dataesr/scientific-paragraphs-categorization huggingface dataset:
RF and GAM required more computation time than the other techniques, especially when the dataset was large, as with N solution in France. Ordinary linear regression had the lowest computational cost. The second advantage of using the meta-models is that they need fewer inputs than the total number required for running Volt'Air. In our case study, Volt'Air required 16 types of input variable, and five of them (i. e. , the weather variables) needed to be provided at an hourly time-step for one month (Section 2. 1) leading to at most a total of 3,611 input variables. The use of Volt'Air is thus relatively costly in terms of data acquisition. The total number of selected inputs in the meta-models ranged from 6 to 15 (if we exclude the quadratic and interaction terms). In the meta-models, weather inputs need to be supplied over at most seven days.
The meta-models produced here combine qualities of both the process-based model Volt'Air and the empirical models developed and used for calculating NH3 volatilization in the field  (Génermont, 2014). Our meta-models include a small number of input variables and have a very short computation time. But they do not suffer from the same drawbacks as purely empirical models based on a limited number of experimental datasets. The domain of validity of our meta-models is related to the domain of validity of Volt'Air and the extent of the dataset used to build the metamodels. Thus we can expect a larger domain of validity than for the empirical models which generally focus on specific conditions, for example, that of Menzi  (Menzi et al. , 1998) , which was constructed for cattle slurry applied to grassland with a splash plate, a total ammoniacal nitrogen (TAN) content of 0.
| Chemical analysis Headspace Solid Phase Micro Extraction (SPME) was performed to collect and characterize the chemical composition of volatiles released from P. halepensis needles and roots  (Jassbi, Zamanizadehnajari, & Baldwin, 2010). A SPME fiber coated with Polydimethylsiloxane/Divinylbenzene (PDMS/DVB, fiber diameter 65 μm, needle size 24 ga, StableFlexTM) was exposed for 4 hr to 10. 0 g DM of suspended plant material in each microcosm 1 hr after the pine material was put into the microcosm (Figure  1 ). The SPME fibers were analyzed on a Hewlett-Packard GC6890 coupled with a HP5973N Mass Selective Detector and equipped with a HP-5MS capillary column (30 m × 0. 25 mm × 0. 25 µm, J&W Scientific). Data were acquired in scan mode from 40 to 300 uma. Retention indexes of compounds were determined relative to Wisconsin Diesel Range Hydrocarbon injection (C8-C20, Interchim) and compared with those reported in the literature  (Adams, 2007).
This study was funded by the CNRS within the framework of the Zone Atelier "Arrière-pays Méditerranéen. " We gratefully thank Sylvie Dupouyet, Elisabetta D'Addio and Flore Corallo for their technical assistance during both material collection and laboratory experiments. , and CF designed the experiment. MS and SG performed the experiment. MS analyzed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. DATA ACCE SS I B I LIT YDryad https ://doi.
org/10. 5061/dryad. s0b179p. Composition of volatile organic compounds released from Pinus halepensis needles and roots. Abbreviations: CAS Number, Chemical Abstracts Service Number; Exp RI, Experimental Retention Index calculated with the formula of van Den Dool and Kratz (1963); Lit RI, Literal Retention Index based on Adams (2007); Peak area, peak area of the compound in the chromatogram; Percentage, peak surface percentage in the chromatogram; Tr. , Traces (peak surface percentage < 0. 1%).
org/10. 5061/dryad. s0b179p. Composition of volatile organic compounds released from Pinus halepensis needles and roots. Abbreviations: CAS Number, Chemical Abstracts Service Number; Exp RI, Experimental Retention Index calculated with the formula of van Den Dool and Kratz (1963); Lit RI, Literal Retention Index based on Adams (2007); Peak area, peak area of the compound in the chromatogram; Percentage, peak surface percentage in the chromatogram; Tr. , Traces (peak surface percentage < 0. 1%). SANTONJA eT Al. | 8213 Percentage Percentage 1. 1 5. 3 Tr. 18. 0 0. 2 Tr. Tr. 0. 8 2. 6 0. 5 Tr. 25. 6 0. 4 2. 7 1. 1 0. 3 4. 4 0. 4 1. 6 0. 8 Tr. 22. 3 0. 2 Tr. 1. 4 0. 3 2. 9 0. 4 Tr. Tr. (Continues) Roots Roots Peak area Peak area 608,193 2,976,659 10,054,231 107,540 465,899 1,485,812 305,225 14,269,696 212,077 1,505,045 643,660 154,872 2,456,448 270,637 876,675 473,752 12,505,286 168,261 812,058 186,682 1,643,386 249,478 Percentage Percentage 0.
