dataesr/scientific-paragraphs-categorization huggingface dataset:
At the daily timescale, n(u) ranged from 6 when interpolating temperature with ORK to 17 when interpolating precipitation with KED and IED. Hence, 10 (17) surrounding neighbours were used to compute altitudinal gradients of temperature (precipitation) based on the daily linear regressions with KED and IED. Figure  5  shows the annual temperature and precipitation maps obtained by interpolating daily data from the meteorological gauges over the period 2000-2016 using the IDW, ORK, KED, and IED methods and their optimized parameters (Table  4 ). The maps of IED estimates of mean temperature and annual precipitation closely resemble the KED maps. Temperature estimates range from -9. 2 to 16. 0 • C with KED and from -11. 2 to 16. 6 • C with IED. Precipitation estimates range from 630 to 3273 mm with KED and from 642 to 3184 mm with IED.
As expected, these ranges are wider than those obtained with the IDW and ORK procedures, which, unlike the KED and IED methods, do not consider either local or seasonal elevation dependency. As a result, the ranges obtained with KED and IED are probably more realistic with respect to temperature, but not necessarily with respect to precipitation, for which daily crossvalidation shows that the simple IDW method provided better results. However, cross-validation was based on gauges sampled only below 2006 (2105) m a. s. l. , meaning evaluation of the precipitation (temperature) gridded datasets at higher altitudes was not possible. Another approach is thus needed to further explore whether elevation dependency should be disregarded when estimating daily precipitation (as suggested by cross-validation), and, if not, whether this dependency should be accounted for in the interpolation process or by inverting the hydrological cycle.
Sensitivity of the snow-hydrological simulations to the orographic gradients For the sake of brevity, here we only present the results obtained with the datasets interpolated with the IDW and IED procedures, since cross-validation at the daily timescale showed that they slightly outperformed the ORK and KED methods, respectively. Table  5  summarizes the six tests per-formed to account for elevation dependency in the T and P inputs via the modelling experiment described in Sect. 4. Figure  6  and Table  6  summarize the efficiency distributions obtained in validation with the GR4J and HBV9 models combined with the snow model in the different tests on the 20 snow-affected Alpine catchments. The results produced by the two hydrological models are in agreement and highlight the following main findings.
Prospects It would be instructive to further explore the sensitivity of snow-hydrological simulations to seasonal variations in the lapse rates, e. g. by using daily altitudinal gradients instead of a uniform constant gradient at the basin scale. However, as shown in the present paper, establishing the relationship between temperature/precipitation and elevation at the daily timescale from a sparse network of gauges is challenging in mountainous regions. For temperature, the methods tested for computing local and daily lapse rates for each prediction point (KED, IED) outperformed the methods that did not account for altitudinal gradients (IDW, ORK) in the leaveone-out procedure. On the other hand, using only a constant lapse rate calibrated from the inverse modelling approach performed substantially better with regards to the snowhydrological predictions than using the interpolated datasets of temperature with external drift.
Acknowledgements. The author is very grateful to  Météo-France  (https://publitheque. meteo. fr,last access: May 2020) and  Banque Hydro  (http://www. hydro. eaufrance. fr,last access: May 2020) for providing the necessary public hydro-meteorological data for the study. NASA's National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC)  is also acknowledged for providing MODIS snow products (https://nsidc. org,last access: January 2018), which were used for model calibration and validation. Finally, the author is sincerely grateful to the two anonymous reviewers for their careful reading of the original manuscript and their insightful comments and constructive suggestions for improvements. Review statement. This paper was edited by  Günter Blöschl  and reviewed by two anonymous referees.
Data availability. The hydro-meteorological data and MODIS snow products used in this study are available via the respective websites of the dataset producers: Météo-France (daily climatological data from all French stations since their opening, for all available parameters, including the mean daily air temperature under shelter and the daily liquid equivalent water depth of total precipitation. Access to the full depth of the database was possible with Météo-France's public data portal (https:// publitheque. meteo. fr, last access: May 2020), via research agreement no. DIRSE/REC/16/09/0. ), Banque Hydro (http://www. hydro. eaufrance. fr, last access: May 2020) (Leleu et al. , 2014), and NASA's National Snow and Ice Data Center (NSIDC, Distributed Active Archive Center (DAAC), https://nsidc.
