dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, a decision tree approach guides the user in choosing the appropriate type of QC for their dataset. All suggested semi-automated checks make particular use of comparisons with historical time-series data. To evaluate the flagging results, the SOP is accompanied by a comparison to the well-established HOT QC results. which enables plotting the data using the web-based ERDDAP tool. In addition to giving the users more degrees of freedom, hosting the dataset on the ERDDAP server has two important benefits. First, the dataset is machinereadable, enabling an automated transfer to other repositories and higher-level infrastructures (e. g. , SeaDataNet, Copernicus Marine Environment Monitoring Service). Second, ERDDAP data managers are working to provide direct access to metadata information stored in the ODIS catalog, which, once achieved, will significantly improve 920 metadata interoperability.
ODIS catalog Through collaborating with ODIS, we developed two json-ld templates to publish time-series program metadata in a schema. org-friendly way (inspired by Science on Schema;  Shepherd et al. , 2022)  and to enable FAIR metadata. 925 The first template (EventSeries) is designed to capture the general information about the time-series programs (e. g. , location, time, principal investigators, funding, and related datasets). A "sub-events" section is used for more details about the individual cruise's location, time, personnel, and vessel. That section also includes details about the applied measurement methodologies for each cruise and provides links to cruise reports. The second json-ld template (Dataset) is designed to describe the metadata of the related BGC discrete bottle datasets.
Here, the 930 included variables and in particular, the applied semantics of the dataset are described. By using and linking these templates for each of the participating sites, we could include the metadata of the time-series sites and related datasets in the ODIS catalog. Here, the time-series programs are exposed on the web and machine-readable (interoperable) access to the metadata is guaranteed. Presently, these json-ld files are hosted by the METS-RCN GitHub repository (https://github. com/earthcube/METS-RCN). Eventually, the individual time-series program's 935 data centers can host (and update) these files and assign unique identifiers. The metadata of the SPOTS pilot itself (Dataset) are also stored in the ODIS catalog, clearly linking all related metadata to the data synthesis product.
By making BGC EOV datasets from multiple sources consistent and ready to use, the SPOTS pilot facilitates an 965 improved understanding of the variability and trends of ocean biogeochemistry. It represents an important and necessary step forward in broadening our view of a changing ocean and maximizing the return on our continued investment in ship-based ocean time-series programs. It also enhances data readiness  (Lindstrom et al. , 2012)  by implementing FAIR data practices for all included data. In particular, the implementation of ERDDAP and ODIS (Sect. 7. 2) enables easy data integration into e. g. , OceanOPS and Copernicus Marine Environment Monitoring 970 Service. On a higher level, this effort facilitates the consolidation of the international ship-based time-series network by collaborating closely with the participating time-series programs, developing, and recommending SOPs, and supporting the network to become more fit-for-purpose.
Acknowledgments The SPOTS pilot would not have been possible without the effort of the many scientists who secured funding, 1010 dedicated time to collect data, and shared the data that are included. Principal investigators of the different stations are listed on the METS RCN website. The development of this pilot product was supported by the  International Ocean Carbon Coordination Project (IOCCP)  by valuable input from its scientific steering group members as well as from the OCB Time-Series Panel. Further, we want to acknowledge  Adrienne Sutton  for leading the development of TOATS and contributing to Sect. 6. 2. 1015 Financial Support NL was funded by  EU Horizon   2020  through the  EuroSea Innovation Action  (grant agreement  862626 ). BF was funded by the  WASCAL MRP-CCMS  project from the  German Federal Ministry of Education and Research (BMBF ; grant agreement no.
01LG1805A ). The work of SRO was supported by the  European Union 's  Horizon 1020 2020 research and innovation program  under grant agreement no. 820989  (COMFORT). FMK and DRR from the  CARIACO  time-series project were supported financially by the  National Science Foundation  ( OCE-1259043 ). The work of IS was supported by  Norwegian Environment Agency  under grant agreement nos. 14078029 ,  15078033 ,  16078007 ,  17018007 , and  21087110. The work of K2 and KNOT was partly supported by a  Grant-in-Aid for Scientific Research  ( 20H04349 ) from the  Ministry of Education, Culture, Sports, Science, and Technology 1025 (MEXT) KAKENHI. The DYFAMED ship-based time series is supported by the  MOOSE program  ( Mediterranean Ocean Observing System for the Environment ) coordinated by  CNRS-INSU  and the  Research Infrastructure ILICO (CNRS-IFREMER).
