dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ). Nevertheless, we identified very close sequences in the GenBank database showing 98 % identity with our reconstructed sequences (identity based on the complete length). All the retrieved sequences originated from human gut samples obtained through different studies. Although most of them were annotated as 'uncultured' , two 16S rDNA sequences originated from isolated strains: that is, strain T2-145 from a study focused on gut butyrateproducing bacteria (AJ270472. 1) and the Lachnospiraceae bacterium OM04-12BH (QULQ01000007. 1) isolated from Chinese samples, the latter appearing the closest to the reconstructed sequences. The 12 other sequences (OTU 146_4 to OTU 146_15, with lengths ranging from 1025 to 1367 bp) showed between 97. 5 and 99% identity with the V3-V5 OTU 146 sequence. They were also close to the OTU 146_1, 146_2 and 146_3 sequences, with 89-94.
4 ). In the phylogenetic tree, all the sequences were placed in the family Lachnospiraceae. Six sequences (OTU393_24 to OTU393_29) were close to each other, with a similarity percentage above 97%, indicating that they probably belonged to the same species. They could represent six different strains of the same species, but we cannot exclude the presence of 16S rDNA gene variants in one or several strains. A search in the GenBank Whole-Genome Shotgun database highlighted the high percentages of identity (97. 07 %-99. 91 %) between these reconstructed sequences and two cultivated species isolated from the human gut in the same study, namely Clostridium sp. AF36-4 (QTVH01000004. 1) and Clostridium sp. AF37-5 (QUDR01000049. 1). Numerous 16S rDNA sequences from the human gut annotated as 'uncultivated' also showed high identity with these sequences, reaching 100 % identity between OTU393_29 and GQ898152 (Data S4).
5% similarity. By targeting the reconstructed sequences with specific primers designed against these targets using a PCR and cloning-based approach, we obtained amplicon sequences by Sanger sequencing that were very close to OTU393_26 (99,8%), _19 (98. 5%), _27 (98%), _3 (98%) and _11 (98%), validating the efficiency of our strategy (Data S5). OTU393_3 is one of the sequences that showed nearly 100 % identity with the original OTU 393 V3-V5 sequence. Sequence variability could be observed for OTU393_26 through five different PCR products obtained after cloning (Data S6). This suggests a larger microbial diversity at the strain level within species. However, we could not exclude PCR or sequencing errors even by using proofreading DNA polymerase and Sanger sequencing. Variation in the copy number of the 16S rRNA gene within species could also be an explanation.
In summary, hidden behind the OTU 393 sequence, we identified 21 new putative genera, including 30 novel species, indicating that one OTU sequence can hide very diverse and distant sequences. Finally, analysis of the eight reconstructed sequences (mean length 1317 bp) after targeting the metabarcoding OTU 1761 sequence initially assigned to the order Clostridiales allowed us to discover a new family (Fig. 5 ). Eight 16S rDNA sequences (OTU1761_1 to OTU1761_8) represented seven new genera in this new family (86. 5-95. 05 % identity between sequences). OTU1761_4 and OTU1761_7 represented two distinct species in the same genus. The eight sequences showed similarities (92. 26-96. 25 %) with sequences from GenBank annotated as 'uncultured' bacteria from the human gut. OTU1761_1 showed the closest proximity (96.
25%) to 'uncultured' bacteria (DQ793257) from the human gut. Using the PCR approach with primers designed from our reconstructed sequences, we amplified two DNA fragments from mixed initial metagenomic samples. Sanger sequencing (OTU1761_PCR1 and OTU1762_PCR2; Data S7) of these amplicons showed 97 % identity with OTU1761_1 and 97. 5% identity with OTU_1761_8, demonstrating that these species were actually present in the faecal microbiome. The most proximal GenBank-assigned sequence [Eubacterium] siraeum (LC515595) showed 94 % identity with the OTU1761_1 and OTU1761_PCR1 sequences. A draft genome (FP929059) similarly assigned by the MetaHIT consortium showed the same proximities. These sequences could be bacterial representatives of a newly discovered family. Another small contig (667 bp) from the human gut metagenome (QRFC01107314) showed very high identity (99%) with our PCR product.
