dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics All statistical tests were done in Prism v9 unless stated otherwise. Two-tailed paired t tests were performed using parametric tests for comparisons of GALT and PBMC in mass cytometry data, as well as for paired comparisons of IMC data. For differential gene expression in spatial transcriptomic data, Wilcoxon tests with Bonferroni correction for multiple comparisons were used in the Seurat package in R version 4. 2. 2  28. Statistically significant gene correlations in spatial transcriptomic data were identified using Spearman correlation in R along with pairwise P values corrected for multiple inference using Holm's method. Percentage of double positive pixels over total pixels for C1Qb + C1R + /total C1R + , C1Qb + C3 + /total C3 + , C3 + C1R + /total C1R + and C3 + C1R + /total C3 +.
Analysis of metabolites from 23 strains using both in silico tools ISDB-DNP (In silico Data Base-Dictionary of Natural Products) and MolDiscovery, revealed occurrence of godavarin K, a limonoid natural product previously isolated from the seeds of an Indian mangrove tree. This annotation was further confirmed by the marine database MarinLit, suggesting that cyanobacteria might be an alternative source of godavarin K and its four isomers. While no significant antimicrobial and cytotoxic activities were observed, some strains exhibited.
Biofilm sampling and cyanobacteria strains isolation Biofilms presumed to contain abundant cyanobacteria based on their green-to-brown color, were sampled in three mangroves on the Mayotte island of Grande-Terre: Dembeni (areas 1 and 2, 20 biofilms), Zidakani (4 biofilms), and Dapani (3 biofilms) between November 8 th and 11 th , 2018 (Table  S1 ; Figure  1 ). Biofilms, either benthic or attached to substrates including mangrove tree bark and debris were sampled using sterile tweezers, photographed, and transferred to sterile Falcon tubes in a cooler. Following Article 17, paragraph 2, of the Nagoya Protocol on Access and Benefitsharing, a sampling permit was issued and published (https:// absch. cbd. int/database/ABSCH-IRCC-FR-246969). Back to the laboratory, biofilm samples were split. One fragment was stored in RNAlater ™ (Ambion, UK) then frozen for DNA extraction.
DNA extraction and 16S rRNA metabarcoding of biofilms DNA was extracted from the 27 biofilms using the ZymoBIOMICS DNA mini kit (Zymo Research, CA) according to the manufacturer's instructions, after a disruption step using glass beads (6 x 1min, 30 Hz, 1,800 oscillations per minute). A ~400 bp fragment of the rRNA-encoding gene corresponding to the V4-V5 variable region of Escherichia coli was amplified using primers 515F and 926R  (Fadeev et al. , 2021)  and sequenced on an Illumina MiSeq platform (2x300 bp, paired end, Genoscreen, France). Companyprovided mock communities of known composition were used as an internal control for the whole sequencing process. Raw reads were deposited into the GENBANK Sequence Read Archive (SRA, Bio project PRJNA947804). Sequence reads were analyzed using QIIME2  (Hall and Beiko, 2018).
The annealing temperature of 58°C was used for primer set 8F (5'-AGAGTTTGATCCTGGCTCAG-3') and 920R (5'-TTGTAAGGTTCTTCGCGTTG-3'), and annealing at 55°C was used for primer set 861F (5'-TAACGCGTTAAGTATCCC-3') and 1380R (5'-TAACGACTTCGGGCGTGACC-3')  (Gugger et al. , 2002; Ludwig, 2007). Both amplicons were sequenced and sequence chromatograms (Genoscreen, Lille, France) were analyzed, assembled by Geneious (https://www. geneious. com/) for each strain, and compared to the GENBANK database with BLAST (default parameters, standard databases, optimize for highly similar sequences program selected). Sequences were submitted to GENBANK under accession numbers OQ693634 to OQ693681. A dataset was constructed consisting of sequences of the 43 isolated strains, their best BLAST hits, and representatives of major cyanobacterial lineages.
Sequences were aligned by the secondary structure-aware Silva tool [Ribosomal Database Project website  (Cole et al. , 2009) ]. After alignment, ambiguously aligned regions were deleted using Bioedit 7. 2. 5. Phylogenetic relationships were inferred using a Maximum Likelihood approach based on General Time Reversible Model (5 categories and invariants, see tree captions for details, selected based on the Akaike Information)  (Posada and Buckley, 2004). A pairwise p-distance matrix (Table  S2 ) was generated to aid preliminary genus and species delimitation. Phylogenetic trees were constructed by using the software MEGA 7  (Newman et al. , 2016). The presence of four genes diagnostic to produce microcystin, anatoxin, cylindrospermopsin, and saxitoxin families, respectively, Strain ID corresponds to the reference number in the Paris Museum Collection (PMC) of cyanobacteria from which strains are available upon request.
