dataesr/scientific-paragraphs-categorization huggingface dataset:
If either the sample transport time or batch were identified as associated with the PFAS concentrations (p-value <0. 2), we then used the measured PFAS concentrations and the estimated effects of the time and batch to predict standardized concentrations (i. e. , concentrations that would have been observed if all samples had the same transport time and were assayed in the same analytical batch)  (Mortamais et al. , 2012). We used these standardized concentrations in our statistical analysis. Sampling and analytical conditions for which each PFAS have been standardized are presented in Supplemental material, Table  S3. We explored patterns of prenatal PFAS concentrations within the study population. We used Variable Selection for Model-Based Clustering of Mixed-Type Data method (package VarSelLCM, R) to create clusters of individuals based on their PFAS concentrations, independently of all health outcomes.
Missing values on covariates were imputed using multiple imputation by chained equations (mice R package, 20 imputed datasets). Since PFAS may act as endocrine disruptors, interaction was investigated by adding an interaction term with child sex. P value of interaction below 0. 20 was considered as suggestive of effect modification, and analyses stratified for child sex were further conducted. Models with sex-PFAS interaction were not performed when analyzing history of respiratory illness given the small numbers in some sub-categories (i. e. , no girl with quantified PFHpA and asthma). Sensitivity analyses were conducted to address the robustness of our associations to 1) the deletion of extreme values (above or below the 99th or 1st percentiles) of lung function parameters and PFAS concentration, 2) the standardization process for analytical and processing conditions by analyzing unstandardized PFAS concentrations and 3) further adjustment on maternal fish consumption during the first trimester of pregnancy that may be a predictor of both PFAS concentrations and respiratory health.
In addition, to evaluate the overall PFAS mixture effect on continuous respiratory outcomes, we additionally run adjusted Bayesian Kernel Machine Regression (BKMR, R package: bkmr). Categorical outcomes (i. e. , incidence of respiratory health diseases in the first 3 years of life) and exposure (PFAS with low frequencies of quantification) were not included because of the relatively small number of cases for some of these outcomes/exposures and the fact that the BKMR package only accommodated continuous outcomes. Each PFAS concentration was divided by its standard deviation (standardization). We performed 50,000 iterations, discarded the first 25,000 iterations and retained every fifth iteration among the last 25,000 for inference. We generated a plot that illustrated the expected change in each respiratory health outcome based on different quantiles of all PFAS, relative to when they are fixed at their 10th percentile.
The main association analysis was based on the cluster-based exposure patterns. The uni-pollutant models were considered as a support for interpreting the results from the main analysis and for results comparison with previous studies. For this reason, we did not correct for multiple hypotheses testing and considered p-values below 0. 05 as significant and p-values below 0. 1 as suggestive of an association. All statistical analyses were conducted with R-4. 0. 5. The source code is available in the following public repository: https://gricad-gitlab. uni v-grenoble-alpes. fr/iab-env-epi/philippat_pfas_2023.
Acknowledgments SEPAGES biospecimens are stored at  Grenoble University Hospital (CHU-GA)  biobank (bb-0033-00069); we would like to thank the whole  CRB team , led by  Pr. P. Mossuz  and  Mr. P. Lorimier , and in particular the technicians for the huge work of biospecimens processing and pooling:  Mrs. W. Jayar  and  Mrs. L. Than , as well as  Mr. G. Schummer. We thank the  Sepages study group :  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  S. Bayat ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble Alpes University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne, Saint-Martin d'Hères ),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux ,  R. Slama  ( Grenoble Alpes University, Inserm, CNRS, IAB ).
