dataesr/scientific-paragraphs-categorization huggingface dataset:
Type genus. Zeloasperisporium R. F. Castañeda. MycoBank MB812487. Genera included in family -Neomicrothyrium, Zeloasperi sporium. Typus. indonesia, Northern Sumatra, Tebing Tinggi District, Negeri Lama, on leaves of Elaeis guineensis (Arecaceae), 10 Aug. 2014, M. J. Wingfield (holotype CBS H-22246, culture ex-type CPC 25094 = CBS 139916; ITS sequence GenBank KR476745, LSU sequence GenBank KR476778, ACT sequence GenBank KR476788, TUB sequence GenBank KR476795, My- coBank MB812459); CPC 25095 (ITS sequence GenBank KR476746, LSU sequence GenBank KR476779, ACT sequence GenBank KR476789, TUB sequence GenBank KR476796). Pedro W. Crous & Johannes Z. Groenewald, CBS-KNAW Fungal Biodiversity Centre, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@cbs. knaw. nl & e. groenewald@cbs.
Scale bars = 10 µm. Paraphaeomoniella capensisCrous & A. R. Wood, sp. nov. Paraphaeomoniella Crous, gen. nov. Classification -Incertae sedis, Phaeomoniellales, Eurotiomy cetes. Type species. Paraphaeomoniella capensis. MycoBank MB812468. = CBS 123535; ITS sequence J372391, LSU sequence GenBank FJ372408, MycoBank MB812469); CPC 15417-15418. Basionym. Phaeomoniella niveniaeCrous, Persoonia 27: 155. 2011. Neophaeomoniella zymoides(Huang B. Lee, J. Y. Park, Summerb. & H. S. Jung) Crous, comb. nov. -MycoBank MB812467 Basionym. Phaeomoniella zymoides Huang B. Lee, J. Y. Park, Summerb. & Etymology. Pedro W. Crous & Johannes Z. Groenewald, CBS-KNAW Fungal Biodiversity Centre, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@cbs. knaw. nl & e. groenewald@cbs. knaw. nl Suzanne Rooney-Latham, California Department of Food and Agriculture, Plant Health and Pest Prevention Services, Plant Pest Diagnostics Lab, 3294 Meadowview Road Sacramento, CA 95832-1448, USA; e-mail: suzanne.
comMaximumlikelihood tree (MEGA v. 6) was obtained from the ITS sequences of our isolate and other sequences retrieved from the GenBank nucleotide database (TreeBASE Submission ID. 17323). In the tree, branch lengths are proportional to distance. Bootstrap support values ≥ 70 % are indicated on the nodes. The new species proposed in this study is indicated in bold face. Fungal Planet 354 -10 June 2015TephrocybellaPicillo, Vizzini & Contu, gen. nov.
Typus. iTaly, Latium, Rome, wood of Castel Fusano, N41°72'379" E12°32'175", under Phillyrea angustifolia, in a mixed wood with Pinus pinea and Quercus ilex, with Laurus nobilis, Cistus incanus, Erica arborea and Rus cus aculeatus also present, on sandy soil, 21 Nov. 2014, B. Picillo (holotype TO HG 21112014, ITS sequence GenBank KR105775, rpb2 sequence GenBank KR105776, LSU sequence GenBank KR476785, MycoBank MB812273). Additional specimens examined. iTaly, Latium, Rome, wood of Castel Fusano, N41°72'379" E12°32'175", under Phillyrea angustifolia, in a mixed wood with Pinus pinea and Quercus ilex, with Laurus nobilis, Cistus incanus, Erica arborea and Ruscus aculeatus also present, on sandy soil, 28 Nov. 2014, B. Picillo (BP 14/419); ibid. , 3 Dec. 2014, B. Picillo (BP14/430); ibid. , 14 Dec.
