dataesr/scientific-paragraphs-categorization huggingface dataset:
10 Be and in situ 14 C age calculations 10 Be and in situ 14 C surface exposure ages are calculated using the Baffin Bay 10 Be production-rate calibration dataset  (Young et al. , 2013a)  and the West Greenland in situ 14 C production-rate calibration dataset  (Young et al. , 2014). All ages are presented using time-variant 'Lm' scaling  (Lal, 1991; Stone, 2000) , which accounts for changes in the magnetic field although these changes are minimal at this high latitude (~64°N). The 10 Be and in situ 14 C calibration datasets are both derived from sites on western Greenland with early Holocene exposure histories, and the in situ 14 C calibration measurements are derived from the same geologic samples as one of the 10 Be calibration sites  (Young et al. , 2014). This combination of calibration datasets ensures that the production rates and the 14 C/ 10 Be production ratio are regionally constrained.
All ages are calculated in MATLAB using code from version 3 of the exposure age calculator found at https://hess. ess. washington. edu/, which implements an updated treatment of muon-based nuclide production  (Balco et al. , 2008; Balco, 2017). We do not correct nuclide concentrations for snow-cover or subaerial surface erosion; samples are almost exclusively from windswept locations and many surfaces still retain primary glacial features. In addition, we make no correction for the potential effects of isostatic rebound on nuclide production because both the production-rate calibration sites and sites of unknown age have experienced similar exposure and uplift histories (i. e. the correction is "built in";  Young et al. , 2020a Young et al. , , 2020b)). Individual 10 Be and in situ 14 C ages are presented and discussed with 1s analytical uncertainties, and moraine ages exclude the 10 Be production-rate uncertainty when we are comparing other 10 Be-dated features.
Hard-water effects on the 14 C ages, which could make age determinations erroneously old, are unlikely in our study area because lake catchments are dominated by Archean gneiss, and the study lakes are all well above local marine limits. All new radiocarbon ages are calibrated using CALIB 8. 2 and the INTCAL20 dataset, and previously reported radiocarbon ages are re-calibrated in the same manner using the INTCAL20 and MARINE20 datasets  (Stuiver et al. , 2020; Reimer et al. , 2020; Heaton et al. , 2020; Table   S1 ; Table  S7 ).
26 Al Our 26 Al/ 10 Be ratios range from 7. 39 ± 0. 33 to 6. 01 ± 0. 25 in the KNS forefield (n=12; Table  S5 ). Because of the extremely close proximity of all of our bedrock samples, these surfaces must have the same exposure and burial histories over glacial-interglacial timescales; the value of 6. 01 ± 0. 25 is anomalously low relative to our remaining measurements. After removing the lowest ratio, remaining ratios range from 7. 39 ± 0. 33 to 6. 71 ± 0. 24 with a mean of 7. 05 ± 0. 24 (n=11; Table  S5 ). Further limiting this dataset to samples that have no detectable inherited 10 Be, results in a mean value of 7. 17 ± 0. 20 (n=6; Table  S5 ). With the exception of our lowest measured ratio (6. 01 ± 0. 25), each of our 26 Al/ 10 Be ratios overlaps with the constant production values at 2σ uncertainty regardless of which constant production is used.
Continued use of model-data comparisons as ice-sheet modeling efforts, paleoclimate datasets, and geologic reconstructions become more refined will improve our understanding of past icesheet sensitivity to climatic and dynamic forcing mechanisms. 3 ). In the foreground is a Kapisigdlit stade moraine crest resting directly adjacent to the historical maximum extent of this sector of the GrIS (dashed line). The GrIS is in the background and, here, there has been minimal retreat from the historical maximum extent. (b) View to the southwest in the Qamanaarsuup Sermia region showing Marshall Lake and Goose Feather Lake. Note the color contrast between the two lakes. Marshall Lake currently does not receive meltwater from the GrIS. Goose Feather Lake is currently a proglacial lake that receives    S9 ).
Acknowledgements We thank the 109 th Airlift Wing of the  New York Air National Guard  for transport to and from  Greenland ,  Air Greenland  for helicopter support, and  CH2MHill Polar Field Services  for additional logistical support. We also thank  J. Hanley  and  J. Frisch  for help processing 10 Be and 26 Al samples. Funding was provided by the  National Science Foundation Arctic Natural Sciences and Arctic System Sciences  programs: Awards # 1417675  and # 1503959  to NEY and JMS, and Awards # 1417783  and # 1504267  to JPB.