As another prerequisite for our study, we verified that the MeRIP-Seq technique was adequate to perform differential methylation analysis. To this end, we compared the recovery of green fluorescent protein (GFP) transcript in vitro transcribed in the presence of m  6  A by m  6  A-immunoprecipitations from different RNA preparations or buffer alone (Supplementary Fig. 2a ). We were able to recover equal amounts of the GFPm  6  A-transcript from total RNA, ribodepleted RNA, purified mRNA, and immunoprecipitation (IPP), indicating that a differential expression analysis from MeRIP-Seq is possible. m  6  A modification profiles in cecum and liver. We used a series of mice with different gut microbiota (see below) and analyzed methylation profiles using MeRIP-Seq. Overall, using 64 datasets for cecum and 33 datasets for liver with a median coverage of 8× and 11×, respectively (Supplementary Data 1), we detected 36,935 m  6  A peaks in the various anti-m  6  A-immunoprecipitates from murine cecum and 25,808 m  6  A peaks in the anti-m  6  Aimmunoprecipitates from liver (Supplementary Data 2).
Fifty-two percent of m  6  A peaks detected in the cecum were also found in the liver, and 74% of m  6  A peaks detected in the liver were also found in the cecum. In total, 80 and 84% of the peaks we detected in the cecum and liver, respectively, are described in the Methyl Transcriptome Data Base (MeT-DB v2. 0)  39  (Fig. 1a ), indicating that we identified bona fide methylation peaks. m  6  A peaks across different murine and human tissues and cell lines have been described to be largely conserved  1, 2  , and we found a strong overlap of the peaks we detected with all the different murine types of tissues and cell lines present in the MeT-DB v2. 0 database, but also a substantial overlap with published m  6  A peaks from human tissues and cell lines (Supplementary Fig. 2b ). GUITAR plots summarizing the positions of all the m 6 A modifications for the cecum (Fig.
1b ) showed that m  6  A peaks were mostly present in the CDS and 3′ UTR of mRNA, and at a lesser extent in the 5′ UTR, in agreement with previous studies  1, 2. We performed motif searches on the list of all detected peaks and assessed presence of known motifs for m 6 A (RRACH)  1, 2  and m 6 Am (NBCAN)  26  separately for the 5′UTR, CDS and 3′UTR (Fig. 1c ). As expected, the RRACH motif was less present in the 5′ UTR of transcripts, where instead the consensus motif for m  6  Am modification (NBCAN) was enriched (Fig. 1c ). Gut microbiota controls m  6  A modifications in mouse cecum. We first compared m  6  A marks in the cecal transcripts of conventional (CONV) and germ-free (GF) mice and found 440 m  6  A peaks on 312 transcripts to be differentially methylated. Multidimensional Scaling (MDS) analysis of m  6  A peaks revealed a clear separation between methylation peaks of CONV and GF mice in the cecum (Fig.
2a ). Since m 6 A modifications are linked to both stabilization and destabilization of transcripts  27, 41  , a complete uncoupling of levels of differential methylation and differential transcript expression, however, would be very surprising. When we examined levels of the residual 38% of peaks that were differentially methylated and at the same time differentially expressed (Supplementary Data 3), increased methylation correlated with an increased abundance of the transcript in most cases. This suggests that a minor proportion of transcripts with m  6  A peaks that we found to be influenced by the microbiota are stabilized, as it is also the case in viral infection  42, 43. We then performed label-free proteomics analysis to assess whether differential methylation and transcript levels correlate with differential protein expression levels in the cecum of CONV and GF mice.
Although the overlap between the two published datasets is low, we found a small overlap of our differentially methylated transcripts with both datasets (Supplementary Fig. 5 ). Interestingly, these transcripts included that for S-adenosylmethionine synthase isoform type-2 (Mat2a), which we found to be hypermethylated in the 3′UTR of CONV, ex-GF and vanco mice (Fig. 4c, d ). We could confirm this finding by MeRIP and subsequent quantitative real-time (qRT) PCR (Fig. 4e ) and found that the Mat2a protein expression was reduced in GF and abx, cecum compared to CONV, ex-GF and vanco, Am and Lp mice (Fig. 4f, g ). Since Mat2a is the enzyme that produces the methyl donor S-adenosylmethionine (SAM) required for methyltransferase activity  56  , a reduced Mat2a expression in GF and abx may be linked to the altered methylation profiles.
