dataesr/scientific-paragraphs-categorization huggingface dataset:
without dry season (Fig  1C ). Average monthly minimum and maximum temperatures and precipitation were downloaded from the WorldClim website (version 1. 4; http://www. worldclim. org) with a resolution of 30 arc-seconds  [39]. SRTM 90m Digital Elevation Data were downloaded from the CIAT-CSI SRTM website  [40]  and elevation ranged from 71-112 meters across the forest (Fig  1D ). Land cover data were taken from Broxton et al. [41]. Wood areas, rivers, roads, railway and buildings spatial data were downloaded from OpenStreetMap website (http://www. openstreetmap. org). Crop types per plot were taken from the "Registre Parcellaire Graphique Bretagne 2013 Contours des ı ˆlots culturaux et leur groupe de culture majoritaire", downloaded from GeoBretagne website (geobretagne. fr/geonetwork/srv/fre/pdf? id=18162).
Sequencing and sequence analysis Qiagen (Hilden, Germany) performed sequencing on all positive samples, either directly on the PCR product or following extraction from agarose gel and purification using the NucleoSpin Extract II kit (Macherey-Nagel, Duren, Germany). Sequences obtained were compared with known sequences listed in the GenBank nucleotide sequence databases via the National Center for Biotechnology Information Blast search option (www. ncbi. nlm. nih. gov/BLAST), and sequence data were deposited in GenBank.
Statistical analysis Prevalence rates and exact binomial 95% confidence intervals were independently calculated for each microorganism in male and female adult ticks using Ecological Methodology software  [44]. Prevalence rates were compared between males and females with the Fisher Exact test, using Genstat version 15 (VSN International Ltd. , Hemel Hempstead, UK). For the pooled nymph samples, we employed the exact method of Hauck, assuming perfect sensitivity and specificity of our pathogen detection methods  [45]. Hauck noted a one-to-one relationship between individual level prevalence, π, and the prevalence of positive pools, P. A point estimate for the prevalence rate can thus be obtained from the pool positive rate by π = 1-(1-P) 1/k where k is the number of nymphs per pool. Exact 95% confidence intervals were then obtained by assuming a binomial distribution for the number of positive pools  [46].
52 nymphs in comparison to adult females, and P = 0. 068 with adult males). In contrast, the percentage of Rickettsia spp. positive samples was significantly higher in adult males (10. 5%) than either females (0%) (P = 0. 007) or nymphs (1. 9%, non-overlapping 95% confidence intervals, Fisher's Exact p-value = 0. 032). Sequencing analysis demonstrated that all amplified sequences corresponded to R. helvetica with 100% identity with sequences present in databanks. B. burgdorferi and Bartonella spp. infection prevalence rates were very low, with no differences between adult males, females, and nymphs. Unfortunately, we were unable to sequence the corresponding amplicons for these two genera. No positive samples of Babesia, Theileria, or Francisella spp. were identified.
Background Thousands of fungal genome sequences, covering 996 different species, are currently available in public databases (April 2017, http://www. ncbi. nlm. nih. gov/genome/ browse/). The majority of these comprise fragmented draft assemblies that were produced from relatively short DNA sequence reads generated by the Sanger method (up to 1,000 bp) or 'second-generation sequencing' (SGS) technology such as 454 and Illumina (up to 500 bp)  [1]. Although fragmented genome assemblies can provide good coverage of the 'gene space' , corresponding to the protein-coding genes, in-depth studies of genome architecture, evolution and speciation of organisms require access to other parts of the genome which until recently were regarded as 'junk' DNA, for example regions containing repetitive sequences such as transposable elements  [2].
In addition to cultivated Brassica and Raphanus species it also infects the model plant Arabidopsis thaliana, providing a tractable model pathosystem in which both partners can be genetically manipulated. Based on optical mapping, we previously reported that the genome of C. higginsianum strain IMI 349063 comprises 12 chromosomes including two minichromosomes <1 Mb in length  [13] , consistent with results obtained more recently from mitotic cytological karyotyping  [14]. This strain was sequenced in 2009 using a combination of short-read data from 454 GS-FLX (350 bp) and Illumina GAII (100 bp) sequencing platforms together with a small quantity of Sanger reads (Table  1 ). The resulting assembly (GenBank accession number CACQ02000000) was highly fragmented, containing 10,269 small contigs (N50 = 6,150 bp) and 376 scaffolds  [13].
