dataesr/scientific-paragraphs-categorization huggingface dataset:
After these quality control steps, the average number of sequences per sample was 143,543. Taxonomy was parsed using the curated SILVA SSU database (v138)  (Quast et al. , 2013 , curated by Hassenrück, 2020). To further investigate taxonomic composition, remaining OTUs were pooled by taxonomic level using taxapooler v1. 4  (Gobet and Ramette, 2011). Functions were predicted using Tax4Fun  (Aßhauer et al. , 2015) , with both KEGG Ortholog (KO) and KEGG pathway reference profiles. The KEGG pathway matrix was curated and functions irrelevant to bacteria were removed. The matrix was further used as community matrix with the same methods as described in the statistical analyses. To investigate targeted functions, the KO matrix was subset to the relevant functions, using the KEGG database. Metabolic functions were determined with KO genes or enzymes related to the KEGG reaction or pathway (Supplementary Table  2 ).
River samples collected in June and August were not separated according to month and were treated as individual samples for further analysis. Marine sediments sampled along the fjord transects were not separated between locations and were analyzed in relation to the water column samples. For statistical analysis of the data, eight samples with low sequence counts were removed, and the OTU table randomly rarefied to 42,990 sequences for further analyses. A metadata table of reads removed during bioinformatic processing and rarefaction is provided in the supplement (Supplementary Table  3 ). The analyses presented here were performed on the rarefied dataset; the analyses with the unrarefied dataset showed similar results. Alpha diversity estimators were calculated after repeated (n = 100) random subsampling of the sequences.
To test for differences in metabolic functions, a Kruskal-Wallis test and the post hoc Dunn's test were performed to account for non-parametric data with heterogeneous variances. P-values were adjusted using the Benjamini-Hochberg (BH) correction  (Benjamini and Hochberg, 1995). Ordinations were performed with the Vegan package in R (v2. 5-6)  (Oksanen et al. , 2019) , using relative abundances of the OTUs and rarefied data. For beta diversity, the OTU turnover between or within sites was calculated using the Jaccard index. Microbial community structure was assessed using non-metric multidimensional scaling (NMDS), based on Bray-Curtis dissimilarities, with the Wisconsin double standardization implemented in the metaMDS function. To test for differences among a priori sample groupings, permutational analysis of variance (PERMANOVA)  (Anderson, 2017)  and Analysis of Similarity (ANOSIM)  (Clarke, 1993)  were computed using adonis and a post hoc modified version  (Hassenrück, 2019)  of the anosim function in Vegan (FDR-corrected p-values).
Indicator species analysis  (Dufrene and Legendre, 1997 ) was used to determine taxa that significantly contributed to seasonal differences in fjord waters. The multipatt function in the indicspecies package (v1. 7. 9)  (Cáceres and Legendre, 2009)  was used to calculate Dufrêne-Legendre Indicator Value (IV), with the control set to 9999 permutations designed by the function how in the permute R package. OTUs with an IV ≥ 0. 7 and a p-value ≤ 0. 001 were considered significant indicators. Highly abundant indicators were filtered based on a 0. 5% relative abundance threshold within their group. Taxonomic affiliation of highly abundant indicator taxa was further analyzed. The correlation between top seasonal indicators (chosen among indicators with relative abundance over 1%) and environmental variables was calculated using Spearman correlations to account for non-homoscedastic repartitions.
Correlations and p-values were calculated using the rcorr function in the Hmisc package (v4. 4-0) (Harrell, 2020). P-values were adjusted using the Benjamini-Hochberg correction with the p. adjust function in the R stats base package. All plots were generated using the ggplot2 package in R (v3. 3. 0) (Wickham, 2016).
ACKNOWLEDGMENTS We thank  Nathalie Carrasco ,  Anne Deininger ,  Hannah Miller ,  Emelie Skogsberg ,  Charlotte Pedersen Ugelstad , and  Emilie Hernes Vereide  for their help with field sampling. We also thank  Julie Bitz-Thorsen  and  Owen Wangensteen  for the help with the library preparation and sequencing,  Christiane Hassenrück  for the help with bioinformatics and  Jørn Dietze  for the help with the Stallo HPC. Finally, we also thank the reviewers for their thoughtful revisions which helped to greatly improve this manuscript.
