dataesr/scientific-paragraphs-categorization huggingface dataset:
Genotyping in HELIAD Genome-wide genotyping was performed at the facilities of the "Center national de recherche en génétique humaine" (Evry, France) using the Illumina Infinium Global Screening Array (GSA, GSAsharedCUSTOM_24+v1. 0), as detailed elsewhere  [40]. Briefly, variants included in the marker list for removal by Illumina were excluded and only variants for which the full-length probes aligned uniquely on the GRCh38 genome without any mismatch were kept. Variant intensity quality control (QC), was conducted for all autosomal variants, according to established methods  [41]. Sample quality control was performed using PLINK v1. 9  [42]. Samples with missingness >0. 05, sex inconsistencies or with heterozygosity rate that deviated more than ±6 SD from the mean, were excluded. To identify population outliers, a Principal Component Analysis (PCA), was run using as reference the 1000 Genomes phase 3 population and the dataset was projected onto two dimensions, using the flashPCA2 software  [43].
To control for cryptic relatedness, we kept one individual from each pair of samples with a kinship coefficient more than 0. 125 (cut-off for second-degree relatives), yielding a final sample size of 1251 unrelated individuals. We excluded variants with a missingness>0. 05 in at least one genotyping center or with a differential missingness test p < 10 -10. The Hardy-Weinberg equilibrium tests (p < 5 × 10 -8 ) were performed only in controls and for each genotyping center/country separately. To improve the accuracy of imputation, we compared the frequencies of variants (chi-square test) against two reference panels, the population of the Haplotype Reference Consortium r1. 1 (HRC)  [44]  excluding 1000 Genomes samples and the Finnish and non-Finnish population of Genome Aggregation Database v3 (gnomAD)  [45].
Variants showing a χ 2 >3000 in both the HRC and the gnomAD or a χ 2 >3000 in one reference panel without being present in the other were excluded. Finally, GWAS analyses were performed between controls across genotyping centers to assess frequency differences between genotyping centers, using the software SNPTEST  [46] , under an additive model and adjusting on associated PCs. Variants with a Likelihood Ratio Test of p < 10 -5 were excluded. Furthermore, we removed ambiguous variants with minor allele frequency (MAF) > 0. 4 and we kept only one copy of any duplicated variants, prioritizing the one with the lowest missingness. All samples and variants, passing the above QC metrics were imputed in the Michigan Imputation Server (v1. 2. 4)  [47]  using the TOPMed Freeze 5 reference panel. Phasing and imputation were performed using EAGLE v2.
Construction of genetic risk score for Alzheimer's disease Imputed dosages for a total of 5611,082 SNPs with MAF>0. 05, call rate >95% and imputation quality score >0. 4 were converted to bestguess genotypes for PRS computation. The PRSice software (http://prs ice. info/) was utilized to construct PRSs for each individual applying the clumping and thresholding (C+T) method  [49]. A PRS for Alzheimer's disease was computed, as the weighted sum of the risk increasing alleles that each individual carries at each SNP locus multiplied by the effect size for the reference allele on the basis of large GWAS meta-analysis summary data (i. e. discovery samples)  [12]. A set of 23 out of 29 SNPs, which reached genome-wide significance in the original GWAS meta-analysis and were available in our dataset, were used to construct the PRS for Alzheimer's disease (Supplementary Table  1 )  [12].
Sources of funding This work has been supported by the following grants:  IIRG-09-133 , 014  from the  Alzheimer's Association ,  189 10 , 276/8/9/2011  from the  NSRF-EU program Excellence  Grant (ARISTEIA), which is cofunded by the  European Social Fund and Greek National resources , and  ΔΥ2β/οικ. 51657/14. 4. 2009 from the  Ministry for Health and Social Solidarity (Greece). MG acknowledges support in form of a  Walter-Benjamin fellowship  from  DFG  (GZ:  GE 3461/1-1 ) and from the  FöFoLe program  of  LMU Munich (Reg. -Nr. 1120 ).
