dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion In this paper, we demonstrate the potential of the New Caledonia lagoon near Noumea to host Cal/Val activities. Using in situ data acquired as part of the GEOCEAN-NC campaign, this study proposes a method to link and compare observations from the Noumea long-term tide gauge site with an offshore altimetry crossover point from Jason and Sentinel-3a missions. With measurements from a GNSS buoy and a bottom pressure sensor, we were able to virtually transfer long-term Noumea tide gauge data to this intersection. A comparison over the common year of measurement of the tide gauge and the pressure sensor show that this method is relevant for estimating an altimeter bias at the mm-level. The use of along-track altimetry product allows us to test and adapt altimeter correction parameters, especially for the wet tropospheric delay.
It is also possible to transpose our method to other study areas, thus increasing the potential number of Cal/Val sites around the world. However, as in Noumea, these new areas must have suitable altimetry data in the vicinity of a long-term tide gauge site, as well as a hydrodynamic context conducive to the realization of a geodetic mission (navigation, sensors deployment…). Finally, although the GEOCEAN-NC campaign is not directly related to the preparation of the future SWOT mission, a better knowledge of the lagoon dynamic and the mapping of the fine-scale geoid will be useful for the exploitation of its future largeswath measurements. Thus, the Noumea lagoon represents a real opportunity to establish an absolute and relatively low-coast Cal/Val site, to better understand current and future altimetry data.
To test this hypothesis, we compared the correction provided by the radiometer with two data sets: (1) the wet tropospheric correction from the ECMWF model and (2) the wet tropospheric correction computed from permanent GNSS stations in Noumea. For the latter, we used the total tropospheric delay extracted from GINS PPP computations, performed by the CNES teams in Toulouse, for the NRMD and NOUM stations. The tropospheric corrections, estimated every 2 hours, are interpolated at the satellite pass times. The dry tropospheric component from GDR files is then subtracted to finally obtain the wet component of the tropospheric correction. Since the GNSS stations are not at sea level elevation, an additional correction is applied to account for the pressure difference with the comparison point (which is at sea level elevation).
Acknowledgements For the GEOCEAN-NC mission in Noumea lagoon in October 2019 (DOI : https://doi. org/10. 17600/18000899 ), the authors want to thank  Etienne Poirier  for the instrument management during the campaign, and the commandant and crew of the  R/V Alis. We acknowledge the help of IRD, Shom and DITTT for logistics and on-land tide and GNSS data collection. We also thank the  US IMAGO Noumea teams  for their logistic support during the campaign, and more particularly  Bertrand Bourgeois  and  Mahé Dumas  for the deployment and recovery of the pressure sensors. We acknowledge the  CNFC (Comission Nationale de la Flotte Côtière), Ifremer, IRD  and the  Government of New Caledonia  for rapidly adjusting and obtaining permissions for the new cruise plan. We also want to thanks the  GINS community  and especially the  GET laboratory  for helping with GNSS computation, and the LEGOS altimetry experts (especially  Florence Birol  and  Fabien Léger ) for their help and advices analysing altimetry dataset in the lagoon.
Proteome data processing MS/MS spectra were searched using MASCOT 2. 5. 1 software (Matrix Science) against the 19N S. epidermidis genome for which 2,469 protein sequences have been annotated totalling 720,252 residues (NCBI database SRX7846931). The following parameters were used for MS/MS spectra assignation: fulltrypsin specificity, maximum of two missed cleavages, mass tolerances of 5 ppm and 0. 02 Da on the parent ion and fragments, respectively, fixed modification of carbamidomethyl cysteine (+57. 0215), and oxidized methionine (+15. 9949) and deamidated asparagine/glutamine (NQ) (0. 9840) as dynamic modifications. Peak lists generated from all spectra of peptides were submitted to MASCOT. Peptides with a score below a p-value of 0. 05 were assigned to proteins. The proteins were validated when at least two different peptide sequences were detected.