Identification of abundant strains in natural biofilms The 16S rRNA-encoding genes sequences obtained from all 43 strains were compared with ASVs obtained from the 27 biofilms to evaluate the relative abundance of isolated strains in natural biofilms. For this, cyanobacterial ASVs were extracted from biofilm datasets and aligned with strain sequences using Clustal W. Number of differences between most similar ASVs and strains were computed based on a 345 bp alignment. ASVs displaying 3 or fewer differences with strain sequences (~1%) were retained, and their respective abundance versus bacterial and cyanobacterial sequences in biofilm samples were checked. All analyses were conducted using MEGA-7  (Newman et al. , 2016).
009798) were used as internal lock masses. A permanent MS/MS exclusion list criterion was set to prevent oversampling of the internal calibrant. LC-UV and MS data acquisition and processing were performed using MassHunter ® Workstation software. Acquisition was performed in "Auto MS/MS" mode on the range m/z 100-1200 with an MS rate of 1 spectra. sec -  and an MS/MS scan rate of 3 spectra. sec -. Isolation MS/MS width was 1. 3 m/z. Fixed collision energies of 45 eV was used. MS/MS events were performed on the four most intense precursor ions per cycle with a minimum intensity of 5,000 counts. The MS data were converted from d (Agilent) standard data format to mzXML format by using the MSConvert software, part of the ProteoWizard package  (Myers et al. , 2017). Then, the converted mzXML files were processed with the MZmine software package v.
0 ppm, an RT tolerance of 0. 3 min (absolute), a maximum charge of 1, and the most intense representative isotope employed. The join alignment method was used to align the peaks (m/z tolerance of 10 ppm, RT tolerance of 0. 12 min). The weight of m/z was set as 50, the weight was set as 50, and the charge state must be the same. The same RT and m/z range gap filler was used to fill in the gaps in the peak list (m/z tolerance at 10 ppm, the retention time range 0. 04 from 15. 7 min). Finally, using the peak-list rows filter option, the aligned peak list was filtered to maintain only features linked with MS2 scans. LC-MS/MS data were deposited on MassIVE under the accession number: MSV000091466.
Molecular networks generation and annotation A feature-based molecular network was generated using Global Natural Products Social Molecular Networking (GNPS)  (Pluskal et al. , 2010; Wang et al. , 2016; Nothias et al. , 2020)  (https:// gnps. ucsd. edu/ProteoSAFe/static/gnps-splash. jsp). The edges of the network were filtered to have a cosine score greater than 0. 6 and more than six matched peaks. Further edges between two nodes were preserved in the network if and only if each node was in the top 10 most similar nodes of the others. The network's spectra were compared to GNPS' spectral libraries. All network spectra and library spectra match had to have a score of at least 0. 6 and at least six matched peaks. The molecular network was visualized using Cytoscape 3. 9. 1 software  (Doncheva et al.
003 to 30µg. mL -1. After 48 hours, cells were dyed with the MTT assay, and the optical density was measured using a Microplate reader SpectraMax Plus384 (GMI, USA) at two wavelengths of 570 and 690 nm  (Bich-Loan et al. , 2021). The IC 50 values (the concentration of extract at which 50% of cell growth was inhibited) were calculated by Excel and GraphPad Prism 8 software.
Cyanobacteria represented between 1. 35% (sample S26) and 61. 07% (sample S30, Table  S1 ) of reads. Interestingly, biofilms that displayed evident blue-green colors, such as samples S2, S21, and the three samples from Dapani (S27, 29, and 30) displayed particularly high abundances of Cyanobacteria compared to samples that did not have a greenish appearance (e. g. , samples S13 and S26 illustrated in Figure  1 ). Over the whole dataset, cyanobacteria clustered into 157 ASVs, each recovered from 1 to 5 biofilms. Of these ASVs, 15 represented at least 5% of all reads in at least one biofilm sample, and could be considered as abundant members of the biofilm (Tables  1, S1 ).