DNA extraction and 16S rRNA phylogenetic analysis DNA was isolated from UR159 T using the using the Quick-DNA fungal/bacterial miniprep kit (Zymo Research, Irvine, CA) according to the manufacturer's recommendations. The 16S rRNA gene of UR159 T was amplified by PCR and sequenced using the universal primers 27F and 1541R, corresponding to base positions 8-27 and 1541-1525 of the 16S rRNA gene of Escherichia coli, respectively  [9]. Both LeBibi-QBPP and NCBI nucleotide collection (nr/nt) databases were used to identify the nearest neighbor taxa with validly published names  [10, 11]. A sequence alignment using 16S rRNA gene sequences was constructed with the MUSCLE software  [12]. All sequences with less than 95% coverage were eliminated, resulting in a final dataset of 139 sequences (consensus sequence = 1,255 bp).
Genome sequencing, assembly and annotation After mechanical DNA shearing (Covaris® ultrasonicator), the DNA libraries were prepared using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina® (New England Biolabs) and The Illumina reads were trimmed using Trimmomatic v0. 32  [14] , quality filtered with the Fastx-toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) and assembled using SPAdes v3. 14. 0 that includes plasmidSPAdes  [15, 16]. Note that contigs <1,000 bp were discarded from the assembly and chromosomal contigs were then scaffolded using the SIS  [17]  and GapFiller v1. 10 [18]  softwares with Flavobacterium marinum CGMCC 1. 10825 T (GenBank accession number NZ_FNXE00000000. 1) as the reference genome sequence. Genome annotation was performed by the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) (www.
ncbi. nlm. nih. gov/genome/annotation_prok/). For mobile genetic elements, prophages were predicted using PHAST  [19]  completed by the Actinobacteriophage database (https://phagesdb. org) and plasmids were searched using the online plasmid search tool (https://plasmid. med. harvard. edu/PLASMID/). Functional analysis was performed using RPSBLAST program on COG database implemented in WebMGA (http://weizhongli-lab. org/webMGA).
Comparative genomics and phylogenomic analysis To confirm the status as a new taxon of UR159 T , Average Nucleotide Identity (ANI), in silico DNA-DNA hybridization (isDDH), Average Amino Acid Identity (AAI) values were determined. The calculation of the ANI between strain UR159 T and 343 genomes of Flavobacteriaceaerelated species was performed using BLASTN and Pyani v0. 2. 10 module (https://pypi. org/project/pyani/) [20] , following the algorithm described by Goris et al. [21]. According to Pyani and 16S rRNA gene similarities, 25 available Flavobacteriaceae genomes were selected and downloaded from GenBank for comparative genomic analysis. The protein coding sequences were predicted for all genomes by using Prokka v1. 14. 5  [22]. The A c c e p t e d M a n u s c r i p t estimation of AAI was determined using the tool AAI calculator (http://enveomics.
ce. gatech. edu/) [23]. The isDDH values were calculated with the same data set for ANI calculation using the Genome-to-Genome Distance calculator (GGDC 2. 1; https://ggdc. dsmz. de/) with the formula 2. Pangenomic analysis of the 26 genomes was conducted using PIRATE software  [24]. To calculate phylogenomic relations between strain U159 T and phylogenetic neighbours, a core-genome tree was constructed as follows. All predicted protein-coding genes annotated from each available genome were compared using the all-versus-all BLAST search  [25]. Only proteins for which coding sequences with >50% amino acid identity and >70% sequence coverage were considered as orthologous. The nucleotide sequences of core gene set were aligned using MUSCLE software and MEGA6 software  [26]  was used for the phylogenomic tree reconstruction using the neighbourjoining method with Jukes-Cantor correction.
