dataesr/scientific-paragraphs-categorization huggingface dataset:
All intensities were reported in terms of Quinone Sulfate Units (QSU). Absorbance data were converted to Napieran absorption coefficients (E in m -1 ) by multiplying raw absorbance values by 2. 303 and dividing by the cuvette path length (m)  56. E 350 was calculated and represents a quantitative measure of the chromophoric fraction of DOM that is positively correlated with DOC and lignin phenol content  57. SUVA 254 was calculated as the decadal UV absorbance at 254 nm divided by the DOC concentration  20. SUVA 254 is positively correlated to bulk DOM aromaticity because aromatic compounds absorb more light in the UVvisible spectra. The DOM derived from plant and soil organic matter dissolution, which is composed of large biopolymers, shows higher aromaticity than autochthonous and microbially derived DOM that comprised small biopolymers and monomers assimilable and produced by microorganisms  20, 58.
PARAFAC modeling was performed using the DOMFluor toolbox within MATLAB  66  in order to decompose EEMs into independent fluorescent components. Excitation wavelengths below 255 nm and emission wavelengths below 280. 81 were removed from matrices in order to avoid deteriorating signal-tonoise ratios  67. Prior to modeling, regions of the spectra influenced by 1st and 2nd order scatter peaks and with no fluorescence (i. e. , emission << excitation) were cut and replaced by missing values. A six-component model was generated incorporating a total of 226 samples. Model parameters were constrained to be non-negative. The model, which identified components with unimodal emission maxima, was validated using residual analysis characterized by instrument noise and a lack of systematic pattern, split-half analysis and random initialization  21, 66  and explained 99.
9% of total the fluorescence signature. PARAFAC component intensities were normalized to the sum of component fluorescence intensities and expressed as their relative contribution to the total fluorescence. The identified component emission and excitation loadings were compared with the open fluorescence database 68 (https://openfluor. lablicate. com/) and with previous studies using PARAFAC analysis that are not included in the database. Components have been previously reported by 97 independent studies from openfluor database in various environments (Tucker Congruence Coefficient, TCC > 0. 95) and in many studies in Arctic ecosystems (Table  S4 ). Statistical analyses. All statistical analysis were carried out using the R free software version 3. 2. 1  69. Bartlett's and Shapiro-Wilk tests were performed to, respectively, evaluate the variance homogeneity and the normal distribution of each variable of interest.
Even log-transformed most variables were not normally distributed. A Principal Component Analysis (PCA) was performed using PARAFAC components and absorbance and fluorescence indices (biogeochemical data and soil properties were not included) was used to examine the differences in optical properties between thermal layers and layer natures. Non-parametric analyses of variance using Kruskal-Wallis (defined by its chi-squared H and P value) and the multiple pairwise-comparisons using pairwise Wilcoxon rank sum test (defined by its p value) were used to determine significant differences in concentrations and yields of DOC, DTN, NH 4 + , NO 3 -and optical properties among thermal layers (active layer vs. permafrost), and between soil layer types (organic vs. mineral). Wilcoxon rank sum test tests (defined by W and P value) were used to assess the significance of difference between two specific groups.
Before performing the PCA auto scaling was conducted on all variables with the FactoMineR package in order to decrease the leverage of high values. Regression analyses were employed to determine relationships between concentrations and yields of DOC, DTN, NH 4 + , NO 3 -, optical properties and soil properties. Canadian Permafrost landscape DOC pool estimates. We estimated the DOC pool in permafrost soils at the pan-Canadian scale using the updated version of the Northern Circumpolar Soil Carbon Database (NCSCDv2)  70  at a spatial resolution of 1 km. NCSCDv2 provides information on SOC storage stratified both by depth layers and soil types. In the database, the class of Gelisol (permafrost soils) is subdivided in 3 suborders: Histel, Turbel, Orthel. We classified the soil type of each borehole as "organic permafrost soils" (Histels) or "mineral permafrost soils" (Orthels and Turbels).
