dataesr/scientific-paragraphs-categorization huggingface dataset:
• Genes that have ketoacyl-synt (PF00109) and Ketoacyl-synt_C (PF02801) but not Acyl_transf_1 (PF00698) were predicted as 'PKS-like' genes. • Genes that have at least the three domains AMP-binding (PF00501), PP-binding (PF00550), and Condensation (PF00668) were predicted as 'NRPS' genes. • Genes that have an AMP-binding (PF00501) domain and at least one of the domains PP-binding (PF00550), Condensation (PF00668), NAD_binding_4 (PF07993), and Epimerase (PF01370) were predicted as 'NRPS-like' genes. • Genes that have both 'PKS' and 'NRPS' domains were predicted as 'Hybrid' genes. • Genes that have a Trp_DMAT domain were predicted as 'DMAT' genes. • Genes that have Terpene_synth (PF01397) or Terpene_synth_C (PF03936) domains were predicted as 'Terpene cyclase/synthase' genes. Secondary metabolite-specific PFAM domains were taken from Supplementary Table  2  of the SMURF paper  61.
Prediction of secreted proteases. Secretome prediction was done using an in-house adaptation of SignalP 62. Gene-compound assignment. Identification of conserved or highly similar fungal gene clusters was performed on the basis of the gene cluster predictions above. The genomes were compared using the BLASTp function from the BLAST+ suite  63. Presence/absence of an orthologous gene to a member in a gene cluster was based on a bidirectional best hit, with e < 1 × 10 -100 and coverage of > 90%. Presence/ absence of a full gene cluster was based on the occurrence of the majority of the predicted members in a gene cluster, including the backbone synthetase in another species. Detection of encoded CAZymes. Each Aspergillus protein model was compared using BLASTp to proteins listed in the Carbohydrate-Active enZYmes database (CAZy)  64.
2. 27 ( e-value cut-off ≤ 1 × 10 -10 ). These 1. 444 whole-genome BLAST tables were analyzed to identify bidirectional hits in all pairwise comparisons. Using custom Python scripts, homologs were identified within and across the genomes and grouped into sequence-similar families using single linkage, if they met the following criterion: The sum of the alignment coverage between the pairwise sequences was > 130%, the alignment identity between the pairwise sequences was > 50%, and the hit must be found in both of the species' BLAST output (reciprocal hits). Singletons were assigned a family having only one gene member. This allowed for identification of species-unique genes as well as genes shared by sections, clades, and sub-clades of species. All homologs were assigned functional and structural domains using InterPro version 48  65  and checked for annotation and sequencing errors by investigating scaffold location and sequence identity.
Primary metabolism. Copy numbers were assessed using the homologous protein families generated during the analysis of genome diversity. The gene pathway associations were taken from the A. niger genome-scale model  41. All proteins in the respective protein families were considered putative isozymes and were included in the copy number analyses. Comparing the putative isoenzymes in the different species, gene sequences were aligned and clustered using neighbor-joining with MUSCLE v3. 8. 31  68. Resulting trees were visualized and edited for publication using the Python ETE Toolkit  67. Subcellular localizations for the genes included in the analysis were predicted using the TargetP  69  web server (see URLs). Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements T. C. V. , J. L. N. , S. T. , and M. R. A. acknowledge funding from The  Villum Foundation/ Villum Fonden , grant  VKR023437. J. B. and M. R. A. acknowledge funding from the  Novo Nordisk Foundation , grant  NNF13OC0004831. M. R. A. and T. C. V. acknowledge funding from the  Danish National Research Foundation  ( DNRF137 ) for the Center for Microbial Secondary Metabolites. J. C. F. acknowledges funding from the  Novo Nordisk Foundation , grant  NNF13OC0005201. Work performed at the  US Department of Energy (DOE) Joint BioEnergy Institute  is supported by the  US DOE, Office of Science, Office of Biological and Environmental Research , through Contract No. DE-AC02-05CH11231  between  Lawrence Berkeley National Laboratory  and the  US DOE. The work conducted by the  US DOE Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the US DOE  under Contract No.
