dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were analysed two times by mass spectrometry. Our final dataset thus comprises label-free quantitative proteomics results for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry). Statistical procedures were applied to identify proteins with differential abundances between stressed and unstressed situations. Considering that C. glabrata and C. albicans are human pathogens, which face important pH fluctuations during a human host infection, this dataset has a potential value to other researchers in the field.
Open Access this project, the interest for technical replicates was more questionable. We induced proteome modifications applying an alkaline pH stress to Candida cells grown in minimal liquid medium. Our final dataset comprises quantitative proteomics for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry, see below)  [9, 10]. We believe it could be useful for other researchers, either interested in a statistical exploitation of the results (to model for instance the variability of protein quantifications associated with biological or technical replicates respectively) or interested in a better understanding of the cellular mechanisms which underly adaptation of pathogenic yeasts to pH changes, a key process during a human host infection  [11].
These time points were chosen because the cells were then in the exponential phase. Finally, REP1 and REP2 referred to two technical replicates, i. e. independent MS acquisition from the same protein extract and trypsin digestion. Overall, two datasets were associated to this paper note (Table  1 ). Data set 1 comprises 24 raw data files, obtained from a Q-Exactive Plus mass spectrometer coupled to a Nano_LC Proseon 1000 equipped with an easy spray ion source (all from Thermo Fisher Scientific); 48 search files, obtained with the Proteome Discovered software (Thermo Scientific, version 2. 1) and the Mascot search engine (Matrix Science, version 2. 5. 1); 2 quantification files obtained with the Progenesis QI for Proteomics software (version 4. 1, Waters) and 2 FASTA files obtained for the CGD website and used for the MS/MS identification step.
Limitations We produced this dataset to assess our ability to properly quantify protein abundances in yeasts Candida glabrata and Candida albicans. An open question for us was the impact of technical replicates compared to biological replicates. We thus performed cell cultures under two different conditions (control and induced stress), collected cells at two separate time points (10 and 60 min) after stress induction, extracted the proteins, performed trypsin digestions and analysed the composition of samples by mass spectrometry. As a result, we were first able to observe a good coverage of proteome in yeasts C. glabrata and C. albicans, respectively. Between 1500 and 2000 proteins were identified in a reproducible way, representing ~ 30% of the total protein repositories in these species.
| Specific identification Collagen peptide mass fingerprinting (PMF) was applied to taxonomically identify the bone artifact. Also known as ZooMS (zooarcheology by mass spectrometry), collagen PMF discriminates taxonomic groups based on differences in the collagen protein sequence observed through Matrixassisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry  (Collins et al. 2010; Buckley et al. 2014). In accordance with the routine procedure established in the PaleoCet project, a photogrammetric survey of the object was carried out before any sampling took place. The data necessary to create a 3D model of the artifact in its complete state will thus be archived, both in the project's database and at the museum, in order to keep a record of the artifact's morphology before sampling for conservation purpose.
1 % trifluoroacetic acid to deactivate the trypsin, and purified using a 100 μl C18 resin ZipTip pipette tip (EMD Millipore). Collagen from both samples was spotted in triplicate using a matrix of α-cyanno-4-hydroxycinnamic acid along with calibration standards on a 384-spot MALDI target plate, and run on a Bruker ultraflex III MALDI TOF/TOF mass spectrometer at the University of York. The mMass software  (Strohalm et al. 2008 ) was used to average spectra replicates from each subsample, which were then compared to the list of m/z markers for marine mammals presented in  Kirby et al. (2013) ,  Buckley et al. (2014)  and  Hufthammer et al. (2018) , in order to taxonomically identify the species used as raw material for the artifact. The bone subsamples from the exterior and interior of the artifact produced similar MALDI-TOF spectra, both of which could be confidently identified as gray whale (Eschrichtius robustus), based on gray whale specific markers presented in  Kirby et al.