https://doi. org/10. 5194/essd-2023-238 Preprint. Discussion started: 31 July 2023 c Author(s) 2023. CC BY 4. 0 License. 7. 4. Fair Data Usage Agreement While the SPOTS pilot is made available without any restrictions (Creative Commons Attribution 4. 0. ), users of 940 the data should adhere to fair data use principles: For investigations that rely on data from a particular timeseries program, principal investigators should be contacted to explore opportunities for collaboration and coauthorship and if there are any uncertainties regarding methodological details or interpretation of datasets. The original dataset DOI and any articles where the data are described should be cited. Contacting principal investigators comes with the additional benefit of expert insight into the specific site under investigation.
Data Access and Availability 7. 1. METS-RCN Website All information regarding the SPOTS pilot and the collaborative NSF EarthCube funded Marine Ecological Time Series Research Coordination Network (METS-RCN) can be accessed at https://www2. whoi. edu/site/mets-rcn/. 890The SPOTS web page (https://www2. whoi. edu/site/mets-rcn/ts-data-product/) includes detailed information on the participating time-series programs, including: Providing the data through ERDDAP enables FAIR-compliant data access services and gives users significantly enhanced capabilities rather than just downloading the dataset directly from the website. Optional constraints 910 within the ERDDAP dataset enable downloading subsets of the dataset. The constraint options include amongst others variable-, station-and time selections.
ERDDAP also enables downloading the dataset in several formats, such as tab-separated or netCDF. The latter format also entails additional metadata attributes, including alternative variable names (NERC P01 or following the recommendations from Liqing et al. (2022)). On the ERDDAP server, users find a link "Make a graph" (https://data. pmel. noaa. gov/generic/erddap/tabledap/bgc_ts_product. graph), 915 • contact person(s) • time-series website URL 895 • relevant data repositories • cruise reports and papers • detailed metadata on the BGC EOVs measured https://doi. org/10. 5194/essd-2023-238 Preprint. Discussion started: 31 July 2023 c Author(s) 2023. CC BY 4. 0 License. 7• recommended SOPs (Sect. 6. 3) and in-depth information on the assigned BP flags • links to AtlantOS QC software and crossover toolbox used 900 The website also provides several options for users to download the SPOTS pilot (DOI: 10.
26008/1912/bcodmo. 896862. 1), including: • Comma-separated value (CVS) format (directly from the website) • Link to the BCO-DMO repository (https://www. bco-dmo. org/dataset/896862, Lange et al. , 2023) • GOOS-relevant ERDDAP server 905 (https://data. pmel. noaa. gov/generic/erddap/tabledap/bgc_ts_product. html) 7. 2. Environmental Research Division's Data Access Program (ERDDAP).
The optical axis was perpendicular to the sample surface. Hard X-ray photoelectron spectroscopy (HAXPES) measurements were performed at the BM-25 SpLine beamline of The European Synchrotron, Grenoble, France  [38]. The experimental set-up includes a high electron kinetic energy analyser working from a few eV up to 15 keV  [39]. All data were analysed using CasaXPS software after subtraction of a Shirley baseline, correcting charge shifts and determining chemical contribution through Gaussian/Lorentzian peak fitting. To measure the IV curve, four gold contacts have been circumscribed in the circular area of the sample and arranged in a gridded cross. The distance between the points of the cross is 2 mm so that they remain inside the irradiated area. A four-point station with millimetric control of electrical contact positioning was used to interconnect with the IV acquisition system (Bio-Logic).
Acknowledgments The current research was funded through grants  PID2020-112770RB-C21  and  PID2020-112770RB-C22  by  MCIN/AEI/ 10. 13039/501100011033. We acknowledge the  Spanish MCIN and CSIC  for provision of synchrotron radiation at the Spanish line at theESRF trough project 25-02-972. We acknowledge the service from the  MiNa Laboratory at IMN , and funding from CM (project  S2018/NMT-4291 TEC2SPACE ),  MINECO  (project  CSIC13-4E-1794 ) and  EU (FEDER, FSE).