DATA AVAILABILITY STATEMENT The sequence data generated for this study can be found in the online ENA European Nucleotide Archive under project number PRJEB40446. The environmental data are published by  McGovern et al. (2020). The processed OTU and Taxonomy tables are openly available in the UiT data archive Dataverse (https://doi. org/10. 18710/JDWLVA). The scripts generated and used for this study are available on GitHub at (https://github. com/lmdelpech/16S).
Strain isolation and identification The human microbiome study was an opportunity for us to isolate bacterial strain from stool sample from a child from Niger with severe acute malnutrition. The child's parents provided an informed and signed consent. The study was validated by the ethics committee of the Institute Federatif de Recherche (Marseille, France) IFR48 under agreement number 09-022. The shipments of the sample, as well as its isolation were carried out as previously described  [20]. Then, strain Marseille-P3556 was seeded in petri dishes containing 5% sheep blood agar (bioMérieux, Marcy l'Etoile, France) and incubated under anaerobic condition (Thermo Scientific, Dardilly, France) at 37°C. Identification by MALDI-TOF MS has failed with this strain despite numerous attempts because this reference spectrum is unknown in the MALDI-TOF database.
The Microflex LT spectrometer (Bruker, Daltonics, Bremen, Germany) was used during the MALDI-TOF identification as previously described  [21]. The obtained spectra were analysed with Biotyper 3. 0 software and added in the local URMS database (https://www. mediterranee-infection. com/urms-data-base). The 16S rRNA gene was amplified using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and then sequenced using the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France), as previously described  [22]. The nucleotide sequences of the 16S rRNA gene were analysed by CodonCode Aligner software (http://www. codoncode. com). An obtained consensus sequence is compared to the NCBI nucleotide database (https://www. ncbi.
Genome characteristics Extraction of the genomic DNA (gDNA) was carried out on EZ1 biorobot using the EZ1 DNA tissue kit (Qiagen, Hilden, Germany). Then, it is sequenced on the MiSeq instrument (Illumina Inc, San Diego, CA, USA) using the Nextera Mate Pair and Nextera XT Paired End (Illumina) sample preparation kit, as previously reported  [17]. The genomic assembly was performed with three softwares such as Velvet  [27] , Spades  [28] , and Soap Denovo  [29]. Sequences were trimmed or untrimmed using MiSeq and Trimmomatic  [30]  softwares. Best assembly was carried out using different criteria as previously described  [20]. Annotation of Genome of Marseille-P3556 was performed as reported elsewhere  [20]. The Genome-to-Genome Distance Calculator web server available online (http://ggdc. dsmz.
Strain identification and phylogenetic analysis Colonies of strain Marseille-P3556 was not identified by mass spectrometry. In other words, the spectral reference of the strain is absent in the MALDI-TOF database. Therefore, its generated reference spectrum was incremented in our local database (Fig. 1 ). A similarity analysis of the 16S rDNA gene  CGView Server  [34]  was used to perform this genome cartography. From outside to the centre: region coding genes and RNA genes from the forward and reverse strands, respectively, GC content (black) and GC skew (green/mauve). of strain Marseille-P3556 revealed a nucleotide sequence identity of 97. 29% with Bacillus subterraneus strain COOI3B (accession number NR_104749. 1), the phylogenetically closest species. This similarity value was lower than the recommended cut-off value (98.
4%), C 14:0iso (20%), and C 16:1n7 (14. 8%) are the major fatty acids detected in cells of Bacillus marasmi sp. nov. The genome of strain Marseille-P3556 was 4. 64 Mbp with 38. 2 mol% of G + C content. The 16S rRNA and draft genome sequences are deposited in the Genbank database under Accession numbers LT671590 and CABHPS000000000, respectively. The type strain of Bacillus marasmi sp. nov. , strain Marseille-P3556 was isolated from a Nigerien child presenting clinical aspects of marasmus.