Fundings This work was supported by the environment-health-work program of the  French Agency for Food, Environmental and Occupational Health & Safety -ANSES  (grant  n2018/1/264 , the  French Research Agency -ANR  ( GUMME  project:  ANR-18-CE36-005 ) and the  European Union 's  Horizon 2020 research and innovation program  ( N • 825 , 712   OBERON  Project). The  SEPAGES cohort  was supported by the  European Research Council  ( N  •  311765-E-DOHaD ), the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 -N • 308 , 333-892 HELIX ), the  European Union 's  Horizon 2020 research and innovation program  ( N • 874 , 583   ATHLETE  Project,  N • 825 , 712   OBERON Project ),  ANR  ( PAPER  project  ANR-12-PDOC-0029-01 ,  SHALCOH  project  ANR-14-CE21-0007 ,  ANR-15-IDEX-02  and  ANR-15- IDEX5 ,  GUMME  project  ANR-18-CE36-005 ,  ETAPE  project  ANR-18-CE36-0005 -EdeN  project  ANR-19-CE36-0003-01 -MEMORI  project  ANR 21-CE34-0022 ),  ANSES  ( CNAP.
Artificial triggering of dry slab avalanches The impact of the dust deposition on the probability of artificial triggering in the region is evaluated using the MEPRA stability indicator. The model MEPRA analyses at every time step the output of the snow cover model, calculates the snow mechanical properties of the snow layers (shear strength and rammsonde penetration resistance), performs a basic stability analysis (shear strength / stress ratio at each layer boundary, with and without skier;  Föhn, 1987)  and based on that data provides stability indicators for the probability of natural release and artificial triggering. Figure  11  of  Morin et al. (2020)  shows an example of a final product of the analysis with MEPRA as it is provided to French avalanche forecasters. The so-called "accidental risk index" in MEPRA provides an estimate of the probability of artificial triggering due to an additional load at the snow surface (e.
Acknowledgements. CNRM/CEN is part of  Labex OSUG@2020. Marie Dumont  and François Tuzet have received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (IVORI; grant no. 949516 ). This work was partly funding by  ANR   JCJ EBONI  ( ANR-16-CE01-0006 ) and APR CNES MIOSOTIS. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-16-CE01-0006 ), the  Centre National d'Études Spatiales  (grant no. APR MIOSOTIS), and the  H2020 European Research Council  (grant no. 949516 ). Review statement. This paper was edited by  Jürg Schweizer  and reviewed by  Kevin Hammonds  and  Ingrid Reiweger.
Code availability. The Crocus model is open-source, and the code is available at https://opensource. umr-cnrm. fr/projects/snowtools_ git/wiki/Procedure_for_new_users  (Lafaysse et al. , 2023). The version used is labeled as s2m_reanalysis_2020. 2. The configuration of the ensemble of models used is described in detail in Lafaysse  et al. (2017). Data availability. The meteorological data used to drive snow simulations are available at https://doi. org/10. 25326/37 Vernay et al. (2020). Processed data are available upon request to the corresponding author.
In previous studies, kinetic DNA methylation studies performed with randomized oligonucleotides have determined systematically which flanking sequences are favored by DNMT1, DNMT3A, and DNMT3B in vitro, and the in vitro preferences are reflected in the cellular DNA methylation patterns  [36] [37] [38] [39] [40]. We have exploited these data in the following manner (Fig. 4C ): for each of the enzymes, we created a table in which the 256 possible NNCGNN sequences are ranked by order of preference in vitro. In parallel, we ranked the 256 possible NNCGNN sequences by average methylation level in each point of our WGBS dataset. Then we calculated pairwise Pearson r-correlation coefficients between the in vitro preferences and the actual WGBS values. This bioinformatic approach quantifies how much the flanking sequence preferences of a particular enzyme match to the actual genome-wide methylation in cells.
040 to -0. 451, indicative of a strong anticorrelation, and meaning that the best DNMT3 sites actually fell among the least methylated sites when UHRF1 was removed. To summarize, this rich dataset shows that UHRF1 depletion leads to profound decreases of DNA methylation not just at the best DNMT1 target sites, but also at the best DNMT3A and DNMT3B target sites suggesting that UHRF1 also has a role in DNMT3A and DNMT3B mediated DNA methylation. We obtained further support for this scenario by examining DNA methylation losses at H3K36me3-marked CpG islands, which are a well-described target of de novo methyltransferases in HCT116 cells  34. We extracted from our WGBS data the methylation values for CpG islands and ranked them in 10 bins according to their H3K36me3 content (Fig. 4D ). CpG islands with low levels of H3K36me3 lost the same amount of DNA methylation after UHRF1 depletion or after DNMT1 depletion: the methylation difference between these two conditions was close to zero.