Gene expression analysis before HIV peptides stimulation revealed that 113 genes were differentially expressed. Ingenuity Pathway Analysis software showed that these genes are significantly involved in inflammation with a downregulation of many genes, such as IL1A (Ϫ2. 28) and IL1B (Ϫ7. 02), IL-6 (Ϫ5. 71), CXCL5 (Ϫ6. 89), CXCL13 (Ϫ1. 97), CCL23 (Ϫ1. 68), CXCL1 (Ϫ4. 19 ), TREM1 (Ϫ1. 66), and CD14 (Ϫ2. 86) (Fig. 2A ). Some of these genes are also related to granulocyte adhesion and diapedesis (IL1A, IL1B, CXCL5, CXCL1, FPR1, FPR2, CCL22, CXCL13, CCL19, and CCL23) and to interleukin-6 (IL-6), HMGB, and TREM1 signaling (IL1B, IL-6, CD14, IL1A, FOS, LAT2, RHOU, and TREM1). We also observed a downregulation of genes involved in the iron homeostasis pathway, such as HBA1/HBA2 (Ϫ12. 93), HBB (Ϫ12.
Cells were then stained with intracellular antibodies: anti-IFNG PerCP Cy5. 5, anti-TNF-␣ PE-Cy7, and anti-MIP1B PE (BD Biosciences). Data were acquired with a LSRII flow cytometer (BD, Le Pont de Claix, France), with a minimum of 100,000 events collected in CD3 ϩ live cells. The data were analyzed using FlowJo software, and the specific response was expressed as the percentage of CD4 or CD8 T cells. For the Luminex assay, 14 cytokines were measured in the supernatants of cell cultures at days 2 and 9 using Millipore reagents (Milliplex human CD8 T-cell panel with IL-2, IL-5, IL-10, IL-13, IFN-␥, TNF-␣, MIP-1␤, perforin, granzyme A, and granzyme B; magnetic beads and antibodies for human IP-10, IL-21, IL-17A, and IL-1␤ [Millipore, Chicago, IL]). Data were acquired with the Bio-Plex 200 system (Bio-Rad, Marnes-la-Coquette, France).
Statistics. Microarray data analyses were performed using R software version 3. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria). Gene transcription data were preprocessed  (51, 52)  and corrected for potential batch effect  (53). Statistical comparisons between groups were based on empirical Bayes moderated t statistics  (54). An adaptive false discovery rate (FDR) procedure was used to control for test multiplicity. Unsupervised hierarchical clustering heatmap analysis was performed on raw scaling expression using a Euclidean distance matrix and Ward's linkage  (55). Canonical pathway and biological function analyses were then carried out using genes differentially expressed between groups with adaptive FDR-adjusted P Յ 0. 05 and a fold change (FC) absolute value of Ն1. 5. Ingenuity Pathway Analysis software (Qiagen, Redwood City, CA) was used for gene pathway and function analyses.
ACKNOWLEDGMENTS We are grateful to the staff and patients at the  Service d'Immunologie Clinique ,  Hôpital Henri Mondor , and the  ANRS CO21 Codex cohort  for their contributions. This study was supported by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77  and by the  ANRS (National Agency for Research on AIDS and Hepatitis). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The authors have no conflict of interest to declare.
Theoretical Estimate of NO The equilibrium concentrations of N, O, NO, and N 2 as a function of temperature were calculated using a computer program that models chemical speciation at thermodynamic equilibrium  (Bale et al. , 2016). The program predicts the chemical species and their mixing ratios that are in thermochemical equilibrium at a given temperature and pressure (1 bar) based on the chemical composition of the initial gas mixture. The rates of reactions as a function of temperature were obtained from the National Institute of Standards and Technology Chemical Kinetics Database  (Manion et al. , 2015).
The authors are grateful to the SAM and MSL teams for successful operation of the SAM instrument and the Curiosity rover. The data used in this paper are listed in the supporting information, figures, and references. SAM Data contained in this paper are publicly available through the NASA Planetary Data System at http://pdsgeosciences. wustl. edu/missions/msl/ sam. htm. We would like to express gratitude to Pierre-Yves Meslin from the Research Institute in Astrophysics and Planetology at Toulouse, France, and five anonymous reviewers whose comments/suggestions on earlier drafts helped improve and clarify this manuscript. The authors declare no conflicts of interests.
Root and rhizosphere sampling and ITS2 sequencing data analysis Rhizosphere and root samples were collected using a fractionation method described previously  (Almario et al. , 2017). The obtained paired-end reads were processed in MOTHUR v1. 37. 3  (Schloss et al. , 2009)  using a custom pipeline and the UNITE fungal ITS database (v. 7. 2, release 1. 12. 2017) (Kõljalg et al. , 2013). The raw ITS2 sequencing data are deposited at NCBI sequence read archive under Bioproject PRJNA489990.