Surprisingly, even in the same species (OTU393_24-29), we observed individual-specific patterns. We cannot exclude that a part of this diversity originates from artificial diversity created in silico during full-length sequence reconstruction, even though we validated the efficiency of this step by detecting similar sequences in the GenBank database and during PCR experiments followed by cloning and Sanger sequencing. As indicated by EMIRGE developers, occasional presence of small indel errors in the reconstructed sequence could occur but, in practice, these rare indels have little effect on taxonomic results  [11]. Other tools for 16S reconstruction could also be used  [46]. In this study, we characterized rare OTUs from the human gut that were previously detected by metabarcoding at abundances between 0.
using a proteomic approach. This study revealed that the composition of secreted lignocellulolytic enzymes was strongly modified in the presence of sea salt, with increased secretion of xylanases and cellulases and lower production of oxidoreductases  [8]. Xylanases (EC 3. 2. 1. 8) are glycosidases that degrade xylans into xylooligosaccharide and xylose and thus help break down hemicellulose, one of the major components of plant cell walls  [9]. Xylanases are classified in the CAZy database in GH families 5, 7, 8, 10, 11, and 43, with a predominance of GH10 and 11  [10]. Microbial xylanases are used in various industrial applications such as in the food industry (production of wine and beer, fruit juice, bread), for animal feed, biofuel production, and in the pulp and paper industry  [9]. These industrial processes are conducted in a range of temperatures and pHs.
This analysis was performed using the VEGAN package (http://cran. r-project. org, accessed on 19 April 2021) in R. The sequence data generated in this study were deposited in the EMBL-ENA public database (PRJEB43346 for the first sampling campaign (March) dataset and PRJEB43343 for the second sampling campaign (November) dataset). The ProtParam tool (http://web. expasy. org/protparam/, accessed on 5 May 2021) was used to predict the theoretical molecular mass, and molar extinction coefficient of Xyn11-29. The signal peptide sequence was predicted using the tool available at http: //www. cbs. dtu. dk/services/SignalP-4. 1/ (accessed on 5 May 2021). N-Glycosylation sites were predicted via the NetNGlyc 1. 0 server (http://www. cbs. dtu. dk/services/NetNGlyc/, accessed on 5 May 2021). For sequence comparison, BlastP was used to search for sequences with similarity to Xyn11-29 at NCBI portal (https://blast.
ncbi. nlm. nih. gov/Blast. cgi, accessed on 5 May 2021) using the non-redundant protein sequence data. For the phylogenetic analysis, Muscle alignment was used at MEGA X (https://www. megasoftware. net/, accessed on 5 May 2021). The evolutionary history was inferred by using the maximum likelihood method and JTT matrix-based model under MEGA X  [16, 17]. The tree with the highest log likelihood (-5562. 80) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model and then selecting the topology with the highest log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.
Diversity of Fungal Expressed Genes Encoding GH11 Xylanases The diversity of expressed genes encoding fungal GH11 xylanases was investigated in surface and deeper mangrove sediments beneath A. marina (A) and R. stylosa (R) trees during the wet (March) and dry (November) seasons in three independent plots. This diversity analysis was performed using a classical functional metabarcoding approach based on cDNAs previously enriched in fungal GH11 sequences. The normalized GH11 xylanase dataset consisted of 247,464 sequences distributed among only 15 different operational functional units (OFUs) corresponding to 15 putative GH11 xylanase encoding cDNAs (Figure  1 , Supplementary Table  S2 ). The number of OFUs per sediment sample ranged from one to seven 7. GH11 diversity, estimated with the Shannon index, was systematically higher in deep layers during the wet season (Figure  1A , Supplementary Table  S3 ).
Xyn11-29 underwent a phylogenetic analysis with the 50 best hit sequences obtained from the BlastP analysis. However, the origin of the sequence could not be inferred by this approach (not shown), probably owing to strong conservation of xylanase amino acid sequences among Agaricomycetes. In the current state of the available data, we can only suggest that Xyn11-29 has a fungal origin and belongs to a basidiomycete of the Agaricomycetes class. Alignment with the biochemically characterized mangrove-derived xylanase XynMF13A confirmed that Xyn11-29 contained the two highly conserved catalytic residues characteristic of GH11 xylanases (Glu120 and Glu217) (Figure  2 ) and an analysis of the domain architecture showed that the 231 amino acid sequence was composed of a catalytic domain without a carbohydrate binding module.