Occurrence and relative abundance of identified species in natural biofilms Out of the 22 species-level taxa identified among isolates, 13 were also identified as ASVs in 16S rRNA metabarcoding datasets obtained from 14 out of the 27 biofilms (Table  1 ). For 10 of these taxa, the corresponding ASV was recovered from at least one of the samples from which the strains were isolated. According to biofilm community compositions, eight species isolated in culture happened to be abundant members of the cyanobacterial community (i. e. , they represented over 5% of cyanobacterial reads) in at least one biofilm sample. In several cases, the species was even the main cyanobacterial member of this biofilm, with taxon Neolyngya sp. (PMC 1132 (PMC. 19, 1134 (PMC. 10, 1131. 19). 19) (PMC 1100 (PMC. 19, 1112 (PMC.
On the other hand, our results suggest that standard isolation procedures can already yield some of the abundant biofilmassociated cyanobacteria, as well as rarer members of the community. Examples of the latter likely include the nine species that we isolated as strains but never detected in biofilm 16S rRNA datasets, in line with the claim that lab-based isolation procedures Overview of the workflow showing Feature-based molecular networking informed with GNPS spectral library and ISDB and MolDiscovery annotations. Compounds dereplicated via in silico tools ISDB and MolDiscovery are visualized as red rectangles and triangles, respectively. Nodes (round shape) are colored according to the mean precursor ion intensity per cyanobacterium genus (For further details see Figure  S1 ). also often yield rare bacteria  (Ferris and Hirsch, 1991) , as the culture media compositions or culture conditions are very different from that of natural environment.
The benthic cyanobacterial strains reveal a high level of chemical novelty The annotation of 2,667 nodes organized into 131 clusters at the genus identification level revealed that many were strain-specific or taxa-specific, suggesting important inter-strain heterogeneity in their produced molecules. The limited coverage of cyanobacteria in existing databases only allowed us reliable annotation of very few compounds. Among the ISDB-DNP annotated nodes, 28 were from the marine field, 15 from cyanobacteria, and 6 compounds from bacteria. For a deeper analysis, the results generated from different in silico annotation tools, namely ISDB, MolDiscovery, and SIRIUS in conjunction with the marine natural product database MarinLit were combined. The three databases (MarinLit, ISDB, and MolDiscovery) consensually proposed godavarin K as the most probable annotated compound for [M+H] + ion m/z 567.
26008 (Text S2). Interestingly, SIRIUS did not suggest godavarin K as a structure candidate. Further exploration of the database PubChem  (Kim, 2021)  revealed that the molecular formula C 29 H 36 O 9 (molecular weight 528. 6 g. mol -1 ) was erroneously attributed to godavarin K. Moreover, it turns out that this entry in PubChem was linked to the reference of godavarin A-J  (Li et al. , 2010) , which does not include godavarin K. In this study, the node annotated as godavarin K was previously isolated from seeds of mangrove tree Xylocarpus moluccensis, that were collected in the wetlands of Godavari estuary, Andhra Pradesh, India  (Li et al. , 2011). In addition to godavarin K, dereplication of the molecular formula C 32 H 38 O 9 against MarinLit resulted in four limonoids including granatumin C  (Li et al.
At this sensitivity, the feature separates into four distinct peaks that are reproducible between the NIR38 NIRCam and NIRSpec spectra, as well as between NIR38 and J110621 (see Methods). These features are consistent with a blend between the C-H stretch of CH 3 OH and a broad component centered at 3. 47µm (Extended Data Figure  9 ). Ammonia hydrates (NH 3 • H 2 O) are considered to be the primary contender for this broad component  30  , but the sensitivity of our observations will enable a differential diagnosis in a future work. As seen in previous dense cloud spectra, methanol ice is detected additionally in isolation at 9. 7 µm and blended with the NH + 4 feature at 6. 85 µm. There is excellent agreement between the column densities derived from both methanol features (See Supplementary Figure  2 ).
Discussion The sum of the column densities for both CO isotopolog ices and their potential reaction products is less than the expected total CO column density from A V for each line of sight, suggesting that at most  46% and 33% of the available CO gas has frozen out into ices towards NIR38 and J110621, respectively. Although NIR38 samples a smaller total column of dust, its line of sight appears to pass closer to the Class 0 protostar (see Extended Data Figure  10 ). If this region contains locally denser or colder dust, it could explain the larger fraction of total CO that is frozen out onto the grains. These results imply that the rich variety of ices that we see likely formed early, prior to catastrophic CO freeze-out, rather than later through purely CO hydrogenation pathways. The other ice column density results are also consistent with efficient, early formation of CO 2 , NH 3 , and CH 4 in water-rich ices through H-addition and abstraction  3, 6, 35, 36  , followed by a small amount of the subsequent CO-based chemistry that we would expect to see at these high extinctions.