Method, Data and Modelling Strategy The first hypothesis put forward by  Pinchinot (1984)   [8]  suggested that the gully resulted from debris-flow initiated by collapses of the Grand Ilet landslide scarps. However, recent observations of different gully formations around the Grand Ilet landslide mainly show regressive destabilizations during rainstorms  [6, 51]. Without totally rejecting the hypothesis of  Pinchinot (1984)   [8] , it seems more likely that the gully formed by headward retreats  [10, 18, 52]. To test this hypothesis, we first reconstructed the morphometric characteristics and failure conditions and then evaluated the stability of the Grand Ilet plateau under these conditions.
Building a Historic Elevation Model To retrieve the characteristics of the pre-and post-gully incision topography we applied SFM on archive aerial photography. Aerial photography campaigns of May 1978 and April 1984 imaged the cirque of Salazie before and after Cyclone Hyacinthe. Digital aerial files are freely available from IGN-France's data portal http://remonterletemp. ing. fr (accessed on 12 February 2021). as JPEG200 files. We selected the 60 pictures of 1978 and the 79 of 1984 that cover the cirque of Salazie (Table  1 ). The camera calibration parameters of these two series are not known and were solved using SFM software. However, the scanned images were slightly rotated and may cover a wider area than the photo diapositives, inducing disparities in the image coordinates. This biases the estimated camera frame, preventing the finding of a relevant solution for camera calibration parameters  [31].
Therefore, the first step for building a 3D point cloud from these photographs consisted of the preparation of the aerial photographs. The images were rotated in order to align the camera's X/Y axis with the line and column of the photo. Then the straightened photographs were cropped to the same origin to remove inscriptions and the black borders of the photographs  [31]. Once corrected, the digital photos were loaded in commercial SfM-MVS software developed by Agisoft: Metashape ®. To build DSM and orthophotography we used the following workflow on Metashape ® : (1) gather the photography of 1978 and 1984 in two respective chunks; (2) on the aerial photos, mask all the moving objects that may induce errors in calculation (e. g. , clouds, vehicles); and (3) add the known 11 ground control points (GCP) and 9 check points (CP), spread as regularly as possible over the study area on a stable surface.
One of the main assumptions considered in this application is that the hydrological behaviour of the basin remains unchanged. We use the rainfall series recorded in Grand Ilet and the measurements of water table level to calibrate the models corresponding to the Ravine de l'Eglise catchment. Rainfall data were recorded at the Grand Ilet meteorological station with a tipping-bucket rain gauge. With Gardenia ® , the daily rainfall series recorded since 2010 was correlated to the daily water table level measured in PZA3, a 100 m-deep piezometer. PZA3 was installed in 2010, 250 m ahead of the main scarp of the gully of the Ravine de l'Eglise (Figure  2 ). There, the water level was monitored at 30-min time steps since monitoring started in December 2010. Previous studies  [46, 50]  have proven the presence of a continuous aquifer for the Grand Ilet plateau.
Above 1, if the FoS is comprised between 1 and 1. 2, the failure is moderately likely to occur and between 1. 2 and 1. 5 is unlikely to occur. This approach is simple to implement and benefits from considerable experience feedback  [41, 57]. TALREN ® software (Terrasol) was used to compute slope instabilities. Based on Bishop simplified equations  [58] [59] [60] , the tool assumes a rotational slip surface, and divides the rigid body into slices to account for its inhomogeneity and to consider the equilibrium of each slice. Generally, this method yields high factors of safety. It is also less conservative than other methods as it accounts for the effects of interslice forces  [59, 61]. To achieve computations, three types of data are required: (1) the initial topography and the internal structure of the slope; (2) the unit weight (Yd), the effective cohesion (C') and the effective friction angle (Φ') of each material; and (3) the groundwater level.
Cultural Resistance to change and acceptance of any perceived increase in risk to the integrity of the data produced can be an issue, especially as the process of drug development and the place that the ADME package has in that remained steadfast for many years. There is a general acknowledgement that, as the pre-work is reduced, there is an inherent increased risk of an issue being raised in the data outcome of the hADME study, but a case-by-case assessment can of course be made prior to committing to any streamlined approach. As inferred earlier, it is clear from a discussion among the authors of this white paper that several companies have already de-prioritized conduct of the in vivo non-rodent  14  C-ADME in favor of conducting only the in vivo rodent study, or at least delaying the non-rodent ADME study until late in development.