DISCUSSION Changes of DNA methylation following plant exposure to abiotic constraint has been observed for various species, different constraints and genomic features such as genes and TEs. However, these studies were either focusing on specific analysis of particular genes or TEs, or on detecting local DMRs from genome-scale datasets, therefore hampering the genome-wide analysis of the methylome response. Here, instead of relying on a window-based approach that does not reflect the feature-based biological role of the epigenome  (Roudier et al. 2011) , we took advantage of the genomic architecture of the maize genome to unravel how genomic features and chromosomal location may affect methylome response to cold. For the first time, we show that an abiotic stress induces a genome-wide hypermethylation through the hypermethylation of TE sequences across all chromosomes.
2015) , suggesting different underlying mechanisms. In apple, drought induced similar amounts of hypo-and hyper-methylated DMRs in genes, but a larger amount of hypomethylated DMRs in TEs  (Xu et al. 2018 ). This contrasts with our observation that hypo-and hypermethylated DMRs share the same annotation and cytosine contexts patterns, and are therefore likely generated from similar mechanisms. This suggests that generation of local methylation changes in response to abiotic constraints may be more complex than previously anticipated. Further analyses of these previously acquired datasets using the same methodology should help deciphering to what extent TE methylation spreading is affected by abiotic stresses in various species. The methylome change we depict occurs together with extensive expression modifications.
DNA extraction, and BS-seq data DNAs were extracted from the last visible leaf samples following manufacturer's protocol using Macherey Nagel midi extraction kit. For each sample, 5 µg of genomic DNA was fragmented by acoustic shearing to a target value of 250 bp using a Covaris S220 (Covaris, Woburn, MA), according to manufacturer's recommendations. Fragmented DNA was purified using the QIAQuick PCR purification kit (Qiagen, Valencia, CA). Then, DNA fragments underwent an NGS library preparation procedure consisting in end repair and methylated adaptor ligation using the TruSeq DNA Sample Preparation kit (Illumina, San Diego, CA). Adapter-ligated fragments ranging from 200 to 300 nucleotides were isolated by agarose gel electrophoresis (Bio-Rad, Hercules, CA) and purified using the MinElute Gel Extraction Kit (Qiagen, Valencia, CA).
Quality and quantity of this amplified library was assessed as above. NGS data were generated by Illumina paired-end sequencing (2x100 bases) on a HiSeq2000 machine. Sequence quality was evaluated using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). was selected based on Bayesian Information Criterion (BIC). Each DMR was assigned to a cluster when its posterior probability was higher than 90%. It was considered unclassified otherwise. For "CG plus CHG" cases, redundancy between cases originally found with CG and originally found with CHG were merged using a 50% overlap threshold using Bedtools intersect  (Quinlan and Hall 2010) , keeping the most extreme boundaries. DNA methylation levels were re-estimated for these new locations using roimethstat (http://smithlabresearch.
4x-25x] dataset used for original DMR detection using a custom Python script (5 GCs for "CG only" cases, 5 CHGs for CHG only cases, and 5 CGs and 5 CHGs for CG plus CHG cases). For analysis of DMRs located inside genes, new random sets were generated, excluding genomic regions located outside of genes. mRNA extraction, RNA-seq library preparation and RNA sequencing mRNAs were extracted from the one before last visible leaf samples with Trizol (Invitrogen ref. 15596018) and β-mercaptoethanol (SIGMA ref. M3148-25ML) reagents. Supernatant was recovered and RNA purified using Qiagen RNeasy Plant Mini kit (ref. 74904) following manufacturer's instructions. Then, a Qiagen RNAse-free DNAse set (ref. 79254) was applied to remove the residual DNA. Library construction and sequencing were generated by the IPS2-POPS platform with Illumina NexSeq500 sequencer using barcoded adaptors and 18 samples per lane.