By combining these datasets, the superlative sensitivity, spectral resolution, and wavelength coverage with JWST now enable us to fully probe the initial conditions of all of the major ices in molecular cloud cores just prior to their collapse to form protostars. These new capabilities open the door to understanding the formation and inheritance of these key CHONS-species through the star-and planet-formation process and, ultimately, address what role they will play in shaping the chemistry on emerging planets.
Observations and data reduction NIRSpec: NIRSpec Fixed Slit (FS)  21  observations of the targets NIR38 (A V = 60) and SSTSL2J110621. 63-772354. 1 (A V = 95) were taken on July 6, 2022 and July 8, 2022, respectively, using the G395H grating, 8/33 combined with F290LP blocking filter. Target acquisition was achieved using the Wide Aperture Target Acquisition (WATA) method and the SUB2048 subarray with the CLEAR filter and a readout pattern of NRSRAPID6 with an exposure time of 14. 5 seconds for both sources. Spectra of the two stars were obtained using the ALLSLITS subarray at four dither positions, spaced along the S200A2 slit. Each integration was composed of 57 groups and 265 groups using the NRSRAPID readout pattern, for total on-source exposure times of 1274. 7 seconds and 5845. 7 seconds, respectively.
The JWST calibration pipeline was used for detector level 1 processing to calculate rate files from the uncalibrated ramps using version 1. 7. 1, Calibration Reference Data System (CRDS) context jwst_0948. pmap, and the PUB CRDS server. The two-dimensional rate spectra were distortion corrected using a second-order trace function derived from a commissioning observation of the standard star TYC 4433-1800-1, observed as part of program PID 1128. The two-dimensional spectral dithers were pairwise differenced to efficiently remove the background, and a one-dimensional spectrum from each dither was then optimally extracted  42  using a cross-dispersion profile calculated by median-collapsing each dither in the spectral direction. An uncalibrated spectrum was then derived using a median for the four separate dithers to remove most cosmic rays.
However, the wavelength calibration does use the solution from the pipeline. Errors were formally propagated from pixel errors estimated by the ramps-to-slopes fits from the level 1 processing. NIRCam: NIRCam Wide-field Slitless Spectroscopic (WFSS)  22  grism observations of the Cha-MMS 1 field were taken on July 3, 2022 with the F322W2 filter (2. 5-4. 0 µm) using Grism C with NIR38 (A V = 60) residing in module A. We obtained 24 individual integrations of the field with a total exposure time of 1. 7 hours. We followed a data reduction routine similar to that in  43. We first reduced the grism spectroscopic data with the standard JWST calibration pipeline v1. 6. 2 to the level of Stage-1, using the default CRDS setup with JWST OPS and no modifications, i. e. CRDS context 0953, and then performed 2D sky-background subtraction using the sigma-clipped median images that were constructed from the obtained WFSS data.
Flat-field correction was also applied using the imaging flat data obtained with the same filter. We then extracted the spectra of the two background stars using the optimal extraction method  42  from each individual integration, and co-added them together using the SpectRes package  44. The wavelength and flux calibrations were performed using the in-flight measurements obtained with JWST Commissioning Program #1076. At this stage, it is important to note that the current background subtraction method has not been fully optimised, so small systematic offsets may exist within data. Additionally, the optimal extraction method reduces, but may not entirely eliminate, potential flux contamination from other nearby sources. Therefore we may be marginally overestimating the flux for our A V = 60 source.
MIRI LRS: MIRI Low Resolution Spectrograph (LRS) Fixed Slit (FS)  23  observations of the targets NIR38 (A V = 60) and SSTSL2J110621. 63-772354. 1 (A V = 95) were taken on July 4, 2022 and July 11, 2022, respectively. Target acquisition was achieved using the F560W filter with a FAST readout pattern with 4 groups per integration for an exposure time of 11. 1 seconds for both sources. These two targets 9/33 used observations with 40 groups per integration and 104 groups per integration, respectively, with 5 integrations per exposure with a two-nod dither pattern along the slit, for a total of 10 total integrations per source and total exposure times of 1132. 2 seconds and 2908. 2 seconds, respectively. The FASTR1 readout pattern was used. We reduced the data with the same procedure for the two sources.
We used the STScI JWST pipeline (https://jwst-pipeline. readthedocs. io) version 1. 8. 2, the PUB CRDS server, and CRDS context jwst_0986. pmap to obtain the Stage 1 and Stage 2 products. We started from the uncalibrated data (Level1b, 'uncal' files). We ran the Detector1Pipeline with default parameters and the Spec2Pipeline step by step. We used each dither position as a background image for the other and subtracted the background pixel-wise. From the calibrated images ('cal' files), we extracted a one-dimensional spectrum from each dither position using the optimal extraction method  42  where a cross-dispersion profile is calculated by median-collapsing the 2D spectral trace in the spectral direction. The spectra from both dither positions were averaged to obtain a final spectrum. As a comparison, we extracted a spectrum using the JWST Spec3Pipeline.