Image analysis One hundred postoperative radiographs were fully anonymized and analyzed by 3 fellowship-trained orthopedic surgeons. Image analysis was performed using the recently introduced online tool Tyche (www. tyche. expert). 29, 30  Using Tyche, images were presented in random order without displaying any metadata inside a web browser with the means to store results on the same window. Observers were able to use standard tools like zoom and contrast, employ angle tools, and create annotations like auxiliary lines. To access the images, observers were provided with a temporarily valid URL. After the analysis, the results were immediately  computed and visible to the project manager. Fifty percent of the images were analyzed twice. On every image, the observers determined 2 established angles: LSA and DSA.
Statistical analysis For statistical analysis, Prism 9. 4 (GraphPad Software, Boston, MA, USA) and SPSS version 27 (IBM, Armonk, NY, USA) were used. For all angles, mean values and standard deviations (SDs) were calculated for every observer individually and for all observers combined. To estimate measurement accuracy, we employed the following method as described by Popovi c 27 : for every measurement on every image, the SD between all 3 observers was calculated. Then, the average from all SDs was calculated and termed the ''mean of individual SDs. '' A lower value indicates higher accuracy. To assess inter-and intraobserver agreement for the measurements, intraclass correlation coefficients (ICC 2,1 ) were calculated. ICCs were interpreted according to Koo and Li  18  as shown in Table  II.
Correct radiographs are crucial The new angles presented in this study were tested and validated on images from our database labeled as true ''anteroposterior'' (AP) views. Research has proven that angle measurements produce discrepant results with different radiographic viewing perspectives. 10, 15, 32  Hence, researchers need to ensure that they only apply the new angles on "true'' AP radiographs. No clinical conclusions should be drawn from angle measurements performed on incorrect images, even though measurements might be radiographically reliable. In case of doubt, CTs should be used. 24.
Study population and data Patients residing in Martinique were recruited at the tertiary-care sleep center of the University Hospital of Martinique. The study population was restricted to patients having programmed functional polysomnography explorations during the study period from 01/01/2018 to 31/12/2022. Patients were referred to the sleep center by their general practitioners, and the indication for functional polysomnography exploration was based on clinical signs evocative of sleep apnea syndrome (sudden awakenings with shortness of breath, snoring, tiredness, observed apnea, high blood pressure, insomnia or hypersomnia, trouble focusing, mood changes and morning headaches in patients aged>50 years with a body mass index >35 and a neck circumference > 40. 6 cm). Patient exclusion criteria were as follows: age < 18 years, inability or refusal to provide informed oral consent for study participation, difficulty to determine living/working addresses of explored patients and unsuccessful polysomnography exploration.
Acknowledgements The authors thank the team of the  Martinican Observatory of Air Quality (Madininair) , the  French Meteorological Agency (Meteo France)  and Info climat for the data available on their websites. The authors wholeheartedly thank the patients and medical teams of the referral sleep center of the  University Hospital of Martinique  who contributed to this work. We convey special acknowledgement to the neurophysiology team of the  University Hospital of Martinique  at the origin of the present research initiative.
Analysis of total serum N-glycans by matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF MS) Total serum N-glycan profiles were studied by MALDI-TOF MS following N-glycan cleavage by peptide N-glycosidase F (PNGase F from Roche Diagnostics (100 U, lyophilized, product reference 11 365 185 001, Mannheim, Germany)), glycan purification by solid-phase extraction and permethylation as previously described in. 15, 16  The MALDI-TOF mass spectra were internally calibrated, and further processed using the GlycoWorkBench software. 17  Mucin core 1 O-glycosylation 2-DE of apoC-III was performed as previously described. 18  Primary antibody (rabbit anti-apoC-III cat #K97113R, 1:5000 v/v in 5% TBST; Meridian Bioscience, USA), and then with the secondary antibody (goat anti-rabbit HRP-conjugated IgG NA934V, Biorad, 1:5000 v/v).
4, supplemented with 4% β-mercaptoethanol (Fluka). For TGN46 and LAMP2 analysis, samples were denaturated for 10 min at 95 • C, separated on 4%-12% Bis-Tris gels (#NP0322BOX Invitrogen), and for TMEM165, samples were not heated and separated on a home-cast 12% gel. β-actin antibody was purchased from MilliporeSigma (Burlington, MA, United States) and used at 1:10000 v/v dilutions. TMEM165 antibodies were purchased fromMilliporeSigma (Ref HPA038299 #000028770) and used at 1:3000 v/v dilution. Mouse anti-LAMP2 antibody from Santa Cruz Biotechnology (#H4B4, sc-188822, Dallas, TX, United States) was diluted at 1:2000 and sheep anti-TGN46 (#AHP500G, Biorad) at 1:1000 v/v. Secondary antibodies (HRPcoupled) were purchased from MilliporeSigma (#12-348 and #12-349) and used at 1/10000 v/v dilutions.