Videomicroscopy analysis For live cell imaging, cells were grown on 35 mm FluoroDish (World Precision Instruments) with 0. 17 mm thick optical quality glass bottom and fitted with a 4-well silicone insert (Ibidi). Timelapse images were taken every 10 minutes for 20 hours using an inverted Eclipse Ti-E microscope (Nikon) equipped with a CSU-X1 (Yokogawa) spinning disk integrated in Metamorph software, and a 4-laser bench (Gataca systems). ∼45 μm Z stacks were acquired (Z-step size: 3 μm) with a 60× CFI Plan Apo VC oil-immersion objective (numerical aperture 1. 4). The microscope has a motorized Nano z100 piezo stage (Mad City Lab), a stage top incubator (Tokai Hit) and an EMCCD camera (Evolve, Photometrics). The images were 3D deconvolved using the NIS Elements software (Nikon).
Cell proliferation assay For cell proliferation studies, HCT116 cells were seeded at a density of 5,000 cells per well in a 96-well plate. They were then treated with 0. 2 µg/mL Dox and 20 µM auxinole for one day. Following this, the medium was replaced with fresh medium containing 0. 2 µg/mL Dox and 500 µM IAA. Throughout the experiment, images were captured every 2 hours using an IncuCyte ZOOM microscope (Essen Bioscience). The IncuCyte ZOOM software was utilized to determine the cell confluency (%) based on the acquired images. To obtain cell count data and assess cell viability, trypan blue staining was performed after every 4 days of auxin treatment. The TC20 Automated Cell Counter (BioRad) was used to obtain the cell count data and calculate the cell viability rate.
DNA methylation analysis LUMA and Pyrosequencing analyses were conducted following standard procedures. Wholegenome bisulfite sequencing (WGBS) libraries were prepared using the tPBAT protocol, as described by Miura et al  66, 67. The library preparation involved using 100 ng of genomic DNA spiked with 1% (w/w) of unmethylated lambda DNA from Promega. Subsequently, sequencing was carried out by Macrogen Japan Inc. utilizing the HiSeq X Ten system. To process the sequenced reads, BMap was employed to map them to the hg38 reference genome. The mapping information was then summarized using an in-house pipeline, which has been previously described  67. Custom scripts for this pipeline can be accessed via GitHub at the following link: https://github. com/FumihitoMiura/Project-2. A summary of the mapping information can be found in Supplementary File 3.
ChIP-seq analysis ChIP-seq data for HCT116 cells was obtained from ENCODE. Upon downloading the data, we performed quality checks on the reads using FASTQC (v0. 11. 9, available at https://www. bioinformatics. babraham. ac. uk/projects/fastqc). Reads with low quality and adaptor sequences were removed using Trimmomatic with default settings (version 0. 38). Subsequently, the reads were aligned to the hg38 reference genome using bowtie 2 (v2. 4. 5). To calculate the histone read coverage within each CGI (CpG island), we utilized the BEDtools coverage function. Initially, CGIs with less than 4 read counts in the ChIP-seq data were excluded to avoid including randomly mapped regions. The read counts were then adjusted to counts per 10 million based on the total number of mapped reads per sample.
Data availability The WGBS data has been submitted to GEO under reference GSE236026 token for reviewer access is eribksakjbgpzgt The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  69  partner repository with the dataset identifier PXD043254 The reviewer account details are: Username: reviewer_pxd043254@ebi. ac. uk PxYbEr4G    Control hUHRF1 UBL TTD PHD SRA RING DNMT3A A DNMT3B Control hUHRF1 UBL TTD PHD SRA RING Transfection: DsRed-DNMT3A + GFP → Transfection: DsRed-DNMT3B + GFP →.
