dataesr/scientific-paragraphs-categorization huggingface dataset:
jax. org/reference/MGI:80863 http://www. ebi. ac. uk/biosamples/sample/SAMEA2397676 <http uri pattern><local ID> no pattern A) Record-level versioning http://purl. uniprot. org/uniprot/A0A022YWF9. 1 http://purl. uniprot. org/uniprot/A0A022YWF9. 2 http://purl. uniprot. org/uniprot/A0A022YWF9 <Prefix> <Local ID> http://10. 5281/zenodo. 12266 http://e86. ensembl. org/id /ENSMUSG00000033577 http://e85. ensembl. org/id /ENSMUSG00000033577 http://ensembl. org/id/ENSMUSG00000033577 http://10. 5281/zenodo. 580337 http://10. 5281/zenodo. 592020 Documentation exists regarding what kinds of record changes prompt a new version to be issued. http://www. uniprot. org/ help/entry_history http://www. uniprot. org/ help/uniprotkb http://www. uniprot. org/ help/fasta-headers https://www. ncbi. nlm. nih. gov/ books/NBK50679/ #RefSeqFAQ.
Participants A total number of 54 employees of a research centre located in northern Italy were recruited on a voluntary basis to participate in a six-week long study (only working days were considered). During introductory meetings, they were provided with detailed information about the purpose of the study; the data treatment and privacy enforcement strategies adopted; and the devices they would be using and the measurements they provide. Following Italian regulations, all participants signed an informed consent form approved by the Ethical Committee of Ca' Foscari University of Venice. The 54 participants belonged to five units; four units were research groups of varying size, while members of the fifth unit were part of the full-time IT support staff. The heads of the units, all the employees within three units, and the majority of the employees within the remaining two units (7 of 10 and 17 of 18, respectively) participated in the study.
The possible transitions are Low to Low (L → L), Low to High (L → H), High  For each of the four types of transitions as described above, we extracted the number of face-toface interactions with other participants (alters) detected between two consecutive surveys. We used the lagged levels of the personality states of the other participants (alters) the individual is interacting with: for a transition taking place between the morning survey (time t) and the midday survey (time t + 1), we consider the personality states' scores of alters recorded up to the morning survey (time t). The data comprise 3220 surveys by the 54 participants. The response rate for the surveys was 83. 9%  [54]. Ideally, the number of filled surveys should be 4860 (54 participants × 3 daily surveys × 30 working days); however, participants reported absences from work for a variety of reasons, such as attending conferences, being absent due to illness, working from home and leaving work early, reducing the number of expected responses.
From the network of face-to-face interactions between our participants, we extract the graphlets for each of them (i. e. specific network configurations observed in their ego-networks), which represent their local interactions. We consider all possible graphlets up to four nodes, as shown in figures 2-8, where the double circle represents the ego, and their alters and the possible multiple patterns of reciprocal interactions. The graphlets embed information on the current (binary) state of the alters, but not of the ego, in order to account for possible social influence effects. Further, we use the terms dyads, triads and tetrads to encode the ego's interaction with one, two or three alters, respectively. More specifically, we extract graphlets from infrared sensor data starting from 30 min before the first survey and until 30 min before the second survey was filled out.
Methods A comprehensive literature search was conducted on the distribution of Leishmania and Phlebovirus species responsible for human infection in the Mediterranean region. An additional search was dedicated to identifying the reservoir(s) of Leishmania and Phlebovirus. We analyzed all articles listed in the PubMed database in English, French, Italian, and Spanish between 1980 and December 2016, when the final search was conducted. The search terms used included leishmanial species, phlebovirus species, reservoir species, and countries included in the study. Titles relevant to the scope of this review were obtained in full text and selected for inclusion. Unpublished data were not considered. Data on the presence of Leishmania and/or Phlebovirus detection were reported on a geographic information system (GIS) at country level.
Nevertheless, the huge numbers of asymptomatically infected individuals constitute a possible source of infection  [37]. Animals. Wild or domestic animals are the main reservoirs of L. infantum and L. major. Recently, some animal species have been implicated as reservoirs for L. tropica  [4, 6, [38] [39] [40]  (Table  3 ). Domestic dogs (Canis lupus familiaris) are the primary domestic reservoir hosts of L. infantum. After infection, dogs may show clinical manifestations with widespread dissemination of parasites both in viscera and dermis, making this animal an excellent source of infection for vectors; infection efficiency for sand flies increases with the increasing clinical severity  [51]. All positions with less than 95% site coverage were eliminated so that there was a total of 55 positions in the final dataset.