Bioinformatics analysis Sequence files are demultiplexed. The alignment of the files obtained on the reference genome (GRCh38) uses a Burrows-Wheeler transform (BWA-MEM). The marking of duplicates is done by Picard MarkDuplicates, the recalibration of the base quality score is performed by GATK4 (Broad Institute) and then annotated using SNPeff and SnpSift: the databases queried are as follows: SNPEff, 1000Genomes, gnomAD, Clin-Var, COSMIC, dbscSNV, dbSNP, dbNSFP, phastCons. CNV calling is done by CNVnator (Mark B. Gerstein lab, Yale University) and annotation is done by AnnotSV.
Tools for predicting protein and peptide structures Model of the Δ80-TMEM165 with the variant A310P was produced with a locally-installed version of the AlphaFold v2. 2 software,  20  as previously described in. 21  The models shown in the figures were rendered with the UCSF ChimeraX v1. 6 software (https://www. cgl. ucsf. edu/chimerax/). The Orientations of Proteins in Membranes (OPM) database (https://opm. phar. umich. edu/) has been used to calculate the spatial arrangement of the AF2-predicted models with respect to the hydrocarbon core of the lipid bilayer. 22  For the prediction of peptide structures shown in Supplementary Fig. 2 , both AlphaFold 2 and ESMFold  22  were used by the mean of the integrated structure features of ChimeraX v1. 6 through the Colab network. 23.
Degradation of the TMEM165-A310P protein is inhibited by chloroquine Our previous results thoroughly established that the turnover of TMEM165 is inhibited by chloroquine treatment. 7  To assess whether the A310P mutated form of TMEM165 falls under the same regulation, the stability of wt-and mutated forms of TMEM165 were analyzed by immunofluorescence in the presence or the absence of chloroquine (Cq). In control fibroblasts, chloroquine treatment led to a slightly higher TMEM165 immunofluorescence signal arguing for an inhibition of its lysosomal degradation. Very interestingly, the same treatment in the patient's fibroblasts fully rescued the TMEM165 expression level and its Golgi subcellular localization as proved by its colocalization with GM130 (Fig. 4A ). Proteasomal inhibition did not rescue TMEM165 expression (data not shown).
Acknowledgments This work was supported by the  French National Agency  ( ENIGMncA  project,  ANR-21-CE14-0049-01  to F. F. ), The  IRP CNRS GLYCOCDG  to F. F. and the  ERA-Net for Research on Rare Diseases  ( ERARE118-117 -EUROGLYCAN- omics) to F. F. Z. D. was supported by a fellowship from  Région Hauts-de-France and Lille university. This research was made possible through access to the data generated by the  France Genomic Medicine  Plan 2025. This work was supported by  Commissariat a` l'Energie Atomique et aux Energies Alternatives   and the   MetaboHUB infrastructure  ( ANR-11-INBS-0010 ) ( F. Fenaille ,  S. Cholet ). We thank  Dr. Olivia Baylet-Fernandez  (pediatrician),  Dr. Bamberger  (pediatrician),  Dr Pinto  (endocrinologist),  Dr Dehoux  (nephrologist),  Dr Girard  (hepatologist),  Pr Cormier-Daire  (genetician),  Manon Tessier  (psychomotrician),  Clarisse Duchon  (neuropsychologist),  Pr Borgel  (hematologist) for their help in patient management and  Mounira Zerguini  ( ARC ).
R [see script at https://github. com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R_an alysis/RandomArray. R]) was used to randomize community member placement in the wells so that each member occupied a total of 31 wells per plate. In total, there were 7 community conditions-3 monocultures, 3 pairwise cocultures, and the 3-member community. A time course was performed for each replicate. The time course included an exponential phase time point (12. 5 h) and 5 time points assessed every 5 h over stationary phase (25 h -45 h). Four biological replicates were performed for each community condition for a total of 28 experiments. For each experiment, 6 replicate filter plates were prepared for destructive sampling for a total of 168 transwell plates.