The false discovery rate for protein identification was below 1% when applying these rules with the corresponding MASCOT decoy search mode. For each validated protein, spectral count (SC) was used as a proxy of their relative abundances in each condition. SC values for each protein were compared between pH conditions by the calculation of a relative protein enrichment, the T-fold change, as defined by Carvalho after normalisation of the dataset as recommended  (Liu et al. , 2004; Carvalho et al. , 2012). Differentially abundant proteins were selected when their adjusted p-value ≤ 0. 05 and absolute T-fold above 1. 5. The proteins differentially abundant in the 3 conditions were considered for the functional interactions network construction using STRINGdb (v11. 0) 1 based on: co-expression, text-mining, biochemical/genetic data ("experiments"), and previously curated pathway and proteincomplex knowledge ("databases"), with S.
epidermidis RP62A as reference organism and a minimum interaction score of 0. 7  (Szklarczyk et al. , 2019). For protein functional analysis, BLATSp analysis was performed against S. epidermidis database from UniProt (22530 entries in 10/06/2019), considering valid Scheme of the workflow followed. Cells from -80 • C glycerol stocks were streaked in Tryptic Soy agar media (TSA) and a single colony was picked to inoculate a new TSA plate. From the plate a single colony was collected to prepare a pre-inoculum in TS broth medium with adjusted pH (pH 5. 5 and 7. 4) and incubated overnight at 37 • C and 225 rpm. The inoculum at pH 5. 5 was used for the cultures grown at 5. 5 (N55) and 7. 4 (N57), and the inoculum at pH 7. 4 for the culture at the same pH (N77). The cells were harvested at mid-exponential phase.
For proteomics the proteins were extracted from 4 biological replicates/experimental condition and analysed by nanoLC-MS/MS. Identified proteins (MASCOT) were quantified by spectral counts. Differential abundant proteins between experimental conditions were submitted to functional clustering analysis (STRINGdb). Metabolites from eight replicates/experimental condition were extracted, after metabolic quenching and biomass normalisation. Metabolites were assigned in 1 H-NMR spectra acquired in an 800 MHz spectrometer (ChenomxNMRsuite). Their determined concentrations were used for pathway analysis (MetaboAnalyst). results for which 75% sequence coverage, 35% sequence identity and p-value ≤ 0. 05 were at least obtained. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (Perez-Riverol et al.
NMR metabolite analysis NMR spectra were acquired on a Bruker Avance II + 800 MHz spectrometer equipped with a 5 mm TXI-Z H/C/N/-D probe. All 1D 1 H were acquired at 298. 15 K and using a noesygppr1d pulse program [256 scans; relaxation delay of 4 s; mixing time of 10 ms; spectral width of 16025. 641 Hz; size of free induction decay (FID) was 128k points]. Processing of spectra was performed with Bruker TopSpin 3. 2. All FID were multiplied by an exponential function, followed by Fourier Transformation. Spectra were manually phased and baseline corrected. Chemical shifts were adjusted according to the TSP chemical shift at 0. 00 ppm. For spectral assignment, 2D NMR spectra were acquired for some samples: 1 H-1 H TOCSY, 1 H-13 C HSQC, and 1 H J-resolved. 1D 1 H NMR processed spectra were grouped and converted into a matrix (each column was a spectrum; each row was one of the 128k points that makes up the FID).
Spectra were aligned and centred according to the chemical shift of TSP at 0. 00 ppm. For untargeted analysis, the region of water (4. 70-4. 95 ppm) was removed. After trimming interfering peaks, only the region between 0. 15 and 10. 00 ppm was used. All the aforementioned steps were completed using R software environment for statistical computing (v3. 6. 2) with homemade scripts. For targetted analysis, resonances assignment, metabolites identification and quantification were performed with Chenomx NMR suite 8. 12. All the metabolites were confirmed based on two-dimensional NMR spectra. Metabolomic data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, 2  where it has been assigned Project ID ST003341.
Statistical analysis The results were evaluated by univariate and multivariate statistical analysis. In the case of proteomic data, a univariate t-Student analysis was performed using bicaudal distribution and homoscedastic samples. For metabolite data, pairwise comparisons were conducted using the Wilcoxon rank-sum test to look for significant differences (p-value ≤ 0. 05) between the groups of samples. Correction for multiple testing was performed using the Benjamini-Hochberg procedure  (Benjamini and Hochberg, 1995). This analysis was performed in the R software environment for statistical computing (v3. 6. 2). Multivariate statistics included principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for both data sets. A cross-validation method was used to evaluate the goodness of prediction (Q2) value of the resulting models, with a permutation analysis to further confirm the validity of the PLS-DA model.