This work was supported by the  Italian-French University (UIF/UFI ,  Vinci Programme ) to SV and RNC, by the  University of Bologna (RFO)  to SV, and by the  Emilia-Romagna Region  ( Lab P3  funds) to MPL. This work was also supported in part by the  European Virus Archive goes Global (EVAg)  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  653316  (to RNC). The work of RNC was done under the frame of EurNegVec  COST Action   TD1303. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
6Λ, and h. i rectification on the given interval. The smoothing ensures that regions that already have local structure are modified less than regions without local structure. Learning was initiated from a homogenous initial set of J i = 0 (where the model produces localized multi-bump states in the stimulated area and sub-population). The learning process converged (e. g. pinwheel density stabilizes) after around 1600 steps as the map takes on structure across the whole domain (after this the term 1 -h|J i,n | ? Gi [0, 1] remains close to 0 across the whole domain, although small changes in the map continue). Final maps (at 6400 steps) were high-pass and low-pass filtered as described in  [46]. The maps used in this study are included as part of the simulation code (see Supplemental information S1 Code), thus allowing the results to be independently reproduced.
The orientation bias was quantified by tuning curves of the orientation preference at axon terminals relative to orientation preference in a region local to the originating cell's body. Tuning curves were quantified by parameter-fits to a von-Mises distribution (circular normal distribution). Our model was developed to be able to provide a direct point of quantitative comparison with these measures. The distribution is defined on a circular domain and parametrized by a tuning coefficient κ ! 0 (0 is untuned, larger is more tightly tuned) and a preferred orientation   κ < 1 were excluded (this exclusion had a very minor effect). The map shows that κ increases with β rec (as expected) and decreases with RW ex. Solid white contours show a band of values for RW ex and β rec where the tuning of connections in the model is consistent with the anatomical data (κ 2 [0.
The decreasing Naka-Rushton function NR used here decays to zero as r increases: NRðrÞ ¼ R max 1 À r n r n þ r n 50 ; ð29Þ where the exponent n > 0 describes the steepness, the maximal value is R max and the half-max r-value is r 50 > 0. Best-fit Naka-Rushton functions were determined by minimising, for example the least-squares distance between Act(r, t final ) and NR(r) varying the parameters n, R max and r 50 using Matlab's lsqcurvefit function.
Orientation selective activation is restricted to stimulus footprint in the anatomical parameter range The measures of the orientation selective component of the lateral spread of activation as studied in individual simulations in Figs 5 and 6 are now quantified over a range of RW ex and β rec. The ranges of these parameters consistent with anatomical data is indicated in Figs  7 8 9 (between the white iso-κ contours reproduced from  Fig 3). The agreement of model simulations with qualitative features from imaging data will be assessed in this and the following section. Fig  7A  shows a map of normalized area (relative to the FFF area) of the selective activation, where each value is an average from simulations at 5 randomly selected map locations. Within the red contour (darker regions) the selective area is roughly equal to or smaller than the FFF of the stimulated region (e.
26 This switch is associated with molecular and genetic changes that facilitate metastatization. 27, 28 However, the molecular mechanisms that mediate differential expression of genes during melanoma progression remain largely unknown. Here we show that FMRP is overexpressed in human melanomas characterized by high Breslow thickness and high Clark level. We found that FMRP levels correlate with prognostic factors of aggressive melanoma and FMRP is often detected at high levels in cells localized at invasive front of the tumor. Moreover, a meta-analysis of The Cancer Genome Atlas (TCGA) melanoma data set revealed that increased FMR1 expression level significantly correlates with metastatic melanoma, risk of tumor relapse and reduced disease-free survival. Reduction of FMRP in two melanoma cell lines revealed decreased cellular migration and invasion and increased adhesion properties.
Finally, using nextgeneration sequencing, we identified the FMRP-regulated transcriptome in melanoma cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases revealed that FMRP affects gene expression of almost 300 proteins involved in invasiveness-related pathways. Our findings suggest that FMRP could affect melanoma progression through the action of proteins involved in plasma membrane plasticity at the leading edges of cancer cells, driving their invasiveness.