Carbohydrate-Active enZYmes Database, http://www. cazy. org; RoerdamAndersenLab GitHub repositories of scripts for comparative genomics, https://github. com/RoerdamAndersenLab/; HGAP Assembly, http://files. pacb. com/software/smrtanalysis/2. 2. 0/ doc/smrtportal/help/!SSL!/Webhelp/CS_Prot_RS_HGAP_ Assembly3. htm; JGI fungal genome portal MycoCosm, http://jgi. doe. gov/fungi; JGI website, http://jgi. doe. gov; Joint BioEnergy Institute website, http://www. jbei. org; Technical University of Denmark (DTU) Bioinformatics TargetP web server, http://www. cbs. dtu. dk/services/TargetP/.
Some tools have been developed through the years to help students to create meaningful artefacts, like Scratch (https ://scrat ch. mit. edu/), a free educational programming language that was developed by the Lifelong Kindergarten Group at the Massachusetts Institute of Technology (MIT). Understanding the need for innovation and the potential of these new approaches, the Italian Ministry of Education (MIUR) put into action the National Plan for Digital School (PNSD) which aims to set up a comprehensive structure of renewal for Italian school. Many activities have been supported by PNSD to introduce and educate students to digital technologies  (Avvisati et al. 2013; Bottino 2014). One of these activities is to train in-service teachers on didactic innovation. PNSD establishes that the professional development of teachers will take place in the poli (schools leading other set of schools) and in the snodi formativi territoriali (schools which host training courses for a particular area of Italy).
The total number of teachers, ranging from kindergarten to upper secondary school, considered for the statistical study described in Statistical methods, is 184. This sample is described in Table  2  and Fig. 2. Personal data and data on their professional life are reported in Table  2. Figure  2  represents the histogram of teachers' usage of technology in the classroom by category. "Robot" stands for use of robots, either in kit or already assembled, in classroom for activities. "LIM" (Lavagna Interattiva Multimediale) is the Italian acronym for the electronic whiteboard. "Tablet/PC" stand for the use of PC or tablet in his/her teaching activities. "Boards" mean electronic boards like Arduino, Raspberry and many other choices the market offer. "SW_Coding" is the software dedicated to computer programming (i.
e. Scratch). "SW_Didactical" is the software dedicated to didactical purposes (for example, learning geometry, to visualize history and geography,…). "Text Editor" is the category for software dedicated to text editing. "AV_Editor" means the software for Audio-Video editing. "Other" collects all those technologies teachers couldn't classify satisfactorily in the previous categories. From Fig. 2  we can see that the clear majority of teacher had previous experience in using tablet and pc to work with students. Also, LIM seems to be very popular amongst teachers' work. This may indicate a good result for the Italian policies aiming at integrating this device into all classrooms. On the other hand, the use of robots, software for coding activities, boards and other   )  and other policies addressing the use of computers at school started before PNSD and they have given results in the past years.
8 ). It's easy to see the connections between the two models: Connect phase corresponds to Think step, Construct with Make step, Contemplate with Improve step and Continue with the loop structure of the TMI model. Workshop then continued with a practical activity: kindergarten teachers built a more complex robot with respect to their previous experience (a robotic lion using Lego WeDo kit), primary and secondary teachers installed a sensor in the robot they had previously built and programmed their artefact to interact with the environment. For example, they used the motion sensor of the Lego WeDo kit to measure the distance from an object (Fig. 9 ) or the robot rover to avoid obstacles on Mars following the code in Fig. 10. Thus, teachers had the possibility to create a simple "intelligence" for their robot, useful to integrate other sensors, and not only an ON-OFF logic.
A factor analysis and reliability analysis were carried out using IBM SPSS v. 25. Items considered for these analyses are those regarding ER, Coding and Tinkering (9 items). Data was gathered from teachers not interested in participating to all the experiment and just filled B1 at the beginning of the course. 285 teachers participated in this part of the study. Sampling was not an issue because this number of observations respects the rule of thumbs of having at least 10-15 participants per item. Moreover, applying the Kaiser-Meyer-Okin (KMO) measure of sampling adequacy, the dataset scores 0. 862, a good value. The correlation matrix (Table  3 ) shows good correlations between items. None of the correlations falls under r = 0. 3. On the contrary, it should be noted that correlation is high (r > 0.
