dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation concept: Different wavelengths diagnose different aspects of the solar coronal magnetic field -strong vs. weak field, disk vs. limb, eruptive vs. noneruptive -and are weighted differently helping to remove line-of-sight ambiguity. By utilizing testbeds of synthetic data at all wavelengths (from radio to extreme ultraviolet), effective measurement and optimization strategies can be developed which will set priorities and for future observational development. Status: DKIST will provide opportunities for polarimetry and testing of observational techniques with high resolution and sensitivity but in a small (5 arc minute) field of view. Proposed new observations include large (1. 5 meter) ground-based coronagraph(s) with narrow-band filter polarimeter and spectropolarimeter to observe the full Sun corona at the limb in the visible and infrared (currently undergoing engineering design and preliminary design review as US-China collaboration).
Such view is useful especially in the cases where the magnetosphere is prone to several subsequent activations in the solar wind. The shape and size of the oval and intensity variations in its different sectors enable simultaneous monitoring of, e. g. , nightside magnetospheric recovery from previous activity, while new energy already enters the system from a new event of dayside reconnection. Objectives: To achieve continuously global UV-images to follow the morphology and dynamics of the auroral oval, at least in the Northern hemisphere, but occasionally also in the southern hemisphere. Imager data combined with ground-based SuperDARN and SuperMAG networks allows solving the ionospheric Ohm's law globally, which yields a picture of electric field, auroral currents and conductances with good accuracy and sufficient spatial resolution.
Key requirements: Multi-point in-situ observations and real-time analysis of energetic particle fluxes (mostly, 0. 1 -10 MeV for electrons) in the inner magnetosphere, of the current geomagnetic conditions (mostly, Dst and AL indices) and solar wind / IMF conditions at L1 point. Implementation concepts: To realize our objectives, maintaining the current observation facilities related to radiation belt dynamics (particle and electromagnetic field measurements in the inner magnetosphere is essential). To fill the gap of observational data, hosting radiation monitor payloads (and/or cubesat missions) at LEO, MEO, GEO, and constructing new ground-based observatories for sparsely covered region is also encouraged. Models (empirical, theoretical, numerical) which are based on these observational data will improve our understanding of transport, acceleration, and loss processes in the radiation belt.
Establishing methodologies and metrics for validation and calibration of models and data is another important issue. Status: Continuing observations of energetic particles from LANL, GOES, ELECTRO-L, POES, Meteor-M. Geomagnetic indices from WDCs. Solar wind parameters from L1 (ACE). Supporting observations: The current constellation of Cluster, Van Allen Probes, THEMIS, with the upcoming MMS, along with data from the geostationary satellites, and ground-based observation networks by magnetometers and HF radars represent a perfect opportunity to achieve a major step forward. Theoretical studies and modeling is underway. What is needed is investment in a few minor expansions and in particular international coordination to push the program forward. Appendix F. 9. Solar energetic particles in the inner heliosphere Rationale: SEPs present a major hazard to space-based assets.
Results Overview of TGCT mutational landscape. The 42 TGCT cases comprised 16 seminomas, 18 non-seminomas, 4 mixed seminoma/non-seminoma histology and 4 tumours of indeterminant classification. Fresh frozen tumour tissue and matched germline blood samples were obtained from each patient and WES was performed on extracted DNA, achieving mean coverage of 72 Â across targeted bases with 86% of targeted bases being covered at Z20. Sequencing was conducted using Ilumina technology, with subsequent alignment, mapping and variant calling performed using Burrows-Wheeler Aligner (BWA)/Stampy/GATK/MuTect software. Across all 42 cases a total of 1,168 somatic single nucleotide variants (SNVs), and 111 small scale somatic insertion -deletions (indels) were identified, resulting in a combined total of 795 non-synonymous mutations, equating to a mean rate of 0.
While p53 mutations have been suggested to be a feature of TGCT  23  , none were observed in our data set, consistent with most recent studies  17, 24, 25. We validated all KIT/KRAS mutations called by next generation sequencing (NGS) using Sanger sequencing of the respective exons across all samples and to ensure no additional mutations were missed. In all cases, Sanger sequencing was 100% concordant with NGS. In addition to KIT and KRAS, there was an over-representation of mutations in cell division cycle 27 (CDC27) (11. 9%; 5 mutations, 5 tumours) and PRKRIR (4 mutations, 2 tumours), neither of which have been previously reported as TGCT drivers. CDC27 is a core component of the anaphase-promoting complex/ cyclosome, a multi-subunit E3 ubiquitin ligase that governs cell cycle progression, through ubiquitination and degradation of G1/ mitotic checkpoint regulators  26.