More recently, flexible methods relying on the joint statistical modelling of species occurrences have gained a lot of popularity. These models are sufficiently flexible to allow several fixed or random predictors to be incorporated in modelling species occurrences, allowing different aspects of habitat variability or species trait differences to be controlled against. In such a framework, one can infer species associations from their residual covariances, that is, the correlation between their residual probabilities of occurrence. One commonly used example of this approach are hierarchical models of species composition, as implemented in the Hmsc r package  (Ovaskainen & Abrego, 2020; Tikhonov et al. , 2020). This is the method we will use in this article. In this framework, we will use four different model specifications.
Note however that this approach requires additional data, namely the age of patches, on top of the species co-occurrence matrix. This would not always be possible. Considering the fair association between patch age and patch richness (Figure  6c ), one could consider using patch richness as a surrogate for patch age. As patch richness is already encoded in the species co-occurrence matrix, this approach, like all previous sets of methods, would not require additional data. However, if using patch richness as a proxy of patch age (model Hmsc M2'), the method fails to provide correct results (see Figure  S1 ). The association presented in Figure  6c  appears not to be strong enough for the model to appropriately capture age-related patch differences. Arguably, in datasets containing even more species (e.
| Effect of changing parameter values and sample size The performances of the main methods, under the different parameter scenarios, are summarized in Table  1. Confirming the previous results, only Hmsc M2 yielded satisfying results in all cases. All other methods generally detected spurious associations, often at quite high rates, sometimes exceeding 70 %. Obviously, reducing sample size down to N = 300 reduced the general statistical power, and therefore the rate of spurious detections. In the case of species distribution model Hmsc M1, this sometimes made the percentage of errors fall to acceptable levels. However, this is just indicative of a lack of power, and would also come at the expense of higher type II errors in actual datasets containing genuine species associations. Decreasing the rate of patch disturbance , as expected, also decreases the overall amount of spurious associations detected (Table  1 ).
Since all species are more likely to occupy older patches, all appear positively associated overall. We found that this signal was detectable even with realistic sample sizes (number of patches), under relatively low levels of patch disturbance, and for TA B L E 2 Are detection methods robust to patch disturbance? Summary of main findings. For each type of approach, we only mention methods tested in this study, but conclusions apply to other similar methods as well. Note that 'Sim2' and 'Sim9' denote the 'fixed-equiprobable' and 'fixed-fixed' algorithms, respectively of spurious species associations detected generally declined with sample size (Table  1 ), but so would the power to detect genuine species association signals. Moreover, the range of sample sizes we used may quickly become conservative, as more and more ecological studies now have datasets with several hundreds or thousands of patches sampled  (Dubart et al.
Microarray Data Analysis Array images were digitized using ScanArray (Agilent Technologies, Les Ulis, France) in XDR mode at 5 µm resolution and then quantified with Feature Extraction software (version 9. 5. 3). Genes differentially expressed between AHR-1 mutant and Wild type condition in the microarray study were identified by a multivariable permutation test controlling the false discovery rate (a moderated t-test with adjustment of p-values using the MAnGO software  [27] ). Genes were selected as significantly differentially expressed when the p-value was less than 0. 05. Microarray data were also examined for gene set enrichment analysis (GSEA, Broad Institute's Molecular Signature Database). The microarray data can be accessed through the Gene Expression Omnibus accession no. GSE195728.
GFP cells were isolated from both strains (WT: pmec-3::GFP vs. mutant: pmec-3::GFP;ahr-1(ia03)) using fluorescent activated cell sorting (FACS). RNA from both cell types were extracted, amplified, and analyzed using a C. elegans array from Agilent (for more details, see STAR Methods). In total, 95 genes were down-regulated, and 76 genes were up-regulated in the ahr-1::gfp mutants, compared to the wild-type GFP, with cut-off values (a fold change ≥ 1. 5 and a p-value < 0. 05). The number of down-regulated genes was higher than the number of up-regulated genes in the ahr-1 mutants, in line with previous microarray or RNASeq data  [24, 25]. Among the most down-regulated genes were several genes encoding guanylate cyclases (gcy) (gcy-35, gcy-36, gcy-34, gcy-32, gcy-33, gcy-37, gcy-17), the neuropeptide-likeprotein gene (npl-20) and an FMRF-like peptide family gene (flp-8).