5. Secondly, pH stability of the enzyme was determined. Activities were estimated after incubation at various pH values (5, 5. 5 and 6) (Figure  4A ). Results show that Xyn11-29 activity was relatively stable at pH 6, exhibiting 80% of its maximal activity after 48 h incubation. By contrast, at pH 5. 5, the recombinant xylanase retained only 64% of its initial activity after 48 h of incubation, and at pH 5, 60% of its activity was lost after only 24 h of incubation (Figure  4A ). Xyn11-29 displayed a relatively high thermal stability at 35 • C, with a half-life longer than 48 h (Figure  4B ). Half-lives decreased with temperature, 1 h at 40 • C and only 3 min at 50 • C (data not presented on the graph for 50 • C). Taking into account the temperature and pH stabilities of Xyn11-29, standard conditions (40 • C, pH 5.
8 ka. The vertical transect patterns thus reveal details of the deglaciation processes. Given these possible explanations for variability in transect ages, we evaluate the potential for a quantitative record of ice thinning in our data set. As mentioned before, the Culross transect appears to be the best-behaved set of data. If we cull additional outlier ages from the other transects, based on the foregoing discussion, the age patterns are clearer (Fig. 8F ). For this plot, we cull PWPI2 and PWPI11 for suspicions of their being too old due to inheritance; PWEP1 for possibly being too young due to a lingering cirque glacier; and PWCI1, PWPI3, and PWGB5 due to snow shielding, as discussed previously. In this version of the plot, the remaining 20 samples yield ages that range between 11. 6 ± 2.
Ice retreat We examined the data for evidence of glacial retreat. If we use the culled data set discussed in the previous section, the transect ages and distance to the paleo-ice source moderately correlate (Fig. 8F ). The Eshamy transect is closest to the paleo-ice source and has the youngest ages. The Granite Bay and Esther Bay transects are similar and are next farthest from the paleoice source. The Fool Island sea-level sample is located 5 km from the mouth of Esther Bay, and its age (PWFI1, 11. 6 ± 2. 8 ka) is within error of the sea-level sample from nearby Esther Bay (PWEI4, 12. 7 ± 1. 3 ka). The similar Perry East and West transects have the oldest ages of any transect and are the farthest from a paleo-ice source. Moreover, the sea-level sample shared by both transects is the oldest of the sea-level population (PWPI1, 14.
We applied the iceTEA calculator to the culled data set discussed earlier using the same method as for ice thinning. The calculated retreat rate is 20 m/yr (Fig. 11 ). The plot shows the regression is strongly influenced by the Eshamy data, which lie considerably closer to the ice source, and moreover, a different ice source than the other sites. Without the Eshamy transect, the ages of the remaining sites are considerably scattered and do not exhibit any clear age-versus-distance trend. Given the relatively small variation in distance to the ice source, perhaps this outcome is expected. If we assume all these ages (except Eshamy) are from one population, the weighted mean age is 14. 3 ± 1. 6 ka. These ages may not all be from one population due to factors already described (different elevations, snow shielding, and inheritance), but given the relative consistency of the ages and the consistent rates of thinning, we infer this is the time these mountainous islands were first exposed in this part of PWS.
If we assume the Eshamy ages are from a separate population, the weighted mean of those ages is 12. 2 ± 0. 4 ka, indicating the Eshamy surface exposure ages are younger than the remainder of our data set. The previously discussed weighted mean of the sea-level ages is 12. 9 ± 1. 1 ka, which we infer is the retreat of marine-terminating glaciers past the sampling sites. The Eshamy transect ages are within the error of the sea-level ages, which indicates rapid collapse of that part of the Sargent Ice Field ice sheet. The retreat rate may have changed as deglaciation progressed. We assume that glaciers extended locally to the edge of the continental shelf 23 ka in cross-shelf troughs  (Kaufman et al. , 2011;  Figs. 1  and 2 ). The distance from Culross Island or Perry Island to the edge of the shelf is about 200 km.