The most frequently mutated pathways were those involved in metabolism (mutated in 93%), pathways in cancer (54%), endocytosis (54%) and PI3K-Akt signalling (54%). The most significantly mutated pathway was RNA degradation (14. 6%), with a biased accumulation of functional mutations (fm-bias, P ¼ 3. 8 Â 10 À 3 ), observed across six different genes (see methods and Supplementary Table  2 ). Copy number variation. The 42 tumours were analyzed for copy number variation (CNV) using software package Exo-meCNV  34. Focal CNVs (up to 3 Mb) were identified in all tumours and large-scale CNVs (Z3 Mb) were detected in 35 (83%) tumours, (Fig. 3 ). Across all 42 cases the proportion of the tumour genome showing CNV ranged from 0. 1 to 48. 4% per genome (mean 10. 8%). The most frequent large-scale chromosome abnormality was 12p copy number gain, present in 30 of the 42 tumours (71%), of which 25 were 12p isochromosomes, a result consistent with previous experimental observations  [7] [8] [9].
Further analysis of one additional treatment-refractory tumour sample revealed some evidence for a second XRCC2 mutation. Cell line studies suggest that the exceptional sensitivity of TGCTs to cisplatin is due to their inability to repair treatmentinduced DNA damage, due to the low expression of DNA repair genes such as ERCC1 (ref. 62). In addition, cisplatin-resistant embryonal carcinoma cell lines show sensitivity to poly(ADPribose) polymerase (PARP) inhibition, through blocking their acquired ability to repair DNA  63. The observation of XRCC2 mutations in our patient tumour data expands on these previous animal and cell line studies, further supporting an important role for this pathway. To our knowledge this study represents the largest comprehensive sequencing study of TGCT conducted to date.
0 Â were achieved across targeted bases for tumour and normal samples, respectively. FASTQ files were generated using Illumina CASAVA software (v. 1. 8. 1, Illumina) and aligned to the human reference genome (b37/hg19) using BWA (v. 0. 5. 10, http://bio-bwa. sourceforge. net/)/ Stampy (v. 1. 0. 23) packages. PCR duplicates were removed and coverage metrics were calculated using Picard-tools (v. 1. 48, http://picard. sourceforge. net/). Coverage metrics demonstrated a mean of 95% of target bases achieved 410 Â coverage and 86% 420 Â. The Genome Analysis Toolkit (GATK, v. 3. 1-1, http://www. broadinstitute. org/gatk/) was used for local indel realignment/base quality score recalibration and SNVs were called using MuTect (v. 1. 1. 4). Data was quality filtered using in-house FoxoG software to remove potential artefactual variants introduced through DNA oxidation  21.
We used Oncodrive-fm  32  as implemented within the IntOGen-mutations platform  67   CNV analysis. CNV analysis was conducted using the CRAN package Exo-meCNV  34  , a statistical algorithm designed to detect CNV, and loss of heterozygosity (LOH) events using depth-of-coverage and B-allele frequencies (https://secure. genome. ucla. edu/index. php/ExomeCNV_User_Guide). ExomeCNV is calibrated to achieve high levels of sensitivity and specificity, with a power to detect 95% for CNVs down to 500 bp in length  34. When recently tested using a matched tumour/normal exome data set with B40 Â coverage, ExomeCNV achieved 97% specificity and 86% sensitivity compared with results from Illumina Omni-1 SNP array  34. To calculate CNVs, we first generated coverage files using GATK, and then used ExomeCNV to calculate log coverage ratios between matched tumour/normal samples and make CNV calls per exon.
Pathway analysis. Pathway analysis was performed using Oncodrive-fm  32  as implemented within the IntOGen-mutations platform  67  , using the 1,168 SNVs and 111 indel mutations called across the 42 tumours. Statistical analyses. Statistical significance of mutations were determined by testing whether the observed mutation counts in a gene significantly exceeded the expected counts based on a gene-specific background mutation rate, as implemented in MutSigCV (v. 1. 4). Plotted in the far section of Fig. 2  are the resulting À log10 (P values), with the dotted red line denoting a significance threshold of P ¼ 0. 05 and the solid red line a genome-wide significance threshold of P ¼ 5 Â 10 À 6. Due to the overall low frequency of mutations observed in our data set, and the way such tumour types are treated by MutSigCV, no genes were significant at the genome-wide level, not even previously known TGCT driver gene KIT.