No further processing is performed during the fitting procedure. In the fits, we assume saturated bands at 3 µm and 4. 27 µm because of negative fluxes. At these two bands, the fit is not limited by the peak of the band. This is an important assumption to make to avoid underestimating column densities of the molecules contributing to the absorption of these bands. ENIIGMA searched for the best combination of experimental data measured at temperatures of 15 K or below. Motivated by previous works, we explored combinations with ice mixtures composed of CO:CO 2  51  , CO:CH 3 OH 52 , H 2 O:CH 3 OH 53 and H 2 O:NH  3 30  , H 2 O:CO 2 :CH 4  56  , and pure CO  55. In addition to these data, ENIIGMA tested other IR spectra measured at temperatures below 16 K. We did not include the spectrum of the ammonium ion (NH + 4 ) in the global fits because it is not a consensus that the 6.
Local continuum fit for weak features: To separate the weaker features from the wing of the water stretch and combination bands, we also fit a local continuum to both spectra. The continuum points were set to the following ranges: 3. 215-3. 231 µm, 3. 252-3. 263 µm, 3. 289-3. 295 µm, 3. 306-3. 311 µm, 3. 610-3. 626 µm, 3. 686-3. 693 µm, 3. 711-3. 727 µm, and 3. 759-3. 795 µm. We calculated a fifth-order polynomial to these regions and took the local optical depths with respect to this continuum. For 13 CO, CH 3 OH, OCN -, and OCS we scaled laboratory data of simple ice mixtures to match the feature profiles. The profile of the best fitting scaled laboratory mixture and band strengths were used to determine local column densities, as described below. 3. 4 -3. 6 µm blended absorption (CH 3 OH and NH 3 • H 2 O): The absorption feature between ∼3.
We model the Gaussian and lab data simultaneously and minimize the residuals of the sum of both fits between 3. 4 -3. 65 µm (Extended Data Figure  9 ). When doing this we find that the column densities for CH 3 OH are lower by ∼ 20 -30% (N= 4. 1×10 17 cm -2 and N= 4. 5×10 17 cm -2 for the A V = 60 and A V = 95 sources, respectively). These column densities agree with those found by using the ENIIGMA fits to the globally determined optical depths. Further follow-up studies will model the full 3. 4 -3. 6 µm absorption feature and constrain the column densities not only for CH 3 OH, but also the other possible absorbing species. 13  CO 2 : Extended Data Figure  4  shows the observed 13 CO 2 feature, around 4. 39 µm, compared with laboratory spectra of CO 2 in different ice mixtures, which peak at slightly different wavelengths depending on the ice mixture.
The resulting 1σ upper limits in the abundances w. r. t. H 2 O ice is less than 1 % for H 2 S in NIR38. Data covering this region is not yet available for J110621. For HCN the upper limits w. r. t. H 2 O ice is less than 1% for both sources. For CH 3 CN the value is less than 2% for both sources. The FWHM and band strengths for the pure ices are taken from  62, 65, 66. The location of the background stars in their larger scale environments: Complementary information about the larger scale environment is critical when interpreting the column densities inferred from the ice observations. Extended Data Figure  10  shows a map of the H 2 column density maps extracted from the larger scale Chamaeleon maps 77 created based on far-infrared data 70 to 500 µm from the Herschel Space Observatory's Gould Belt survey  78.
Acknowledgements The  Ice Age ERS  team would like to thank our support team at  STScI  ( William Januszewski ,  Beth Sargent ,  Norbert Pirzkal , and  Mike Engesser ) for their technical suggestions and improvements to the program since 2017. We would also like to thank the anonymous referees for suggestions that improved the manuscript. MKM acknowledges financial support from the  Dutch Research Council (NWO ; grant  VI. Veni. 192. 241 ). MR acknowleges support from the  Netherlands Research School for Astronomy (NOVA). SI, HL, and EvD acknowledge support from the  Danish National Research Foundation  through the  Center of Excellence "InterCat"  (Grant agreement no. :  DNRF150 ). EvD acknowledges support from  ERC  grant  101019751 MOLDISK. The research of LEK is supported by a research grant ( 19127 ) from  VILLUM FONDEN.