dataesr/scientific-paragraphs-categorization huggingface dataset:
Clustering of subpopulations The obtained dissimilarity matrices of genetic distances were then subjected to the clustering procedure developed by  Rodriguez and Laio (2014)  to delineate conservation units (CUs) at both scales. Based on the automatic identification of local density peaks, this method allows the detection of clusters and outliers based on the distance between data points. It is similar to density-based algorithms such as DBSCAN  (Ester et al. 1996) , however it delineates clusters without introducing a noise-signal cutoff, thus decreasing the probability of low-density clusters being classified as noise  (Rodriguez and Laio 2014). The only variable parameter d c (cutoff distance) was fixed so that the average number of neighbors represents 5% of the total number of points in the dataset  (Rodriguez and Laio 2014, Du et al.
2017). A framework that incorporates IBR models and direct estimates of migration rates based on coalescent simulations would be a significant step forward in seascape genetics, potentially enabling the estimation of much higher migration rates than F ST -based methods, while taking into account the demographic history of all populations. There are however potential issues to consider. As  Momigliano et al. (2021)  recently demonstrated, unaccounted demographic events may cause strong biases in parameter estimation, although migration rates are among the least affected demographic parameters. During our model optimization, we used F ST as the sole metric of genetic differentiation. We could have used other measures such as Jost's D estimator  (Jost et al. 2018 ), but we found that F ST and Jost's D were highly correlated in our dataset, especially as we did not include samples from Chagos in the IBR framework (R 2 = 0.
Acknowledgements -We thank the crew of the  Research Vessel Amborella (Government of New Caledonia) , the many volunteers that contributed to field trips in New Caledonia, and  Laurent Millet  of the genetic lab at  IRD Nouméa  for help in the processing of samples. We appreciate the support of  Pierre-Edouard Guerin  ( CEFE/CNRS ) for SNPs filtering and of  Jérôme Lefèvre  (ENTROPIE) and Eric Mermet (EHESS) on map computation.
Funding -Sampling in New Caledonia was funded by research grants of  Laurent Vigliola  ( Programme APEX ,  Government of New Caledonia, Pew Charitable Trust ,  Total Foundation , IRD core funding), and  David Mouillot  ( Programme APEX ,  Total Foundation ). Data collection in other countries was supported by the  Great Barrier Reef Marine Park Authority, Cooperative Research Centre (CRC) Reef ,  Australian Coral Reef Society and PADI Aware. PM was funded by the  Sea World Research and Rescue Foundation , the  Academy of Finland  (grant no. 316294 ), the  Spanish Agencia Estatal de Investigación  ( JC2020-042611-I/ MCIN/AEI /10. 13039/501100011033 ) and the  European Union  ( NextGenerationEU/PRTR ). 12 Conflict of interest -The authors declare no conflict of interest. Ethics statement -Genetic samples in New Caledonia were collected under permits to  Laurent Vigliola  by the  Government of New Caledonia  (permit no.
Data availability statement All raw sequence data from New Caledonia are available from the Zenodo Digital Repository (<https://doi. org/10. 5281/ zenodo. 5935133>) and GenBank Sequence Read Archive (Bioproject PRJNA795958). All SNP data are also available in public repositories (Zenodo for data from New Caledonia, <https://doi. org/10. 5281/zenodo. 5943789>; Dryad Digital Repository for other locations, <https://doi. org/10. 5061/ dryad. mt2m3>).
The study is reported in accordance with ARRIVE guidelines. Participants and apparatus. The data provided by 24 Guinea baboons (Papio papio, 17 females; age range: 2-24 years) from the CNRS primate facility (Rousset-sur-Arc, France) were analyzed. The participants were tested using ten automatic computerized learning devices for monkeys  27  that were freely accessible from the baboons' living enclosures. Each test system comprised a touch screen, a food dispenser, and an automated radio frequency identification (RFID tag) of the participants. This allows us to test the individuals without capturing them, which improves animal welfare in experimental research  33. The experiment was made available to all 25 animals in our facility, who participated in the study on a voluntary basis. Of the 25 participants, 1 failed to complete the first condition they were assigned to (see 'Procedure and learning criteria' below).