12 ). Their work utilized 40 10 Be exposure ages and 25 radiocarbon ages and is currently the most robust terrestrial data set constraining deglaciation of the northern marine-terminating part of the CIS. The 17 ka age is the same as the Sanak Island age as well as the oldest phase of deglaciation in the Anchorage region. Despite the larger uncertainties in our data set, the younger southeast Alaska 10 Be ages appear to precede those of PWS by 1-2 ka. Because the oceanographic, storm trajectory, and bathymetric conditions of southeastern Alaska and PWS are similar, the higher latitude and higher mountains of northern PWS may be the cause of the difference in ages between the two regions. The timing of ice retreat in PWS and other marineterminating parts of the CIS was preceded by warming sea-surface temperatures.
Praetorius et al. (2020)  compiled records of northeastern Pacific sea-surface temperature data that show warming from 17 ka into the Bølling event, which started at 14. 6 ka. The data show slight cooling through the Younger Dryas period, followed by warming at about 12. 1 ka. These time periods and temperature transitions are reflected in the deglacial history in the North Pacific, with the oldest pulses of deglaciation around 17 ka in southern southeast Alaska, Katalla, the Anchorage region, and Sanak Island. Widespread, and perhaps catastrophic deglaciation occurred during the Bølling period in northern PWS, offshore in core EW0408 85JC, Wingham Island, Valdez, and slightly later in the Anchorage area (Fig. 12 ). Our study contributes to a growing body of evidence that collapse of the CIS was an important precursor to global climate change events.
We calculated ice-thinning rates using a Monte Carlo approach implemented in the iceTEA calculator. We grouped data into four vertical transects and found thinning rates are between 120 and 160 m/ka based on the median and mode of the probability distributions (Fig. 10 ). We also found evidence of a correlation between the ages of each transect and the distance to the paleo-ice source. Considering all the sites and using the same Monte Carlo approach implemented in iceTEA, we calculated a retreat rate of 20 m/yr (Fig. 11 ). The Eshamy transect is unique among our sample transects, in that it is located closer to its paleo-ice source, the Sargent Icefield, than the other more northerly transects, which have an ice source in the College Fiord region. The Eshamy transect has a younger set of ages, which give a weighted mean of 12.
Acknowledgments. Thanks to Kara and  Katie Haeussler  for help in sample collection and to  Keith Labay  for help with drafting of some of the figures. Thanks to editor  Derek Booth , associate editor  Yeong Bae Seong , two anonymous reviewers, and  John Jaeger  for exceptionally constructive journal reviews;  Adrian Bender ,  Sue Karl ,  Marti Miller , and  Janet Slate  for their reviews; and  Jason Addison  for additional input. Fieldwork was funded by the  U. S. Geological Survey ; the  Hebrew University Jerusalem  funded analysis of samples. Any use of trade, firm, or product names is for descriptive purposes only does not imply endorsement by the  U. S. government.
These results suggested that if a snorkel manufacturer has passed a CO2 accumulation test with their mask and snorkel tube, it is very likely that a Pneumask-G based on their snorkel mask will also pass a CO2 accumulation test. Under the same logic, it was proposed that during relaxed or resting states, insufficient quenching of the mask secondary to the low minute ventilation expected may be conducive to CO2 accumulation. The data is available in the supplementary document on CO2 data. We note an increase in the inspired CO2, consistent with rebreathing and insufficient quenching of exhaled CO2, with a maximum value of 9 mmHg of inspired CO2 at the minute 15 of the test, but the EtCO2 values remained stable, as did all the other vital signs values for the entirety of the test. Our explanation is that the inspired CO2 rise triggered an increase in minute ventilation and respiratory rate, maintaining EtCO2 within the normal range.
Acknowledgments This has been a large community projects with support from global community including clinicians who have given us feedback throughout. We thank everyone who has engaged with the  Pneumask community , especially international community working to build solutions for the current PPE shortage. We thank  Schmidt Futures ,  Moore Foundation ,  Autodesk  and  CZ Bio-Hub  for financial support of the project. We thank  Boston Scientific and Medtronics  staff for support in development of injection molding parts and overall consultation. We thank  MITRE and Helena  for conversations around equitable distribution and members from  Pall corporation  for discussions around filter efficiency. Map data in Fig 7 is from OpenStreetMap1, is licensed under the Open Data Commons Open Database License (ODbL) by the  OpenStreet-Map Foundation (OSMF).