During an experimental campaign performed at the LOHENGRIN recoil spectrometer of the Institut Laue-Langevin (ILL), a kinetic energy dependence of 132 Sn fission product isomeric ratio (IR) has been measured by inducing thermal fission of 241 Pu. The IRs are deduced using gamma ray spectrometry in coincidence with the ionisation chamber. To interpret these data, we use the FIFRELIN Monte-Carlo code to simulate the de-excitation of the fission fragments. Combining the IRs with the FIFRELIN calculations, the angular momentum distribution with kinetic energy of the doubly magic nucleus of 132 Sn was deduced. This will be compared with the angular momentum distribution obtained for the reaction 235 U(n th ,f) for 132 Sn.
The shift in the mean value of the kinetic energy between the first and the last experimental days is equal to (4. 6 ± 1. 0) MeV. It should be noted that the evolution of the mean kinetic energy was linear with a slope of (-0. 52 ± 0. 09) MeV/day. For each kinetic energy selected with the LOHENGRIN spectrometer, IRs are measured. By combining all the experimental data (weighted by the kinetic energy distribution) the mean IR can be derived and is equal to 0. 0719 ± 0. 0016. It should be noted that covariance was not taken into account for the uncertainty propagation.
Spin extraction using FIFRELIN FIssion FRagment Evaporation Leading to an Investigation in Nuclear data (FIFRELIN)  [16]  is a Monte-Carlo code developed at CEA. This code simulates the fission process and gives information on fission observables such as prompt neutrons, gammas, neutron multiplicity. For this work, this code has only been used to carry out the de-excitation simulation of the nuclei of interest ( 132 Sn). The inputs provided for this code are the mass number (A), the atomic number (Z), excitation energy (E ⇤ ) and spin along with parity (J ⇡ ). It also requires additional files from the RIPL-3 2015  [17, 18]  library to get information on the nuclear levels and the gammas and electrons emitted at lower energies. Additional models are also required such as the Composite Gilbert-Cameron Model (CGCM), which is propositioned in RIPL-3  [17]  for nuclear level densities, Back Shifted Fermi Gas Model (BSFGM) for spin cut-o↵ and the Enhanced General Lorentzian (EGLO) model  [19]  for the gamma strength functions.
The BRICC code  [20]  is used to calculate the internal conversion coefficients. FIFRELIN EPJ Web of Conferences 256, 00011 (2021) https://doi. org/10. 1051/epjconf/202125600011 Theory-5 uses these input libraries and assumes that the experimental level scheme provided in the RIPL-3 2015 file is complete up to cut-o↵ energy (E cut ). After this E cut , it is assumed that the experimental level scheme is incomplete and this is then filled by FIFRELIN using the CGCM model. The BSFGM is used to attribute a J ⇡ value to these new levels and the EGLO model is used to get the gammas and their respective intensities originating from these levels. The probability to emit neutrons are obtained from the neutron transmission coefficients which are derived from an optical model; the Koning-Delaroche global neutron optical model potential  [21]  but in our case, no neutrons are emitted as the simulations were run below neutron separation energy.
Code availability. The code used for this study is available at https://doi. org/10. 5281/zenodo. 5638845  (Fossa, 2021). The pyclits Python code used for cross-scale interactions is available at https: //github. com/jajcayn/pyclits (last access: 17 March 2021;  Jajcay and Lavicka, 2018). Data availability. The Safran precipitation and temperature data set can be obtained from http://www. meteofrance. fr (last access: 1 October 2015). Discharge data are available from http://www. hydro. eaufrance. fr (last access: 15 October 2015 Ministère de la Transition Energetique, 2015). Supplement. The figures presented in this article are available in a colorblind-friendly palette in the Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-25-5683-2021-supplement.
Most of the force applied during invasive ventilation is needed to expand the chest wall, and only a lesser amount to inflate lung tissue  [7]. When the chest wall elastance increases, e. g. , during pneumoperitoneum, the ΔP increases, even when V T is left unchanged  [8]. This rise in ΔP is often interpreted as 'innocent', and therefore accepted during intraoperative pneumoperitoneum. However, the cephalad shift of the diaphragm could induce, or worsen atelectases during intraoperative ventilation, and the resulting increase in ΔP is related with a rise in lung applied force  [9]. In other words, it should be questioned if a rise in ΔP during pneumoperitoneum with closed abdominal surgery can be accepted. To determine and compare the independent associations of ΔP with PPCs in patients undergoing open abdominal surgery versus patients undergoing closed abdominal surgery, we reassessed the database of the 'Local ASsessment of Ventilatory management during General Anaesthesia for Surgery' (LAS VEGAS) study  [10].
