dataesr/scientific-paragraphs-categorization huggingface dataset:
Mass spectrometry analysis Tryptic peptides were analyzed on a Q Exactive Plus instrument (Thermo Fisher Scientific, Bremen) coupled with an EASY nLC 1000 chromatography system (Thermo Fisher Scientific). Sample was loaded on an in-house packed 50 cm nano-HPLC column (75 μm inner diameter) with C 18 resin (1. 9 μm particles, 100 Å pore size, Reprosil-Pur Basic C18-HD resin, Dr. Maisch GmbH, Ammerbuch-Entringen, Germany) and equilibrated in 98% solvent A (H 2 O, 0. 1% FA) and 2% solvent B (ACN, 0. 1% FA). Peptides were first eluted using a 2 to 18% gradient of solvent B during 112 min, then a 18 to 30% gradient of solvent B during 35 min, a 30 to 45% gradient of solvent B during 15 min and finally a 45 to 60% gradient of solvent B during 5 min all at a 250 nL/min flow rate. The instrument method for the Q Exactive Plus was set up in the data dependent acquisition (DDA) mode.
Data processing for protein identification and quantification All data were searched using Andromeda  (Cox et al. , 2011)  with MaxQuant software  (Cox and Mann, 2008; Tyanova et al. , 2016)  version 1. 5. 3. 8 against a Neosartorya fumigata (strain CEA10 CBS 144. 89 FGSC A1163) (Aspergillus fumigatus) Uniprot database (Proteome ID UP000001699, 9942 entries) concatenated with mutated RodA protein, usual known mass spectrometry contaminants and reversed sequences of all entries. Andromeda searches were performed choosing trypsin as specific enzyme with a maximum number of two missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), oxidation (Met, variable) and Nter acetylation (variable). The mass tolerance in MS was set to 20 ppm for the first search then 6 ppm for the main search and 10 ppm for the MS/MS.
, 2011). Since we were particularly interested in secretory proteins among all these conditionspecific proteins, we used the SignalP algorithm to predict proteins with secretory signal peptides  (Petersen et al. , 2011). This algorithm was used with the default cut-off value, which optimizes the performance measured as Matthews Correlation Coefficient (MCC). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Table  1 ) partner repository with the dataset identifier PXD008503.
conidia and (B) 10 days old conidia. The most abundant proteins present in both 30 and 10 days old conidia are listed in (C). Proteins are ranked in decreasing order of abundancy based on iBAQ (intensity Based Absolute Quantification) values. The mean iBAQ is calculated from the three biological replicates. Over 1000 proteins were identified and quantified in the formic acid extracts from each strain and were deposited in the PRIDE data base PXD008503 (http://proteomecentral. proteomexchange. org/cgi/GetDataset). A Signal-peptide-containing proteins extracted with 2h in formic acid from 30d old conidia, present in I115S/I146G and absent in parental.
Acknowledgements The authors thank  Olaf Kniemeyer  ( HKI, Jena, Germany ) for providing unpublished MS data of the secretome of A. fumigatus,  Remi Beau  and  Anne Beauvais  ( Aspergillus unit, Institut Pasteur ) for providing the anti-RodA polyclonal antibody,  Prof. Chiaro Neto  for use of the  AFM  and the staff of  Sydney Microscopy and Microanalysis  for maintenance of the TEM.
Funding This work was supported partially by the grant Aspergillus by  Aviesan , the grant  DEQ20150331722 LATGE Equipe FRM 2015  and the  French Australian FAST  project  FR110012. This work was additionally funded by the  French ANR  ( HYDROPHOBIN ANR-10-BLAN-1309  and  FUNHYDRO ANR-16-CE110020-01 ) and the  Institut Pasteur   PTR 529. AP was a recipient of an  Université Pierre et Marie Curie (UPMC)  fellowship. The NMR spectrometer was partially supported by the  Région Ile de France  ( SESAME grant ).
DNA was extracted from the 19 muscle samples using the Wizard Genomic DNA Purification kit (Promega). Each purified DNA sample was assessed by agarose gel electrophoresis. DNA concentration was measured with a Nanodrop ND-100 instrument (Thermo Fisher Scientific). Sequencing libraries were prepared using TruSeq SBS v3-HS Kit (Illumina) and the whole-genome sequenced using a 2 × 100 bp paired-end approach on an Illumina HiSeq2000. Sequence alignments were carried out using the Burrows-Wheeler Alignment tool (BWA-v0. 6. 1-r104)  21 with the aln option with default parameters for mapping reads to the UMD3. 1 bovine reference genome  22. Potential PCR duplicates were removed using the MarkDuplicates tools from the Picard package version 1. 4. 0  23. Only properly paired reads with a mapping quality of at least 30 (-q = 30) were retained.