The initial differential gene expression analysis compared each coculture to the monoculture control, separately. It was revealed that were was a subset of genes that were only differentially expressed in the 3-member community. These genes were parsed from the dataset and used to perform two additional analyses in ImpulseDE2. Case-control analyses were performed to analyze differential expression between the 3member community to each of the pairwise cocultures as controls. Functional enrichment analysis was performed on the collection of genes determined to have unique differential expression in the 3-member community using the BiNGO package in Cytoscape  (Maere et al. , 2005).
Biosynthetic gene cluster (BSGC) analysis NCBI accession numbers were uploaded to antiSMASH 6 beta bacterial version  (Blin et al. , 2021)  to identify genes involved in BSGCs using default parameters. Where possible, literature-based evidence and BSGCs uploaded to MIBiG  (Kautsar et al. , 2020)  were used to better inform antiSMASH predictions. Log2 fold-changes (LFCs) were calculated for all predicted biosynthetic genes within each predicted cluster by comparing coculture expression to monoculture expression at each time point. Average LFCs were calculated from all predicted biosynthetic genes within a predicted BSGC at each time point. Temporal LFC trends were plotted using ggplot2. An upregulated BSGC was defined as a BSGC that had at least two consecutive time points in stationary phase with a LFC > 1.
To ensure robust detection of co-expressed genes, a resampling approach was used as previously described  (McClure et al. , 2016). Then, a Z-score was computed on the mutual information matrix. A Z-score threshold of 4. 5 was used to determine an edge in the interspecies network. Interspecies networks were uploaded into Cytoscape version 3. 7. 1. for visualization, topological analysis, and enrichment analysis  (Shannon et al. , 2003). Gene annotation and gene ontology files were obtained for B. thailandensis, P. syringae, and C. violaceum for enrichment analyses. For B. thailandensis, annotation and ontology files were downloaded from the Burkholderia Genome Database (https://www. burkholderia. com). For P. syringae, annotation and ontology files were downloaded from the Pseudomonas Genome Database (http://www.
At each time point, a Z-scored distribution of FCs was plotted in R. Z-scores were extracted from exometabolite of interest (thailandamide, pyochelin, and capistruin) to plot as vertical ablines on the Z-scored distributions. In addition, the non-additive features were extracted from the dataset to generate a heat map in MetaboAnalyst. Ward's clustering algorithm was used with Euclidean distances from Z-scored data. Data for each sample are the averages from independent time point replicates (n = 2 to 4). To find statistical support for non-additive exometabolite production in the 3member community, we created an "expected" time course abundance for identified exometabolites that had non-additive upregulation (thailandamide, pyochelin, and capistruin). The "expected" time course abundance was determined by calculating the summation of peak abundances across pairwise cocultures with B.
We note each iteration of repeated measures ANOVA would result in different statistical result. This was because the replicate identifiers for the "expected" time course were arbitrarily assigned. For this reason, we only considered a test significant if it was consistently below a P threshold of 0. 05 across 10 iterations of the code. analyses can be viewed at and downloaded from GitHub (see Dataset 7 at https://github. com/ShadeLab/Paper_Chodkowski_MonocultureExometabolites_2020/t ree/master/Datasets). Large data files (e. g. , MZmine project files) are available upon request. Supplementary files are also available on GitHub (https://github. com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021/tree/maste r/Supplemental_Files).
Acknowledgements This material is based upon work supported by the  National Science Foundation  under grant  DEB 1749544  and by  Michigan State University. In addition, metabolite analysis and transcript sequencing were provided by a  DOE-JGI Community Science Program   award  (proposal identifier  502921 ). The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported under contract number  DE-AC02-05CH11231. J. L. C. was supported by the  Eleanor L. Gilmore Fellowship  from the  Department of Microbiology and Molecular Genetics. We thank  Katherine B. Louie  and  Benjamin P. Bowen  for support in mass spectral analysis.
Code availability Computing code, workflows, and data sets are available at [https://github. com/ShadeLab/Paper_Chodkowski_3member_SynCom_2021]. R packages used during computing analyses included DEseq2  (Love et al. , 2014) , ImpulseDE2  (Fischer et al. , 2018) , Minet  (Meyer et al. , 2008) , vegan 2. 5-4  (Oksanen et al. , 2019) , ggplot2  (Wickham, 2016) , VennDiagram  (Chen, 2018) , RVAideMemoire  (Herve, 2020) , rtracklayer  (Lawrence et al. , 2009) , viridis  (Garnier et al. , 2021) , and helper functions  (Wickham, 2007 , Wickham, 2019;  Wickham et al. , 2019A; Wickham et     al. , 2019B).