Power analysis was conducted using a binomial power model, based on recent methods published by the Cancer Genome Analysis group at the Broad Institute 68 , incorporating the average background somatic mutation rate specifically observed for TGCT, sample size and assuming a genome-wide significance level of Pr5 Â 10 À 6. Significance of focal copy number events by gene was calculated under a binomial distribution. Meta-analysis was conducted using the Fisher method of combining P values from independent tests. Statistical analysis were carried out using R3. 0. 2 (http://www. r-project. org/) and Stata12 (StataCorp, Lakeway Drive College Station, TX, USA) software. Continuous variables were analyzed using Student's t-tests. We considered a P value of 0. 05 (two sided) as being statistically significant.
Acknowledgements We thank the patients and their clinicians for participation in this study. We acknowledge the  National Health Service  funding to the  National Institute for Health Research Biomedical Research Centre. We acknowledge the facilities and expertise of the  Cancer Genetics Core Laboratory Facility  and the  Cancer Genetics Sequencing Facility made available at the Institute of Cancer Research by Professor Nazneen Rahman. This study was supported by the  Movember foundation and the Institute of Cancer Research. K. Litchfield is supported by a PhD fellowship from  Cancer Research UK. R. S. H. and P. B. are supported by  Cancer Research UK  ( C1298/A8362   Bobby Moore Fund for Cancer Research UK ).
Enzyme profiles during growth on wheat bran and sugar beet pulp A preliminary test in which eight plant biomass degrading enzymes were measured at day two, three and four of cultivation, demonstrated that at day three activities were maximal for all fungi (data not shown). This time point was therefore selected for the full enzymatic analysis. Nineteen extracellular, lignocellulose-active enzyme activities of the liquid cultures were measured (Additional file 1: Figures  S1-S3 ). Comparison of these profiles demonstrated strong differences among the species, not only in the quantities of the activities, but also in the induction of specific enzymes. For instance, the highest activity levels for most enzymes of A. terreus were observed during growth on sugar beet pulp, while wheat bran resulted in higher levels of most enzymes for A.
CAZy annotation The identification step of CAZymes followed the procedures previously described  [6]  where sequences are subject to BlastP analysis  [54]  against a library composed of modules derived from the CAZy database, the positive hits are then subjected to a modular annotation procedure that maps the individual modules onto the peptide using hits against libraries of catalytic and carbohydrate models derived from CAZy using BlastP or Hidden Markov models  [54, 55]. The functional annotation step involves BlastP comparisons against a library of modules derived from biochemically characterized enzymes  [6].
Proteomics analysis Proteins from 3 ml of culture filtrate were precipitated with cold TCA/acetone and the amount of protein recovered was determined using the RCDC kit assay (BioRad, Mississauga, ON, Canada). Five micrograms of protein were digested with trypsin and an aliquot analyzed by LC-MS/MS as previously described  [60]  on a Velos LTQ-Orbitrap mass spectrometer (Thermo-Fisher, San Jose, CA, USA). MS/MS data were processed using Proteome Discoverer Quant 1. 3 (Thermo-Fisher) and spectral data were searched against Aspergillus protein databases downloaded from the Aspergillus Genome Database (AspGD). Search parameters used were 0. 80 Da for fragment ion tolerance and 10. 0 ppm for parent ion tolerance, fixed iodoacetamide cysteine modification and variable methionine oxidation. Protein and peptide identification confidence filters were applied to satisfy a 1% false discovery rate at the Peptide and Protein level.
Phylogenetic analysis Sequences of the RPB1, RPB2 (RNA polymerase II genes), Tsr1 (putative ribosome biogenesis protein), Cct8 (putative chaperonin complex component TCP-1) and AguA (α-glucuronidase) genes were downloaded from the full genome data sets and aligned using the Muscle software in the MEGA5 package  [65]. After aligning, the data sets were combined and maximum likelihood analysis was performed using RAxML version 7. 2. 8  [66]. Each locus was treated as a separate partition. The number of bootstrap replicates was set on 1,000 replicates. Sequences of Penicillium chrysogenum Wisconsin 54-1,255 were used as outgroup.