, on naked soil in tropical lowland semi-deciduous forest, 14 May 2014, V. Trunov (LE 302078, ITS sequence GenBank KP676037, LSU sequence GenBank KR052824); Lam Dong Province, Cat Tien District, Cat Tien Biosphere Reserve (Cat Loc Sector), 1. 3 km NE of Phuoc Son ranger station, N11°37'51" E107°18'26", alt. 214 m, on naked soil along a path in bamboo thickets, 19 June 2011, E. Popov (LE 302079); Dak Lak Province, Krong Bong District, Chu Yang Sin National Park, 7 km NW of Chu Yang Sin Mt, N12°25'31" E108°21'22", alt. 913 m, on soil along the roadside in tropical montane evergreen broadleaf forest, 29 May 2014, A. Alexandrova (LE 302080, ITS sequence GenBank KP676039). Additional specimens examined. china, Guangdong Province, Shenzhen, Yangtaishan Forestry Park, N22°53' E113°18', 21 Sept. 2006, C.
Y. Deng & T. H. Li (GDGM 24473, as E. flavidum (He et al. 2012), ITS sequence GenBank JQ281481); Guangdong Province, Zhuhai, Niutoudao, N22°00' E113°48', 29 July 2013, T. H. Li, H. Huang & Y. W. Xia (GDGM 57154, ITS sequence GenBank KJ845723). -VieTnaM, Binh Phuoc Province, Bu Gia Map District, Bu Gia Map National Park, N12°12'15" E107°12'13", alt. 370 m, 26 May 2011, O. Morozova (LE 302076); ibid. , N12°11'37" E107°12'46", alt. 550 m, on naked soil in bamboo thickets, 14 May 2014, O. Morozova (LE 302077, ITS sequence GenBank KP676038); ibid. , N12°12'19" E107°12'16", alt. 500 m along the roadside in bamboo thickets, 30 May 2011, E. Popov (holotype LE 302075: ITS sequence GenBank KP676036, LSU sequence GenBank KR052823, MycoBank MB811395).
rubescens RPC13), R. sp. 3 0. 99/95 AF058315 (R. roseolus JMT8227), R. sp. 4 0. 9/99 0. 9/69 Bolbitius vitellinus AY194519 AF071439 (P), R. ochraceisporus 0. 97/79 1/91 Bolbitius variicolor JF907768 1/94 AF071444 (P), R. diabolicus AF071441 (H), R. colossus 0. 97/83 AJ810035 (H), R. vinicolor Bolbitius vitellinus DQ200920 Bolbitius cf. vitellinus FJ235157 Subg. VILLOSULI 0. 03 1/97 1/100 0. 2 AF071447 (P), R. hawkerae Bolbitius callistus EU477860 Bolbitius aleuriatus JF907770 Bolbitius excoriatus KC456418 -/80 AM085523 (P), R. fragrans The phylogram inferred from the Bayesian analysis of nrITS for 3 million generations, using MrBayes v. 3. 1 software. Maxi-related species. Analysis was performed under a GTR model, dataset sequences of Bolbitius aurantiorugosus and closely mum likelihood analyses were run in the PhyML v.
Typus. cape Verde, Santo Antão, Porto Novo, Aguas das Caldeiras, alt. 434 m, N17°06'45" W25°04'08", on soil close to Pinus canariensis, 29 Nov. 2010, M. T. Telleria (holotype MA-Fungi 87402; ITS sequence GenBank KP325105, LSU sequence GenBank KR069062, MycoBank MB811056). Additional materials examined. cape Verde, Santo Antão, Porto Novo, Aguas das Caldeiras, alt. 434 m, N17°06'45" W25°04'08", on soil close to Pinus canariensis, 29 Nov. 2010, M. P. Martín MPM3264 (MA-Fungi 87403; ITS sequence GenBank KP325104, LSU sequence GenBank KR069061); ibid. , I. Salcedo (MA-Fungi 87404; ITS sequence GenBank KP325103, LSU sequence GenBank KR069059); ibid. , M. Dueñas 13426MD (MA- Fungi 87405; ITS sequence GenBank KP325106, LSU sequence GenBank KR069060).