Statistical Methods Data from B1-BL, B1-PT, B2-BL and B2-PT were recoded. The 10-points Likert scale that respondents to B1-BL and B1-PT used was discretized and divided in five classes: class I was associated with very low level, class II with low level, class III with medium level, class IV with good level, class V with a very good level. This was necessary due to the granularity of data. Also the answers to B2_BL and B2_PT were classified considering different situations. Each subject could in fact give two correct answers (class III), only one correct answer (class II) or no correct answers, both missing and/or wrong answer to the two questions (class I) for each methodology. So, class I represents the lowest level of knowledge, class III the highest and class II the medium level. To establish if the differences among the values collected for a specific variable are due to chance (null hypothesis, H0) or not (alternative hypothesis, H1) some statistical tests were applied: the p value indicates the significance level at which H0 is rejected and H1 is accepted.
Acknowledgements Authors would like to show their gratitude to  Dr. Marta Balietti  for her professional support in analysing data. A special thank you goes out to the  Headmaster of Istituto Comprensivo "Giovanni Pascoli" (Silvi, TE) and I. I. S. "Crocetti-Cerulli" (Giulianova, TE)  for having made possible the fulfilment of the project. This material is based upon work supported by the  Italian Ministry of Education  with programme:  Fondi Strutturali Europei -Programma Operativo Nazionale "Per la scuola , competenze.
II. NUMERICAL METHODS FOR A WAVE PROPAGATION In our study, we simulate a wave propagation with three dimensional spectral-element method (SEM) for the discretization scheme in spatial dimension. We used a numerical code called SPECFEM. SPECFEM is a generic name of numerical codes, belonging CIG (Computational Infrastructure for Geodynamics), for a wave propagation simulation which uses the SEM. Initially this was developed by Dimitri Komatitsch and Jean-Pierre Vilotte at Institut de Physique du Globe (IPGP) in Paris, France from 1995 to 1997 and then by Dimitri Komatitsch and Jeroen Tromp at Harvard University and Caltech, USA for a seismic wave simulation. [16, 17, 30]  On SPECFEM, continuous Galarkin typed SEM with higher (4 th )-order Gauss-Lobatto-Legendre is implemented. The higher order basis function brings better accuracy for surfacewave, acoustic-elastic coupling problems than the other types of finite-element method using lower-order polynomials.
A. PLAJEST experiment / CFD simulation We selected an experimental and numerical study called PLAJEST as the modeling target of our study since PLAJEST is also targeting the same object of the same condition i. e. the upper-core region of SFR in operation. This study program is collaboratively worked between Japanese Atomic Energy  Agency (JAEA), the U. S. Department of Energy (DOE) and CEA. The configuration of this experiment is shown in Figure  1. The main purpose of PLAJEST is to observe heat conduction at a liquid-solid boundary in a sodium cooling circuit because the thermal fluctuations lead to high frequency thermal fatigue and crack in adjoining structures of SFR. After the experiment by JAEA  [14]  [15], thermofluid analysis has been carried out by CEA/STMF  [4]. STMF used a CFD tool called TrioCFD (known as Trio_U by 2015) for their calculation and LES was employed as a turbulent model.
The Finite-Element (FE) Mesh for SPECFEM3D is generated by FE meshing software CUBIT (developed by Sandia National Laboratories, United States). Both 1st and 2ndorder hexahedral FE may be imported to SPECFEM. We selected 1st-order elements for our simulation because the targeted model does not have any curved geometry. It is necessary to convert the data format while the mesh data is exported from CUBIT to SPECFEM3D. In order to speed-up this conversion process, we used IOSS (IO Systems) library included the FE analysis supporting software SEACAS (also developed by Sandia National Laboratories, United States). After being imported into the SPECFEM, the GLL points are inserted into each element. Determination of an element size is done following 2 conditions, which are CFL condition  (3) where  is the interval of each time increment.
D. Interpolation of temperature field from CFD to wave simulation The element type of TrioCFD is a tetrahedral FE with the average element size  = 1. 400 × 10 -3 meter. The temperature value is defined on the central point of each element, while the element type of SPECFEM3D is a hexahedral with average element size  = 8. 050 × 10 -4. The temperature values need to be defined on the corner nodes and every internal GLL point. Thus the CFD temperature data needs to be interpolated for importing to SPECFEM3D. For this process, we used a software library so called MEDCoupling, which is one of the libraries included in a scientific numerical method supporting tool SALOME. SALOME is the open source software developed by CEA. By using MEDCoupling, interpolation of physical fields between several types of FE or nodes is possible.