Acknowledgements IB, GAO, EB and BSG were supported by grants of the  Dutch Technology Foundation STW, Applied Science division of NWO  and the  Technology Program of the Ministry of Economic Affairs   UGC 07063 & 07938  to RPdV. IB was also supported by a grant of  Kluyver Centre for Genomics of Industrial Fermentation   KCII 2. 2. 27  to RPdV. GAO was also supported by a grants from  DGAPA-UNAM.
g. , PCR chimeras, sequencing errors, OTU overestimation). In order to efficiently compare the datasets and avoid biased community comparisons, the samples reads were reduced by random selection closed to the lowest datasets (4,000 and 8,000 reads for 16S-and 18S-rRNA gene sequences respectively). The retained high-quality reads were used for: (i) taxonomy-independent analyses, to determine diversity indices using the defined OTU composition, and (ii) taxonomy-based analysis using similarity approaches against dedicated reference databases from SILVA (R114 for 16S, and R111 for 18S). The raw data sets are available on the EBI database system under project accession number [PRJEB6118].
Heat maps were built up by applying the heatmap. 2 function implemented in the gplots R package to the relative abundance values of the most dominant bacterial and fungal phyla across the samples (relative abundance > 1%). For the analysis, the datasets (soil histories) were analysed together, so the mean values of Z scores are from the total dataset. Each row was scaled so that the mean of each taxonomic group across sample types was calculated and colored according to the corresponding Z score. All these analyses were performed with the R free software (http://www. r-project. org/). Physicochemical parameters, diversity metrics, Unifrac distance and relative abundance were compared between treatments and land use histories by two-way ANOVA and the differences between them by Fisher test (P<0.
Impact of land use history and seasonal climatic variations on soil microbial diversity Pyrosequencing yielded a total of 1,844,885 and 2,020,216 sequences of 16S-and 18S-rDNA with 4000 and 8000 quality sequences per sample, respectively. Diversity indices calculated from the sequence datasets obtained from control plots (without wheat addition) showed that, overall, both bacterial and fungal richnesses were higher in cropland plots than in grassland  The taxonomic affiliation of 16S-and 18S-rDNA sequences at T0 showed that overall the microbial phyla distribution within these two soils was dominated by Proteobacteria, followed by Actinobacteria, Firmicutes, Acidobacteria, Bacteroidetes, and Chloroflexi for bacteria, and by Basidiomycota and Ascomycota for fungi. However, the difference between the two soils was mainly due to an increase in the relative proportions of Proteobacteria (58.
plots whatever the sampling dates (Fig1). Contrastingly, bacterial evenness was similar in the two soils whereas fungal evenness was lower in grassland than in cropland soil. Interestingly, bacterial and fungal diversity indices did not vary significantly during the 12-month experiment whatever the soil history. NMDS analysis of the full bacterial-(Fig2A) and fungal-sequences datasets (Fig2B) highlighted distinct microbial community structure between the control subplots depending on the land use history (confirmed by Pairwise ANOSIM test (R = 0. 805, P = 0. 001 for bacteria and R = 0. 916, P = 0. 001 for fungi)). A difference between the two soils was also observed at the OTU level, with only 21. 4% of bacterial and fungal OTU being shared at T0 (data not shown).
Acknowledgments This work was supported by the  Agence Nationale de Recherche (ANR)  as part of the  ANR Systerra  project  DIMIMOS  ( ANR-08-STRA-06 ) and by a grant from the  Regional Council of Burgundy. It benefited from the technical facilities of the  GenoSol platform of the infrastructure ANAEE-France [ANAlysis and Experimentations on Ecosystems, ANR  project  'Investments for the Future ' ( ANR-11-INBS-0001 )], as well as from the  ORE-ACBB  (http://www. soere-acbb. com/index. php/fr/). The authors have declared that no competing interests exist. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This work was supported by the  Agence Nationale de Recherche (ANR)  as part of the  ANR Systerra  project  DIMIMOS  ( ANR-08-STRA-06 ) and by a grant from the  Regional Council of Burgundy. It benefited from the technical facilities of the  GenoSol platform of the infrastructure ANAEE-France [ANAlysis and Experimentations on Ecosystems, ANR  project  'Investments for the Future ' ( ANR-11-INBS-0001 )], as well as from the  ORE-ACBB  (http:// www. soere-acbb. com/index. php/fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
For many years Staphylococcus aureus has been recognized as an important human pathogen. In this study, the surfacome and exoproteome of a clinical sample of MRSA was analyzed. The C2355 strain, previously typed as ST398 and spa-t011 and showing a phenotype of multiresistance to antibiotics, has several resistance genes. Using shotgun proteomics and bioinformatics tools, 236 proteins were identified in the surfaceome and 99 proteins in the exoproteome. Although many of these proteins are related to basic cell functions, some are related to virulence and pathogenicity like catalase and isdA, main actors in S. aureus infection, and others are related to antibiotic action or eventually resistance like penicillin binding protein, a cell-wall protein. Studying the proteomes of different subcellular compartments should improve our understanding of this pathogen, a microorganism with several mechanisms of resistance and pathogenicity, and provide valuable data for bioinformatics databases.