Typus. Morocco, Souss-Massa-Drâa, road R 105, from Aït Baha to Tafraoute, Km 63, N30°03'28. 6" W09°05'06. 6", on Euphorbia officinarum subsp. echinus (Euphorbiaceae), alt. 760 m, 17 Apr. 2010, J. Cardoso, M. Dueñas, I. Melo, M. P. Martín, I. Salcedo & M. T. Telleria, 13062MD (holotype MA-Fungi 88007; ITS sequence GenBank KJ701188, LSU sequence GenBank KJ701189, MycoBank MB810992). specimens examined. Morocco, Souss-Massa-Drâa, road R 105, from Aït Baha to Tafraoute, km 63, N30°03'28. 6" W09°05'06. 6", on Euphorbia officinarum subsp. echinus, alt. 760 m, 17 Apr. 2010, 13057MD, 13058MD, 13059MD, 13060MD, 13061MD, on Euphorbia sp. , 18451Tell; Additional Souss-Massa-Drâa, from Tiznit to Bou Izakarn, N29°24'13. 8" W09°43'35. 8", alt. 930 m, on Euphorbia sp. , 19 Apr. 2010, 18691Tell; Souss-Massa-Drâa, road Tiznit to Anglou Plage, N29°45'29.
Profiling by TaqMan Human MicroRNA Arrays Expression levels of 754 miRNAs (Sanger miRBase v14) were quantified using the TaqMan Human MicroRNA Array A + B Card Set v3. 0 (Applied Biosystems, Foster City, CA) as per the manufacturer's instructions (including pre-amplification) (S1 Methods). Quantitative miR-NAs expression data were acquired by ABI 7900HT SDS software v2. 4. Cycle threshold (Ct, cycle in which there is the first detectable significant increase in fluorescence) values were set using ExpressionSuite software (Applied Biosystems) on the first 60 samples (automatic baseline and threshold) and these thresholds for Ct were used for the remaining series. The Taq-Man Human MicroRNA Array experiments are MIAME compliant and have been deposited at the NCBI Gene Expression Omnibus (GEO) database (http://www.
Statistical analysis Descriptive comparisons of study variables between cases and controls used the Chi-squared test for categorical data, and the Student's t-test for continuous data. The data were analysed in HTqPCR package  [24]  using R Bioconductor  [25]. miRNAs with undetermined Ct values in more than 120 samples were filtered out. Data were quantile normalized. Following normalization Ct values with an interquartile range (IQR) of less than 1. 5 and endogenous controls were removed from subsequent analysis, and limma analysis was performed to identify differentially regulated miRNA between cases and controls. With limma, a one-factorial linear model is fitted for each miRNA and the standard errors (SE) are moderated using an empirical Bayes model resulting in moderated t-statistics for each miRNA  [26].
Acknowledgments We would like to thank the  IARC Genetic Services Platform  for providing the laboratory equipment. We are indebted to  Mrs Priscilia Chopard  for technical assistance,  Dr Patrick van Uden  for useful comments on the manuscript and we would like to thank all the patients for their participation. We are also grateful to the staff of hospitals, interviewers, data managers, pathology departments, and primary care centres that supported this study. The abstract was presented at the 2014 Annual Meeting of  American Association for Cancer Research.
Using the sequence datasets, we identified cases where fungal OTUs were sampled across multiple geographical sites and between different sampling depths. This was especially clear in one relatively abundant and diverse phylogroup tentatively named Novel Chytrid-Like-Clade 1 (NCLC1). For comparison, a subset of the water column samples was also investigated using fluorescent microscopy to examine the abundance of eukaryotes with chitin cell walls. Comparisons of relative abundance of RNA-derived fungal tag sequences and chitin cell-wall counts demonstrate that fungi constitute a low fraction of the eukaryotic community in these water column samples. Taken together, these results demonstrate the phylogenetic position and environmental distribution of 71 lineages, improving our understanding of the diversity and abundance of fungi in marine environments.
Both second-generation SSU V4 rR/DNA diversity tag sequencing  [33]  and metatranscriptome sequencing  [34]  suggest fungi dominate eukaryotic communities in deep-sea sediments. Edgcomb et al. targeted the eukaryotic community of sediment cores using both RNA and DNA templates and demonstrated that the diversity recovered was dominated by fungi, specifically basidiomycete yeasts branching close to Cryptococcus and Malassezia  [31]  and similar results have been recovered in additional studies  [23, [30] [31] [32] 35]. Furthermore, fungi have also been recovered from marine animals, algae, muds and hydrothermal vents  [16, 18, 19, 36, 37]. Here, we use BioMarKs V4 SSU rR/DNA derived Roche/454 sequence tag dataset  [38]  from 130 samples from six European marine sites combined with fluorescence microscopy for the detection of eukaryotic microbes with chitin cell walls to investigate the diversity and abundance of fungi in water column and surface sediment samples.