Results and discussion Strain UR159 T was not identified by the MALDI-TOF mass spectrometry using the Bruker database (version 2. 3) but the profile was reproducible (Figure  S1 ). The almost-complete sequence of the 16S rRNA gene was obtained (1,516 bp) and used for sequence comparison and phylogenetic analysis. Strain UR159 T exhibited the highest level of nucleotide pairwise similarity (93. 1%) with Flavobacterium marinum SW105 T  [27]. Sequence similarity was lower with the next closest relatives (Flavobacterium spp. , Myroides spp. ) with percentages of 16S rRNA gene sequence similarity in all cases lower than 92. 9%. The ML and NJ phylogenetic trees were almost identical and both confirmed the affiliation of UR159 T to the Flavobacteriaceae (Figure  1 ). The sequence similarity between the 16S rRNA gene of UR159 T and the closest phylogenetic neighbors, which was distinctly below the predetermined cutoff levels in order to classify bacterial isolates as novel taxa at the genus (<95%) and (<97-98.
species previously described. Hence, this strain represents a novel taxon within the Flavobacteriacae family, for which the name Avrilella dinanensis gen. nov. , sp. nov. is proposed. The descriptions of the Avrilella gen. nov. andAvrilelladinanensis sp. nov. aregiven in Table5. genome size, G+C content, ANI, in silico DDH, AAI and orthologous proteins. Strain Genbank accession no. Size G+C Mean ANI identity Mean ANI coverage % In silico DDH Mean AAI identity Mean AAI coverage Orthologous proteins Avrilella dinanensis UR159 T NZ_NIPO00000000. 1 2,331,799 37. 7 100. 0 100. 0 100. 0 100. 0 100. 0 100. 0 Aequorivita sublithincola DSM 14238 T CP003280. 1 3,520,671 36. 2 70. 7 11. 2 23. 5 54. 5 68. 0 47. 2 Aequorivita viscosa DSM 26349 T FQYV01000000. 1 3,526,954 36. 5 70. 5 10. 5 38. 7 54. 6 69.
We therefore used mouse exon arrays to acquire the global gene expression profiles of developing forelimb buds taken from cultured E10. 5 and E12. 5 mouse embryos at different time points. We found that the molecular dynamics taking place in cultured embryos very rapidly show signs of diverging from that of embryos developing in utero. The analysis and comparison of the different datasets obtained from forelimb buds after 0, 1, 3 and 6 h of culture nevertheless allowed us to discriminate groups of genes for which expression dynamic changes likely reflected developmental progression from others for which these changes resulted from the in vitro culture procedure. A number of candidate genes that had not been previously associated with early aspects of limb development were thus identified. Two of them, Tbx1 and Fgf11, were tested for their implication in limb bud development and found to be involved.
5 after conception (0. 5 dpc). The pregnant mice were euthanized by cervical dislocation, before collecting E10. 5 and E12. 5 embryos. Experiments were performed in accordance with European and French Agricultural Ministry guidelines for the care and use of laboratory animals (Council directive 2010/63). The procedure for embryo culture was conducted as previously described  9. RNA extraction, array hybridization and data processing. Total RNA was isolated from each sample using RNeasy Mini Kit (Qiagen). The quantity and purity of the total RNA were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop ND, Thermo Fisher Scientific) and their integrity by using the Agilent 2100 Bioanalyzer (Agilent Technologies; http://agilent. com/). cDNA synthesis, amplification, fragmentation and biotinylation were performed using the Ambion WT Expression Kit (Ambion, Austin, TX, USA).
Microarray experiments were performed in the IRMB (Montpellier University Hospital) DNA microarray platform. Samples were hybridized to Affymetrix GeneChip ® Mouse Exon 1. 0 ST Arrays according to Affymetrix recommenda- tions. These mouse exon arrays consist of an average of 4 probes per exonic region of every primary mRNA transcript allowing to interrogate the entire length of every genome transcript. The experiment was run in triplicate and included the forelimbs of E10. 5 mouse embryos, the forelimbs of E10. 5 embryos cultured for 1, 3 or 6 hours, the forelimbs of E12. 5 mouse embryos and the forelimbs of E12. 5 embryos cultured for 1 or 3 hours. Data was acquired on a GeneChip ® Scanner 3000 and CEL file generation performed using AGCC. Affymetrix Expression Console ™ following standard Exon Array protocols with Robust Multi-chip Average (RMA) was used to extract probe intensity data.
Microarray data were obtained and analyzed in agreement with the minimal information about microarray experiment (MIAME) recommendations  10. The microarray data were subjected to K-means algorithm and MultiExperiment Viewer (MeV_4_8, version 10. 2) software to subdivide the probe set into relevant clusters based on the expression profiles of (i) the forelimb from freshly dissected E10. 5 mouse embryos and (ii) the forelimbs from E10. 5 and E12. 5 mouse embryos cultured for 1, 3 or 6 hours. MeV is a versatile microarray data analysis tool, integrating sophisticated algorithms for clustering  11. Only transcripts with a significant p-value <0. 01 were retained. The appropriate workflow for each analysis is described in the corresponding figure. The gene ontology (GO) enrichment analysis, the biological processes and networks of differentially expressed genes were analyzed through the use of IPA (QIAGEN Inc.