Interestingly, of the 311 more relevant FRGs, 165 genes (i. e. 52. 7%) were upregulated and 146 (i. e. 47. 3%) were downregulated (Figure  7a ). A validation of the transcriptomic analysis was performed on a subgroup of 91 annotated genes using the Nanostring nCounter approach, a method that avoids sample amplification (http://www. nanostring. com). Of note, the RNA-seq data set showed a good correlation with the Nanostring nCounter (Pearson's coefficient, 0. 70) (Figure  7b ). Pathway analysis of the 311 more relevant FRGs using Web Gestalt bioinformatics database (http://bioinfo. vanderbilt. edu/ webgestalt/) showed that the dysregulated genes were associated with diverse groups of biological processes and pathways. A Disease Enrichment Analysis (DEA) showed that FRGs were enriched in 'neoplasms', 'neoplasm invasiveness', 'carcinoma', 'neoplastic process' and 'epithelial cancer' categories (Figure  7c  and Supplementary Table  1 ).
Seventy-five genes of the FRG set were found in this group against an expected number of 42. 16 (R = 1. 78; rawP = 9. 52e -08; adjP = 0. 0001), supporting a role of FMRP in regulating cancer cellular dynamics in response to different chemical stimuli (paracrine and/or endocrine) that occur during migration and invasion. 39 inally, the analysis in the KEGG pathway database showed that the FMRP-affected proteins were part of pathways involved in peripheral cell plasticity during migration and invasion processes, such as 'regulation of actin cytoskeleton' and 'focal adhesion' (Figure  7e ). Intriguingly, 11 FRGs were present in the 'lysosome' category, against an expected number of 1. 88, and therefore with an enrichment of 5. 84 fold changes (rawP = 1. 62e -06; adjP = 0. 0001). Similar results for GO and KEGG pathway analysis were also obtained using DAVID online bioinformatic database (https://david.
61 nterestingly, FMRP appears to also regulate the expression of MAPK pathway-related mRNAs (i. e. MAPK4) (Supplementary Table  1 ) that have a key role in the development of melanoma. 62 However, further studies are required to investigate the exact role of these molecules in FMRP-overexpressing melanoma cells. Another wellrepresented pathway in the KEGG database is the 'lysosome' pathway. These data are particularly interesting because endolysosomal trafficking in melanoma is hyperactivated and particularly distinguishes this disease from over 35 different cancer types. 63 As melanoma cells are revealing intrinsic vulnerabilities of the endolysosomal machinery, this pathway can be harnessed for tumor-selective drug delivery and cell death. 64, 65 n conclusion, FMRP regulates molecular processes that impact upon the ability of tumor cells to interact with their environment and switch from a sessile, stationary to a migratory and invasive phenotype.
Materials and Methods Immunohistochemical analysis. FMRP-IHC was performed using polyclonal antibodies  29  (1 : 50 dilution), followed by detection with labeled polymer in accordance with the standard UltraVision AP detection system (Thermo Fisher (e) KEGG analysis. Pathways analysis of 311 FRGs, using KEGG functional annotation of Web Gestalt bioinformatics database. A pie chart of more representative pathways affected in KEGG functional annotation. In red, the adjP value are indicated for each category Scientific, Runcorn, UK). Immunohistochemical reactions were visualized by using Liquid Fast-Red Substrate System (Thermo Fisher Scientific) as the chromogen. Hematoxylin and Azure B were used as counterstains to visualize nuclei and melanin, respectively. A semiquantitative approach was used to evaluate FMRP protein expression.
68 ta-analysis of TCGA. FMR1 expression data (RNA-seq V2) and clinical and pathological information of a cohort of 472 patients with skin cutaneous melanoma (TCGA, provisional) was downloaded from cBioPortal website (http:// www. cbioportal. org/). Exclusion criteria were applied to the cohort, that is: (i) patients who received neoadjuvant treatment before sample collection; (ii) metastatic melanoma with an unknown origin; (iii) secondary metastatic melanoma. In total, 402 out of 472 samples were retained for all analyses described herein. FMR1 RPKM-level data were log 2 transformed. For the survival analysis, samples with an FMR1 expression lower than the 25th percentile of the distribution were assigned to the LOW class, whereas patients with expression higher than 75th percentile were assigned to the HIGH class.
