dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the contents of Ca, Mg, and Mn were determined in sediments and deposit extracts by atomic absorption spectrometry using previously developed methodology  (Niedzielski et al. 2015a ) and the factors Ca/Mg and Fe/Mn have been calculated. For quality control, the certified standard materials (for total concentration of elements) and standard addition method (for iron speciation) have been used. The statistical analysis has been provided using Statistica 10 software.
Reads were aligned onto the assembled sequences and 500-bp nonoverlapping sliding windows along the assembled sequence were used to calculate GC content and average read depth. Repeat rate was calculated based on contig length and depth analysis. Each contig was categorized as unique, repeat, similar, or error. Error was based on contigs with less than 0. 1-fold coverage; repeat was based on contigs with more than 1. 8-fold coverage; similar was defined as having similarity higher than 0. 95% between contigs of the same length with the average coverage between 0. 1-and 1. 8fold; the remaining were classified as unique. Repeat contigs included short contigs with lengths <100 bp and coverage no less than 1. 5-fold and long contigs with length >100 bp and coverage no less than 1. 8-fold. The complete nucleotide sequence assemblies from both species and their annotations can be found online at the genome resource site of NCBI (BioProject ID: PRJNA318028 and PRJNA318026).
Ab Initio Putative Gene Model Prediction The ab initio putative gene model (PGeneM) prediction was performed using two different approaches. Initially PGeneMs were predicted from the assembly results using AUGUSTUS version 2. 6. 1  (Stanke et al. 2004 ), trained with P. sojae gene models (Joint Genome Institute Phytophthora sojae v3. 0 database). Secondly, PGeneMs were predicted using MAKER  (Cantarel et al. 2008). The MAKER pipeline masked repeats with RepeatMasker  (Cantarel et al. 2008) , used SNAP  (Korf 2004 ) and AUGUSTUS for de novo gene prediction, and used Phytophthora peptides and Pmeg/Ppal RNA-Seq transcriptomes for evidence-based gene prediction. SNAP was trained with Core Eukaryotic Genes Mapping Approach (CEGMA) genes identified from either the Pmeg or Ppal genome, respectively, whereas AUGUSTUS was trained with the previously identified genes of P.
infestans, P. parasitica, P. capsici, P. cinnamomi, P. sojae, and P. ramorum. The predicted proteins were compared against NCBI nonredundant (NR) protein databases by BLASTp to identify biological functions  (Altschul et al. 1997). The predictions in overlapping PGeneMs from the two different approaches were ranked according to the following criteria and the "best" ab initio PGeneM according to the stipulated criteria was selected for functional annotation. The criteria for the ranking of the PGeneMs were the following: 1) Manual annotation had priority over all other evidence; 2) Prediction with similarities to known proteins then had priority. A prediction was considered to be similar to a known protein if it had an E-value of at most 1e-10 (BLASTP against NR+ Phytophthora protein set); 3) If two overlapping PGeneM predictions had similarity with known proteins, the one with better a coverage score had priority; 4) In clusters without similarity with known proteins and without expressed sequence tag (EST) evidence, the priority was for the MAKER PGeneM prediction.
RNA reads from RNA-Seq libraries ranging from 50 to 70 million reads in fastq format were aligned using memory-efficient short-read aligner Bowtie-2-2. 1. 0 (Langmead and Salzberg 2012)  to the coding sequences (CDS) of the Pmeg and Ppal genomes. Tabulated raw counts of reads to each CDS were obtained from the bowtie alignment. Raw counts were normalized using the DESeq package in the R statistics suite  (Anders and Huber 2010). For DESeq's default normalization method, scaling factors are calculated for each lane as median of the ratio, for each gene, of its read count of its geometric mean across all lanes and apply to all read counts.
Determining Secretomes Pmeg and Ppal protein-coding sequences were scanned for possible signal peptides using SignalP, version 3. 0  (Petersen et al. 2011). The amino acid sequences containing predicted signal peptides were scanned for transmembrane proteins using the TMHMM program (prediction of transmembrane helices in proteins)  (Sonnhammer et al. 1998). Proteins with no more than one transmembrane domain were considered potential components of the secretome.
RxLR Motif Finding The predicted RxLR effector protein set of P. sojae and P. ramorum was used to perform PSI-BLAST against the Pmeg and Ppal ab initio PGeneM. Significant hits (Evalue <1e-5) were manually checked for the presence of an RxLR-dEER domain and N-terminal signal peptide. By using the program HMMER 2. 3. 2 (http://hmmer. org/download. html, last accessed 16 February 2017) (Eddy 1998), two hidden Markov models (HMMs) were built from the candidates mentioned above, one using the RxLR motif and ten amino acids on the left side and the other using the complete RxLR-dEER domains, with variable spacing arbitrarily placed in between. The predicted protein set, as well as all six-frame translations from both genomes were screened using the HMMs. Proteins with a significant HMM score (E-value <0.