, https://www. qiagenbioinformatics. com/products/ ingenuitypathway-analysis)  12. The Pathway Studio 9. 0 software (Ariadne Genomics) was used for the gene interactions and visualization of interactive signaling pathways. Venn diagram are generated using Venny 2. 1 Software. All our data are accessible at the gene expression Omnibus (GEO) repository (https://www. ncbi. nlm. nih. gov/geo) with the provisional accession series number GSE117750.
Finally, samples were acquired on the FACS Canto II and analyzed using the FlowJo software (Ashland, OR, USA). SiRNA injection in E9. 5 mouse embryos. For in vivo transfection reagent studies, 100 μM siRNA solutions were prepared in sterile PBS. Formulation of Injectin (BioCellChallenge) complexes with 100 μM siRNA solutions was prepared according to the manufacturer's instructions and injected in 4 sites surrounding the left bump on the flank of E9. 5 embryos where the forelimb will start to appear at E10. 5. For the 4 local injections of 5 nL each, we used the Eppendorf ® FemtoJet ® microinjector and then cultured the E9. 5 mouse embryos in the roller culture system for 24 hours. We used an untargeted siRNA labeled with the fluorescent dye CY3 (MWG Biotech) both as a control, for the injection -hence its designation as CTL siRNA -and, to track the injection site.
Monitoring of forelimb development of E9. 5 mouse embryos after siRNA injection and culture. Left forelimb development was monitored by measuring the surface of the left forelimb of E9. 5 mouse embryos cultured for 24 hours (beginning of the bump on the flank) up to the most proximal end of the forelimb. To exclude the size variability between mouse embryos and quantify the effect of the siRNA injection on the development of the left forelimb, the measured area of the left forelimb was normalized by the surface of the untreated right forelimb of mouse embryos. The measure of the forelimb surface was done with the ImageJ software. Significance testing was done using Mann-Whitney unpaired t-test, two tails using GraphPad Prism 6 Software. Graphs show mean ±Standard Error of the Mean (SEM).
Statistical analysis. Results are expressed as the mean ± SEM and all experiments were performed at least 3 times. Generated P values were obtained using Mann-Whitney unpaired t-test, two tails using GraphPad Prism 6 Software. Graphs show mean ±Standard Error of the Mean (SEM). P-values <0. 05 (*), P < 0. 01 (**) or P < 0. 001 (***) were considered statistically significant. Analysis and graphical representation were performed using Graph-Pad Prism TM software (Graphpad).
2A ). Using Multi-Experiment Viewer software and K-means algorithm, we identified differentially expressed genes that could be grouped into eight clusters according to their respective expression dynamics over the 6-hour time course (Fig. 2B  and Supplementary Table  1 ). For six of these clusters, the gene expression profiles went back to their original level after just 6 hours of culture. For the other two, gene expression went up to a certain level within the first hour of culture and maintained this level for the rest of the time-course (Fig. 2B  and Supplementary Table  1 ). These observations indicate that culture conditions affect developmental dynamics quite rapidly. Extended embryo culture triggers stress-induced cellular response. Are the gene expression dynamics we observed the result of a culture-induced stress? A cell's response to stress involves a choice between death and survival, with competing processes leading either to apoptosis or to cell dedifferentiation and proliferation  15.
Analysis and comparison of the E12. 5 datasets with their E10. 5 counterparts allowed us to identify 83 genes showing a similar expression profile over the 3-hour time course (Fig. 3B , Supplementary Table  2 ). The expression of these genes thus appears to be modulated by the procedure undergone by the embryos, including their harvesting and culture, not by stage-specific developmental processes. These 83 genes were then categorized based on their functions, interconnectivity, and involvement in canonical pathways using the Ingenuity Pathway Analysis application (IPA) (Fig. 3B , Supplementary Fig. S1 ). The genes were mapped in the Ingenuity Pathways Knowledge Base (IPKB) with their respective expression values and designated focus genes. Based on gene connectivity, the algorithm generated networks of focus genes and assigned them a score taking into account the number of focus genes and the size of each network (Fig.