Results and discussion (a) Sampling of multi-provenance operational taxonomic unit-clusters Initial clustering of the tag sequences identified 1752 fungal SWARM-OTUs encompassing 10 840 reads from European marine waters (table  1 ). Figure  1  summarizes the taxonomic assignment of these 1752 fungal SWARM-OTUs. Taxonomic assignment was accomplished by using the most numerous sequence read within each SWARM-OTU for VSEARCH against a copy of the PR2 V4 SSU rRNA database  [39]. The fungal-assigned OTUs recovered were dominated by Chytridiomycota and unclassified fungi followed by Ascomycota and Basidiomycota. These reads were filtered in two additional ways: first, representative sequences from each OTU were aligned and masked using the approaches described in the electronic supplement material.
The 10 remaining sequences affiliated with chytrid-like sequences (eight specifically with CRA) but their phylogenetic placement was not supported by greater than or equal to 50% bootstrap support. Fifty per cent is a low level of bootstrap support for identifying phylogenetic affiliations; it was used here as the phylogenies are calculated from short sections of the SSU rRNA encoding gene with relatively few positions sampled for the alignment (i. e. 342 and 316). As such phylogenetic analysis of these datasets is unlikely to consistently identify strong bootstrap results for even established phylogenetic relationships.
Indeed, taxonomic assignment analysis of the total BioMarKs fungal-assigned dataset showed that a very low proportion of the total sequences was assigned to fungal groups known to form filamentous structures in terrestrial environments, for example Pezizomycotina represented only 2. 32% and 3. 52% of the total DNA and RNA reads, respectively (figure  1b ). It also could explain why Dikarya yeasts and chytrid-like sequences were preferentially recovered in these datasets, as both cellular forms are relatively fragile and therefore more readily sampled for RNA/DNA sequencing.
As such, targeted single cell genomics/transcriptomics  [62]  represents a useful tool for sampling marine fungi. Data accessibility. Electronic supplementary material that accompanies the online version of this article includes materials and methods, a description of the environments sampled (electronic supplementary material, tables S1 and S2), and a table showing the per cent similarity within each of the OTU clusters (electronic supplementary material, table  S3 ). Electronic supplementary material, figure S1 eukaryotic cells with putative chitin cell walls and filamentous cell structures. Representative sequences of the 71 have been submitted to the European Nucleotide Archive: LN827819-LN827889. Additional supporting data are available at GitHub (https:// github. com/guyleonard/marine_fungi with doi:10.
5281/zenodo. 16817). These data include: (i) all the sequences grouped into the 71 OTU clusters (fasta files), (ii) a spread-sheet showing the recovery of tag sequences classified in the 71 OTU clusters from the 130 environmental samples, (iii) the water column cell counting data, (iv) the two V4 SSU rDNA alignments as MASE files with mask details available using SeaView  [63] , and (v) the tree files of the phylogenies shown in figures 1 and 2.
The estimate is similar when we use BAC-located genes that have S. vulgaris homologs, which excludes genes that were gained by the X chromosome. Conclusions: Our BAC sequencing identified 59 new sex-linked genes, and our analysis of these BAC-located genes, in combination with RNA-seq data suggests that gene losses from the S. latifolia Y chromosome could be as high as 30 %, higher than previous estimates of 10-20 %.
Gene densities in S. latifolia X, Y and S. vulgaris BAC clones We found an average of 34 genes/Mb in the S. latifolia X BAC sequences and 74 genes/Mb in those from S. vulgaris (Table  1 ). The gene densities we observed in both species' BAC sequences are quite high, which suggests that we have sequenced gene-dense regions. The 2. 2-fold lower gene density in the S. latifolia X is, however, consistent with the expectation based purely on the genome sizes of the two species (2. 7 Gb for S. latifolia and the 1 Gb for S. vulgaris; see the Plant DNA C-value Database, http://data. kew. org/ cvalues/). Assuming the same total number of genes in both species (which is likely as they are closely related species with an identical chromosome number of 2n = 24), and neglecting possible inter-chromosomal translocations in S.