2 ). Integration of annotation. We annotated variants for indicators of putative biological functionality using data from publically available databases. Intragenic variants were ascribed to genes relative to GENCODEv19, miRNA variants using MirBasev20 and variants situated within segments of the genome under evolutionary conservation were annotated using conserved element outputs generated by four algorithms (GERP++, SiPhy Omega, SiPhy Pi and Phastcons)  [20] [21] [22]. For information derived from tissue-based experimental data sets, we focused primarily on those conducted in prostate cell lines; specifically DNaseI hypersensitivity sites in three prostate cell types from seven experiments in the ENCODE project, chromatin-state characterisations by ChromHMM from Taberlay et al. 23  , ChIP-seq peak locations for a variety of transcription factor (AR, CTCF, ERG, FOXA1, GABPA, GATA2, HOXB13 and NKX3.
2 , Supplementary Data 2). In particular, enrichment was found for eQTLs in the TCGA data set (P = 1. 15 × 10 -23 ); intragenic variants within protein-coding genes (P = 8. 15 × 10 -11 ; P = 6. 03 × 10 -5 for protein altering variants exclusively) but not non-coding transcripts (P = 0. 29); promoter (P = 1. 66 × 10 -8 ), enhancer (P = 3. 42 × 10 -6 ) and transcribed (P = 3. 07 × 10 -7 ) ChromHMM states in prostate epithelial cells; DNaseI hypersensitivity sites from all seven ENCODE prostate data sets (P = 1. 28 × 10 -7 to 7. 61 × 10 -17 ); for Published GWAS SNPs for which the signal or region replicated in our EUR meta-analysis are indicated, alongside the region co-ordinates assigned for fine-mapping analyses (GRCh37/hg19 assembly). The final priority pruner thresholds used and numbers of variants and priority pruner tags included in the analysis are shown.
Region boundaries for dense genotyping were defined as the greater of ±500 kb from the index SNPs or the maximum distance of any variant with r 2 > 0. 3 to the index SNP in 1KG (phase 1 version 3, March 2012 release). All SNPs within these regions with MAF > 0. 01 in any ancestral population were extracted and then we obtained Illumina Design Scores for all variants from the 1000 Genomes Project (phase I version 3, March 2012 release). From designable variants with a Design Score ≥ 0. 8, we used Snagger  44  to select (a) all variants correlated with the known hits at r 2 > 0. 6 and P < 0. 05 in the iCOGS study, (b) all variants from lists of potentially functional variants, defined through ENCODE and RegulomeDB and (c) a set of SNPs to tag all remaining variants at r 2 > 0. 9. The 23 risk loci reported in a recent multi-ethnic meta-analysis study  12  were not densely genotyped as these loci were reported after the OncoArray design; however, these regions were also fine-mapped in this study.
html) or an in-house software C++ programme. OR estimates and standard errors were combined by a fixed effects inverse variance meta-analysis using METAL  45. All statistical tests conducted were two-sided. IMPUTE2 was used to impute non-genotyped SNPs within a boundary flank of ±500 kb or the maximum distance of any variant with r 2 > 0. 3 to the index SNP in 1KG phase 1 from the originally reported GWAS index SNP in the meta-analysis cohort. For the OncoArray data, un-phased imputation was carried out for all the fine-mapping regions. Where the boundaries of adjacent associations to fine-map overlapped, these were merged for imputation; therefore, imputation was performed as 82 discrete chunks. Within 3 of these chunks the separate signals to analyse were sufficiently dispersed to enable clear demarcation of the individual signals and retention of an appropriate flank distance; these 3 imputation chunks were therefore split prior to statistical analysis and the 92 original index SNPs analysed were fine-mapped as 85 separate regions.
Overall, 3761 of the 4142 unique SNPs selected by either JAM run were retained in the combined top models from both runs (90. 8%), with a further 61 variants with BF < 1 removed to achieve the final 95% credible set. Annotation of variants for functional features. Variants were annotated for a number of putative indicators of biological functionality or importance, using a range of publically available data sources. These annotations focussed on either the likely consequence or relevance of the variant resulting from its primary genomic context, or the proximity to annotated regulatory features within cell lines derived from normal prostate or PrCa tissues. Gene-based annotation of variants was performed using wANNOVAR in relation to GENCODEv19 transcripts  58. Variants residing within miRNA transcripts were subsequently added in relation to miRBase release 20 (ftp:// mirbase.