When comparing the methylation profiles in liver of monoassociated mice (Am, Lp) to GF mice, Lp mice displayed a stronger effect on host m 6 A modifications of the two tested bacterial strains (Supplementary Data 2 and 5), and influenced pathways in insulin signaling, lipid metabolism and cell differentiation (Supplementary Data 5). Fig. 3  Differentially methylated transcripts in mouse cecum. a Pathway analysis of "molecular and cellular function" and "diseases and disorders" of transcripts differentially methylated between GF and CONV mice was performed using Ingenuity pathway analysis (IPA). log -p of the 10 most enriched pathways are displayed. Lists of genes contained in each category and comparison are given in Supplementary Data 4; b Expression log ratio of transcripts differentially methylated between GF and CONV mice related to lipid metabolism as defined in IPA.
8402; GF: 0. 0002; abx: 0. 0019; vanco: 0. 7800; Am: 0. 3569; Lp: 0. 0070; details for statistical analysis are given in the source data file associated to this manuscript. f, g Pathway analysis of "molecular and cellular function" and "diseases and disorders" of transcripts differentially methylated between Am and GF mice (f), and LP and GF mice (g) was performed using Ingenuity pathway analysis (IPA). log -p of the 10 most enriched pathways are displayed. Lists of genes contained in each category and comparison are given in Supplementary Data 4.
Extracted Ion Chromatograms (EIC) of base peaks of the following masses: A (m/z 268. 08-268. 12), C (m/z 244. 08-244. 11), m 1 A and m 6 A (m/z 282. 10-282. 13), m 6 Am (m/z 296. 12-296. 15), and m 5 C (m/z 258. 09-258. 12), were used for quantification. In all cases, coefficients of variations for peak areas were always below 15%. Experimental data (peak area versus injected quantity) were fitted with a linear regression model for each compound leading to coefficient of determination (R 2 ) values better than 0. 997. Accuracies were calculated for each calibration point and were always better than 15%. Western blotting. For Western blotting, we performed acetone precipitation of the flow-through of the tissue lysate from RNA preparation according to the manufacturer's instructions. Samples were solubilized, separated on NuPAGE (Thermo Fisher) or criterion (Bio-Rad) 4-12% Bis-Tris Protein Gels and transferred to PVDF membranes using iBlot (Thermo Fisher).
Antibody dilutions were: rabbit anti Mettl3 (abcam ab195352), rabbit anti Mettl16 (abcam ab186012), rabbit anti Alkbh5 (abcam ab195377) and rabbit anti Mat2a (abcam ab154343): 1:1000; mouse anti β-actin (Sigma-Aldrich A1978): 1:2000; rabbit anti Mettl14 (Sigma Aldrich HPA038002): 1:500. Proteomics analysis. Protein digestion: precipitated proteins were solubilized with 8 M urea in Tris HCl 50 mM pH 8. 5. Proteins disulfide bonds were reduced with 5 mM tris (2-carboxyethyl)phosphine (TCEP) for 20 min at 23 °C and further alkylated with 20 mM iodoacetamide for 30 min at room temperature in the dark. Subsequently, LysC (Promega) was added for the first digestion step (protein to Lys-C ratio = 80:1) for 3 h at 30 °C. Then the sample was diluted to 1 M urea with 100 mM Tris pH 8. 0, and trypsin (Promega) was added to the sample at a ratio of 50:1(w/w) of protein to enzyme for 8 h at 37 °C.
1% FA) from 3 to 6% in 5 min, 6 to 29% in 130 min, 29 to 56% in 26 min, and 56 to 90% in 5 min (total length of the chromatographic run was 180 min including high ACN level steps and column regeneration). The instrument method for the Q Exactive Plus was set up in the data dependent acquisition mode. MS spectra were acquired at a resolution of 70,000 (at m/z 400) with a target value of 3 × 106 ions. The scan range was limited from 300 to 1700 m/z. Peptide fragmentation was performed using higher-energy collision dissociation (HCD) with the energy set at 27 NCE. Intensity threshold for ions selection was set at Fig. 5 m 6  A modifications in the liver. a Positions of detected m  6  A peaks on all methylated transcripts in liver were determined and visualized using the GUITAR package. b Motif enrichment in m  6  A modification was determined by calculating total occurrence of motifs in m  6  A peaks on the 5′UTR, CDS and 3′UTR of in the liver.