Acknowledgements We are grateful for field and lab assistance from  Jonathan Roger ,  Steve Koziar ,  Daniel Lamhonwah ,  Megan Rueckwald ,  Yvette Chirinian ,  Tova Pinsky ,  Olivia Rodee , and  Gillian Thiel. We thank  Warwick Vincent ,  Mickaël Lemay , and all ADAPT contributors for providing research infrastructure and science support. This research was funded by the  Natural Sciences and Engineering Research Council (NSERC) Discovery Frontiers ADAPT program , the  ArcticNet Network of Centres of Excellence , and  NSERC Discovery  grants to M. J. L. , S. F. L. , and P. G. C. T. C. was financed by an  Ontario Trillium Scholarship  from the  Ontario Ministry of Training, Colleges, Universities. Logistics support was provided by the  Polar Continental Shelf Program ,  Natural Resources Canada.
Data availability The authors declare that all data supporting the findings of this study other than the excitation-emission matrices (EEMs) and the PARAFAC parameters are available within the paper and its supplementary information and supplementary data files. The EEMs and the PARAFAC code that generated the 6-component model are available from the corresponding author upon request. All data associated with this study will shortly be posted on the Polar Data Catalogue metadata website (https://www. polardata. ca) and Nordicana-D, the online data report series archived by the Centre for Northern Studies (http://www. cen. ulaval. ca/nordicanad/en_index. aspx).
Recently, RNA-seq data have been used for the selection and validation of suitable reference genes in some land plant species such as Lycoris aurea  (Ma et al. 2016)  and Oxytropis ochrocephala Bunge  (Fu et al. 2015). In marine macroalgae, some genes have been experimentally validated as reference genes in few model species such as Ectocarpus siliculosus (Le  Bail et al. 2008) , Chondrus crispus  (Kowalczyk et al. 2014) , Heterosigma akashiwo  (Koeduka et al. 2015) , and Pyropia yezoensis  (Gao et al. 2018). However, up to now, there is no study about the identification and validation of RT-qPCR reference genes in S. latissima whereas this kelp is becoming an important model species for studying molecular responses to environmental changes  (Monteiro et al. 2019 , Li et al. 2020). In order to conduct studies on the expression regulation of targeted metabolic pathways, we looked for putative stable reference genes in RNA-seq datasets of this species, obtained from both biotic and abiotic stress experiments, using in silico analyses.
Subsequently, the expression stability of selected genes was further validated using RT-qPCR under different light, temperature, and endophyte co-cultivation conditions as those used to generate RNA-seq data and following defense elicitation using endogenous elicitor, oligo-guluronates (GG) treatment. Indeed, GG applications have been shown to induce rapid oxidative bursts in kelps  (Küpper et al. 2001 (Küpper et al. , 2002) )  and later metabolic regulations  (Goulitquer et al. 2009) , including conserved defense and signaling pathways, such as the release of free fatty acids and oxylipins. In order to validate RT-qPCR normalization in S. latissima in a physiological context of both abiotic and biotic stress, all selected reference genes were then re-assessed to monitor the expression of two target genes encoding a lipoxygenase (LOX) and a glutathione s-transferase (GST), which are involved in oxylipin pathway and oxidative stress responses, respectively.
Bioinformatic selection of candidate reference genes Different RNA-seq data sets were used to screen for potential candidate reference genes. RNA sequencing of control and co-cultured samples were previously performed using Illumina Hi-Seq 3000 platform  (Bernard, Xing et al. unpublished data; ENA at the EBI; ID: PRJEB37483). After transcriptome assembly and annotation, the reads were mapped on the transcriptome using bowtie2  (Langmead and Salzberg 2012). The read counts were calculated by RSEM  (Li and Dewey 2011)  and converted into transcripts per million (TPM) for each unigene. The microarray data of S. latissima under abiotic stress (control, high temperature and high light, high temperature and low light, low temperature and high light, and low temperature and low light) were downloaded from the EMBL database (ArrayExpress at the EBI; http://www.
ebi. ac. uk/microarray-as/ae/; ID: E-MEXP-3450) and the probes of each gene were mapped to the reference transcriptome to link the expression of probes with unigenes. The expression stability of all unigenes in two datasets was calculated and ranked separately by the Pattern Gene Finder (PaGeFinder). PaGeFinder is a web-based server for the online detection of gene expression patterns from serial transcriptomic data generated by high-throughput technologies like microarray or next-generation sequencing. In PaGeFinder, the dispersion measure (DPM) was calculated to evaluate the variability of gene expression profiles. A value of DPM close to 0 suggests a similar expression level of a gene over all RNA-seq samples, and then a strong stability of its expression whatever the treatments. In the end, genes with putative and reliable protein annotation (nr databases), appropriate expression levels (TPM > 10), and a low dispersion measure (DPM ≤ 0.