JMP 12 (SAS Institute Inc, Cary, NC, USA) software was used for all statistical analyses including Kaplan-Meier survival analysis. Silencing of FMR1 mRNA using siRNAs. siRNA-mediated silencing of FMRP were performed with FMR1-specific siRNAs from Life Technologies (Carlsbad, CA, USA) (AM 16708, ID nos 10824, 10919 and 11010). As a nonspecific control, a scrambled siRNA was used (no. 4390843; Life Technologies). siRNA duplex was transfected into melanoma cells (501 mel or A375) using Lipofectamine RNAiMAX (Life Technologies), according to the manufacturer's instructions. Transfections were carried out in six-well plates at 50-60% confluency with 90 pmol of siRNA, and cells were harvested after 24, 48 or 72 h. Wound-healing assay. For assessing the migratory ability in a scratch assay, 7 × 10 4 melanoma cells (501 mel or A375) were transfected with FMR1-specific siRNAs or a scrambled siRNA or untransfected cells (CTR) and were harvested to silence FMRP expression.
jp/kegg) was used to identify pathways that were most significant to the data set. Nanostring nCounter analysis. Hundred nanograms of total RNAs (previously RNA-seq analyzed) from 501 mel cells were subjected to Nanostring nCounter analysis, using the GX Human Cancer Reference Kit (Nanostring Technologies (Seattle, WA, USA); no. GXA-CR1-12). Among the 230 genes of this GX Kit, 91 overlap with the RNA-seq gene set. Nanostring nCounter analysis was performed following the manufacturer's protocol. Raw counts for each mRNA were normalized and analyzed using nSolver Analysis Software (Nanostring Technologies) to obtain differential expression analysis of 91 genes in FMR1-depleted cells versus WT cells. Human tissues collection and patient information. The studies described in this paper that involve human samples have all been performed with informed consent from the patients.
Human melanomas and cells from patients used in this study were provided by Pathological Anatomy Units of Campus Bio-Medico University (Rome, Italy) and by the University of Turin (Turin, Italy). All experiments involving human specimens were conformed to the principles described in the NMA Declaration of Helsinki and the NIH Belmont report. The histopathological diagnoses of the tumors were described according to the World Health Organization International Classification of Disease for Oncology. The clinical staging was determined in agreement with the AJCC 2009 staging system. 69 All human tissues were collected following standardized procedures and informed consent was obtained for all specimens linked with clinical data according to procedures approved by the Institutional Ethical Board of the European Institute of Oncology.
A correlation plot for the 91 genes present in both RNA-seq gene set and Nanostring nCounter gene set. Ninety-three percent of the data points were present in the same dial of the graph (i. e. change in the same direction). (c) DEA of 311 more relevant FRGs showing the mRNA enriched in different disease categories. Brackets indicate the number of genes in each category. The x-axis represents the Log 10 of P-value adjusted by the multiple test (adjP), and the dashed line indicates the significance threshold (Log 10 (adjP) = 1. 3). (d) Pathway analysis of 311 FRGs, using Gene Ontology (GO) functional annotation of Web Gestalt bioinformatics database. Partial diagram of 'cellular component' and 'biological process' categories, with the most significantly affected pathways. Brackets indicate the number of genes in each category.
The x-axis represents Log 10 (adjP), and the dashed line the significance threshold (Log 10 (adjP) = 1. 3). (e) KEGG analysis. Pathways analysis of 311 FRGs, using KEGG functional annotation of Web Gestalt bioinformatics database. A pie chart of more representative pathways affected in KEGG functional annotation. In red, the adjP value are indicated for each category.
Acknowledgements. This research was supported by  FWO G. 0532. 12N ,  KU Leuven OT/12/089 ,  Etat de Vaud ,  Telethon GGP15257 ,  Associazione Italiana Sindrome X Fragile  (to CB),  Associazione Romana Ricerca Dermatologica  (to VP), FIRB 2010 (to RAR),  Diagnostic Molecular  fund (to GP) and by private funds (to SM). We thank  Florian Rambow  for the in silico analysis on melanoma cell lines and  Fiona Hollis  for discussions and proofreading of the manuscript.