We annotated variants that intersected DNaseI peaks data in seven individual ENCODE prostate data sets from three cell lines (LNCaP, PrEC and RWPE1; GSM816637, GSM816634, GSM1008595, GSM736565, GSM736603, GSM4742 and GSM4743)  61, 62. Peak data from ChIP-seq experiments for transcription factor-binding sites and histone modifications in the LNCaP, PC3, PrEC and VCaP cell lines and human prostate tumour tissue was downloaded from the Cistrome Data Browser (http://cistrome. org/db/); a resource that accumulates publically available ChIP-seq data sets and re-analyses their raw data through a standardised pipeline and QC procedure  24. Downloaded CistromeDB data were converted from GRCh38 to GRCh37/hg19 reference assembly co-ordinates for compatibility with our variant data set using the UCSC Genome Browser LiftOver tool (https://genome.
ucsc. edu/cgi-bin/hgLiftOver). Transcription factor-binding site data were obtained for the Androgen Receptor (GSM1236922, GSM1328945 and GSM1576447), CTCF (GSM1006874 and GSM1383877), ERG (GSM1193657 and GSM1328978), FOXA1 (GSM1068136, GSM1274873 and GSM1716762), GABPA (GSM1193660), GATA2 (GSM1600544), HOXB13 (GSM1716763 and GSM1716764) and NKX3. 1 (GSM699633 and GSM989640). Histone modification data were obtained for H3K27Ac (GSM1249447 and GSM1249448), H3K27me3 (GSM1383866 and GSM1383872) and H3K4me3 (GSM1383874 and GSM945240). Finally, to facilitate deeper categorisation of the genomic context of variants within prospective regulatory features, they were annotated with their chromatin state categorisations by ChromHMM from two prostate cell lines (PrEC and PC3; GSE57498), alongside three ENCODE tier 1&2 cell lines (GM12878, H1HESC and HUVEC) to enable comparison of tissue specificity for prospective regulatory elements  23, 63, 64.
Second, we ranked the P-values in descending order for each eQTL. Finally, we calculated the colocalisation score for each pair of eQTL and imputed SNPs as: Colocalisation score ¼ N À rank ð Þ =N where N is the total number of imputed SNPs in that region and rank is the rank of the imputed SNP we are including. In general, if an eQTL and an imputed SNP represent the same signal, this will be reflected by the imputed SNP having a high P-value, a low rank and consequently a high colocalisation score. Quantile regression. Conditional QR across variant annotations was performed for the 75 regions successfully fine-mapped using JAM, with the 5 regions in which JAM was unable to resolve candidate variants excluded from this analysis. To minimise correlation between annotations, single data sets for each transcription factor, histone mark, DNaseI, conserved element and chromatin state by ChromHMM category were selected for investigation with conditional QR.
Specifically, the GSM736603 DNaseI, GSM1328945 AR, GSM1383877 CTCF, GSM1193657 ERG, GSM1068136 FOXA1, GSM1193660 GABPA, GSM1600544 GATA2, GSM1716763 HOXB13, GSM989640 NKX3. 1, GSM1249447 H3K27Ac, GSM1383872 H3K27me3, GSM945240 H3K4me3 and GERP++ conserved element annotation fields were selected, as these were observed to be most informative for variants within the 95% credible set, whilst the PrEC cell line ChromHMM annotation was selected over the PC3 data set due to its origin from normal prostate rather than cancerous tissue. Similarly, CADD RawScore was selected, with CADD PHRED score excluded prior to the analysis. Information on whether variants were an eQTL in the TCGA data set was included. Finally, new categories were computed to ascertain whether a variant was situated within a protein-coding transcript (intronic, exonic or untranslated region), within a non-coding transcript, and whether the variant altered protein structure (non-synonymous, non-sense, frameshift or non-frameshift insertion/deletion coding variants).
91, 1. 10]. For the FRR calculation, we specified a prior distribution as λ 0 $ N 2:5; 0:14  2  ð Þ , which places a 95% prior density in the range [2. 22, 2. 78] on the FRR of PrCa. This calculation was performed using the JAGS software  70. To collapse our catalogue of credible variants identified through fine-mapping into a parsimonious set of SNPs matching the observed number of independent signals, we selected single representative lead variants to represent each signal. For the 63 regions in which JAM identified only a single signal, these were designated as the tag with the highest posterior evidence for association, whereas for the 5 regions in which JAM did not resolve candidate variants the variant most strongly associated with PrCa in the original meta-GWAS was designated as the novel lead variant.