ACKNOWLEDGEMENTS We thank  INRA Saint Martin de Hinx  for seed stocks. The POPS platform benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). We thank  Daniel Grimanelli  for providing a list of DNA methylation genes,  Kelly Dawe  for discussion on centromeres, and  Karine Alix ,  Nicolas Bouché  and  Leandro Quadrana  for helpful discussions on the manuscript.
[ 4x-25x] dataset used for original DMR detection using a custom Python script (5 GCs for "CG only" cases, 5 CHGs for CHG only cases, and 5 CGs and 5 CHGs for CG plus CHG cases). For analysis of DMRs located inside genes, new random sets were generated, excluding genomic regions located outside of genes. mRNA extraction, RNA-seq library preparation and RNA sequencing mRNAs were extracted from the one before last visible leaf samples with Trizol (Invitrogen ref. 15596018) and β-mercaptoethanol (SIGMA ref. M3148-25ML) reagents. Supernatant was recovered and RNA purified using Qiagen RNeasy Plant Mini kit (ref. 74904) following manufacturer's instructions. Then, a Qiagen RNAse-free DNAse set (ref. 79254) was applied to remove the residual DNA. Library construction and sequencing were generated by the IPS2-POPS platform with Illumina NexSeq500 sequencer using barcoded adaptors and 18 samples per lane.
In this study, we evaluated the effects of tagging on apparent survival, condition, phenology and breeding performance for small bird species (<100 g) in a robust dataset of both published and unpublished studies to minimize the impact of publication bias. Moreover, we assess whether the tagging effects are related to species' ecological and life-history traits, type of control treatment as well as geolocator and attachment designs. We build on the most recent advances in meta-analytical statistical modelling to get unbiased estimates of the geolocator deployment effects controlled for phylogenetic non-independence and its uncertainty  (Doncaster & Spake, 2018; Guillerme & Healy, 2017; Hadfield, 2010; Viechtbauer, 2010).
| Data search We conducted a comprehensive search for both published and unpublished studies deploying geolocators on bird species with body mass up to 100 g. We searched the Web of Science Core Collection (search terms: TS = (geoloc* AND (bird* OR avian OR migra*) OR geologg*)) and Scopus databases (search terms: TITLE-ABS-KEY (geoloc* AND (bird* OR migra*) OR geologg*)), to find published studies listed to 18 February 2018. Moreover, we searched reference lists of studies using geolocators on small birds and included studies from previous comparative studies  (Bridge et al. , 2013; Costantini & Møller, 2013; Weiser et al. , 2016). In order to obtain information from unpublished studies, we inquired geolocator producers and the Migrant Landbird Study Group to disseminate our request for unpublished study details among their customers and members, respectively.
5. The variable of interest was presented outside the interaction with another variable. In order to obtain robust and unbiased results, we asked the corresponding authors for missing data or clarification when the criteria were not met or when it was not clear whether the study complied with the criteria (70% response rate  [n = 115] ). In addition, we excluded birds that had lost geolocators before subsequent recapture as we did not know when the bird lost the geolocator, and excluded all individuals tagged repeatedly over years because of possible interannual carryover effects of the devices. VBr assessed all studies for eligibility and extracted data; the final dataset was cross-checked by JK and PP. A list of all published studies included in the meta-analysis is provided in the Published Data Sources section.
| Trait categories; effect size calculation; explanatory variables We divided all collected data into four trait categories: apparent survival, condition, phenology and breeding performance based on the response variables reported (e. g. interannual recapture rates, body mass changes, arrival dates or clutch sizes; Supporting Information Table  S2 ). These categories represent the main traits possibly affected in the geolocator-tagged individuals. Subsequently, analyses were run separately for each trait category. We calculated the effect sizes for groups of tagged birds from the same study site and year of attachment, of the same sex (if applicable) and specific geolocator and attachment type accompanied with the corresponding control groups. For simplicity, we call these units records throughout the text.