Hymenoscyphus fraxineus version2 genome approaches chromosome level contiguity The Hymenoscyphus fraxineus version 2 genome assembly (Hf-v2. 0) is 62. 08Mbp (Fig. 1 ). Hf-v2 contains the information present in 1672 contigs of Hf-v1. 1 in just 23 scaffolds (Supplementary Information 2). Both Hf-v1. 1 (published as a pre-print  29  and Hf-v2 were released (open) online and hosted on the OADB github repository (https://github. com/ash-dieback-crowdsource  30 ). The Hf-v2. 0 gene annotation pipeline built on that of Saunders et al. 29  and identified 10,945 genes (11,097 transcripts, 1516. 50 mean CDS length, 3. 49 mean exons per gene; Supplementary Information 3). Telomeric repeats are identified at both ends of 14 scaffolds and, at one end of all but one of the remaining scaffolds (Supplementary Table  2 ).
1  29  was replicated on Hf-v2 adding newer RNA-seq data available via the Open Ash Dieback repository (Supplementary Information 3). The Hf-v2 genome was repeat masked using de novo modelled repeats, known fungal and low complexity repeats (REPEATMODELER v1. 0. 8, REPEATMASKER v4. 0. 5  68  ). Annotations (9,737) from Hf-v1. 1 were transferred to Hf-v2 using GMAP  69. AUGUSTUS v2. 7  70  was used to predict additional protein-coding genes using protein and RNA-seq alignments (16 libraries , Supplementary Table  16 ) with the previously trained Hymenoscyphus fraxineus model. We ran a transcript extension protocol on all genes to identify whether those genes within 100bp of an upstream gene had evidence for extension. In addition, we checked this extended gene dataset for potential secreted proteins.
This protocol allowed the identification of 36 genes that had not previously been recognised as having a methionine start codon which were included into our effector-mining pipeline. We used the pipeline described by Saunders et al. 29, 37  to identify putative effectors. Briefly, SIGNALP4 (-t euk -s notm -u 0. 34) was run to identify transcripts with a signal peptide and from these TMHMM and TARGETP were used to remove transcripts with transmembrane or mitochondrial localisation signals  71. Finally, secreted proteins were clustered into tribes using TRIBEMCL v12. 135 (e-value 10 -5 )  72. We used T-REKS  73  to identify repeats, the Disulfide algorithm  74  to predict disulfide bonds and PREDICTNLS  75  to predict nuclear localisation signals.
Phasing fruiting body samples SHAPEIT2  79  , used to phase fruiting body data, first applies a phase informative read step (extractPIRs) to group SNPs present on the same read pair and then phases remaining SNPs using population level polymorphism in the second step (assemble --states 1000 --burn 60 --prune 60 --main 300 --effective-size 564000 --window 0. 5). All Japanese samples including the haploid strain were used in the phasing step. As part of the phasing process indels must be removed from the dataset. Effective population size (ϑ=2Neμ) was calculated assuming a mutation rate of 5×10 -9 per generation per site  80  using the mean nucleotide diversity present in 100Kbp windows in the Japanese population.
SNP diversity and divergence analysis Polymorphism statistics and sliding windows i. e. sites per individual, SNP depth, transition/transversion (Ts/Tv) ratio, linkage (--thin 1000; --ld-window-bp 500000), π and FST were conducted using VCFTOOLS v0. 1. 13 & BEDTOOLS v2. 22. 0  81  & custom scripts (https://github. com/mcmullan0). SNPEFF V4. 2  82  was used to categorise the impact of variants. Effectors are, on average, shorter than other genes and so we controlled for the number of variants present per feature by dividing by feature length. DNASP v5. 10. 01  83  was used to calculate population genetic statistics per gene after fasta conversion using GATK v3. 5. 0 FastaAlternateReferenceMaker 84 and PAML v4. 9  85  was used (YN00) in pairwise mode to calculate the average ka:ks ratio nonsynonymous (ka) and synonymous (ks) values for each gene with at least one synonymous mutation.
The sm package  86  was used in R v3. 2. 1 to compare the density distributions of ka:ks. Output data for each gene (including effectors) from SNPEFF, DNASP & PAML are combined into a single table. Data are represented using SPLITSTREE v4. 13. 1  87  using default parameters (CDS of all genes and separately for each CEG) and CIRCOS v0. 67-5  88.