To understand which metabolites were contributing the most to the separation observed by PLS-DA, a variable importance in projection (VIP) analysis was conducted using the loadings derived from the PLS-DA. For the proteomic data, Hierarchical Cluster analysis was performed with MetaboAnalyst 4. 0 3   (Chong et al. , 2019). Pathway analysis was done to identify the putative pathways altered between the different conditions based on the metabolites levels using the Staphylococcus aureus N315 as the pathway library reference in MetaboAnalyst 4. 0  (Xia and Wishart, 2010). Proteins differentially abundant between pH conditions were considered to the network construction using STRING database  (Szklarczyk et al. , 2019)  with (0. 9) confidence minimum required interaction score.
978 h -1 ) compared to the N55 (0. 817 h -1 ) and N77 (0. 705 h -1 ) steady conditions. The growth rate is also significantly different between these two last conditions. Measured media pH values at the middle-exponential phase have dropped 0,3 and 1,0 units, respectively for N55 and N77. Cells were harvested at the midexponential phase and subjected to shotgun proteomics and NMR-based metabolomics. With the proteomic workflow used a total of 771,440 MS/MS spectra were acquired for the 12 analysed samples. A total of 441,537 Peptide-to-Spectrum Matches (PSMs) were interpreted, resulting in a ratio of 57% of assigned MS/MS spectra. Such a ratio is usually obtained in similar conditions when a highly confidently annotated genome is used as a database. The dataset gave confident identification of 20,194 unique peptides and 1,372 proteins that could be quantified.
Differential proteomic analysis A multivariate statistical analysis showed that the proteomic datasets for the three conditions were clearly distinguished by the PCA test, and no biological replicate should be considered as outlier. Five components from the PCA test explains 81% of the variance among the three experimental conditions (Supplementary Figure  2 ). The Volcano plots showing the differential abundance of proteins between each pair of experimental conditions (N55 vs. N77, N55 vs. N57, and N57 vs. N77) are represented in Figures  2A-C. N55 (skin pH condition) vs N77 (blood pH condition) showed 243 proteins with significantly different levels, 131 proteins are increased in N55 condition and 112 increased in N77 (Figure  2A ). This constitutes the pair with a higher number of differences by opposition to the comparison N57 vs N77 with only 88 protein abundance differences.
Funding This work was partially supported by project  PTDC/CVT-CVT/29510/2017  funded by Fundação para a Ciência a Tecnologia; Projects  LISBOA-01-0145-FEDER-007660  ( Microbiologia Molecular ,  Estrutural e Celular)  and  UID/Multi/04378/2019 ) funded by  FEDER  funds through  COMPETE2020 -Programa Operacional Competitividade e Internacionalização (POCI) ; by  ONEIDA  project ( LISBOA-01-0145-FEDER-016417 ) co-funded by  FEEI -"Fundos Europeus Estruturais e de Investimento" from "Programa Operacional Regional Lisboa2020 "; by national funds through FCT; and by  Transnational Cooperation Portugal/France -Pessoa Program , supported by  FCT/Campusfrance. LGG was financed by an  FCT  contract according to  DL57/2016 , ( SFRH/BPD/111100/2015 ).
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034825 and doi: 10. 6019/PXD034825. Metabolomic data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www. metabolomicsworkbench. org where it has been assigned Project ID ST003341.
, 2020). Section 2 presents our data, i. e. , the 20 GCM-RCM pairs for Representative Concentration Pathway (RCP) 8. 5 adjusted from EURO-CORDEX, and the S2M reanalysis set as the reference observational dataset  (Vernay et al. , 2019 (Vernay et al. , , 2022)). In Sect. 3, we detail our statistical methodol-ogy. Finally, results, discussions and conclusions are introduced in Sects. 4, 5 and 6, respectively.