Acknowledgments Work in the lab of Pierre-Antoine Defossez is supported by the  Fondation ARC  ( PGA1 RF20180206807 ),  Agence Nationale de la Recherche  ( ANR-15-CE12-0012-01 ),  Institut National du Cancer  ( INCa PLBio 2015-1-PLBio-01-DR A-1 ), and  Fondation pour la Recherche Médicale. This study was also supported by the  LabEx "Who Am I? " # ANR-11-LABX-0071  and the  Université de Paris IdEx  # ANR-18-IDEX-0001  funded by the  French Government  through its " Investments for the Future" program. KY was supported by  ARC foundation CDD Postdoctorant en France  (Ref. No. PDF20181208337 ),  Labex  "Who Am I?" postdoctoral support, and a  JSPS   Postdoctoral Fellowship for Research Abroad  (Ref. No. 202260432 ). The work of FM and TI was supported by  Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS))  from  AMED  under Grant Number  JP20am0101103  (support number 2652).
the development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity.
Further, coarse-resolution imagers with a nominal resolution near 1 km are limited in nearshore and narrow systems where modern high-resolution missions like Landsat-8 and Sentinel-2 offer valid observations 21. Overall, the retrieval of water quality in lakes, rivers, estuaries, lagoons and nearshore coastal waters remains an active area of research where improvements are needed so that satellite observations can fulfill their potential and become part of routine monitoring programs for ecosystem states, trends, and public-health alerting systems  [22] [23] [24] [25] [26]. Large and globally representative in situ datasets are essential for the development and validation of bio-optical algorithms to support large-scale monitoring using satellite Earth observation technologies. Such.
datasets are particularly scarce and geographically fragmented from inland and coastal waters as radiometric measurements are not part of most routine sampling programs and many lakes are remote and difficult to access. We address these shortcomings with our GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA). GLORIA includes over 7000 curated hyperspectral remote sensing reflectance (R rs , sr -1 ) and co-located chlorophyll a (Chla, mg m -3 ), total suspended solids (TSS, g m -3 ), absorption by colored dissolved organic matter (CDOM) at 440 nm wavelength (a CDOM (440), m -1 ) and Secchi depth (m) measurements. The data were contributed by researchers affiliated with 59 institutions in 20 countries who made the measurements for a range of objectives under diverse funding sources and resource levels, but shared attention to strict sampling protocols, tenacity to reach remote and inaccessible sites, commitment to establish long-term trend monitoring sites, and the recognition of the value of open-access datasets for public benefit.
With its almost global coverage, geomorphic range of water bodies, and 30-year time span (Fig. 2 ), GLORIA represents the de-facto state of knowledge of in situ coastal and inland water bio-geo-optical diversity. Subsets of the data have already produced significant contributions to global algorithm development for the satellite-based estimation of Chla, TSS, and a CDOM (440) using data-intensive machine-learning methods  [27] [28] [29] [30] [31]  or global semi-analytical approaches  32. Where they were available, we also provide uncertainty estimates of R rs and water quality measurements as standard deviations and means from replicate measurements. Nevertheless, some methodological detail which is currently considered relevant may not have been recorded at the time of observation, which limits our ability to retrospectively assess sources of uncertainty to subsets of the global dataset.
GLORIA builds upon the existing data repositories aimed at remote sensing studies of aquatic environments. We address poorly represented optically complex coastal and inland waters in existing open-data platforms such as the SeaWiFS Bio-optical Archive and Storage System (SeaBASS, https://seabass. gsfc. nasa. gov)  33, 34. In contrast to other relevant data repositories, such as the Lake Bio-optical Measurements and Matchup Data for Remote Sensing (LIMNADES, https://limnades. stir. ac. uk) database, GLORIA is open-access. By carrying out consistent quality control across the entire dataset, and providing comprehensive methodological details associated with each measurement, we have produced an analysis-ready, standalone data package for the community. The commitment of space agencies towards maintaining and enhancing optical Earth observing systems and the burgeoning fleet of commercial platforms indicate that our coupled reflectance-water quality attribute dataset fills a strong need to facilitate algorithm and application development.
Methods The GLORIA dataset was collated from the aquatic optics community of researchers or research groups working towards a range of goals including the routine monitoring of high-priority sites, one-off bio-optical characterization of a range of water bodies, data gathering to support algorithm development, or designated sampling for validating equivalent satellite-derived products. Efforts to gather this data started in 2018 with the second atmospheric correction intercomparison exercise (ACIX-II Aqua), an international collaboration to test processors that generate aquatic reflectance products from radiance measurements made at the top of the atmosphere  19. Requests for contributions were made at pertinent conference sessions and via the research networks of individuals. These requests were for quality assured remote sensing reflectance spectra at 1 nm intervals within the 350 to 900 nm wavelength range and at least one co-located water quality attribute (Chla, TSS, a CDOM (440), or Secchi depth), and associated uncertainties.