6 in positive trials and 0. 5 in negative trials. Participants' performance in neutral trials is 0. 55. The iconicity indexes for positive trials is thus 0. 6 -0. 55 = 0. 05 and 0. 5 -0. 55 = -0. 05 for negative trials. That is, the participant was more likely than chance to choose an iconic response on positive trials (iconicity index greater than 0) but less likely than chance to do so in negative trials (iconicity index less than 0). We modeled such an iconicity index using a mixed model specified as: using the lme4 package  35  in R. Because the model was singular, we built on recent advances in Bayesian statistics (using the brms package in R  36  ) to model our by-participant estimates  37. We tested whether the intercept for each level of trialType (positive; negative) was significant.
Method. Data availability. The pre-registration, the data, and the script for the analysis in this paper are available online https:// doi. org/ 10. 17605/ OSF. IO/ C43GW. Participants. 6 Guinea baboons (Papio papio, 4 females; age range: 4-11 years) participated in the study. An additional 18 animals participated in the study but were not included in the analysis because either they failed to complete the experiment ( n = 6 ), or they were part of a second group of participants ( n = 12 ) for whom the stages were presented in reverse order (stages B followed by stages A; see Task and conditions) that underwent a technical issue (no phase (i) was presented, see Task and conditions below, and there were implementation errors in the learning criteria). Note that despite this technical error, the final sample size remains comparable to other primate cognition studies  40.
The samples with fewer than 10,000 final valid sequences were removed from the statistical analysis. Clusters were affiliated to Operational Taxonomic Units (OTUs) using the silva 138. 1_16S reference database and a taxonomic multi-affiliation procedure (Blast+with equal multi-hits that were manually verified and corrected if necessary). OTUs were agglomerated to examine differential abundances in response to maternal diet at the OTU and the family rank. Community profiles (including α and β diversity) and differentially significant features were further analyzed using the R packages Phyloseq (v1. 34. 0) and DESeq2 (v1. 30. 1) respectively. Within sample community richness was assessed by the Chao-1 index and examined by two-way ANOVA analysis. Divergence of microbiota composition between samples was explored using the Unifrac distance matrices, visualized using multidimensional scaling (MDS) and assessed statistically using permutational multivariate analysis of variance (Adonis test with 9999 permutations).
Profiling of fecal reactive aldehydes through liquid chromatographymass spectrometry Free reactive aldehydes in fecal supernatants were stabilized and selectively labelled using a bromine atom according to Chevolleau et al. [43]. Briefly, derivatized aldehydes were selectively detected on the basis of their 79 Br/ 81 Br isotopic pattern through high performance liquid chromatography coupled to with high resolution mass spectrometry (HPLC-HRMS). Identification experiments were then realized via tandem mass spectrometry (MS/MS) using the iron trap mass analyzer of the LTQ-Orbitrap XL mass spectrometer. Features corresponding to brominated compounds were extracted (XCMS software) and structural characterization of potential aldehydes was carried out by processing MS n spectra using Xcalibur QualBrowser (Thermo Scientific).
Multi-omics analyses Correlation structure between omics data sets was explored using the R package mixOmics DIABLO framework (Data Integration Analysis for Biomarker discovery using Latent cOmponents, v6. 15. 45)  [50, 51]. This integrative multivariate method is able to maximize correlations between different data-types and perform discriminant analysis with the aim of identifying a signature specific to the male offspring maternally exposed to heme iron. Only mice for whom both physiological traits (metadata without data associated with glucose challenge test), 16S rRNA gene amplicon data and aldehydomics data were available at PND60, were included in this framework (12 females Control, 10 females Maternal Heme, 11 males Control, 10 males Maternal Heme). Prior to fitting multi-block DIABLO model, 16S rRNA gene amplicon and aldehydomics datasets were preprocessed according to the mixMC framework and log-ratio transformed respectively  [52] , and independent PLS-DAs were built.