Searching for the BAC-located genes in RNA-seq data We blasted our BAC-located genes to the RNA-seq contigs from previous studies (see Methods), which produced Gene density was computed using all available BAC data. When only triplets are used, the results are similar significant matches for 54 out of 63 genes (Table  2  and Additional file 1: Table  S3 ), showing that most of our BAClocated genes (~85 %) are expressed in flower buds. Only half of these genes were identified as sex-linked by any of the previous studies (Table  2 ). As predicted (see Background) the genes not detected as sex-linked in any of the RNA-seq studies have much lower expression levels (as estimated by  [8] ) than those where sex-linkage was detected (RPKM values: 3008. 3 versus 11251. 2, respectively; the difference is significant by a one-tailed Student's t test, p-value = 0.
004). This suggests that failure to ascertain genes as sex-linked when they have low expression affects inferences using RNA-seq, in addition to absence of expression of some genes in flower buds. Re-evaluating Y gene loss using both BAC and RNA-seq data Two RNA-seq studies have used X-linked genes to estimate Y gene loss in S. latifolia. Only 10 to 20 % of Xlinked genes were estimated to have no Y transcripts, suggesting that Y degeneration and male hemizygosity may be modest in S. latifolia  [6, 7]. Correct inference of X-hemizygous genes is critical for reliably estimating Y gene loss. If the Y copy of an X/Y gene pair is not expressed, or is expressed at low levels in the tissue(s) used for RNA-seq analysis, hemizygosity will be incorrectly inferred and gene losses from the Y will be overestimated.
We found some examples of this when comparing the BAC and RNA-seq data (using stringent Blast criteria, see Methods). Two BAC-located genes matched contigs inferred as X/Y gene pairs from one study but with contigs inferred as X-hemizygous in others, and one Y-linked gene matched a contig inferred as X-hemizygous (Table  2 ). Among X-linked BAC-located genes, five matched contigs inferred to be X-hemizygous (Table  2 ). Using our BAC-located genes that match RNA-seq contigs detected as sex-linked, this yields an estimate of 20 % of Y gene loss, the same as in the published RNA-seq studies  [6, 7]. However, if coverage is low due to a low expression level, SNPs may not be identified; individuals cannot then be genotyped and no inferences about sex-linkage are possible. Recent data from animals suggests that average expression levels are lower for X-hemizygous genes than for X/Y gene pairs  [25, 26] , and therefore the RNA-seq approach may fail to detect X-hemizygous genes more often than X/Y gene pairs, resulting in an underestimation of gene losses from the Y.
Assembly and annotation of BAC sequences For each BAC clone, the reads were assembled de novo using Newbler v. 2. 5. 3 (2010), except for three BAC clones sequenced using Sanger sequencing (19P24, 93 L17 and 78D08), which were assembled with phrap v. 16 (2007). The assembly statistics in Additional file 2: Table  S1  were obtained using QUAST  [31]. Annotation (see Additional file 3: Table  S2 ) was done using both homology-based and expression-data-based strategies using Uniprot and S. latifolia RNA-seq data from  [8]. Truncated genes and genes with premature stop codons and/or frameshifts were annotated as pseudogenes. DNA repeats (including transposable elements) were annotated using the latest update of the database of DNA repeats in S. latifolia, based on an extensive search using genomic library screening and low coverage sequencing of the S.
Sequence analysis Homology among BAC clones from the same X/Y probe gene pair was assessed by aligning the BAC sequences with MAUVE 2. 3. 1  [32]  after masking the repeats using RepeatMasker v3. 3. 0 (http://www. repeatmasker. org/) with the Silene DNA repeat database mentioned above. Homology between X/Y BAC pairs was also assessed by performing an all-against-all Blast search (with the default parameters) among the genes found in the X/Y BAC pair. The results are shown in Additional file 1: Figure  S2 , and the Xvulgaris and Y-vulgaris pairs that we found are listed in Additional file 1: Table  S6. To obtain the results shown in Table  2 , we performed a Blast search of all coding sequences (CDS, obtained by annotating the BAC sequences, see previous section) against the RNA-seq data from the three previous studies  [6] [7] [8]  using data available in Genbank  [7]  and our own data  [6, 8].