Inclusion and exclusion criteria The LAS VEGAS study recruited consecutive patients undergoing general anaesthesia with mechanical ventilation during anaesthesia for surgery during a seven-days timeframe between 14 January and 4 March 2013. Exclusion criteria of the LAS VEGAS study were: (1) age < 18 years, (2) having received mechanical ventilation in the preceding month, (3) obstetric or ambulatory surgical interventions, and (4) cardiothoracic surgery cardiopulmonary bypass. For the current analysis, inclusion was restricted to patients undergoing abdominal surgery. The following additional exclusion criteria were used: (1) insufficient data to calculate ΔP, i. e. , on at least two timepoints sufficient data had to be available to calculate the driving pressure for a patient to be included;  (2)  to increase the homogeneity of the compared patient cohorts and avoid using erroneous data, patients who received intraoperative ventilation through an airway device other than an endotracheal tube as well as patients under an assisted or spontaneous ventilation mode were excluded; (3) patients in whom laparoscopy only assisted the surgery, i.
Data recording and calculations Full details on data collection can be found in the original publication of the LAS VEAGS study  [10] , and in Additional file 2. In the LAS VEGAS study database, ventilatory parameters at every hour of surgery, from induction up to the last hour of surgery, were recorded. Data in the LAS VEGAS database was validated through two rounds of extensive data cleaning to check for invalid or incomplete data. Local investigators were queried on incorrect or missing data and had to correct those in the cleaning rounds. The following calculations were performed. ΔP was calculated by subtracting PEEP from plateau pressure or inspiratory pressure at every hour in volume-controlled and pressure-controlled ventilated patients, respectively. ΔP TW , i. e. , the pressure that is proportional to the amount of time spent at each driving pressure in relation to the total time, was calculated by summing the mean values between consecutive time points multiplied by the time between those points and then dividing by the entire time  [11].
Analysis plan The analysis plan was prespecified before data access, and we used data of all available patients in the LAS VEGAS database without formal sample size calculation. Also, as the purpose of the analysis was exploring a physiological hypothesis, we did not specify any a priori effect size. Continuous variables were reported as median and interquartile ranges; categorical variables expressed as n (%). Normality of distributions was assessed by inspecting quantile-quantile plots. If variables were normally distributed, the two-sample t-test was used; if not, the Wilcoxon rank sum test was used. We used the Chisquare test or Fisher's exact test for categorical variables, or when appropriate, as relative risks. Statistical uncertainty was expressed by showing the 95%-confidence intervals (CI).
For both outcomes, we fitted the model for each of the compared patient cohorts respectively, i. e. , patients who underwent open surgery intervention and those who underwent closed surgical intervention. We used a Wald z-test to test the difference between odds ratios from models fitted on closed and open surgery cohort. Models' goodness of fit was assessed by residual diagnosis based on scaled quantile residuals (R DHARMa package v. 0. 2. 4) and simulated residuals (R sure package v 0. 2. 0) for logistic and ordinal models, respectively. To build the CBPS to relate exposure variable, i. e. , ΔP TW , with potential confounders, we included by clinical judgment the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) risk class  [13, 14] , and the average intraoperative V T.
Discussion The main findings of this posthoc analysis of the LAS VEGAS study can be summarised as follows: (i. ) the    finding, though, was not confirmed in the matched cohort analysis. This analysis uses the database of a worldwide international multicentre prospective observational study as a convenience sample  [10] , strictly followed a plan, and was characterised by a robust method accounting for the multilevel data structure and allowing precise estimation and confounder control, even with seven or fewer events per confounder  [17, 18]. Also, the outcome of interest, i. e. , PPCs, was predefined, well-described, and largely followed the European Perioperative Clinical Outcome (EPCO) group definitions  [19]. Furthermore, the study population was defined to minimise information and selection bias and to have a sufficient number of patients while keeping an acceptable number of timepoints at which ΔP TW could be calculated per patient.