5A ). In order to directly target both putative reactive aldehydes and bacterial taxa specifically associated with the leaky gut phenotype identified in males from heme iron-enriched fed mothers, a multiblock supervised model allowing datasets integration was built  [50]. In contrast to aldehydomic data structuration, PLS-DA applied to microbiota dataset showed a separation of mice primarily by maternal diet and then by sex (Supplemental Fig. 5B ), but connections between datasets were clearly found (Supplemental Fig. 5D ) and allowed to capture signatures describing key features relevant to each mice group (Fig. 6A ): One signature outlined the sex-associated features including mainly putative reactive unidentified aldehydes that were under-expressed in males and negatively correlated with body weight (Fig.
Using databases, 2 out of the 3 brominated compounds that correlated negatively with gut hyperpermeability were putatively annotated (namely cervonoyl ethanolamide and nutriacholic acid), but none of the other 5 candidates that could potentially explain the increase in fecal TBARS, corresponded to the previously identified lipoperoxidation products resulting from direct dietary heme iron uptake  [43] , suggesting a profile specific to the maternal exposure.
Acknowledgments The authors thank  Xavier Blanc  ( UE 1298 ,  Sciences de l'Animal & de l'Aliment, INRAE)  for providing custom experimental diets and all members of the  EZOP (Animal facility ) for assistance with the animal experimentation. The authors thank the  Get-PlaGe platform (Toulouse)  for 16S rRNA gene libraries and sequencing and to the  Genotoul bioinformatics platform Toulouse Occitanie and Sigenae group  for providing help and storage resources thanks to Galaxy instance (https://gala xy-workbench. toulouse. inra. fr). The authors are grateful to  Alyssa Bouville ,  Carla Orlandi  and  Jean-François Martin  from the  Metatoul-AXIOM analytical platform (Metabolomics and Toxicology)  for their careful help with this project. All MS experiments were performed on the instruments of the MetaToul-AXIOM platform, partner of the national infrastructure of metabolomics and fluxomics:  MetaboHUB [MetaboHUB- ANR-11-INBS-0010 , 2011].
Participants were asked reasons for missing ART at least once in the past 30 days, or ever switching ART. Data were also collected on various ART-related concerns, comorbidities, and perceived current treatment priorities. The last was assessed among those who had been diagnosed for at least one year and was measured as follows: 'Imagine that you were starting HIV treatment today, other than ensuring that it is effective, what would be your most important considerations?'. Response options were: 'To ensure that the virus was suppressed enough so that I could not pass it on to a partner'; 'To ensure side effects would be minimal'; 'To ensure it was compatible with other medications/drugs/pills I am taking'; 'The cost of the medication'; 'To keep the number of HIV medicines in my treatment to a minimum'; 'To minimize the long-term impact of HIV treatment'; 'To allow flexibility as to when I have to take the HIV medication (time of day, with or without food, etc.
Exploratory multivariable logistic regression analyses were performed to assess factors associated with past DDI experience using data from the Unmet Needs Study. Because of high polychoric correlation (>0. 3) between several independent variables, separate logistic regression models were fitted for each characteristic, adjusting for country of residence, age, gender, and sexual orientation. In the Positive Perspectives Study, adjusted odds ratios (AOR) were calculated to measure the relationship between polypharmacy and various ART-related concerns, switching patterns, and reasons for missing ART, adjusting for age, gender, country, and presence of comorbidities. Statistical significance was set at p<0. 05. All analyses were conducted using SAS, Cary, NC, v9. 4 and R v3. 4.