RNA-seq data analysis Datasets of the short reads have been deposited at NCBI under accession number PRJNA518045. RNA-seq analysis was performed as described previously  (Xue et al. , 2018). Genes with log 2 fold change ≥ 2 or ≤ À2 (adjusted P ≤ 0. 05) were identified as differentially expressed genes (DEG) (Tables  S5, S6 ). In order to draw the heatmaps, values of log 10 (counts per million (CPM) + 1) were used. The CPM values were calculated by edgeR after normalizing for library sizes. To retrieve the overrepresented gene ontology (GO) terms, agriGO was used (Fisher's test with Hochberg-FDR correction)  (Du et al. , 2010) , using a customized annotated reference, and the -log 10 transformed Pvalues were used to draw the heatmap. Re-analysis of published datasets was used to establish reference gene sets: inoculated vs non-inoculated roots fertilized with low Pi for R.
Statistical analyses The RSTUDIO (v. 3. 2. 1) was used for statistical analyses  (RStudio, 2015). The operational taxonomic unit (OTU) table was used to quantify OTU relative abundances, which were log 10 (X + 1) transformed. This final transformed OTU table was used to calculate Bray-Curtis dissimilarities between samples using the 'vegdist' function of the vegan package  (Oksanen et al. , 2016). The 'dudi. pco' and 's. class' functions from the ade4 package were used to conduct the principal coordinates analyses (PCoA). Permutational multivariate analysis of variance (PerMANOVA) on Bray-Curtis dissimilarities was conducted using the 'adonis' function of the vegan package (at P < 0. 05, 10 000 permutations; Tables  S9, S10 ). Taxa showing differences in their relative abundance between samples from arb + and arb À plants were identified using the DESeq function in the DESEQ2 package (P < 0.
Acknowledgements We thank  Drs Martin Parniske  ( Ludwig-Maximilians-University M€  unchen),  Stig U. Andersen  and  Jens Stougaard  ( Aarhus University ) for providing seeds of mutants,  Ren e Flisch  ( Agroscope Reckenholz ) for providing natural soil,  Janine Altm€ uller  ( Cologne Center for Genomics at University of Cologne, CCG ) for sequencing,  Yuxia He Gan  ( SeqData Co. ) for GO analysis. We are grateful to  Meike Hahn ,  Ksenia Kolesnikova ,  Ulrike Hebbeker ,  Stefanie Junkermann  and  Yvonne Arlt  for technical support. This research was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Ger-many´s Excellence Strategy -EXC-2048/1project ID 390686111 (to JA and MB), by the  Plant Fellows Program  ( FP7 Marie Curie Actions , grant  GA-2010-267243  to JA) and by the  German Science Foundation  (grant  BU-2250/12-1  to MB).
For this purpose, we use the standard Kmeans method, whose main idea is to build N C clusters (N C being a user-defined constant) in such a way that their total within-cluster variance is minimised min ∑ i = 1 N C ∑ f ∈ C i ∑ t = 1 N T f t -μ C it 2 , (4) where N T is the time set, N C is the number of clusters, f is the feeder consumption, C i is the cluster i, μ C i is the consumption of the cluster i. There are several variants and implementations of the K-means method. We used the K-means++  [30]  algorithm, which is straightforward to understand, easy to implement and fast to compute -which are important attributes when dealing with large datasets such as ours. Our method is however general and does not depend on the specific choice of the K-means method and the K-means++ implementation of this method: any other clustering algorithm could have been used.
The K-means method depends on two main user-defined parameters: first the number of clusters N C , and second the initialisation of the algorithm. The K-means algorithm usually performs well compared to other clustering techniques  [31]  provided that the number of clusters  [32]  is large enough for the considered dataset. According to our experiments discussed in Section 4. 3. 5 (see also  [33] ), setting this parameter to N C := 20 appears to be a good choice for our application. The impact of the initialisation of the algorithm is discussed in Section 5. 5 and is shown to have a limited impact on the results. As a result of the clustering phase, the feeders are categorised into N C = 20 groups with similar scaled profiles. Each cluster is characterised by its members (i. e. the individual feeders that belong to the considered cluster) and by its mean profile; the mean profile of a cluster may be interpreted as a prototypical profile that was not present in the input data but to which all the members of a cluster are similar.