For each record, we extracted a contingency table with the treatment arm continuity correction  (Schwarzer, Carpenter, & Rücker, 2014)  or mean, variance, and sample size, to calculate the unbiased standardized mean difference-Hedges' g  (Borenstein, Hedges, Higgins, & Rothstein, 2009) -and its variance with correction for the effect of small sample sizes  (Doncaster & Spake, 2018). We used the equation from  Sweeting, Sutton, and Lambert (2004)  to calculate variance in pairwise comparisons. When raw data were not provided, we used the reported test statistics (F, t or χ 2 ) and sample sizes to calculate the effect size using the r package compute. es (Del Re, 2013). Besides the effect size measures, we extracted additional variables of potential interest-ecological and life-history traits per species, methodological aspects of the study, geolocator and attachment designs and harness material elasticity (Table  1 ).
| Overall effect sizes and heterogeneity We calculated the overall effect size for each trait category from all available records using meta-analytical null models. We employed the MCMCglmm function from the MCMCglmm package  (Hadfield, 2010)  to estimate overall effect sizes not controlled for phylogeny (model 1, Supporting Information Table  S3 ). We then used the mul-Tree function from the mulTree package  (Guillerme & Healy, 2017)  to automatically fit a MCMCglmm model on each phylogenetic tree and summarised the results from all these models to obtain phylogenetically controlled overall effect size estimates (model 2, Supporting Information Table  S3 ). We used weakly informative inverse-Gamma priors (V = 1, nu = 0. 002) in all models. All fitted MCMCglmm models converged and Gelman-Rubin statistic was always <1.
1 for all parameters. As our data contained many effect sizes based on small sample sizes, which could lead to a biased estimate of the overall effect size variance, all effect sizes were weighted by their meanadjusted sampling variance  (Doncaster & Spake, 2018). We considered effect sizes (Hedge's g) of 0. 2, 0. 5 and 0. 8 weak, moderate and large effects, respectively. Moreover, we calculated the amount of TA B L E 1 Explanatory variables used in the multivariate meta-analysis of apparent survival extracted from published and unpublished geolocator studies or from the literature. N presents the number of records specified as the groups of tagged birds from the same study site, year of attachment, of the same sex and the specific geolocator and the attachment type accompanied with the corresponding control groups between-study heterogeneity in all null models using the equation described in  Nakagawa and Santos (2012).
The PC1 explained 54. 4% of the variability and expressed a gradient of species characterized mainly by increasing body mass, egg mass and clutch mass (Supporting Information Figure  S4 ). The PC2 explained 18. 7% of variance and was characterized mainly by increasing clutch sizes, number of broods and decreasing migration distances (Supporting Information Figure  S4 ). These axes together with the categorical ecological traits (Table  1 ) were then entered into the ecological model to estimate their effect on apparent survival (model 4). Finally, we tested for differences in effect sizes between published and unpublished results in each trait category using all available records (model 5). In these models, we employed the rma. mv function from the R package metafor  (Viechtbauer, 2010)  weighted by the mean-adjusted sampling error  (Doncaster & Spake, 2018).
D. K. was supported by the  Russian Science Foundation  grant (project no. 17-14-01147 ) and by a  Leverhulme Trust  research grant to  Richard Holland  ( RPG-2013288 ). The study was funded by the  Czech Science Foundation  (project no. 13-06451S ) and by the  Institutional Research Plan  (RVO:  68081766 ). We are grateful to the funders, supporters and researchers of the many studies included herein. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
They are reported during the early and middle Cambrian, but most importantly during the Terreuneuvian, the Cambrian interval devoid of trilobites and agnostoids, which are the main biostratigraphic tools for the rest of the System. The International Subcommission on the Cambrian Stratigraphy has emphasized the high potential of SSFs for biostratigraphic subdivision of the early Cambrian and SSF First Appearance Data (FAD) have been proposed for the definition of the base of several Cambrian stages (summary in  Peng et al. , 2012; Devaere et al. , 2013; Clausen et al. , 2015; Zhang et al. , 2017). Two FAD of SSFs are proposed for the definition of the base of the Cambrian stage 2: the FAD of the micromollusc Watsonella crosbyi or the FAD of Aldanella attleborensis. Trilobites are first recorded in the Cambrian stage 3 along with SSFs.