Strengths and limitations The strength of this study is exploring both PLHIV and HCP perspectives regarding barriers to the HIV care cascade in four countries that together account for the highest HIV burden in Western Europe 31. Self-reported HIV diagnosis was followed by a confirmed ascertainment of HIV status for all PLHIV. Nonetheless, limitations exist. First, the HCP and PLHIV data in each country may not be directly comparable as the institutions from which the HCPs were mostly sampled from may not necessarily reflect places where sampled PLHIV routinely access care. Second, these are cross-sectional analyses and only associations can be drawn. Third, neither the HCP nor the PLHIV data may be fully representative of the respective countries or the region, because of the non-probabilistic sampling.
In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300-m resolution indicate the percentage cover (0-100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e. g. , re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models -ORCHIDEE and JULES -to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks.
Thus, in this paper, the term "plant functional type" is applied even to the abiotic surface types to cleanly differentiate between the land types derived from Earth observation data (i. e. , land cover classes) and the land types required by models (i. e. , PFTs). Finally, these new PFT maps have been used in two land surface models (ORCHIDEE and JULES) to demonstrate their benefit over the conventional maps based on a generic cross-walking table. For brevity, the new PFT product is referred to as "PFTlocal" due to the new localised nature of the PFT fractions at the pixel level. Products derived by using the global cross-walking approach (using the same version 2. 0. 8 of the CCI MRLC map series) are referred to as "PFTglobal. " The following sections describe the auxiliary inputs and method used to quantitatively determine the PFT fractional composition for each 300 m pixel globally; a description of the new PFT data product; and modelling results from the application of the new PFT distribution for the year 2010 to the ORCHIDEE and JULES land surface models.
Methods The PFT distribution was created by combining auxiliary data products with the CCI MRLC map series. The land cover classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and https://doi. org/10. 5194/essd-2022-296 Preprint. Discussion started: 16 September 2022 c Author(s) 2022. CC BY 4. 0 License. cultivated vegetation. The applied auxiliary data products (described in Sect. 2. 1; e. g. , surface water cover and tree cover) are of higher resolution than the 300 m land cover product and therefore serve as the basis for computing the fractional covers of the contributing PFTs at 300 m resolution.
Surface water product (30 m) The Landsat-based surface water product developed by the Joint Research Centre  (Pekel et al. , 2016)  is used to derive the permanent inland water fractions at 300 m resolution (calculation details in Sect. 2. 2). The surface water occurrence layer (obtained at https://global-surface-water. appspot. com) indicates the frequency of water occurrence in each 30 m pixel (80°N-60°S) over the period March 1984 to December 2019. The frequency occurrence data is reported as integer values of 1-100 %, where a value of 100 % occurrence indicates a permanent water surface that existed over the entire analysis period, which encompasses all but the most recent year (2020) of the time series of the MRLC product. https://doi. org/10. 5194/essd-2022-296 Preprint. Discussion started: 16 September 2022 c Author(s) 2022.
The permanent snow and ice class (code 220) is assigned as 100 % snow and ice PFT. All other classes are assigned as 0 % snow and ice PFT. Nearly all pixels classified as permanent snow and ice class in the CCI MRLC time series are static pixels; that is, such pixels are snow and ice cover for every year of the land cover map series. This is due to a lack of temporally resolved input data available at the global scale to track the evolution of this surface type. Therefore, neither the CCI MRLC classification nor the associated PFT product should be used to track changes in glaciers over time. For all pixelsof any classthat have a non-zero tree fraction, the total tree fraction is assigned as a single tree type (broadleaved or needleleaved leaf type, deciduous or evergreen phenology). For the tree cover classes coded 50-82, the specific tree type follows the class legend.