RT-qPCR and data analysis For each reference candidate gene and stress-related target genes, a pair of specific primers was designed in the 3′ end coding region of sequences using Primer5 software (Table  1 ). Primers were designed to have a melting temperature around 60 °C, a length between 18 and 26 nucleotides, a GC content between 40 and 60%, avoiding secondary structures and selfand cross-annealing. The specificity of each primer was tested in silico, using the BLASTN search on the reference transcriptome of S. latissima. The RT-qPCR reactions were performed in 384-well plates in a LightCycler480 (Roche Molecular Biochemicals, Germany) with LightCycler480 SYBR Green I Master kit (Roche, Germany). The protocol was: 95 °C for 5 min, followed by 55 cycles of 95 °C for 10 s, 60 °C for 10 s, and 72 °C for 20 s.
Four biological replicates were performed for each treatment. The specificity of the amplification was verified with a dissociation curve obtained by heating the samples from 65 to 95 °C. A 1:5 dilution series of the S. latissima cDNA was prepared and tested for each gene to determine the amplification efficiency of the selected primers pairs, which was calculated by the LightCycle480 gene scanning software (version The error value of amplification efficiency for each primers pair was determined by LightCycler480 gene scanning software (version 1. 5) with an acceptable value of < 0. 2 1. 5), using the Fit-points method. These amplification tests on cDNA dilution series are also important to set the linearity limits of the amplification. The gene expression level was determined by the number of amplification cycles (Cq), which was extracted from the raw fluorescence data of the RT-qPCR curve.
Four statistical methods (NormFinder, geNorm, BestKeeper, and comparative delta Ct method) were used to evaluate the expression stability of selected reference genes. BestKeeper is an Excelbased tool that analyzes absolute raw Cq values directly using pairwise correlations. It calculates three variables related to the expression level of genes: the standard deviation (SD), the coefficient of correlation (r), and the coefficient of variance (CV). The gene with the lowest CV ± SD is considered the most stable transcript gene. In the other three methods, the Cq value is converted into relative quantities according to the formula: 2 -ΔCt (ΔCt = the corresponding Cq value-minimum Cq). In geNorm, the M values are calculated to evaluate the expression stability of genes. The most stable expressed gene exhibits the lowest M value.
In NormFinder, the genes are ranked according to their stability value (SV) which is calculated using an ANOVA model based on ΔCt values. A lower SV is correlated with higher expression stability. The last method is based on the pairwise comparisons of ΔCt, ranking the genes from the most to the least stable transcripts, based on the average standard deviations, the lowest average SD corresponding to the most stable. Finally, the online software RefFinder (http://leonxie. esy. es/RefFinder/) was used to obtain the overall final ranking of all candidate genes based on the results of the four computational programs, NormFinder, BestKeeper, GeNorm, and comparative ΔCt. For target genes, the raw data were divided by the Cq value of corresponding reference genes as a normalization step. And then the relative expression level of treated samples versus control samples was calculated by comparing the normalized Cq of treatment and control samples according to the formula: 2 -ΔΔCt (ΔCt = normalized Cq value of treated samplenormalized Cq value of control sample).
Results In silico S. latissima transcriptome-wide selection of candidate reference genes A total of 23,049 unigenes from S. latissima was analyzed based on RNA-seq and microarray datasets: the dispersion measure (DPM) was calculated to evaluate their expression variability according to different experimental conditions, using PaGeFinder. The DPM value of 3,641 unigenes in biotic stress data, i. e. , cocultivation datasets including corresponding control datasets, and 2576 unigenes in abiotic stress data, i. e. , temperature, light stress, and control conditions, were lower than 0. 3 and were first selected as a pool of most stable transcripts. Among them, the unigenes without any function annotation or whose expression level was too low (TPM < 10) were removed, leading to 225 and 788 remaining candidate genes.