5. 3. 8  72, 73  against Uniprot proteome database of mouse. Usual known mass spectrometry contaminants and reversed sequences were also searched. 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), N-terminal acetylation (variable), and ubiquitin (variable). The mass tolerance in MS was set to 20 parts per million (ppm) for the first search then 6 ppm for the main search and 10 ppm for the MS/MS. Maximum peptide charge was set to 7 and 5 amino acids were required as minimum peptide length. The "match between runs" feature was used between condition with a maximal retention time window of 1 min. One unique peptide to the protein group was required for the protein identification.
A false discovery rate (FDR) cutoff of 1% was applied at the peptide and protein levels. The MaxLFQ, Maxquant's label-free quantification (LFQ) algorithm was used to calculate protein intensity profiles across samples  71. Data were filtered by requiring a minimum peptide ratio count of 2 in MaxLFQ. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016099. Statistical analysis: for the statistical analysis of one condition versus another, proteins identified in the reverse and contaminant databases and proteins only identified by site were first discarded from the list. Then, proteins exhibiting fewer than 2 LFQ intensities in at least one condition were discarded from the list to avoid misidentified proteins.
After log2 transformation of the residual proteins, summed intensities were normalized by median centering within conditions (normalizeD function of the R package DAPAR  74  ). Remaining proteins without any LFQ intensities in one of the two conditions have been considered as proteins present in one condition and absent in another. They have therefore been set aside and considered as differentially abundant proteins. Next, missing values were imputed using the impute. MLE function of the R package imp4p. Proteins with a log2 (fold-change) inferior to 1 have been considered as proteins which are not significantly differentially abundant. Statistical testing of the remaining proteins (having a log2 (fold-change) superior to 1) was performed using the limma t-test within the R package limma  75.
An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values using the function adjust. p of R package cp4p to estimate the proportion of true null hypotheses among the set of statistical tests  76. The proteins associated to an adjusted p-value inferior to an FDR level of 1% have been considered as significantly differentially abundant proteins. Finally, the proteins of interest are therefore those emerging from this statistical analysis in addition to those which are considered to be absent from one condition and present in another. Immunoprecipitation of m 6 A-methylated mRNA. Immunoprecipitation was performed as previously described  77. Briefly, 3 μg of rabbit anti m  6  A antibody (Synaptic Systems) were bound to 25 μl washed Protein G Dynabeads (Thermo Fisher) in immunoprecipitation buffer (1x IPP; 150 mM NaCl, 0.
Directional RNA-seq libraries were prepared using NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs) according to the manufacturer's instructions. Libraries were sequenced on an Illumina HiSeq 2500 platform generating single end reads (65 bp). MeRIP-Seq processing. The mouse mm10 genome and list of transcripts were downloaded from Gencode (Mus Musculus VM13  78  ). Only the 21968 'pro-tein_coding' genes were kept for the analysis. After the sequencing of every MeRIP-Seq (IP dataset) and RNASeq (Input dataset) the resulting reads were trimmed (AlienTrimmer 0. 4. 0  79  , default parameters). They were mapped on mouse mm10 genome using STAR mapper 2. 5. 0a (-sjdbOverhang 100 parameter)  80. Mapping files were filtered to keep uniquely mapped reads using SAMtools 0.
1. 19 (samtools view -b -q 1 parameters)  81  , and saved to BAM files after indexation. The quality of the sequencing and mapping was assessed using FastQC 0. 10. 1 and MultiQC 0. 7  82, 83. Gene expression was calculated with HTSeq 0. 9. 1(-s no -m union --nonunique all parameters)  84. m 6 A peak detection. The original three reference papers on MeRIP-Seq analysis have used three different workflows for m  6  A modification peak detection (Fisher test 2 , MACS2 software  85  , and Peak Over Input technique  77  ). Each of these techniques has a certain bias and identifies different types of methylation peaks. We implemented all of them https://gitlab. pasteur. fr/hub/MeRIPSeq and developed our own technique using the fold of Reads Per Million (RPMF). We prepared the peak detection by first generating windows of 100 bp overlapping in their middle using all 21,968 'protein_coding' genes.