8; minimum sub-read length = 500 bp) and then long, highly accurate sequences were pre-assembled by mapping the single-pass reads onto longer 'seed' reads. The Overlap Layout Consensus algorithm (WGS-Celera Assembler 7. 0) was then used to perform an HGAP assembly of the pre-assembled reads. Finally, InDel and base substitution errors remaining in the draft assembly were reduced by polishing the consensus sequence using Quiver (https:// github. com/PacificBiosciences/GenomicConsensus). The assembly was validated by Polymerase Chain Reaction (PCR) with primers shown in Additional file 1 using standard molecular biology techniques  [16]. For Illumina sequencing, libraries were prepared from 1 μg of genomic DNA at the Max Planck Genome Centre Cologne and sequenced on the Illumina Hiseq 2500 platform to produce 100 bp paired-end reads.
Genome assembly comparison and sequence accuracy Whole-genome alignments between contigs of the old and new genome assemblies were performed using MUMmer 3. 0  [17]. To assess the accuracy of the PacBio-derived genome sequence, we mapped Illumina paired-end 100 bp reads to the new assembly using BWA-MEM v. 0. 7. 15  [18]. After filtering to retain only uniquely mapped and properly paired reads, Freebayes  [19]  was used to detect sequence polymorphisms (SNPs, Indels) between the Illumina reads and the PacBio assembly. To limit false-positive detections, two filters were applied sequentially, as recommended for wholegenome variant calling  [20]  using the VCFFiltering script (https://urgi. versailles. inra. fr/download/gandalf/VCFtools -1. 2. tar. gz). The first-pass filter eliminated variants located in low complexity regions detected by mdust  [21]  or in annotated TEs (AN <2, AF >0.
Detection and annotation of transposable elements and simple sequence repeats Two pipelines from the REPET package (http://urgi. versailles. inra. fr/tools/REPET) were used to annotate transposable elements (TEs). The TEdenovo pipeline  [22]  was Independently determined by optical mapping  [13]  and cytological karyotyping  [14]  b Estimated by optical mapping c TEs were detected using RepeatMasker for assembly CACQ02000000 and REPET for assembly LTAN01000000 d SSRs were detected using REPET for assembly LTAN01000000 (not analyzed for assembly CACQ02000000) e Different gene annotation pipelines were used for each assembly f Five or more mapped Illumina reads g Gene annotation completeness was estimated using a set of 3,725 Sordariomycete Benchmarking Universal Single-Copy Orthologs (BUSCOs) used to detect repeats in the genome, build consensus sequences and to classify them  [23].
Analysis of Repeat-Induced Point mutation (RIP) Phylogenetic trees for DNA methyl transferases were built as described previously  [25]  with PhyML  [26]  from a multiple alignment generated with T-Coffee  [27]  and manually edited to remove non-informative sites. Sequence divergence plots were drawn as described by Maumus et al. [28]. RIP analyses followed the steps described previously  [25]. Briefly, (i) each TE copy was aligned against its consensus in a pair-wise manner with REPET tools (refalign, refalign2fasta) to derive a multiple alignment, (ii) TE copies smaller than 400 bp and with less than 80 % identity with the consensus were filtered out, (iii) RIPCAL  [29]  was applied to each TE family, using the copy with highest GC content to compute base transition, and (iv) in-house Perl and R scripts were used to calculate dinucleotide bias and produce graphical outputs.
Gene prediction Coding genes were predicted using the MAKER2 pipeline, version 2. 31. 8  [30]. The SNAP ab initio gene finder  [31]  was trained with protein homology evidence from 47,455 fungal annotated genes in the UniProt database (http://www. uniprot. org/; release 2015_08) and 16,172 predicted genes from the previous C. higginsianum annotation (http://fungi. ensembl. org/Colletotrichum_higginsianum/Info/Index), and transcriptomic data derived from Sanger and 454 GS FLEX ESTs [32], and Illumina RNA-seq reads  [13]. For the latter, we made a genomeguided transcript assembly by mapping the Illumina reads to the genome sequence with TopHat2  [33]  followed by assembly with Cufflinks v. 2. 2. 1  [34]. MAKER2 was run with the SNAP models and the resulting gene models were used to train Augustus 3.