, 2009)  and various snow depth observations  (Vionnet et al. , 2016; Quéno et al. , 2016; Revuelto et al. , 2018; Vionnet et al. , 2019; Vernay et al. , 2022). The S2M reanalysis focuses on the elevation dependency of meteorological conditions. Indeed, this reanalysis is not produced on a regular grid but provides data for each massif every 300 m of elevation between 600 and 3600 m. ADAMONT  (Verfaillie et al. , 2017 ) is a bias-correction and downscaling method that aims to adjust daily climate projections from a regional climate model against a regional reanalysis of hourly meteorological conditions using quantile mapping. This method was used to adjust the EURO-CORDEX dataset  (Jacob et al. , 2014)  against the S2M reanalysis to provide daily values of snow load that spans historical  and future (2006-2100) time periods.
Specifically, the EURO-CORDEX dataset consists of RCMs forced over Europe by GCMs from the CMIP5 ensemble  (Taylor et al. , 2012)  for the historical scenario and several representative concentration pathway (RCP) scenarios  (Moss et al. , 2010). We focus on the RCP8. 5 emission scenario and consider a total of 20 GCM-RCM pairs, with 6 GCMs and EURO-CORDEX meteorological data are used as input to the snow cover model Crocus  (Vionnet et al. , 2012)  every 300 m of elevation for each massif. This provides estimates of the time evolution of the snow cover  (Verfaillie et al. , 2018) , enabling us to compute the maximum annual value of snow load at 1500 m elevation. For simplicity, we often refer to the S2M reanalysis as our observation reference. We note that we discard the two most southern massifs because many projected annual maxima are equal to zero.
Acknowledgements. We are grateful to  Ben Youngman  for his "evgam" R package. INRAE, CNRM and IGE are members of  Labex OSUG. We are indebted to  Raphaëlle Samacoïts  from  Météo France  for providing us the latest version of the climate projection data. The authors also thank the editor,  Tamas Bodai ,  Antonio Speranza  and an anonymous referee, who provided constructive and useful comments.
Code availability. The code is publicly available at the following link: https://doi. org/10. 5281/zenodo. 6769830 (Evin, 2022). Data availability. The full S2M reanalysis on which this study is based is freely available from AERIS  (Vernay et al. , 2019). For each GCM, the global mean surface temperature can be computed from https://climexp. knmi. nl/CMIP5/Tglobal/index. cgi (KNMI, 2022). For the observations, the global mean surface temperature from HadCRUT5 can be downloaded from the following web page: https://www. metoffice. gov. uk/hadobs/hadcrut5/ (last access: 28 June 2022) (Morice et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-13-1059-2022-supplement.
A fuller understanding of this becomes more evident in the Italian study. The São Paulo data was quite small in scale and sample. A much more comprehensive evaluation, with over 100 respondents and focusing exclusively on the psychological ramifications of this work, was conducted by social scientists affiliated with the University of Turin and published in 2019. 12  Their findings represent perhaps the most comprehensive attempt to date to assess the mental health consequences upon those who bury the dead. It is not surprising that such a study would come from northern Italy, where, as noted above, this occupation has an exceptionally well-documented history. Unlike in many other places, the cemetery workers in Florence were often entrusted to be the keepers of the vital municipal record known as Grascia morti -'Books of Dead' -where accounts and dates of death and burial were recorded.
14  In this research the various categories of cemetery workers were evaluated through interview data and questionnaires that were assessed according to the Secondary Traumatic Stress (STS) Scale. Presenting their overall findings, it was revealed that these workers in general score at the highest level on the tests that identify stress levels, with pervasive evidence of 'anxiety, sadness, insomnia'. Among those who bury the dead, unlike other workers within the cemetery, there are specific circumstances that contribute to this: 'they directly face grieving clients during the entombment process or during other procedures such as exhumation'. 15  And these workers scored amongst the highest in two of the most crucial marker categories for STS: first, 'intrusion', referred to as 'thoughts and images of others' trauma', and second, the category of 'avoidance', defined as 'a general depletion due to an emotional and situational fatigue with others' pains'.