Finally, eight genes were selected based on the combination of genes with low DPM values, either in biotic stress datasets only, or in abiotic stress datasets only or in all datasets. PCR amplification and melting curve analysis were performed to confirm the specificity of the eight designed primer pairs, i. e. , presenting a single band of amplification at the expected size (Sup. Fig. 1 ) and a single peak of the melting curve of the PCR product (Sup. Fig. 2 ). The mean amplification efficiency of each gene ranged from 1. 95 to 2. 03 (Table  1 ).
Integrating computational methods for a comprehensive ranking of candidate reference genes Based on these analyses, the prediction of the most stable genes revealed some variations according to experimental conditions tested and methods used (Fig. 2 ). Some overall trends appear such as the relatively bad ranking of the ACT gene as a potential reference gene, except for one method (delta Ct) in abiotic conditions (Fig. 2b ). Indeed, the final ranks of the eight candidate reference genes were sometimes very different as observed for the LISK gene, classified from 1 to 4 in biotic conditions (Fig. 2a ) or for the ATPase gene, ranked from 1 to 6 in abiotic conditions (Fig. 2b ). Therefore, RefFinder, online software based on statistical approaches, was used to compare these data and establish a comprehensive ranking of all candidate   5 ).
In this latter condition, the relative expression levels of LOX seem to be significantly underestimated, when using the most stable reference gene (NDH). While using the least stable reference gene (LISK), the expression fold changes showed significant differences in two conditions out of four tested (Fig. 3 ), confirming the low-quality prediction of this gene as a reference gene in S. latissima. In S. latissima samples cultivated with endophytes for 2 days, the GST expression levels showed strong upregulations, 5. 73-fold and 9. 41-fold changes, respectively, when using the most stable reference gene (ATPase) and the best reference gene combination (ATPase and EIF5B) as calibrators. These expression patterns agreed with that obtained from RNA-seq datasets (Fig. 4 ). The use of the least stable reference gene (ACT) for normalization significantly affects the calculation of the relative expression level, leading to a much lower value (2.
39-fold change). A similar underestimation bias of the GST relative expression level was found in the GG-treated samples, where the best reference gene combination (11. 31-fold) and ATPase (6. 66-fold) normalization showed the highest upregulation shifts, compared to the expression results normalized by ACT only. In that case, however, we could not compare these results with the corresponding GG RNA-seq datasets as a reference.
2005 , Silveira et al. 2018). In agreement with these results, our study also showed that a classic housekeeping gene coding for actin (ACT) did not exhibit high stability in S. latissima in any of the six experimental conditions analyzed, covering both abiotic and biotic stress conditions (Fig. 1 ). Therefore, it is not only necessary to select novel reference genes for S. latissima, but they also need to be adapted to each experimental setup, by validating the absence of variability of their expression in corresponding physiological conditions. As an important source of transcriptomic information, RNA-seq analysis has been performed to explore suitable reference genes for specific experimental conditions in other model species  (Liu et al. 2018 , Zhan et al. 2019). In this study, we analyzed RNA-seq datasets related to biotic (co-cultivation with endophyte) and abiotic stress (light and temperature) in the brown alga S.
latissima. Eight candidate reference genes were selected considering credible protein annotation (nr database), appropriate expression levels (TPM > 10), and a low dispersion measure (DPM ≤ 0. 3), and their expression stability was further experimentally validated by RT-qPCR in S. latissima plantlets submitted to different stress treatments carried out in laboratory-controlled conditions. These treatments were chosen to cover a range of different physiological conditions, such as temperature and light environmental changing parameters, which could be encountered during intertidal exposures. For biotic stress validation, plantlets were cultivated with their natural endophyte L. elsbetiae  (Bernard et al. 2019 ) using laboratory-controlled bioassays. In addition, oligo-guluronate (GG) elicitation was performed as previous studies indicated that this chemical treatment was able to induce an oxidative burst in S.