Each library pool was used for 2 × 100 bp paired-end sequencing on one lane of the Illumina HiSeq2000 with a TruSeq SBS v3-HS Kit (Illumina). After sequencing, the samples were demultiplexed and the indexed adapter sequences were trimmed using the CASAVA v1. 8. 2 software (Illumina). The quality of the raw sequence reads was assessed using FastQC and Qualimap  27. The Bos taurus reference genome sequence was downloaded from Ensembl (release 91, Bos taurus-UMD3. 1. dna. toplevel. fa). To align the reads to the assembled reference genome the STAR RNA-Seq (version 2. 4. 2a) aligner was used  28. Default values were used for mapping except for the intron alignment (align-IntronMin: 20 and alignIntronMax: 500,000). Reads for each sample were mapped separately to the reference genome sequence. Only paired reads were retained for alignment.
The number of paired-reads uniquely aligning to transcribed regions of each transcript was calculated for all genes of the annotated transcriptome. The transcript paired-read count was calculated as the number of unique paired-reads that aligned within the exons of each transcript, based on the coordinates of mapped reads. SNP identification and annotation. SNPs were called following the best practices from GATK (version 3. 4-46) with HaplotypeCaller for DNA and RNA sequence data respectively  29, 30. First, reads were subjected to local realignment, coordinate sorting, base quality score recalibration and indel realignment. We then performed SNP and indel discovery and genotyping. In the GATK analysis, we used a minimum confidence score threshold of Q30 with default parameters. We also used multi-sample variant calling in order to distinguish between a homozygous reference genotype and a missing genotype among the analysed samples.
SNPs were annotated with VEP  31  using the transcript set from Ensembl 87. Detection of ASE SNPs. We used ASEReadCounter 9 to calculate read counts per allele. We performed an N-masking (replacing for each identified variant the nucleotide of the bovine genome reference sequence by N) to remove mapping bias and we only kept overlapping heterozygous SNPs from DNA and RNA to remove discordant genotypes, possibly due to imprinting or RNA editing. We only kept candidates with minimum 10 reads for at least one allele. To determine if the imbalance was significant, we used a binomial test against an allelic ratio of 0. 5 with a p-value of 5% (Python).
Correlation analysis. The SNP being tested for ASE might not be the variant regulating the expression of the gene. So in order to determine the SNPs within the regulatory regions or potentially the regulatory variant itself, we detected SNPs in linkage disequilibrium with our ASE SNPs using PLINK 1. 9  32  (intra-chromosomal analysis and r 2 > = 0. 75). We used HTSeq-count  33  to determine the number of reads for each transcript per individual and normalised this using DESeq2  34. We computed the Spearman's rank correlation coefficient between the genotypes of ASE SNPs or SNPs in LD and expression level of the corresponding transcript. We performed a correction for multiple testing, for the same transcript, using the Bonferroni correction.
Purified single-stranded PCR product was annealed to the pyrosequencing primer (diluted to 0. 3 μM) and then sequenced using the PyroMark Q24 system (Qiagen), following the manufacturer's instructions. For validating candidate ASE SNPs, DNA and RNA (cDNA) from each sample were pyrosequenced simultaneously. The proportions of individual alleles for each SNP were obtained using the PyroMark Q24 software version 1. 0. 10 (Qiagen). Genomic DNA was examined to confirm the heterozygosity. The final ASE ratio for each SNP of each sample analysed was calculated using the following formula: ASE ratio = (allele 1%/allele 2%) RNA/(allele 1%/allele 2%) genomic DNA. Prediction of microRNA binding sites. Prediction of microRNA (miRNA) binding sites was done as follows: first, for SNPs within 3′UTR regions, flanking sequences (+/-100 bases) were retrieved using the whole-genome reference sequence (UMD3.