We next used quantitative RT-PCR (using the same mRNAs extracted from ahr-1 wild type and mutant worms) to confirm and better quantify the microarray data. Seven genes were tested (including gcy-32, gcy-33, gcy-35, gcy-36 and cyp-13A1, cyp-13A10) and their downregulation identified using microarrays was confirmed (Supplementary Data Figure  S1 ). In order to analyze the potential impact of AHR-1 on regulatory pathways, we used several methods including Gene Set Enrichment Analysis (GSEA). The results highlighted several processes that may be altered in the mutant animals as compared to their wild-type counterpart (Table  2 ). In mutant animals, gene sets associated with nervous system function were significantly depleted and functions associated with major metabolic pathways (fatty acid metabolism, oxidative phosphorylation, and glycolysis) were significantly enriched (Supplementary Data Figure  S2 ).
Again, we did not observe any increase in the mRNA expression of the prototypical target gene cyp1a1 in Cos-7 cells (Supplementary Data Figure  S3 ), compared to the strong induction observed in the human HepG2 cell line. Finally, to check that Cos-7 cells could represent a good model to test the activity of the AhR, we compared the potentiality of AHR-1 and of the human AhR. Experiments were performed with the empty vector, AHR-1/AHA-1 or human AhR/ARNT, and cells were treated with 10 nM of TCDD. We observed a basal activity of the human AhR/ARNT without treatment. Treatment with TCDD resulted in a 1. 79-fold activation of human AhR compared to the vehicle when there is no effect on AHR-1 (Figure  3 ). There is no strong activation in our system but it is well known that Cos-7 cells transfected with vertebrate AhR (human, fish, bird, etc.
Identification of AHR-1 Positive and Negative Modulators To guide the choice of molecules used in the screening test, we firstly chose to test some of the human AhR agonists or antagonists such as environmental, dietary, or endogenous compounds. We also tested bacterial compounds or virulence factors which are part of the natural environment of C. elegans (and are human AhR ligands). First, we verified that such molecules did not exert any toxicity in our model. Two cell viability assays have been carried out: Alamar Blue and Neutral Red (Supplementary Data Table  S1 ), for each molecule identified subsequently as a modulator of AHR-1. A PI/Hoechst assay was performed for pyocyanin especially, which interferes with the other.
Identification of AHR-1 Positive and Negative Modulators To guide the choice of molecules used in the screening test, we firstly chose to test some of the human AhR agonists or antagonists such as environmental, dietary, or endogenous compounds. We also tested bacterial compounds or virulence factors which are part of the natural environment of C. elegans (and are human AhR ligands). First, we verified that such molecules did not exert any toxicity in our model. Two cell viability assays have been carried out: Alamar Blue and Neutral Red (Supplementary Data Table  S1 ), for each molecule identified subsequently as a modulator of AHR-1. A PI/Hoechst assay was performed for pyocyanin especially, which interferes with the other assays, by its oxidative properties. Second, we performed an inhibitory enzymatic assay of the Firefly luciferase (Supplementary Data Table  S2 ) to confirm that the resulting bioluminescence is related to an activation of AHR-1 and not a direct effect of the molecules tested on the enzyme (because some chemicals identified as AhR ligands, such as β-naphthoflavone or resveratrol, also inhibit luciferases, and this effect is often neglected)  [44, 45].
Acknowledgments: We thank  Sophie Coudouel ,  Hervé Delacroix ,  Jean Laurent Ichanté  and the  DNA Microarray Platform (GODMAP, CNRS ;  Université Paris-Sud 11, Gif-sur-Yvette F-91198, France ) for C. elegans microarray analysis. We thank  Anne Louise  and the  Cytometry Platform  ( Pasteur Institute, Paris, France ) for C. elegans FACS sorting experiments. Graphical abstract created with BioRender. com(accessed on 9 March 2022). N. V. acknowledges funding from the  Deutsche Forschungsgemeinschaft  ( VE366/8-1 ). V. B. was supported by a Ph. D. scholarship from the  Jürgen Manchot Foundation.