2011 , Lim et al. 2015). According to the RNA-Seq dataset analysis, the LOX gene was significantly upregulated under light and temperature stress conditions and GST was upregulated under co-cultivation stress. Therefore, these two target genes were used to validate the use of selected reference genes for normalizing their expression. In light and temperature stress, the results showed that the expression normalized by the least stable gene LISK was significantly underestimated comparing to the one normalized by the best reference gene combination (BRG), including the three genes NDH, ATPase, and EIF5B. It is worth noting that the normalization by a single reference gene, even if being the most stable one, NDH, also conducted to a significant underestimation compared to the normalization by the BRG, indicating the necessity of using multiple reference gene combination as a calibrator.
Conclusions In this study, eight candidate genes with low expression variance were screened using large transcriptomic datasets generated in the kelp S. latissima under biotic and abiotic stress conditions. Among these genes, two combinations of reference genes were identified according to their expression stability under the different physiological conditions tested: EIF5B and ATPase are the best-adapted reference genes under biotic stress-related conditions, while NDH, ATPase, and EIF5B are the optimal combination for RT-qPCR normalization under temperature and light stress. Moreover, our study clearly showed that a commonly used reference gene, coding for Actin, is not suitable to normalize RT-qPCR gene expression under the different physiological conditions in S. latissima, its expression being too much variable.
Acknowledgments We would like to thank  L. Dartevelle  and  E. Rolland  from the  LBI2M's Algal Culture  facilities for technical support. This work has also benefited from the computational resources of the  ABiMS bioinformatics platform  ( FR 2424 ,  CNRS-Sorbonne Université, Roscoff ), which are part of the  Biogenouest core facility  network.
Data availability The RNA-seq datasets analyzed during the current study are available in the EMBL databases under the accession ID: PRJEB37483 and E-MEXP-3450 (ArrayExpress at the EBI; http:// www. ebi. ac. uk/microarray-as/ae/; ID: E-MEXP-3450). All other data generated during this study are either included in this published article and its supplementary information files, or available from the corresponding author on request.
In addition, microbial communities in HMA and Eel River Basin sediment may differently respond to the presence of manganese oxide. Among the reactive iron minerals present in the methanic zone, magnetite, which quantitatively is the least important iron oxide fraction in the sediments (Figure  2C ), stimulated Fe-AOM most strongly, suggesting that this iron oxide mineral could be important for in situ Fe-AOM. Similar findings were obtained from incubation experiments with lake sediments  (Bar-Or et al. , 2017)  and supported by a geochemical modeling study on Baltic Sea sediments  (Egger et al. , 2017). The underlying mechanism for magnetite dependent AOM is unclear from the dataset presented; however, 'cryptic' methane cycling is feasible  (Beulig et al. , 2018 (Beulig et al. , , 2019)). This process would be fueled by direct interspecies electron transfer (DIET;  Kato et al.
, 2012; Tu et al. , 2017). While dissimilatory iron oxide reducers (e. g. , Desulfuromonadales) have not been clearly shown to act as partners for ANMEs, the increased relative abundance of Desulfuromonadales 16S rRNA genes (Figure  5B ) cannot be ignored. However, whether they are syntrophic partners of ANME in Fe-AOM requires further research as a clear indication for bacteria partner organisms was not obtained from the long-term incubations. Possibly, ANME-2a completely oxidize CH 4 to CO 2 without a bacterial partner. A previous study showed that ANME-2a, like ANME-2d, possess a variety of multi-heme cytochromes in their genome  (Wang et al. , 2014) , and based on their metatranscriptome datasets they argued that ANME-2a may exist without bacterial partners in specific marine sediments.
Sampling From the Helgoland Mud Area Sediment samples were obtained from gravity cores collected during RV HEINCKE cruises HE406 (July, 2013), HE443 (May, 2015), HE461 (April, 2016) and HE483 (April, 2017) (Table  2 ). As data from previous campaigns show  (Oni et al. , 2015 ; Figures  2B, C ), the geochemical zonation of the sediments at the study sites are consistent over the years. Pore-water sampling of gravity cores HE443-010-3 and HE461-004-1 for dedicated geochemical analysis was done on board using rhizon samplers  (Seeberg-Elverfeldt et al. , 2005; Dickens et al. , 2007). Sediment samples for solid-phase geochemical analysis (Table  2 ) were collected as described in reference  (Oni et al. , 2015). Geochemical and molecular assessments were done on samples that were directly taken on board after the gears were retrieved.