We also found 39. 61% of SNPs identified from our RNA-Seq data in intronic regions. These SNPs could be from premature transcripts (before splicing). RNA-Seq and DNA-Seq SNP comparison. We compared SNPs detected from WGS with SNPs from RNA-Seq data for each individual. On average, we detected 11,306,326 SNPs only from WGS (out of 11,943,766 detected SNPs), 15,516 SNPs only from RNA-Seq reads (out of 269,390 detected SNPs), and 38,217 of the SNPs from both (Table  2 ). We focused on overlapping SNPs identified from WGS and RNA-Seq data and checked the concordance between their genotype. This overlap is on average 90% (75. 7% to 96. 0%) concordant (69% for both homozygous and 31% for both heterozygous). For the 10% discordant SNPs, 84. 3% are homozygous from DNA-Seq and heterozygous from RNA-Seq data.
Cis-regulation of genes showing allele specific expression. Our detected ASE SNPs are probably not the causative variants, but rather markers in cis with the causative polymorphisms. It is known that the majority of causative SNPs are in regulatory regions instead of coding regions  45. Therefore, we were looking for a link between ASE SNPs and the putative causative SNPs in cis. With this in mind, we used PLINK to identify all the SNPs in linkage disequilibrium (LD) (r 2 > = 0. 75) with our predicted ASE SNPs. We obtained 2,955 SNPs (including ASE SNPs) with genotypes for all the 19 individuals. For each transcript showing allele-specific expression, we calculated the Spearman correlation coefficient score between expression level of genes containing ASE SNPs and genotypes of SNPs in LD with ASE SNPs.
4a ) showing an expression bias in favour of the C allele. We investigated how this SNP (rs41691181) in the upstream gene region could cause this allelic imbalance by testing if the SNP could alter Transcription Factor Binding site (TFBS) using TFBS-match  46  with the SNP flanking sequences (+/-10 bases). None of the allele-specific sequences of these SNPs were located in predicted TFBS. We extended the TFBS search for 5 other SNPs in 5 different genes (5 S rRNA, LRRC66, ENSBTAG00000026637, GLOD4 and PLK1) with a significant correlation in the upstream region without detecting any TFBS. In another example, we found one SNP (G/A, rs109763272) in LD (r 2 = 0. 86, distance of 274 bases) with a SNP (G/A, rs378125518). Both SNPs are in 3′UTR region of the PRNP gene and show ASE in four individuals among the nineteen.
The genotypes of the first SNP (8 G/G, 6 G/A, 5 A/A) is significantly correlated (ρ = 0. 61 and q-value = 0. 0057966) to the PRNP expression level. On average, the PRNP expression level was 4,641 transcripts for G/G individuals, 4,455 for G/A individuals and 3,324 for A/A individuals (Fig. 4b ) showing an expression   bias in favour of allele G. Given that this correlated SNP is also an ASE SNP, we looked if allele counts estimated with ASEReadCounter is in agreement with the transcript expression level. Indeed, transcripts with the G allele are 1. 54 times more expressed than transcripts with the A allele. We investigated how this SNP (rs109763272) in 3′UTR region could cause this allelic imbalance. It is known that polymorphisms in microRNA (miRNA) binding sites may affect miRNA/target gene interaction  47.
Acknowledgements The sampling of the Limousin Longissimus thoraci biopsies was part of the  Qualvigene  project, funded by  Agence Nationale de la Recherche  (contracts  ANR-05-GANI-005  and  ANR-05-GANI-017-01 ) and  APIS-GENE  (contract  01-2005-QualviGenA-02 ). The WGS work was funded by the  French National Research Agency  ( Regulomix project , contract  ANR-09-GENM-011 ). The RNA-Seq work was funded by the  INRA Animal Genetics Department  ( BovRNA-Seq  project). We are grateful to the  Genotoul bioinformatics facility  for providing computing and storage resources. We would also like to thank the  ABIES doctoral school  for funding  G. G. PhD  fellowship. We would like to thank the two anonymous reviewers whose insightful comments and suggestions helped improve this manuscript.
argodatamgt. org/Documentation, and are summarized in Table  6. Code/software that can aid in the QC process are, for example, the SOCCOM Assessment and Graphical Evaluation tools 6  or Scoop-Argo  (Detoc et al. , 2017). The Argo Data Management Team coordinates and approves the BGC-Argo processing and QC/adjustment approaches on the network level through the two key documents, the BGC-Argo cookbooks and the QC manuals, in order to ensure consistency between DACs/float operators and to incorporate and approve new scientific approaches. Finally, as highlighted in the previous parts, the BGC-Argo data flow implies a lot of transformation of the data files from the real time to the delayed mode status. A file checker is developed and made available to the Argo community 7  so that before submitting modified files to the GDAC, anyone can check that their files are compliant with the Argo rules and documentations.