05) were considered as candidates. BLASTp searches within and between Pmeg and Ppal RxLR PGeneMs were conducted to identify relationships within and between the Pmeg and Ppal RxLR gene family members. Similar RxLR PGeneMs were identified based on at least 50% sequence identity with E-value less than 1e-10 between two or more PGeneMs. Transcript Profile Analysis by Self-Organizing Map Analysis Tabulated raw counts of CDS reads were obtained from the bowtie alignment. Read counts were normalized using the DEseq package  (Anders 2010 ) and R x64 2. 15. 2 program (http://www. r-project. org/, last accessed date 16 February 2017). Normalized RNA-Seq count were log 10 transformed and neural network self-organizing map (SOM) analysis  (Kohonen 1982 (Kohonen , 1990 ) was employed (using MATLAB Neural Network Toolbox) to provide an unsupervised multivariate and nonlinear classification of the transcriptome into 24 different classes.
ramorum (65 Mb)  (Tyler et al. 2006)  which are from clades 7 and 8, respectively. They are also larger than another clade 4 species, P. litchii, which has a 58-Mb genome  (Ye et al. 2016). However, they are considerably smaller than P. infestans (240 Mb)  (Haas et al. 2009 ) from clade 1  (Blair et al. 2008). The average GC% was similar for both Pmeg and Ppal (48. 9%) as well as for P. litchii  (Ye et al. 2016) , but less than three other Phytophthora genomes (51-54. 4%) (Tyler et al. 2006; Haas et al. 2009 ). The sequence and annotations are available from NCBI (BioProject ID: PRJNA318028 and PRJNA318026).
Transcription of the Expanded Genomes of Pmeg and Ppal Transcripts from 14,915 Pmeg PGeneMs and 25,617 Ppal PGeneMs could be detected. If all PGeneMs with at least one read in the raw data were considered transcribed (see supplementary Excel file S2, Supplementary Material online) it would increase the number of potentially expressed PGeneMs to 17,531 and 28,534 in Pmeg and Ppal, respectively. Considering that both members of duplicated gene pairs in Ppal would be identified as transcribed by the sequence matching software, the 25,617 transcribed genes in Ppal could correspond to 12,808 gene pairs, comparable to the 14,915 transcribed PGeneMs in Pmeg. As the RNA-Seq experiment estimated transcript expression levels via 90-bp read counts, reads aligning similarly to PGeneM pairs were sorted randomly.
Transposons in the Pmeg and Ppal Genomes Phytophthora genomes carry many TE-related sequences  (Tyler et al. 2006; Haas et al. 2009 ). Both the Pmeg and Ppal genomes contain rich and diverse populations of TEs (supplementary Excel file S1, sheet 3 & 4: Pmeg/Ppal TE, Supplementary Material online). TE-related PGeneMs represent 15. 0% (6,288; 174 expressed) and 13. 6% (6,025; 281 expressed) of the Pmeg and Ppal PGeneMs, respectively. There are 550 Helitron helicase-related PGeneMs in Pmeg compared with 246 in Ppal, and 273 in P. infestans  (Haas et al. 2009). Similarly there are 35 Ty3-gypsy retrotransposons identified in Pmeg compare with just 9 in Ppal. Many gene duplications might have resulted from transposon activity  (Van Hooff et al. 2014). Class I retrotransposons, such as Gypsy elements, create duplicate genes in new genomic positions through the reverse transcription of transcripts from expressed genes.
Eleven out of the 13 and 17 out of 21 PGeneMs selected on the basis of RNA-Seq results showed correlation coefficients greater than 0. 80 between the RNA-Seq and RT-qPCR results for Pmeg and Ppal, respectively (fig. 3  and supplementary Excel file S1, sheet 1: RNA-Seq-RTqPCR comparison, Supplementary Material online). In general, PGeneMs with limited treatment response variation or low RNA-Seq read alignments across treatments gave lower correlations between RNA-Seq and RT-qPCR results. Five Ppal homeologous pairs were included in the RNA-Seq/RT-qPCR analysis (fig. 3 ; supplementary Excel file S1, sheet 1: RNA-Seq-RTqPCR comparison, Supplementary Material online). As expected, RNA-Seq could not distinguish the transcript levels of the individual homeologs, and near identical profiles were assigned by the software.