Illustration of the clustering method: In order to complement the above theoretical description of the clustering method with more concrete examples, this section provides some illustrative results. We also discuss the choice of parameter N C , the user-defined number of clusters. The data is composed of the historical load curves of 1274 feeders over 2 years, with a time step of half an hour (see  Section 5). Let us first consider the influence of parameter N C. Fig. 4  shows one of the performance indicators -ratio within the boundaries -depending on the chosen number of clusters. Note that this simple study uses a non-causal allocation method, in the sense that the data used to perform the allocation (training dataset) is the same as the data used to compute the performance metric (simulation dataset).
Total allocated consumption We apply the three above-mentioned methods on our dataset and first consider the total load allocated for the UFLS, regardless of individual UFLS steps. The corresponding probability density function is depicted in Fig. 6. The boundaries of the NC-ER are also indicated. The traditional and mean methods provide very similar, and relatively disappointing, results: a very significant fraction of time is spent outside of the NC-ER boundaries. On the contrary, the clustering-based method is performing much better: the consumption allocated to UFLS almost always lies within the boundaries.
Reduction of the chemical scheme We have used the kinetic model of  Venot et al. (2012)  to perform the chemical scheme reduction presented in this paper. The initial full chemical scheme contains only neutral chemical reactions and we have excluded photodissociations. There are several methods that exist to reduce a chemical scheme, like the one pro- posed by  Dobrijevic et al. (2011)  for Solar System Giant Planet stratospheres. However such methods are not adapted to cases with reversible reactions. To reduce our chemical scheme, we have used the package ANSYS Chemkin-Pro Reaction  Workbench (2017). We have used the method DRGEP (Directed Relation Graph with Error Propagation), followed by a Sensitivity Analysis (SA). Initially, the Directed Relation Graph (DRG) method has been developed by  Lu & Law (2005)  and extended to the DRGEP method by Pepiot-Desjardins & Pitsch (2008) followed by  Liang et al.
e. [300-2500] K and [0. 01-100] bar). The temporal evolution of abundances with time are very similar with both chemical schemes and they are both able to reproduce thermochemical equilibrium (Fig. 2 ). Finally, we obtained a reduction factor of about 3 for the number of species and about 5 for the number of reactions, which leaves hope for a possible coupling of such mechanism in a 3D model. For a given atmosphere, using the reduced scheme instead of the full chemical scheme in our 1D kinetic model permit ∼30× faster runs. This reduced scheme is available in the KIDA Database  (Wakelam et al. 2012)  1.
(2003) , applied to a chemical scheme whose reactions are reversible. The rate constant of each forward chemical reaction was pseudo-randomly estimated according to its own temperature-dependent uncertainty factors F(T ) following the data evaluation and the numerical method described in  Hébrard et al. (2015). The corresponding reverse rate constant was determined using the reaction equilibrium constant. This 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 10 2 10 4 10 6 10 8 Time (s) 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 10 2 10 4 10 6 10 8 10 10 10 12 10 14 Time (s) 10 -10 10 -8 10 -6 10 -4 10 -2 10 0 10 2 10 4 10 6 10 8 10 10 10 12 10 14 Time (s)  Abundances are calculated with the full chemical scheme (red solid lines) and with the reduced scheme (blue dot-dashed lines). These abundances are compared with the thermochemical equilibrium value (black dotted lines).
Conclusion We have developed a reduced chemical scheme for exoplanetary atmospheres from the C 0 -C 2 chemical network of  Venot et al. (2012). This reduced scheme has been designed to reproduce the abundances of H 2 O, CH 4 , CO, CO 2 , NH 3 , and HCN and is available in the KIDA Database  (Wakelam et al. 2012). For these species of interest, we have validated this new scheme through a Table  4. For Uranus, Neptune, and SD 1110 models, maximum variations of abundances (in %) for each species for which the reduced scheme is designed. The pressure level (@level in mbar) where the maximum variation is reached is indicated in parenthesis. These values are calculated within the region where quenching occurs for the Giant Planets ([10 6 -10 7 ] mbar) and within the region probed by infrared observations ([10 2 -10 4 ] mbar) for the Brown Dwarf.
Acknowledgements. The authors thanks the referee for his careful reading of the manuscript which allowed its improvement. O. V. and T. C. thanks the  CNRS/INSU Programme National de Planétologie (PNP)  for funding support. PT acknowledges support from the  European Research Council  (grant no. 757858 -ATMO ). BD and EH acknowledge support from the  STFC  Consolidated Grant  ST/R000395/1.