Acknowledgements We thank the  MRI  facilities for their assistance and  SMARTY platform and Network of Animal facilities of Montpellier. This work was supported by  Inserm , the  University of Montpellier  and grants from the  Région Languedoc-Roussillon Chercheur (se)s d'Avenir. It contributes also to the  Labex  " Who am I?" # ANR-11-LABX-0071  and the  Université de Paris Idex  # ANR-18-IDEX-0001  funded by the french government.
Serology testing Serum samples were run on the Abbott Architect instrument using the Abbott SARS-CoV-2 IgG assay (Abbott, Sligo, Ireland) following the manufacturer's instructions. The assay is a chemiluminescent microparticle immunoassay for detection in human serum or plasma of IgG against the SARS-CoV-2 nucleocapsid protein. IgM detection was not performed. Based on our local evaluation of 500 serum samples (data not published), the result was considered negative if the IgG index value was < 0. 8, equivocal if the IgG index was between 0. 8 and 1. 39, and positive if the IgG index value was � 1. 40 (0. 40 is the Abbott determined positivity cutoff).
[14] ). It was used as our routine test during the day. The Xpert Xpress SARS-CoV-2 assay (Cepheid) and the GenMark ePlex SARS-CoV-2 assay are sample-to-answer molecular diagnostic platforms. The former is based on the detection of 2 gene targets, N2 and E, according to an algorithm in parallel with the sample process control, and the latter is based on the detection of the N gene of SARS-CoV-2. They were used during the night, on weekends and for urgent results. All data (clinical, laboratory, from both mothers and newborns) were prospectively collected from medical records. All statistical analyses were performed using R software (Version 3. 5. 1, R Core Team [2018] https://www. R-project. org/). Continuous variables are expressed as medians with the interquartile range (IQR). Categorical variables are expressed as numbers with percentages.
The consideration of all the covariates did not decrease the bias but significantly reduced the power. We apply these methods to two real-world examples that have clinical relevance, thereby illustrating the real-world importance of using these methods. We propose an R package RISCA to encourage the use of g-computation in causal inference. The randomised controlled trial (RCT) remains the primary design for evaluating the marginal (population average) causal effect of a treatment, i. e. , the average treatment effect between two hypothetical worlds where: i) everyone is treated and ii) everyone is untreated 1. Indeed, a well-designed RCT with a sufficient sample size ensures the baseline comparability between groups, thus allowing the estimation of a marginal causal effect. Nevertheless, it is well established that RCT is performed under optimal circumstances (e.
This result was concordant with the recent clinical literature  53, 54. impact of barbiturates in the icU on the functional status at three months. We define an unfavourable functional outcome by a 3-month Glasgow Outcome Scale (GOS) lower than or equal to 3. We used the data from the French observational cohort AtlanREA (www. atlanrea. org) to estimate the ATT of barbiturates because physicians recommended these drugs to a minority of severe patients. The second part of Table  3  presents the baseline characteristics of the 252 included patients. The second part of Table  4  presents the results according to the different possible methods and covariate sets. G-computation and TMLE lead to the conclusion of a significant negative effect of barbiturates regardless of the covariate set considered with an OR [95% CI] ranging from 0.
Other perspectives of this work are to extend the problem to i) time-to-event, continuous or multinomial outcomes and ii) multinomial treatment. However, implementing GC using continuous treatment raises many important considerations concerning the research question and resulting inference  64. To facilitate its use in practice, we have implemented the estimation of both ATE and ATT, and their 95% CI, from a logistic model in the existing R package entitled RISCA (available at cran. r-project. org/web/packages/ RISCA). We provide an example of R code in the appendix. Note that the package did not consider the inflation of the type I error rate due to the modelling steps of the Q-model. Users also have to consider novel strategies for post-model selection inference. In the applications, we classified covariates into sets based on experts knowledge  33.
Patients with an aggressive disease, non-responding to firstline platinum-based chemotherapy, have a poor prognosis. It has been reported that, if time since initiation of the latest treatment is inferior to 6 months, patients have a poor prognosis on ICI with a median OS of 4. 6 months  [13]. Furthermore, it has been shown that response to chemotherapy immediately before nivolumab, particularly when combined with bevacizumab, increases the likelihood of disease control under nivolumab  [14]. It is unclear to date whether patients with aggressive disease (progression within 3 months of start of first line treatment) can obtain a better survival with chemotherapy or ICI. Therefore, we analysed data from advanced NSCLC patients with progression within the first three months of first line chemotherapy initiation and who received ICI or chemotherapy as second line treatment.