We retained only manually checked Blast hits with e-values < 10 -5 , % identities > 90 %, and alignment lengths > 50 bp. Multiple corresponding RNA-seq contigs were allowed for a single BAC CDS to account for assembly problems in the RNA-seq data. The three RNA-seq studies were then combined to infer each CDS gene as being X/Y, X-hemizygous, or not detected as sex-linked in the RNA-seq data (Additional file 3: Table  S2 ). A gene was classified as X/Y in RNA-seq data if any one of the matching RNA-seq contigs was classified as X/Y, and as X-hemizygous if it satisfied two criteria: (i) at least one matching RNA-seq contig was classified as X-hemizygous, and (ii) all other matching RNA-seq contigs were not classified as X/Y gene pairs. Finally, the gene was classified as not having been detected as sex-linked in RNA-seq data whenever all matching RNA-seq contigs failed to be detected as sex-linked.
latifolia TE database mentioned above) and with more than 10 mapped reads in one of the libraries (to remove noisy expression), N = 3,400. Only sequences with Blast hits of > 100 bp, e-values < 10 -4 , scores > 80 and identities > 80 % were retained. Fisher's exact tests and Student's t tests were done using the relevant statistical functions in R (http:// www. r-project. org/).
Acknowledgements This work was supported by the  French National Research Agency  (grant numbers  ANR-08-JCJC-0109 ,  ANR-11-BSV7-013-03  to G. A. B. M. ); the  Czech Science Foundation  (grant number  P501/12/2220  to R. H. ) and the  Ministry of Education, Youth, and Sports  (grant number  LH14002  to R. H. );  ETH Zurich  (grant number  TH-07 06-3  to A. W) and the  Swiss National Foundation  (grant number  31003A-116455  to A. W. );  Operational Program Education for Competitiveness -European Social Fund  (project  CZ. 1. 07/2. 3. 00/20. 0165  to N. B. ).
Methods To study how neuronal oscillations and their concurrent firing rate as well as hemodynamic response relate to each other, we employed a model of neuronal dynamics coupled through a thalamo-cortical network. The neural mass model, used to describe the dynamics at the network nodes, was the Stefanescu-Jirsa population model. This neural mass model comes in two flavours, the Stefanescu-Jirsa 2D model composed of FitzHugh-Nagumo neurons and the Stefanescu-Jirsa 3D model composed of Hindmarsh-Rose-neurons  [13]  and can be found as download packages at http://www. thevirtualbrain. org/tvb/ [56]  after registration, corresponding source code under https://github. com/the-virtual-brain/tvb-library. The authors used techniques derived from nonlinear system theory  [57] , in which coupled neurons with parameter dispersion (for instance distributed firing thresholds) reorganize themselves into clusters displaying similar dynamics.
) and a constant scaling factor c. Noise is added by term ε(t). To drive the system, noise was inserted into each node. The noise level was chosen to not saturate the system, while providing sufficient dynamic behaviour. The noise source was white with Gaussian amplitude (mean = 0, standard deviation = 0. 1). Numerical integration of the system was performed using Heun's method  [60] , with an integration step size of 0. 05 ms. Each run comprised a time window of 300s. All simulations were run on a Matlab-based version (R2014a, Mathworks, Natick, USA) of the above mentioned TVB toolbox packages.
The authors acknowledge the support of the  James S. McDonnel Foundation (Brain Network Recovery Group   JSMF22002082 ) to PR and VJ, the  German Ministry of Education and Research (Bernstein Focus State Dependencies of Learning   01GQ0971 ) and the  Max-Planck Society (Minerva Program)  to PR. Further, VJ acknowledges support by the  EU-projects FET BrainScaleS and Human Brain Project. RB acknowledges support of the  Boehringer-Ingelheim-Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.