Functional annotation Functional annotations for the predicted proteins were obtained using BLASTP to search the UniProt/SwissProt protein database and Blast2GO. The Fungal Transcription Factor Database  [40]  and Fungal Cytochrome P450 Database  [41]  were used to annotate transcription factors and cytochrome P450 enzymes, respectively. The CAZy annotation pipeline (  [42] , http://www. cazy. org) was used to annotate the repertoire of carbohydrate-active enzymes. Secondary metabolism key enzyme-encoding genes (SMKGs) and gene clusters were identified by combining predictions from SMURF  [43] , antiSMASH v. 3. 0  [44] , SMIPS  [45] , CASSIS  [45]  and an in-house pipeline reported previously  [46]. Clusters were further defined based on gene co-expression  [47]. Extracellular secreted proteins (with no predicted transmembrane domain or a GPI-anchor) were predicted using SignalP v.
4. 1  [48]  and PredGPI  [49]. Candidate Secreted Effector Proteins (CSEPs) were defined as extracellular secreted proteins that were not present in species outside the genus Colletotrichum, based on BLAST searches against the NCBI nr database  (27. 07. 2016). We further categorised the CSEPs as either genus-or species-specific based on the BLAST results. To identify secreted proteases, sequences of extracellular proteins were subjected to batch BLAST against the MEROPS database  [50].
Phylogenetic analysis of secondary metabolism key genes Concatenated sequences of the KS and AT domains of predicted C. higginsianum PKS and PKS-NRPS hybrids were aligned with well-characterized enzymes experimentally linked to a metabolite using Muscle  [24]. Further sequences from well-annotated fungal genomes were included in the dataset (Additional file 3). Evolutionary history was inferred using the Maximum Likelihood method based on the Le and Gascuel model with 1,000 iterations  [51]. The initial tree for the heuristic search was obtained by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (gamma parameter, 4).
Relationship of TEs to genes and gene clusters The distances between TEs and (a) genes encoding candidate secreted effector proteins, and (b) all genes contained within secondary metabolism gene clusters were analysed using permutation tests implemented in the R package regioneR  [54]. The mean distance between each gene in each functional category and the nearest TE was compared to the mean distance of a random sample of genes taken from the whole genome. Ten thousand random permutations were sampled from the whole genome to establish a distribution of means, which was then used to calculate a p-value for each gene class.
Segmental duplication analysis To detect segmental duplications (SDs) we developed a new tool called SDDetector (https://github. com/nlapalu/ SDDetector), based on the protocol of Khaja et al. [55]. Using this tool, we performed a soft-masked megablast (version 2. 3. 0+) alignment of the PacBio unitigs to the TE-masked genome. Matches were then chained together based on the following parameters: minimum sequence identity = 90%, maximum gap size between fragments = 3 kb, minimum fragment size = 3 kb. SD gene content was analysed to detect sequence polymorphisms among duplicated genes, and their potential effects were manually inspected. Genomic regions (≥ 5 kb) bordering each SD were extracted and compared to the TE annotation. In cases of overlap with TEs, extracted regions were extended up to the end of TE features and corresponding sequences involved in the SD were subjected to Blast in a pair-wise manner.
Transcriptome analysis For gene expression profiling, we re-analysed previously published RNA-Seq data  [13] , corresponding to four developmental stages of C. higginsianum, namely appressoria in vitro  (22 hpi) , appressoria in planta (22 hpi), the biotrophic stage of infection (40 hpi), and the switch from biotrophy to necrotrophy (60 hpi). These data sets comprising 100 bp single reads (3 replicates per stage) are available under GEO accession number GSE33683. Filtered reads were mapped with TopHat2  [33]  (version 2. 0. 14, I = 5000, a = 10, g = 5) against the new annotation of the C. higginsianum genome. HTseq  [56]  (version 0. 5. 3p9) was used to count reads per gene before statistical analysis with DESeq2 version 1. 1. 0 [57]  using default parameters. Genes were considered differentially expressed if |log2 FC| ≥ 2, q-value < 0.
01. To evaluate TE expression, we used three of the above-mentioned RNA-Seq data sets, namely appressoria in vitro, appressoria in planta and the biotrophic phase. After read mapping with TopHat2, read counts were obtained for TEs using FeatureCounts  [58]  with or without the option 'multi-mapped reads' (-M). Counts were then transformed into average log(CPM i ) according to the formula below, where i = total number of TE copies, n = number of replicates, N = number of mapped reads, X = (read counts +1). average log CPM i ð Þ¼ 1 n X j¼1 n log X ij N j :10 6 Expressed and non-expressed TE copies were discriminated according to their log (CPM) distribution. Expressed TEs in at least one condition were clustered by k-means using R scripts.