Branches are labeled with bootstrap values. There were a total of 30 informative positions in the final data set. Evolutionary analyses were conducted in MEGA5  (Tamura et al. 2011). (B) For the relative transcription profiles, RNA-Seq read counts for zoospore and in planta libraries were normalized by read counts in the mycelium library and values were LOG 10transformed. The heat map was generated using CIMminer (http://discover. nci. nih. gov/cimminer). Self-organizing classes were as in figure  4. Large bold numbers indicate phyletic groups. White blocks indicate no detectable transcription.
RxLR Effectors The largest and most diverse family of virulence-related genes identified in the Pmeg and Ppal genomes is the superfamily of RxLR effectors, also known as Avh genes. They possess signal peptides and N-terminal amino acid motifs (RxLR and dEER) required for targeting into host cells together with diverse, rapidly evolving carboxy-terminal effector domains  (Tyler et al. 2006; Jiang et al. 2008 ). These C-terminal domains exhibit virulence activities such as suppression of host cell death  (Bos et al. 2006; Dou, Kale, Wang, Chen, et al. 2008; Wang et al. 2011). Using HMM searches, 1,181 and 991 RxLR PGeneMs were detected, of which, 336 and 415 matched transcripts in Pmeg and Ppal, respectively (see supplementary Excel file S1, sheet 11 & 12: Pmeg/Ppal RxLRs, Supplementary Material online).
Instead, we generated a percent identity matrix of the combined 751 Pmeg-and Ppal-expressed RxLRs (see supplementary Excel file S1, sheet 13: %IM Pmeg & Ppal RxLRs, Supplementary Material online) using the sequence alignment program Clustal Omega (http://www. ebi. ac. uk/Tools/msa/clustalo/, last accessed 16 February 2017). Ppal exhibited 248 expressed RxLRs having !70% sequence identity with at least one paralog. In contrast Pmeg RxLRs were highly diverse; only 96 expressed RxLRs had !70% sequence identity with at least one paralog. There was only one pair of expressed RxLRs sharing !70% sequence identity between Pmeg and Ppal. Out of the 751 transcribed RxLR PGeneMs, 245 and 228 from Pmeg and Ppal, respectively, fell within the SOM classes exhibiting elevated expression in both zoospores and in planta or in planta alone (fig.
Acknowledgments This work was funded by the  USDA/ARS  and  The Pennsylvania State University's College of Agricultural Sciences , The  Huck Institutes of the Life Sciences, Penn State Endowed Program in Molecular Biology of Cacao. Special thanks are given to the  Hershey Company  which supported the sequencing of the Phytophthora megakarya genome through a gift to the  Penn State Endowed Program in Molecular Biology of Cacao. This work was supported by a grant from the  Hershey Company  to MJG, The  Pennsylvania State University's College of Agricultural Sciences , The  Huck Institutes of the Life Sciences , the  Penn State Endowed Program in Molecular Biology of Cacao  and by the  USDA National Institute of Food and Agriculture Hatch  project  1003147. References to a company and/or product by the  USDA  are only for the purposes of information and do not imply approval or recommendation of the product to the exclusion of others that may also be suitable.
A C B D Presence of core eukaryotic genes (CEG) within different Phytophthora spp. Phytophthora spp. Detected CEGs % Complete Total CEGs Average orthologs per CEG % CEGs with >1 ortholog P. megakarya 234 94. 35 351 1. 5 33. 76 P. palmivora 239 96. 37 564 2. 36 69. 78 P. sojae 240 96. 77 308 1. 28 22. 08 P. ramorum 236 95. 16 300 1. 27 20. 34 P. infestans 238 95. 97 314 1. 32 23. 53 Source: CEGMA http://korflab. ucdavis. edu/datasets/cegma/.
An extended description of the implementation of the deep geothermal wells GRT-1 and GRT-2 respectively drilled in 2012 and 2014 in the framework of the ECOGI project is given. These wells located in Rittershoffen (France) offer a unique opportunity to gather high quality datasets on a deep geothermal system in the Upper Rhine Graben at the transition between the Buntsandstein sandstone and the Palaeozoic granite basement. We present the extensive logging and well testing program that was applied and focus on hydraulic and thermal characterization of the targeted deep reservoir. Well architectures of GRT-1 and GRT-2 are described. Temperature logs in both wells are discussed in details. In particular, temperature in the production well GRT-2 is shown to reach 177 • C at 3196 m MD at thermal equilibrium.