Allele-specific expression of imprinted genes in embryonic tissues For allele-specific expression, we applied a computational pipeline for unbiased allele-specific quantification of transcripts that we developed previously, obtaining quantitative allelic information for up ~ 5000 genes per sample (Additional file 2: Fig. S9 )  [42]. On average, individual genes show equal expression from the B6-and DBA2derived alleles in all samples, including in the heterozygous chromosomal regions of the PGA-derived lines, with only a small number of genes showing allelic bias in expression toward B6 or DBA2 as would be expected for imprinted genes (Additional file 2: Fig. S9 ). To determine allele-specific expression of imprinted genes, we compiled a list of 98 imprinted genes based on available data present in the two most widely used imprinting databases (www.
Information on allele-specific expression of imprinted genes in NT and PGA lines is largely absent. Importantly, most of the studies investigating imprinting in ESCs and EpiSCs did not include analysis of the embryonic tissues and therefore cannot discriminate between lack of imprinting in embryonic tissue versus loss (or gain) of imprinting during derivation of ESCs and/or EpiSCs. The analysis performed here therefore extends previous studies by providing a comprehensive overview of imprinted gene expression in pluripotency. The use of isogenic tissues and cell lines in the current study allows for direct comparisons between pluripotent cells and as such provides a benchmark to allow identification of cell lines that faithfully maintain imprinted gene expression. A notable example showing the power of the dataset we generated represents the known imprinted gene Ube3a, for which the biallelic expression observed in the ESC and EpiSC lines is not due to a loss of imprinting, but due to absence of imprinting in the embryonic tissue of origin.
RNA was isolated using the PureLink ™ RNA Micro Kit (Invitrogen). 100 pg RNA of the embryo samples was used to prepare cDNA according to the SMARTer (Clontech) method and sequenced paired-end on Illumina Hi-Seq 2000. All RNA-seq of the embryonic tissue samples (FASTQ and BedGraph or wig files) are present in the NCBI GEO SuperSeries GSE53387. An overview of this data is present in Additional file 1: Table  S1.
Generation of FASTQ files and demultiplexing was performed using Illumina CASAVA. All sequencing analyses were conducted based on the M. musculus NCBI m37 genome assembly (MM9; assembly July 2007). Additional file 1: Table  S1  summarizes the sequencing output. All RNA-seq data and ChIP-Seq data (FASTQ and WIG files) are present in the NCBI Gene Expression Omnibus (GEO) SuperSeries GSE101292. An overview of these profiles is present in Additional file 1: Table  S1. Allele-specific mapping using polymorphic sites between the B6 and DBA2 genomes Known polymorphic sites between the Mus musculus domesticus mouse strains B6 and DBA2  [37]  (nucleotide substitutions, not indels) were collected using polymorphic sites determined by  (1)  the Sanger mouse sequencing project using the March 2011 release using the strains C57BL and DBA  [35, 36] ; and (2) the NIEHS/ Perlegen mouse resequencing project  [34]  (we used the b04_Chr*_genotype.
The genotype present after the breakpoints was determined by applying the same 3 criteria as at the start of the chromosome, or by the ≥ 4 consecutive bins of the same genotype at the proximal end of the chromosome. Breakpoints within 15 MB were discarded if the genotype of the genomic regions neighboring the ≤ 15 MB region was same. The methods as described in this section were also applied for genotyping of the EpiSCs using genomic DNA (obtained from MethylCap-seq experiments present in NCBI GEO SuperSeries GSE47793)  [94].
ChIP-Seq performed on 3 B6D2F1 ESCs lines. Example of tag-normalized ChIP-Seq data over known pluripotency markers as well as imprinted genes. For the highly expressed pluripotency markers, we detect clear H3K4me3 peaks on the promoters, but no H3K27me3, as expected. Dependent on the gene, the imprinted genes contain promoter-associated H3K4me3 (active), H3K27me3 (silent) or both (bivalent; associated with low level of expression)  [48]. Figure  S12. (Allelic) epigenetic landscape of fertilized ESC, ESC-NT and ESC-PGA lines, showing that the majority of the H3K4me3 and/or H3K27me3 enriched loci associated with imprinted loci are equally present at both alleles. (a) Allelic bias of H3K4me3 or H3K27me3 enriched loci (ChIP-Seq "peaks") associated with known imprinted genes plotted by the percentage of B6 as compared to the total coverage of B6 and DBA2 per peak.