Windows of 100 bp overlapping in their middle were generated. The total number of reads per window was calculated for each dataset. The number of reads for each window in IP and Input was determined using HTSeq 0. 9. 1  84  (-s no -m union --nonunique all parameters). Only the windows with coverage higher than 10 reads in IP datasets were kept. Fisher, POI, and RPMF techniques were then run on these windows to assess for the presence of methylation peaks. The Fisher exact rank test was applied on each of the 100-bp windows. The p-values were corrected with Benjamini-Hochberg multiple testing correction  86. Only peaks with a corrected p-value < 0. 001 were kept for Fisher analysis. The reads per million (RPM) for each window in IP and Input was calculated. The reads per million-fold (RPMF) was determined by subtraction of RPM of each window in the IP dataset and RPM in the Input.
Only the peaks with RPMF > 10 were kept. We prepared the POI peak detection by first calculating the raw coverage on each dataset, IP and Input, using BEDTools 2. 17. 0 87 (genomcov -d -split -ibam) and removing all positions with null coverage. Following the workflow described in Schwartz et al. 77  , the Peak over median (POM) was calculated by dividing median expression in the window by median expression of the gene, only considering exonic regions of genes. The peaks with a POM score <4 in the IP dataset were removed. The Peak Over Input (POI) score was then calculated by dividing POM score in the IP dataset by POM score in the Input dataset. Only the peak with POI >2 were kept. MACS2 methylation sites detection was run (-g 282000000 -nomodel parameters) on bam files of IP datasets with the Input datasets serving for assessing the whole RNA distribution.
Each of the four methods of peak detection was detecting small windows with potential methylation sites in very few datasets. To keep only robust methylation sites, the occurrence of each peak was assessed by counting in how many IP samples a specific window was detected as a methylation site. Only windows found in three or more IP samples were kept. BEDtools merge software  87  was run to regroup all overlapping windows. A Median coverage across all IP samples was calculated using WiggleTools  88. With an in-house python script, the maximum coverage position of every peak for every technique was computed and the peak region was redefined as the 150 bp region centered at the position of maximum coverage. Methylation sites found in at least three of the four techniques were combined using BEDTools merge function, followed by another search for maximum coverage for each site.
Finally, a general list of 150-bp-long peaks centered at the maximum coverage in every IP dataset was extracted. The overlapping region on their corresponding gene was searched: 5′UTR, 3′UTR, CDS, intron. The presence of the m 6 A consensus sequence RGACW in the sequence of the peak was assessed by calculating a motif score by adding score presence when one of this sequence was found: 'GAACA': 2, 'GGACA': 3, 'GAACT': 5,'GGACT': 8. The union of all the methylation sites found by each of the four techniques was determined using BEDTools merge function. Two lists of potential methylation sites were then extracted: 37,796 methylation peaks for the cecum and 31,394 peaks for the liver tissue. We determined the overlap of these peaks with the m 6 A modification peaks previously detected by MeRIP-Seq stored in the MeT-DB v2.
0 database 39 using the Bioconductor R package GenomicFeatures  89. Differential methylation and expression analysis. Statistical analyses were performed using the R package limma  75. https://gitlab. pasteur. fr/hub/MeRIPSeq. Read count data were first normalized with the trimmed mean of M-values normalization method  90  (edgeR package) and transformed with the voom 91 function (limma package). Limma was then used to assess the statistical significance of observed differences in read counts. Two different linear models were derived to address two different questions. Differentially expressed genes between all pairs of the four conditions were first detected with y ~BioCond + Sequencing + Library_batch + IP_procedure, where y is the normalized and transformed Input read counts (expression data), BioCond refers to the seven (CONV, GF, ex-GF, abx, vanco, Am, Lp) biological conditions under study for cecum, or 4 (CONV, GF, Am, Lp) biological conditions under study for liver.
Genes or methylation peaks were considered statistically different when their adjusted p-value was lower than 0. 05 and the absolute log-fold change was >1. The batch effects were removed from the data for plotting the multidimensional scaling (MDS) and heat maps plots using the removeBatch-Effect function of the limma package. The MDS plots were done with the function plotMDS. The GUITAR plot was generated using the GUITAR package  39. Pathway analysis was performed using the Ingenuity Pathway analysis software (IPA; Qiagen) using the Diseases & Functions tool after performing expression analysis of differentially methylated peaks with the same parameters as above (absolute log-fold change >1, p-value < 0. 5). For pathways analysis of differentially methylated transcripts in liver, KEGG mouse 2019 and GO Biological Process analyses in Enrichr were used  92, 93.