The drilling of the first vertical well GRT-1 started in September 2012 and ended in December 2012 when the well reached a depth 2580 m MD within the fractured granite basement (see Fig. 6 ). The drilling rig was a MR8000 (hook load capacity 200To). No deviation tool was used. The maximum deviation is 8 • to the West (see Fig. 5 ). Thus, GRT-1 intersects the Rittershoffen fault just below the top basement, roughly around 2400 m MD. The drilling rig was demobilized after the first well in order to be able to perform a series of extended hydraulic tests and apply a coherent reservoir development strategy. Two new seismic profiles were acquired during summer 2013 in order to produce a better structural image of the reservoir for targeting the second well, GRT-2. Several post processing strategies were applied to this newly acquired seismic data which improved the geometrical understanding of the major local faults.
The downhole pressure recorded in GRT-1 shows a very clear tide signal. Thus, the signal was corrected using BETCO (Barometric Earth Tides Correction) software  (Toll and Rasmussen, 2007). The input data were GRT-1 downhole pressure data, surface barometric pressure and earth tides values. The corrected pressure showed in Fig. 12  was obtained using a sampling interval of 15 mn and a maximum lag time of 8 h. After these corrections, it appears that recorded pressure in GRT-1 is clearly correlated with production in GRT-2 (see interpretation below).
Injectivity and productivity indexes The well development strategy applied on GRT-1 was successful. As a result, the injectivity of the well could be multiplied by a factor 5 to reach 2. 5 l/s/bar at nominal flowrate of 70 l/s. The downhole overpressure versus the injection flowrate is showed in Fig. 14  for all injection phases. As no downhole pressure data were recorded during the thermal stimulation, a downhole pressure has been estimated using the fluid density measured at the surface (neglecting friction losses in the well as the flow-rate was quite low during this experiment). The final downhole differential pressure at nominal flowrate is estimated to 29 bar. The following observations can be made on this stimulation experiment: • The thermal injection experiment clearly shows a well injectivity increase over time.
Reservoir properties In order to characterize hydraulic properties of the reservoir, classical pressure transient analysis interpretations have been carried out with pumping test data from each well using AQTESOLV software based on the direct use of analytical solutions  (Baujard et al. , 2016). For GRT-1, only downhole drawdown data of the 9/01/2013 and both recovery data of the 5/01/2013 (unfortunately incomplete) and of the 9/01/2013 have been interpreted. As the data quality is quite poor, the interpretation of GRT-1 production tests was realised using a single permeability confined aquifer model  (Dougherty and Babu, 1984). For GRT-2, the step-drawdown test (phase 2. 1) as well as drawdown and recovery data (phase 2. 2) have been used for the interpretation. This time, the best results are obtained using a fractured confined aquifer model based on a double porosity approximation including a fracture skin effect  (Moench, 1984).
Acknowledgements The authors would like to thank  ECOGI, which is a joint venture between "Electricité de Strasbourg", "Roquette Frères ", and " Caisse des Dépôts et Consignation ". The authors also warmly acknowledge researchers of the  EOST team in Strasbourg ("Ecole et Observatoire des Sciences de la Terre "), which is a main actor of the  Labex ("Laboratoire d'Excellence") G-Eau-Thermie Profonde  for fruitful discussions and scientific support. The works described in this paper are supported by "ADEME" with the " Fonds Chaleur " as well as the " EGS Alsace" research program. A part of this work was done in the framework of the  DESTRESS Eu  project which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  691728.
The type 2 fraction The observed Type 2 fraction for the NuSTAR hard bandselected subsample described above is F Type 2 = 53 +14 -15 % (binomial uncertainties). If we instead use the sources selected in the full band (3-24 keV), a similar fraction is obtained (F Type 2 = 48 ± 11%). In Figure  16  we compare with the Type 2 fraction for nearby (z < 0. 05) AGNs similarly selected at high X-ray energies (> 10 keV). To obtain this data point we calculate the observed Type 2 fraction for the 70-month Swift BAT all-sky survey. Importantly, we use a luminosity-matched subsample of the Swift BAT survey (2 × 10 43 < L 10-40 keV < 2 × 10 44 erg s -1 , as for the NuS-TAR subsample), since the Type 2 fraction likely varies with luminosity. We apply a redshift cut of z < 0. 05 to ensure X-ray completeness (redshifts above this threshold push below the flux limit of Swift BAT for the adopted L 10-40 keV range; see Figure  12 ).