Radiometric data collection and processing. The central radiometric quantity reported in our dataset is remote sensing reflectance, R rs (sr -1 ). It is defined as the ratio of the water-leaving radiance just above the water surface (L w (0+), W m -2 sr -1 nm -1 ) over above-water downwelling irradiance (E s , W m -2 nm -1 )(Eq. 1, Fig. 1 ). We use the symbology of Ruddick et al. 41  with slight modifications: λ φ λ φ λ Θ = Θ R L E ( , , ) ( , , ) ( ) (1) rs w s R rs and L w are dependent on the viewing nadir angle Θ (measured from the downward vertical axis) and azimuth viewing angle ɸ (measured clockwise from the sun); λ identifies the wavelength dependence. For aquatic remote sensing applications, it is conventional to define R rs as derived from a sensor looking straight down L w (λ, Θ = 0) where ɸ is not defined  42.
Therefore, we omit λ, Θ, and ɸ for notational brevity. Several methods and instruments were used for the measurement of the downwelling and upwelling radiometric quantities reported in our dataset. Here we provide brief descriptions of the broad types of methodologies used for their measurement, and a list at the end of this section gives a formal summary. L w can be measured directly using a radiometer just above the water surface, looking vertically down and shielded from light reflected off the water surface  43. Other common techniques include measurement of the upwelling radiance at nadir below the water surface (L u (0-))  44  , or from above the water surface where the sensor is directed at a non-zero nadir angle (L t )  45. Both of these radiance measurements require conversions to L w , which are referenced in the list at the end of this section.
In brief, L u (0-) can be derived by extrapolating upwelling radiance from measurements at practical depths below the water surface to just below the water surface  46. Propagation through the water-air interface by accounting for the reduction of radiance by internal reflection off the water surface yields L w. The estimation of L w from L t is more involved, as L t contains a considerable amount of sky radiance reflected off the water surface into the sensor field of view (reflected sky radiance) in addition to L w (Θ, ɸ), where we note the angular dependence to emphasize the need for conversion to Θ = 0. Sky radiance (L sky ) is therefore usually measured simultaneously with L t at the same azimuth angles and at zenith angles Θ z (from the upward vertical axis) near 40°4  2. Three different approaches were used to measure E s in the present dataset and a detailed review is provided by Ruddick at al.
The instruments used for the radiometric measurements for each entry of the GLORIA dataset are part of the metadata (file GLORIA_meta_and_lab. csv) and are provided in the list at the end of this section. These include those customarily used for validation of satellite-derived aquatic reflectance, such as RAMSES (TriOS, Germany), HyperOCR (manufactured by Sea-Bird Scientific, USA; previously manufactured by Satlantic Inc. , Canada) and C-OPS (Biospherical Instruments Inc. , USA). The RAMSES and HyperOCR have 256 channel silicon photodiode array detectors with a 10 nm spectral resolution and a spectral sampling of 3. 3 nm per pixel. The typical setup for RAMSES instruments for our dataset is an above-surface installation with a vertical E s sensor and L sky and L t sensors at 40-42° zenith and nadir angles, respectively (Fig.
Some investigators integrated compact spectrometers (manufactured by Ocean Insight, Inc. , formerly known as Ocean Optics, Inc. , USA) with data loggers and optical fibers on frames or poles that can be pointed away from observation platforms. Measurements would either be accomplished through several instruments and optical fibers oriented for the respective radiometric quantities, or a single sequentially reoriented fiber. Data contributors provided radiometric measurements interpolated to 1 nm intervals over the 350 to 900 nm wavelength range. The instrument-specific bandwidths of the original measurements are provided in the data table (file GLORIA_meta_and_lab. csv, column 'Spectral_resolution_nm'). Due to instrument and processing constraints, some spectra span the range from 400 to 750 nm, or nearby bounds.