Confocal microscopy Spores of the pChPKS38::mRFP reporter strain were inoculated either onto dialysis tubing (Visking, Roth) or the cotyledons of 7-day old seedlings of Arabidopsis thaliana Col-0 as described previously  [32]. Images of mRFP fluorescence (excitation: 532 nm; emission: 588-621 nm) were recorded using a Leica SPE confocal microscope with a 63x/NA 1. 15 water-immersion objective. Images were analysed using Fiji software and the FigureJ plugin  [61, 62].
The new annotation includes 2,289 new genes with no match in the previous annotation. The majority of these were not previously predicted due to their fragmentation between contigs or because transcript evidence from RNA-Seq was not used in the previous annotation. The new genes include all but one of the 133 genes on chromosome 12, which was covered by only two small contigs in the old assembly (Additional file 6). Conversely, 944 genes from the old annotation are absent from the new annotation, most of which correspond to putative ORFs inside TEs or transposases and reverse transcriptases that were excluded by the MAKER2 pipeline. The correspondence between old and new gene IDs is shown in Additional file 7. Taken together, these data indicate the quality of the revised annotation is dramatically improved compared to the previous version, largely due to the absence of gaps in the new assembly.
Expression profiling secondary metabolism genes and clusters Previous RNA-Seq datasets representing different infection stages were re-analysed (Additional file 8A) to define the expression patterns of the SMKGs. Four distinct waves of expression were recognised: (a) appressoria in vitro and in planta, (b) appressoria in planta and the biotrophic phase, (c) biotrophic and necrotrophic phases, and (d) the necrotrophic phase (Fig. 4a ). Remarkably, out of the 59 significantly regulated SMKGs (|log2 FC| ≥ 2, q-value ≤ 0. 01), 42 (71%) were expressed exclusively during plant infection and not in appressoria formed in vitro. To evaluate the expression patterns of entire SM gene clusters, we used the Transcript Per Million (TPM) normalisation method, where a cluster was considered to be significantly expressed if most genes in the cluster had a TPM greater than 1 % of the expression level of the actin gene, and significantly regulated if |log2FC| ≥ 2 (q-value ≤ 0.
It is important to note that this approach can only provide a clue to the family of molecules likely to be produced rather than a definitive structure. One striking example is the cluster 19 which contains ChPKS13 and homologs of all the   [74]. In D. amygdali, genes are dispersed at two distinct loci in contrast to C. higginsianum. Protein identity is moderate to high and genes were extensively rearranged. Shading indicates syntenic blocks and genes pairs. Yellow: acetyl-transferase depudecin biosynthetic genes with 78% mean aminoacid identity (Fig. 2b ). Depudecin is a histone deacetylase inhibitor produced by Alternaria brassicicola  [69]. Based on the co-expression criterion, cluster 19 possesses an additional gene (CH63R_06318) coding for a 110 amino acid protein with no homolog in public databases and containing no functional domain that could be identified using InterProScan or the Conserved Domain Database.
Annotation of transposable elements The content of transposable elements (TEs) in the previous genome assembly (1. 2%) was significantly underestimated due to the poor assembly of repeat-rich regions  [13]. Using REPET to annotate the new gapless assembly, TEs were found to cover 7% of the C. higginsianum genome, while simple sequence repeats (SSRs) cover 12. 7% (Table  1 ; Additional file 13). The TEs were classified by REPET into 41 families and named using the three-letter code of the Wicker et al. [75]  classification (Additional file 14). The 20 families of class I retrotransposons occupy 67% of the total TE space compared to only 33% for the 20 families of class II DNA transposons (Table  4 ). LTR (Long terminal repeat) retrotransposons and TIR (Terminal inverted repeat) DNA transposons are the two most abundant TE orders, with 636 and 474 copies, respectively.