We used the parent LAS VEGAS definition of PPCs. This definition differs from what was somewhat recently proposed  [1] , but they remain reasonably comparable. The protocol of the LAS VEGAS study did not include the collection of oesophageal pressure recordings. Information regarding surgical positioning was not collected, and intra-abdominal pressure levels were also not recorded in the database of the LAS VEGAS study. Both could influence ΔP TW , though  [35] [36] [37]. Due to the additional strict exclusion criteria, we excluded a considerable number of patients. Thus, the findings of this analysis need confirmation in other studies. Also, some patients had only a few timepoints at which ΔP could be calculated. Furthermore, we only included patients with an endotracheal tube and patients who received controlled ventilation, limiting our focus on a specific type of intraoperative airway device and ventilation mode.
Fabry disease (FD) is an X-linked genetic disease due to pathogenic variants in GLA. The phenotype varies depending on the GLA variant, alpha-galactosidase residual activity, patient's age and gender and, for females, X chromosome inactivation. Over 1000 variants have been identified, many through screening protocols more susceptible to disclose non-pathogenic variants or variants of unknown significance (VUS). This, together with the non-specificity of some FD symptoms, challenges physicians attempting to interpret GLA variants. The traditional way to interpreting pathogenicity is based on a combined approach using allele frequencies, genomic databases, global and disease-specific clinical databases, and in silico tools proposed by the American College of Medical Genetics and Genomics. Here, a panel of FD specialists convened to study how expertise may compare with the traditional approach.
Several GLA VUS, highly controversial in the literature (p. Ser126Gly, p. Ala143Thr, p. Asp313Tyr), were re-analyzed through reviews of patients' charts. The same was done for pathogenic GLA variants with some specificities. Our data suggest that input of geneticists and physicians with wide expertise in disease phenotypes, prevalence, inheritance, biomarkers, alleles frequencies, disease-specific databases, and literature greatly contribute to a more accurate interpretation of the pathogenicity of variants, bringing a significant additional value over the traditional approach.
Databases associate p. (Ala143Thr) to FD, except dbFGP (International Fabry Disease Genotype-Phenotype Database) and LOVD (Leiden Open Variation Database) (Table  3 ). The variant p. (Ala143Thr) has long been considered pathogenic and disease-modifying treatments were unnecessarily indicated. 8 As in the case of the 41-year-old male patient described above, various studies of patients hemizygotes for p. (Ala143Thr) with stroke/TIA demonstrated only slightly decreased α-Gal A activity when compared to other GLA missense variants. Those patients had normal lyso-Gb 3 levels, no typical FD symptoms, Gb   30  in line with 46% reported for the 41-year-old male and 35% for the 72-year-old male described above. This expert panel considers p. (Ala143Thr) to be likely benign. An important evidence is that the prevalence of p.
55 e high residual enzyme activity associated with p. (Ala143Thr) is in favor of a pseudo-deficiency rather than a disease-causing allele per se. 35, 56 Of note, the dbFGP database, which includes inputs from FD experts and researchers, clearly states p. (Ala143Thr) as benign (Table  3 ). The missense p. (Asp313Tyr) variant was historically considered pathogenic for a while. 57 It was first described in 1993 in a classic FD male patient with only partial sequencing of the GLA gene, though clinical description was not provided. However, another group subsequently published a case with the additional presence of another GLA variant [c. 1232G>A; p. (Gly411Asp)], creating an uncertainty regarding pathogenicity of p. (Asp313Tyr). 58 Functional studies showed that p. (Asp313Tyr) is a sequence variant associated with 75% of normal α-Gal A activity (pseudo-deficiency).
p. (Ser126Gly) has been found in 74 cases (exomes frequency of 0. 033%, n = 60; genomes frequency of 0. 063%, n = 14) in GnomAD, above the global frequency of FD (Table  3 ). While several publications claimed p. (Ser126Gly) to be pathogenic when associated with a given haplotype (though no convincing clinical evidence was provided),  63  this expert opinion authors considers p. (Ser126Gly) as likely benign, in line with all major databases which do not associate p. (Ser126Gly) with FD (Table  3 ). GLA genotyping revealed a novel hemizygous 4-base insertion (c. 1281_1282insCTTA) predicted to preserve the entire coding sequence of α-galactosidase A, with the exception of a conservative change of the last amino-acid (leucine to isoleucine) at codon 429, together with the addition of a 22-amino-acid tail.