ACKNOWLEDGEMENTS The authors firstly thank the anonymous referee for the constructive comments. We acknowledge financial support from: the  Science and Technology Facilities Council (STFC)  grants  ST/K501979/1  (G. B. L. ),  ST/I001573/1  (D. M. A. ), and  ST/J003697/2  (P. G. ); a  Herchel Smith Postdoctoral Fellowship  of the  University of Cambridge  (G. B. L. ); the  ERC Advanced  Grant  FEEDBACK 340442  at the  University of Cambridge  (J. A. ); a  COFUND Junior Research Fellowship  from the  Institute of Advanced Study, Durham University  (J. A. ); the  Leverhulme Trust  (D. M. A. );  CONICYT-Chile  grants  FONDECYT 1120061  and  1160999  (E. T. ), 3140534 (S. S. ), and Anillo  ACT1101  (E. T. and F. E. B. ); the  Center of Excellence in Astrophysics and Associated Technologies  ( PFB 06 ; E.
T. and F. E. B. ); the  NASA Earth and Space Sci-ence Fellowship Program , grant  NNX14AQ07H  (M. B. ). We extend gratitude to  Felipe Ardila ,  Roberto Assef ,  Eduardo Bañados ,  Stanislav George Djorgovski ,  Andrew Drake ,  Jack Gabel ,  Audrey Galametz ,  Daniel Gawerc ,  David Girou ,  Marianne Heida ,  Nikita Kamraj ,  Peter Kosec ,  Thomas Krühler ,  Ashish Mahabal ,  Alessandro Rettura , and  Aaron Stemo  for their support during the ground-based follow-up observations. We thank  John Lucey  for unearthing the J1410 spectrum, and  Sophie Reed ,  David Rosario ,  Mara Salvato  and  Martin Ward  for the informative discussions. Additional thanks to  Eden Stern  for lending a hand during the August 2015 Keck run. This work was supported under  NASA  Contract No. NNG08FD60C , and made use of data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by the  National Aeronautics and Space Administration.
Differential gene expression in skin and epidermis among: nude, B6, E14 and E18 mice In the first step of analysis, we compared the transcriptomes from the skin of nude mice (regenerative) to transcriptomes from both adult B6 (reparative) and E14 (regenerative) mice. Subsequently, the transcriptomes from E18 (reparative) skin samples were compared with transcriptomes from both E14 (regenerative) and adult B6 (reparative) skin (Fig. 1a ). In this manner, we generated lists of genes showing statistically significant differences in expression (http:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE71619). For further analysis we used genes considered as a differentially expressed when changes in the expression were greater than or equal to two-fold. The comparison of skin gene expression between nude (model of skin regeneration) and B6 (model of skin reparation) showed 2243 genes that were variably expressed (up and down) (Fig.
Changes in genes and protein expression-validation of sequencing data To confirm and validate sequencing data we performed qRT-PCR, Western Blot and immunohistochemical detection experiments using independent biological samples from nude, B6, E14 and E18 skin. First, in order to establish and verify the levels of Foxn1 mRNA expression in the examined skin tissues, we performed qRT-PCR analysis (Fig. 2a ). We detected no Foxn1 transcripts in E14 skin samples, whereas its expression was detected in E18 and adult skin (Fig. 2a ). Foxn1 mRNA expression was observed not only in B6 but also in Foxn1 deficient nude mice although at lower levels. The highest levels of Foxn1 transcripts were detected in enzymatically separated epidermis, the host tissue for the Foxn1 expression (Fig. 2a ). Foxn1 mRNA was detected in the nude skin tissue because a single base pair (G) deletion in the exon 3 introduces a frameshift and a premature stop codon causing the encoded protein to terminate upstream of the DNA-binding domain (http://jaxmice.
Data analysis-SAGE-seq dataset Sequenced fragments were aligned to the mouse NCBI37/ mm9 SAGE reference sequence using SOLiD™ SAGE™v1. 10 software (Life Technologies). Raw counts were used as the input for differential expression analysis using GeneSifter, Geospiza, Inc. Libraries were normalized using the geometric mean. Reads with up to 1 mismatch which mapped uniquely to a single transcript were counted. Fold changes of expression at the gene level were calculated. For the preliminary selection, genes met a fold-change in expression of > or =1. 5 between experimental groups and a p-value or Benjamini-Hochberg adjusted p-value of 0. 05. The obtained gene data set were deposited in the Gene Expression Omnibus (GEO) repository: GSE71619. For further analysis we used genes considered as a differentially expressed based on: 1) A greater than or equal to two fold change in expression; 2) The p value was less than or equal to 0.