We present the results of this intercomparison in an effort to demonstrate the quality of these individual datasets and of the overall trace gas monitoring activities during the expedition. In addition, we provide merged datasets (which combine redundant individual datasets and limit gaps in time series) for further use by the community. As summarized in Fig. 1 , trace gas measurements described hereafter were performed on-board Polarstern in three distinct sea-container laboratories, and on the sea ice itself from a 10-meter flux tower at Met City (meteorological station housing numerous atmospheric measurements located 300-600 m away from Polarstern 17 ). While the Atmospheric Radiation Measurement (ARM) Program and Swiss containers were located on the foredeck (D-deck) with sampling inlets pointing upwards (inlet height of approximately 18 and 15 m above sea level (asl) in the ARM and Swiss containers, respectively), the University of Colorado (CU) container was installed below deck in the forward cargo hold.
Note that instruments located in the CU container were connected to sampling inlets on the bow crane. Measurements included nitrous oxide (N 2 O), ozone (O 3 ), carbon monoxide (CO), carbon dioxide (CO 2 ), methane (CH 4 ), dimethylsulfide (DMS), selected volatile organic compounds (VOCs), gaseous elemental mercury (Hg(0)), and sulfur dioxide (SO 2 ). The post-cruise analysis of discrete whole air samples collected in background air (upwind from research activities) was performed at the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML). Note that in addition to continuous DMS measurements, discrete samples were also occasionally collected for independent DMS analysis in the CU container. In case of redundant measurements (e. g. , CO 2 ), the cross-evaluated individual datasets were used to generate a merged dataset in order to limit gaps in time series and facilitate further use by the community.
Photo credit: Jan Rohde. As described in detail in the instrument handbook  36  , the ARM instrument was checked twice a day for zero and span checks. Measurements during the zero measurement periods were used to calculate the instrument baseline with a 3-6 week moving average. This instrument baseline was then subtracted from the ambient air measurements. Measurements during zero and span checks were assessed for possible drifts. Note that measurements in the first 105 seconds after a zero and in the first 30 seconds after each span check were discarded. In addition, a linear calibration coefficient (determined from a five-point span check at the New York State Department of Environmental Conservation standards laboratory) was applied to O 3 values. This final, quality checked, minute-averaged O 3 dataset was used as reference to adjust O 3 mole fractions from the Swiss and CU instruments.
This is further discussed below in the sub-section "cross-evaluation of redundant measurements and merged datasets". Note that zero and flow rate checks were performed every 2 weeks in the Swiss container. Dimethylsulfide. Continuous DMS measurements were performed using an Atmospheric Pressure Ionization Mass Spectrometer with an Isotopically Labeled Standard (APIMS-ILS). The description of this custom-built instrument can be found in Appendix A of Blomquist et al. 37. Briefly, the APIMS-ILS monitors the DMS mole fraction of a dried sample air stream at 10 Hz for analysis of the eddy correlation turbulent DMS flux. The air sample was drawn from an inlet at the top of the Polarstern bow sampling tower (Fig. 1 ), adjacent to a sonic anemometer. A known concentration of isotopically labeled DMS (d3-DMS, mass 65) was continuously injected at the inlet tip.
Lastly, sampling was uninterrupted in the Swiss container. As a result, all datasets were carefully screened for local pollution during post processing as described hereafter. As described in Beck et al. 30  , local pollution typically leads to rapid fluctuations in measurements of many parameters. Local pollution in such a remote environment can often be detected based on the time derivative of the ambient air mole fraction. For each data point, the time derivative was calculated. Data points corresponding to an abnormally high derivative (>1. 5 times the interquartile range) and neighboring points were discarded. The function "despike" from R package oce 61 (version 1. 3-0) was then applied to the time-series to remove any remaining local pollution spikes. Briefly, this function first linearly interpolates across any gaps (missing values).
Then, it calculates a running median spanning k elements. The result of these two steps is the "reference" time-series. The standard deviation of the difference between values and the reference is then calculated. Values that differ from the reference by more than n times this standard deviation are considered to be spikes and eliminated. The function was applied once with n = 1 (n = 3 for CO  62  ) and k = 61 (~1 hour). This procedure was applied to all datasets, unless mentioned otherwise below. As the time derivative method is better suited for primary pollutants  30  , O 3 time-series were cleaned from local pollution influence using the above-mentioned "despike" function only. The function was applied twice     9. List of attributes in the files originating from the CU container or Met City, and archived on the Arctic Data Center.