5 ). The c-files contain the float pressure, temperature and salinity data. The b-files contain the pressure and all BGC data, including the raw intermediate sensor data and the computed ocean-state variables. The c-and b-files record the raw float-transmitted data. However, there are subtle differences between how various float types/manufacturers/firmware transmit their data. For example, NKE PROVOR floats transmit the exact pressure reading at the moment a BGC parameter is sampled, while APEX floats transmit only the pressure at which a sequence of sensors samplings are initiated, i. e. , all samples around a pressure level are reported with the same pressure despite being sampled at slightly different moments. To (a) give a homogeneous structure to all BGC-Argo floats, independent of the level of detail of their transmitted data, (b) account for vertical displacement between T/S and BGC sensors due to different vertical position on the float, (c) co-align quasi-concurrent BGC observations for easy multi-parameter analysis, the c-and b-profile data are synthesized to produce the so-called simplified-or s-profiles at the GDACs  (Bittig et al.
Data Modes and Quality Flags Biogeochemical-Argo data come in one of three data modes (Figure  6 ). These are given within the PARAMETER_DATA_ MODE variable and provide an indication on the data quality to expect: "R" or "Real-Time": Designates data of the real-time data stream that is unadjusted. These data were converted by the DAC from raw transmitted sensor data to an ocean state BGC parameter without any further adjustments. While real-time QC was applied to remove gross outliers, these data should be treated with care, as adjustments are often needed before these data can be used for meaningful scientific applications. "A" or "Real-Time Adjusted": Designates data of the realtime data stream that is adjusted in real-time. These data were converted by the DAC from raw transmitted sensor data to an ocean state BGC parameter and subsequently received an adjustment in an automated manner.
While accuracy of such data is a priori much better than for "R"-mode data and should allow scientific use, no guarantee is given that they are free of sensor malfunction or other flaws (e. g. , unidentified biofouling causing drifts/offsets). They can be provided with an uncertainty estimate (typically 1 σ). "D" or "Delayed-Mode (Adjusted)": Designates data of the delayed-mode data stream that have been adjusted (Note: Could be x + 0 if the raw data are fine) and quality controlled (e. g. , flagging of data biased by biofouling; see details in QC manuals, Table  6 ) by a scientific expert. "D"-mode data are validated for scientific exploitation and represent the highest quality of data at the given point in time. They are provided with an uncertainty estimate (typically 1 σ). Note that BGC-Argo is an evolving network, which is also true for its data.
Data Access Guide Data access begins with the search for appropriate floats and profiles. A good starting point is the exploration of various Argo index files at the GDACs, e. g. , argo_bio-profile_index. txt lists profile location, time, parameters, and parameter data modes for floats at the GDACs. Once identified, data access for individual files should follow: (1) What parameters are in the file? (→ PARAMETER variable), (2) What's the processing state ("R, " "A, " or "D") of the respective parameter? (→ PARAMETER_DATA_MODE variable), and (3) Depending on (  2 ), the actual access to both the parameter values and their quality flags (<PARAM> and <PARAM>_QC variables for "R, " <PARAM>_ ADJUSTED, <PARAM>_ADJUSTED_QC, and <PARAM>_ADJUSTED_ERROR for "A" and "D") (Figure  7 ).
Data Sources Argo data can be accessed by either one of the two GDACs (ftp: //ftp. ifremer. fr/ifremer/argo or ftp://usgodae. org/pub/outgoing/ argo) or through the Argo GDAC doi  (Argo, 2000). To allow traceability of research, the Argo doi may be sub-referenced to one of the monthly snapshots with its own doi key 8. Other sources may aggregate Argo data for specific purposes (e. g. , regional). However, only those sources should be used where the data origin is traceable and.
Data Acknowledgment Being part of Argo, BGC-Argo data are freely available without restrictions. Still, Argo (and thus BGC-Argo) asks users to acknowledge their usage by stating In many cases, however, the float PI or operator may have more detailed knowledge about a BGC-Argo float or the area of operation. For regional studies or studies involving just a limited number of float programs, it is recommended to contact the respective float PI or programs.