The remaining water molecules were kept fixed in the LL. The generally contracted basis set ANO-L adopting valence double-ξ contractions was utilized  42. The simulations were performed for 57 geometry realizations (per system) selected out of 500 snapshots generated via Wigner sampling (Supplementary Notes 2. 3 and 3. 1) on top of a representative geometry taken from a classical molecular mechanics simulation (Supplementary Note 2. 1) and refined within the QM/MM framework at the Möller-Plesset second order perturbation theory (MP2, Supplementary Note 2. 2). The geometry optimizations and simulations were conducted with the COBRAMM program  43  , interfacing the QM software Molcas 8  44  with the AMBER suite of classical force fields  45. Wigner sampling was realized with a stand-alone script, part of the program JADE  46.
Acknowledgements We thank  Dr. Stefano Santabarbara  for preparing the PBS. P. K. , G. C. , and M. G. acknowledge support from the  H2020  Grant Agreement number  765266  (Light-DyNAmics). G. C. and M. G. acknowledge support from the  European Research Council Advanced  Grant  STRATUS (ERC-2011-AdG No. 291198 ) and R. B. V. from the  Marie Curie Actions  (grant no. 328110 ). M. G. acknowledges support from  PRIN 2017  project  PHANTOMS ,  Prot. 2017A4XRCA. I. R. and M. G. acknowledge the  Agence National de la Recherche  project  FEMTO-2DNA  ( ANR-15-CE-29-0010 ). We acknowledge the use of HPC resources of the " Pôle Scientifique de Modélisation Numérique " at the  ENS-Lyon, France.
2016 ). Drosophila suzukii is native from Asia and has invaded independently the American and European continents where it was introduced probably in the late 2000s  (Fraimout et al. 2017). Using the recently released high-quality genome assembly Dsuz-WT3_v2. 0 based on Long PacBio Reads  (Paris et al. 2020) , we constructed a de novo TE database and found that TEs represented 47% of the genome. We further assessed TE insertion frequencies and TE abundance in 22 worldwide populations representative of the native area (n ¼ 6) and of the two main invaded areas in Europe (n ¼ 8) and America (n ¼ 8). The study of TE frequencies showed that the repeatome is highly active in D. suzukii: 75% of insertions segregated at a frequency of <0. 25. We found that the TE content was significantly higher in invasive populations and was correlated with a reduction of N e.
An Active Repeatome in the Watsonville Reference Population The female used to establish the WT3 isofemale strain corresponding to the genome assembly was collected in Watsonville (CA, USA)  (Paris et al. 2020 ). To thoroughly evaluate TE activity in this reference population, we assessed TE insertion frequencies in a PoolSeq sample of 50 D. suzukii individuals from Watsonville. Because TEs are mostly deleterious, rare TE insertions are likely to be recent insertions, not yet eliminated by selection, whereas fixed TE insertions are presumably old insertions weakly submitted to selection. It is worth stressing that, for the study of TE frequencies and abundances, we first used simulated PoolSeq data to validate our pipelines and to evaluate their performance and their sensibility to parameters such as sequencing coverage or number of individuals (see Appendix A in supplementary material, Supplementary Material online, for details).
05). To evaluate the effect of genetic variation on TE abundance we performed a genome-wide scan for association using methods controlling for population structure. To that end, we relied on the 13,530,656 bi-allelic variants (mostly SNPs) previously described on the same data set  (Olazcuaga et al. 2020). We searched for an association between allelic frequencies and the population abundance of the 83 TE families/pseudofamilies mentioned above using the BayPass software. Globally, we found 1,950 genomic regions showing evidence of association with population abundance of at least one TE family. Each region included one or several significant SNP/InDel separated by less than 1 kb. On average, each region was associated with the number of insertions per HG of 2. 51 families (min ¼ 1, max ¼ 44) and contained 1.
Creation of a TE Database A TE database was created by merging previously established consensus of Drosophila TE families and de novo reconstructed consensus of D. suzukii TE families. The previously established consensus were obtained by extracting all Drosophila consensus from Dfam and Repbase databases (release 2016-2018 for both)  (Hubley et al. 2016 ; https://www. girinst. org/repbase/, last accessed December 01, 2019). De novo reconstruction was performed using an assembly of an American strain from Watsonville, sequenced using PacBio long reads technology, and the REPET package (v2. 5) (Flutre et al. 2011; Paris et al. 2020). Obtained TE sequences were annotated by homology to previously established consensus of Drosophila TE families. Finally, all consensus were clustered in families using UClust  (Edgar 2010).