Avefallotaspis? orbis indicates the Cambrian stage 3 Avefallotaspis maria Zone (part of the outdated Nevadella Zone;  Webster and Bohach, 2014). In addition to the trilobites, two levels of archaeocyathan-microbial buildups occur in unit 3 of the PBF in Cerro Rajón. Their systematics, palaeoecology and depositional settings have been studied by  Debrenne (1987)  and  Debrenne et al. (1989). The identified archaeocyaths mainly occur in Cambrian Age 4 rocks worldwide, but some genera also occur in the Cambrian stage 3  (Debrenne et al. , 1989). New archaeocyathan material has been collected along with the SSFs and is being studied; the preliminary data obtained confirm that unit 3 corresponds to the Cambrian stages 3-4 based on the occurrences of the species. In addition, McMenamin (1987, note added in proof) reports the occurrence of "Silicified fragments of irregular archaeocyathans […] from a kutorginid brachiopod-rich, thin limestone bed 26 m above the base of unit 2 of the Puerto Blanco Formation," indicating a Cambrian Age 3.
ACKNOWLEDGEMENTS The authors are grateful to  Dr. G. I. Espinoza  from the  University of Sonora  for leading the authors to the location of the studied section. E. Locatelli  and  P. Recourt  from the  University of Lille  are thanked for their valuable technical support. LD benefited from fundings from the  Alexander von Humboldt Foundation  and SC from grant from  Spanish Ministry of Economics, Finance and Competitiveness   CGL2017-87631-P. The study was supported by projects  UNAM-DGAPA-PAPIIT  No. IN105012 ,  CONACyT  No. 165826  and ECOS-France-Mexico No. M13U01. The authors acknowledge the four anonymous reviewers for their constructive comments that greatly improved the manuscript.
In contrast to known cellulases, which are hydrolytic enzymes, lytic polysaccharide monooxygenases (LPMOs) degrade cellulose via an oxidative mechanism  [6] [7] [8]  involving molecular oxygen or hydrogen peroxide and redox-active molecules acting as electron donors  [9, 10]. LPMO-catalyzed cleavage leads to oxidation of one of the carbons in the scissile β-1,4glycosidic bonds, i. e. , oxidation of C1 and/or C4 of the glucose units, leading to carboxylic acid and/or keto functions at the cellulose surface  [9, 11, 12]. LPMOs are widespread in the fungal kingdom, with five families of LPMOs (AA9, AA11, AA13, AA14, AA16) described in the CAZy database (www. cazy. org) [13, 14]. All characterized LPMOs that belong to the AA9 family are able to oxidatively cleave cellulose  [15] [16] [17] [18] , and recent studies have focused on their use to defibrillate cellulose and facilitate the production of nanocelluloses  [19] [20] [21].
Acknowledgements The growth regulatory machinery is immensely complex and the authors apologize for not having cited all the relevant work in this field. The authors would like to thank  Annick Bleys  for proofreading and submitting this manuscript, as well as the present and former members of the  System Biology of Yield group  for fruitful discussion and contributions. This work was supported by  Ghent University  ( BOF-Methusalem  projects  BOFMET2015000201 & B/09140/02 ) and by the  Research Foundation Flanders  ( FWO research  project  3G038719 ).
Typically, structural analysis of peptides in complex mixtures, such as digests or hydrolysates, is performed using non-targeted, datadependent UPLC-MS/MS. In MS/MS, precursor ions are subjected to a fragmentation method, such as collision-induced dissociation or electron transfer dissociation  [16, 17]. Structural analysis of short peptides is less straightforward than the analysis of longer peptides as the often singly charged short peptides require more energy to fragment  [18] , which frequently results in a decrease in sequence information through interresidue bond cleavages and in an increase of the formation of immonium ions  [19]. Predicted fragmentations, e. g. , for di-and tripeptides  [20]  and/or retention time prediction  [21] , are helpful to assign peptides. When MS data are extracted from a non-targeted hydrolysate data set, single m/z values will often generate multiple peaks in the chromatograms, which is especially true for collagens because their primary structure contains many slightly different repetitions.
Non-targeted data analysis Start products, TIM dialysates, and serum samples were analyzed by LC-MS as described in the Materials and methods section. Chromatograms were converted to MS spectra. MS data were exported as intensities per nominal m/z value. A solvent blank was used to correct for solvent background signals  [26]. Division by the background signal provides smoother data than background signal subtraction. All signals related to solvent will give a value close to 1 and all signals related to the products will provide values > 1. The data used to correct for background signals is referred to as correction reference in the remaining part of the text and the outcome of the division of sample data by the correction reference data as signal ratios. An advantage of this approach is that a quick and global view of the similarities and differences between samples is obtained.