TE expression analysis To evaluate TE expression patterns, we used available RNA-Seq data from appressoria in vitro (VA), appressoria in planta (PA) and the biotrophic phase (BP). Genes (ORFs) within TEs are not annotated and no good tools are available to annotate them. Without well-defined gene models, units such as FPKM (Fragments Per Kilobase per Million mapped reads) cannot be used. We therefore calculated expression units as CPM (Counts Per Million mapped reads), either including or excluding reads mapping to multiple genomic locations. The log(CPM) distribution of multi-mapped reads showed a bimodal distribution (Additional file 19A), and we selected the central inflection point (antimode), i. e. 1. 35 log(CPM), as the threshold to discriminate expressed vs non-expressed TE copies. In striking contrast, a bimodal distribution of log(CPM) was not obtained for uniquelymapped reads (Additional file 19B).
Acknowledgements We thank  Bruno Huettel ,  Hans Sommer  and  Julie Vallet  for their expert advice and technical assistance with the isolation of high molecular weight genomic DNA. We are also grateful to  Marc-Henri Lebrun  and  Muriel Viaud  for their assistance with the annotation of secondary metabolism genes. We acknowledge the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing resources.
Funding This research was supported by funding from  Agence Nationale de la Recherche  grant  ANR-12-CHEX-0008-01 ,  France (RJO) ,  Ministerio de Economía y Competitividad  grant  AGL2015-66362-R ,  Spain (MRT)  and  NHGRI  grant  5R01HG000225 ,  USA  (DCS and SZ). The Funders had no role in study design, the collection, analysis and interpretation of data, or writing of the manuscript.
Availability of data and materials The assembly and annotation generated in this study are available in the Genbank repository under the accession number LTAN00000000, with locus tags in the format CH63R_00000. These data are associated with NCBI BioProject PRJNA47061 and BioSample SAMN04420120. All PacBio and Illumina sequencing reads are available from the Genbank short read archive under SRA study number SRP069807. The annotation of transposable elements can be accessed from INRA-URGI server (https://urgi. versailles. inra. fr/download/fungi/TEs/). Biological materials used in this study are available from the corresponding author on reasonable request.
Synaptic efficiency critically depends on the AMPAR nanodomains to glutamate release sites distance To examine theoretically the effects of delocalizing AMPAR nanodomains from pre-synaptic glutamate release sites, we performed Monte-Carlo based simulation using the MCell software (Figure  6 ). The synaptic shape and perisynaptic environment was obtained from 3D electron microscopy images reconstructing the neuropil of a hippocampal CA1 stratum radiatum area, previously developed to model synaptic transmission  (Bartol et al. , 2015a; Bartol et al. , 2015b; Kinney et al. , 2013 ) (See Methods). AMPAR chemical kinetic properties were obtained from a well-established model  (Jonas et al. , 1993)  (Figure  6B ) and the kinetic parameters were adjusted to fit both the recorded mEPSCs, and the AMPAR organization map extracted from the d-STORM data (Figure  1  and  (Nair et al.
The dyes were sequentially imaged (Alexa 647 first, followed by Alexa 532) to collect the desired single molecule frames and to avoid photo bleaching. Multi-color fluorescent microspheres (Tetraspeck, Invitrogen) were used as fiducial markers to register long-term acquisitions and correct for lateral drifts and chromatic shifts. A spatial resolution of 14 nm was measured using centroid determination on 100 nm Tetraspeck beads acquired with similar signal to noise ratio than d-STORM single molecule images. Details of experimental procedure and data analysis was followed as described before  (Nair et al. , 2013). Tesselation analysis of d-STORM experiments are done as described in the original paper  (Levet et al. , 2015). uPAINT 13-14 Days in Vitro (DIV) dissociated neurons were imaged at 37°C in an open Ludin Chamber (Ludin Chamber, Life Imaging Services, Switzerland) filled with 1 ml of Tyrode's.
Dendritic ROIs were selected based on fluorescence from GFP. ATTO-647 coupled to antibody against AMPAR subunit GluA2 was added to the chamber after appropriate cell was identified and region selected. Adding a suitable amount of probes controlled density of labelling. The fluorescence signal was collected using a sensitive EMCCD (Evolve, Photometric, USA). Acquisition was driven with MetaMorph software (Molecular Devices, USA) and acquisition time was set to 20 ms. Around 000 frames were acquired in typical experiment, collecting up to few thousands of trajectories. Sample was illuminated in oblique illumination mode. Angle of refracted beam varied smoothly and was adjust manually to maximize signal to noise ratio. The main parameters determined from the experiments was the diffusion coefficient (D) based on the fit of the mean square displacement curve (MSD).