VARIABLE-SPECIFIC ASPECTS Descriptions of biogeochemical sensors available on BGC-Argo floats are provided below. Note that accompanying temperature, salinity and pressure data are required to support the transformation of raw data returned from a respective BGC sensor into a meaningful BGC quantity. The CTD instrumentation used on typical Argo floats is described in  Roemmich et al. (2004). An outline of the associated core-Argo variable quality control procedures can be found in  Wong et al. (2019). Such details will not be summarized here, as they are beyond the scope of this paper. For all sensors it is good practice to always transmit the raw data, following the sensors' sensing principle (e. g. , temperature, phase shift, spectral absorption, currents, counts). The processing capabilities of the data management system will always be superior to on-board capabilities and raw data can be reprocessed at any time as improved algorithms become available as long as the calibration information is available.
Nitrate is determined by measuring the ultraviolet absorption spectrum of seawater from around 217 to 240 nm. In oxic waters the shape of this spectrum is dominated by the Br -and NO 3 -ions and the spectrum can be deconvolved to determine the nitrate concentration  (Johnson and Coletti, 2002; Sakamoto et al. , 2009). The calculation requires co-located measurements of pressure, temperature, and salinity from the CTD sensor. Additional sensor diagnostic data, such as the fit error and baseline absorbance, can be useful for automated quality control of the raw data and should be retained, if possible. Despite careful calibration in the laboratory, these sensors can suffer from an initial calibration offset once deployed and a calibration drift over time. Therefore, the raw data returned from the float should not be considered "science quality" and should be used cautiously to address scientific questions until these offset and drifts have been accounted for.
Fortunately, for any given profile, the correction is an additive offset: a correction at one depth can be applied to the whole profile  (Johnson et al. , 2013). Since it is unlikely that any validation data will be collected for a float once it is deployed, one useful correction approach is to compare the raw float data to reference estimates for nitrate at 1500 m where temporal changes in concentration are minimal. Two global model estimates are LINR (Locally Interpolated Nitrate Regression)  (Carter et al. , 2018)  and CANYON-B (Bayesian version of CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network)  (Bittig et al. , 2018b). Both of these models are trained on the GLODAPv2 dataset  (Key et al. , 2015; Olsen et al. , 2016) , provide error estimates and require an accurate oxygen concentration as a model input for best results.
, 2017) , and it is recommended that POC samples be collected at deployment. Additional quality control checks include comparison of sensor output with optical sensors deployed on the ship rosette if present, and for data measured in the upper ocean, comparing observed chl a -b bp relationship with published ones (e. g. ,  Westberry et al. , 2010). Also, if a radiometer is present, it could be used to reduce uncertainties in chlorophyll by providing an independent bio-optical proxy  (Xing et al. , 2011). For research purposes, it would be useful if expert users measured the dark counts pre-deployment with sensor integrated on the float, face covered with black electrical tape (only the light receiver covered, but not the light emitter) and submerged in water  (Talley et al. , 2017). If dark counts are measured by the user, the user should make sure no glue residue is left on the optical surface by the electrical tape after removal.
We envision that by the end of the next decade BGC-Argo will achieve the operational budget necessary to deploy and maintain at least 1000 floats distributed evenly in the ocean and with data being distributed with similar processing from all DACs via the GDACs. To achieve these ambitious objectives, important efforts will need to be spent on improving coordination, data management, as well as sensor and float performances. The BGC-Argo community will also need to support emerging manufactures by rigorously testing new sensors in order to diversify suppliers, which is expected to increase standards, reduce costs and minimize risks to the BGC-Argo network. A crucial challenge will be to raise awareness about the objectives and requirements of the BGC-Argo network, and special attention should be dedicated to engaging with and training early career scientists.
ACKNOWLEDGMENTS Float data were collected and made freely available by the  International Argo Program  and the national programs that contribute to it (http://www. argo. ucsd. eduand http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. The authors would like to thank  Mathieu Belbéoch  ( AIC/JCOMMOPS ),  Siv Kari Lauvset (NORCE) ,  Nathan Briggs (NOCS) , and  Nick Hardman-Mountford  ( CSIRO Oceans and Atmosphere/University of Western Australia ) for helpful discussions and comments on the manuscript.