Acknowledgements The author wishes to thank  Bridget Conley , whose ongoing commitment to this project made it possible to realise its several manifestations, and  Élisabeth Gessat Anstett , who generously afforded us the opportunity, with this publication, of making 'In Their Presence' accessible to a much wider audience. To all the authors and participants, and the two anonymous reviewers of this article, my sincerest gratitude, as well as for the ongoing support of the  Jonathan M. Tisch College   of Civic Life , and with it the  Springboard and Diversity Funds  and the  World Peace Foundation , all of  Tufts University.
54 image features were TH+ cell type specific (43%) and specifically represented mDAN biology. We hypothesized that a weighted combination of all 126 cellular image features might allow the generation of a unique phenotypic fingerprint per cell line. Secondly, we hypothesized that the generated phenotypic fingerprint of GS/+ neurons would be significantly different from all control lines. To test our hypotheses, we evaluated the two supervised ML classifiers LDA and SVM. In a first step, we determined the Pearson correlations of all image features to remove strongly correlated image features. Both LDA and SVM algorithms were then trained repeatedly on shuffled sets of 80% of the imaging data and tested on 20% of the imaging data. In total, training and testing were repeated 25 times on shuffled slices of the dataset in a process referred to as cross-validation (CV) (Figure  2B ).
CV is useful to detect and prevent overfitting and to increase robustness since the ML models are trained on multiple slightly different datasets. We observed that training variability over all cycles was generally low, indicating that sufficient training data were provided to both the LDA and SVM algorithms. Alternatively, we used data from one plate for model training, evaluated the model on two unseen plates, and obtained similar results (Figure  S5A ). Although by eye the four mDAN lines appeared similar (Figure  3A ), both LDA and SVM classification algorithms distinguished GS/+ neurons from control cell lines. Overall, control cell lines appeared more similar compared to each other than the GS/+ neurons. Next, we calculated the Z-factor between the GS/+ and +/+ neuronal classifications  (Zhang et al.
Imaging and image analysis Imaging was performed on a Yokogawa CV7000 microscope in scanning confocal mode using a dual Nipkow disk. 384-well plates (Perkin Elmer, #6007558) were mounted on a motorized stage and images were acquired in a row-wise ''zigzag'' fashion at RT for fixed cells and 37 C and 5% CO 2 for living cells. The system's CellVoyager software and 405/488/561/640-nm solid laser lines were used to acquire single Z-plane 16-bit TIFF images through a dry 403 objective lens using a cooled sCMOS camera with 2,560 3 2,160 pixels and a pixel size of 6. 5 mm without pixel binning. Nine images in a 3 3 3 orientation were acquired from the center of each well. Image segmentation and feature extraction was performed with an in-house software written in C++. Except for the detection of mitochondrial structures, image segmentation was performed on illumination-corrected raw images based on fluorescent channel intensity thresholds empirically determined per plate.
ML analysis To support the reproducibility of the ML method of this study, the ML summary table is included in the supplemental information per data, optimization, model, and evaluation (DOME) recommendations  (Walsh et al. , 2021)  (Table  S4 ). Multiple datasets were generated differing in terms of the used mDANs, chemical compound treatment, and fluorescent staining (Table  S5 ). Input data were normalized, outliers were removed, and the number of input features was reduced by removing strongly correlated features (Figure  2 , Table  S4 ). We applied the Python-written ML library scikit-learn to train and test all models  (Pedregosa et al. , 2011). We primarily used supervised binary classification algorithms. Figure  2B  summarizes the overall ML workflow. All models' hyperparameters were optimized using scikit-learn's GridSearchCV module and evaluated using k-fold cross-validation.
Statistics All data were generated at least in duplicate with neurons from a single differentiation batch. All data are represented as boxplots. The notches of the box represent the 95% confidence interval of the median obtained by bootstrapping with parameter value 1,000. Each data point represents the mean of a single well of a Stem Cell Reports j Vol. 17 j 2349-2364 j October 11, 2022 2361 384-well plate comprised of nine images. Data from different plates were median normalized to allow comparison across plates acquired on different days. Data processing and plotting were carried out with Python packages Pandas  (McKinney, 2010) , Matplotlib  (Hunter, 2007) , and Seaborn  (Waskom, 2021). Null hypothesis significance testing was performed with the freely available Python package Statannot  (Weber, 2022).