The morphometric characteristics of the network were assessed by manually encircling each core and estimating their radius from the selected area. Analysis of 457 cores in 9 different cells yielded a median radius of 114 nm (Figure  2D ). Furthermore, applying Delaunay's tessellation to each dataset allowed for the characterization of the average inter-core distances within the same cell. Direct neighbor pairs were 705 nm apart (Figure  2E ), and first neighbors were 443 nm apart (Figure  2F ), similarly to what was described previously  28. F-actin distribution in human osteoclasts unroofed on bone was then observed by a new superresolution method called random illumination microscopy (RIM). RIM consists in illuminating a sample with a series of random speckles and processing the stack of images using signal processing and statistical tools, to gather a lateral and axial resolution of 100 and 300 nm, respectively.
Analysis of DONALD 3D super-resolution images The super-localization of molecules and drift correction were performed on raw images via NEO Software (Abbelight, France) to achieve a final pixel size of 15 nm. Images were grouped in batches accounting for 5% of the total frames, and were submitted to drift correction by comparison batch per batch and according to a sliding window. Eventually, z correction was performed thanks to a dedicated Python script, to account for the calibration value of the SAFe module. The spatial organization of proteins within the sealing zone like was characterized following an adapted version of the algorithm described previously  27. Briefly, actin cores in-plane coordinates (x core ,y core ) were determined from actin epifluorescence images from the 488 nm channel after Gaussian-filtering.
The angle between the podosome belt portion and the horizontal was measured by the user. Then, centered on each core, a rectangle bounding box of length 10 µm and width 200 nm was drawn and the 3D coordinates (x i , y i , z i ) of the molecules inside this box were converted to a local r-z space (r i , z i ). This operation was repeated twice for each core: once along the structure direction, and once in the transversal direction. This analysis was performed for all cores of a given cell. To further the analysis of the spatial organization of molecules within the podosome belt, a dedicated Python script was written to extract statistical information. Each cross-section direction for each cell was treated independently, except for distributions along r-axis and along z-axis for which all molecules detected for all cells were considered.
Analysis of SEM images Actin cores were manually encircled using ImageJ oval selection tool, then the area and its center coordinates of these selected regions were extracted. A dedicated Python script was written to extract statistical information. The inter-core distances were computed using Delaunay tessellation on each image, using SciPy spatial algorithm "Delaunay" in order to create the interconnection matrix, then the distance between each pair of connected vertices was computed and stored in a matrix. In order to avoid plausible errors, the edges of the Delaunay tessellated space were excluded of the distance computations. To identify them, the SciPy spatial algorithm "ConvexHull" was applied to the coordinates, and whenever two points were identified as pertaining to the convex envelope, the distance between them was not computed.
A piezoelectric Z stage (Z INZERT PIEZOCONCEPT) is used for fast z stack acquisition. For triggering, the camera sCMOS is used as master, and a rolling shutter output is used to trigger binary phase sequence to the SLM. The SLM output triggers the laser when binary phase mask is stable. A script from micromanager software was written to select the number of speckles used, the temporal resolution for each frame, the depth of z stack, the step of each z stack and the number of colors used for the acquisition. For stream recording, speckle frames were acquired continuously over the whole duration of the movie. Widefield time-lapse acquisitions were also carried out with the same microscope, but using a 60x objective (CFI PLAN APO LBDA 60XH 1. 4/0. 13 NIKON). Images were captured every 2 seconds, and were were restored with Huygens Software (classical maximum likelihood estimation with 30 iterations and theoretical PSF).
Analysis of RIM images of fixed samples Reconstruction of raw images was carried out as described in  29. Briefly, the method is based on decreasing the computational cost of the inversion method described in  36. This new method uses a variance matching process, instead of the marginal minimization methods based on the full covariance matrix of the data  29. The only input for the super-resolution reconstruction process is the knowledge of spatial statistics of speckle patterns limited by the OTF of the imaging system (Fourier transform of the PSF). Contrary to SIM, the exact knowledge of the illumination function is not necessary, and the protocol of the reconstruction is therefore drastically reduced. The inversion code is implemented on Matlab. The input are the excitation and collection PSF, generated with Gibson and Lanny 3D optical model implemented in the plugin PSF generator  37.