Protein identification by LC-MS/MS and data analysis For LC-MS/MS analysis, peptide mixtures were analyzed by nanoflow liquid chromatography using the Ultimate 3000 RSLC (Dionex, Voisins le Bretonneux, France) with nanocapillary columns (15 cm long Â 75 μm internal diameter; Acclaim Pep Map RSLC, Dionex). The solvent gradient was increased linearly from 4% to 90% ACN in 0. 5% formic acid at a flow rate of 300 nL/min for 38 min. The elute was electrosprayed inside an LTQ-VELOS mass spectrometer (Thermo Fisher Scientific, Courtaboeuf, France) through a nanoelectrospray ion source. Thermo Proteome Discoverer v1. 3 was used for raw data file processing. The EBI_Bacteria protein database (containing 25590467 sequences) was used for protein identification. The following parameters were set for the searches: peptide mass tolerance of 1.
Bioinformatics analysis Functional annotation of the proteins was determined using the Universal Protein Resource database (UniProt) (http://www. uniprot. org/). Subcellular protein localization was predicted by PSORTb version 3. 0 (http://www. psort. org/psortb/). Prediction of non-classical protein secretion not triggered by signal peptides was detected by the use of the SecretomeP 2. 0 server (http://www. cbs. dtu. dk/services /SecretomeP-2. 0/).
Surface proteins The surface of the bacterial cell forms the interface between pathogen and host, the site of interaction during infection of host tissues. In this study, the surfaceome and exoproteome of a clinical sample of MRSA were analyzed. Strain C2355, which had previously been typed as ST398 and spa-t011, shows a phenotype of multiresistance to antibiotics and carries several resistance genes. A cell shaving protocol was used to obtain surfaceome fractions. The peptide mass peaks obtained by LC-MS/MS of the fractions were compared with UniProt and NCBI databases. The database identification (ID), molecular weight (MW), isoelectric point value (pI), function, biological process and cellular location of 236 proteins from this subcellular proteome were identified by searching the Uniprot database (Table  A1 ).
The proteins identified in the cell surface fraction have predicted functional activities in amino acid biosynthesis, carbohydrate processing, cell division, glycolysis, nucleotide biosynthesis, protein biosynthesis, transcription, translation, and 40 other functions (Fig. 1 ). Translation was the most common function in the proteins identified (15%), although no biological function was associated with about 21% of the identified proteins. The locations of the 236 proteins were predicted using PSORTb software. Overall, 198 proteins (83. 9%) were predicted to be cytoplasmic. No location could be predicted for 25 proteins (10. 6%). However, 2 proteins (0. 8%) are thought to be components of the cell wall and 11 (4. 7%) components of the plasma membrane. Gram-positive bacteria such as S. aureus have a cell wall morphology that is strongly resistant to lysis compared to Gram-negative bacteria or animal cells.
In the fractionation procedure used here the growth of S. aureus was stopped in the mid-exponential phase, a stage when the bacteria do not secrete much protein and there is less bacterial turnover than during the stationary phase. However, it is possible that phase-dependent growth regulation can make it difficult to identify specific proteins of the stationary phase or proteins expressed in response to extracellular signals, such as cell density or biofilm formation. The shaving technique is still too recent to know how specific the fractionation is  [9] , so it was necessary to use PSORTb software that predicts the cellular location of the proteins based on the amino-acid sequence (http://psort. hgc. jp/). After the LC-MS/MS analysis and the PSORTb predictions, 13 proteins were identified that can be readily related to a membrane or surface location.