dataesr/scientific-paragraphs-categorization huggingface dataset:
Regressors of no interest (e. g. , 6 head movement parameters obtained during volume registration and AFNI's baseline estimates) were also included in the GLM. Data from all fifty-two participants were entered in a group whole brain analysis. Group whole brain contrasts were generated to quantify the neural responses across the experimental conditions. Scene-selective areas were defined using a contrast of dynamic scenes greater than dynamic objects, and object-selective areas were defined using a contrast of dynamic objects greater than scrambled objects, following convention  (Epstein and Kanwisher, 1998; Malach et al. , 1995). Activation maps were calculated using a t-statistical threshold of p = 0. 001 and a cluster correction of 50 contiguous voxels (as these thresholds have been successfully used in other studies to characterise activation in the visual perception literature, e.
This data has been used in previous studies to examine the neural basis of memory and mind-wandering, including region-of-interest based connectivity analysis and cortical thickness investigations  (Evans et al. , 2020; Gonzalez Alam et al. , 2018 , 2021 , 2022 , 2019; Karapanagiotidis et al. , 2017; Poerio et al. , 2017; Turnbull et al. , 2018; Vatansever et al. , 2017; Wang et al. , 2018). Study 3: Pre-processing Pre-processing and statistical analyses of resting-state data were performed using the CONN functional connectivity toolbox V. 20a (http://www. nitrc. org/projects/conn; Whitfield-Gabrieli and Nieto-Castanon, 2012) implemented through SPM (Version 12. 0) and MATLAB (Version 19a). For pre-processing, functional volumes were slice-time (bottom-up, interleaved) and motion-corrected, skull-stripped and co-registered to the high-resolution structural image, spatially normalized to the Montreal Neurological Institute (MNI) space using the unified-segmentation algorithm, smoothed with a 6 mm FWHM Gaussian kernel, and band-passed filtered (.
Cognitive Decoding Connectivity maps were uploaded to Neurovault  (Gorgolewski et al. , 2015  https://neurovault. org/collections/13821/) and decoded using Neurosynth  (Yarkoni et al. , 2011). Neurosynth is an automated meta-analysis tool that uses text-mining approaches to extract terms from neuroimaging articles that typically co-occur with specific peak coordinates of activation. It can be used to generate a set of terms frequently associated with a spatial map. The results of cognitive decoding were rendered as word clouds using in-house scripts implemented in R. We excluded terms referring to neuroanatomy (e. g. , "inferior" or "sulcus"), as well as the second occurrence of repeated terms (e. g. , "semantic" and "semantics"). The size of each word in the word cloud relates to the frequency of that term across studies.
DTI pre-processing: We used data from a subgroup of 164 HCP participants who underwent diffusion-weighted imaging at 3 Tesla  (Uǧurbil et al. , 2013  http://www. humanconnectome. org/study/hcp-young-adult/). The imaging parameters were previously described in  Uǧurbil et al. 2013, and involved acquiring 111  near-axial slices with an acceleration factor of 32, an isotropic resolution of 1. 25 mm3, and coverage of the entire head. The diffusion-weighted images were obtained using 90 uniformly distributed gradients in multiple Q-space shells  (Caruyer et al. , 2013) , and this process was repeated three times with different b-values and phase-encoding directions. We used a pre-processed version of this dataset, previously described  (Karolis et al. , 2019; Thiebaut de Schotten et al. , 2020; Vu et al.
, 2015) , that included steps to correct for susceptibility-induced off-resonance field, motion, and geometrical distortion. We used StarTrack software (https://www. mr-startrack. com) to perform whole-brain deterministic tractography in the native DWI space. We applied an algorithm for spherical deconvolutions (damped Richardson-Lucy), with a fixed fibre response corresponding to a shape factor of α = 1. 5 × 10-3 mm2. s-1 and a geometric damping parameter of 8. We ran 200 algorithm iterations. The absolute threshold was set at three times the spherical fibre orientation distribution (FOD) of a grey matter isotropic voxel, and the relative threshold set at 8% of the maximum amplitude of the FOD (Thiebaut de  Schotten et al. , 2011). To perform the whole-brain streamline tractography, we used a modified Euler algorithm  (Dell'Acqua et al.
, 2013)  with an angle threshold of 45°, a step size of 0. 625 mm, and a minimum streamline length of 15 mm. To standardize the structural connectome data, we followed these steps: first, we converted the whole-brain streamline tractography into streamline density volumes, with the intensity corresponding to the number of streamlines crossing each voxel. Second, we generated a studyspecific template of streamline density volumes using the Greedy symmetric diffeomorphic normalization pipeline provided by ANTs. This average template was created for all subjects. Third, we co-registered the template with a standard 1mm MNI152 template using the FLIRT tool in FSL to produce a streamline density template in the MNI152 space. Finally, we registered individual streamline density volumes to the template and applied the same transformation to the individual whole-brain streamline tractography using ANTs GreedySyn and the Trackmath tool in the Tract Querier software package  (Wassermann et al.
ROI-based ANOVA analyses ROI-based analyses of activation and intrinsic connectivity in Studies 1 and 3 were performed using FSL's "Featquery" tool for Study 1 and REX for Study 3, which we used to extract the percentage signal change within unweighted, binarised masks. The ANOVAs were carried out using IBM SPSS Statistics version 27. The results of post-hoc tests to interpret significant interactions were corrected for multiple comparisons using the Holm-Bonferroni method  (Aickin and Gensler, 1996). All the p values reported in the Results section are Holm-Bonferroni adjusted p values.
Data Availability: The scripts used in the presentation of the task, the analysis of the neuroimaging data and the visualisation of the results reported here can be consulted in the OSF collection associated with this paper (https://osf. io/sh79m/). We do not have sufficient consent for the public release of individual-level data; researchers wanting access to these data should contact the Research Ethics Committee of the York Neuroimaging Centre (rec-submission@ynic. york. ac. uk). Data will be released when this is possible under the terms of the UK and EU General Data Protection Regulations. Group-level brain maps used to produce the figures are available from the following Neurovault collection: https://neurovault. org/collections/13821/.
The error bars in the bar plots depict the standard error of the mean (Note: ***p<. 001, * p <. 05); the ROIs are shown in supplementary FigureS3. Panel e: Brain maps depicting the seeds and intrinsic connectivity results for the semantic and spatial context decision regions. Panel f: Word clouds depicting the cognitive decoding of unthresholded connectivity maps for semantic and spatial context decision seeds using Neurosynth (bigger words reflect stronger correlation of the functional maps with the terms); the colour-code follows that of the brain maps. Panel g: Brain maps showing the overlap of these intrinsic connectivity maps with the visual network from the 7-network parcellation from Yeo et al. (2011).
) and MRIcroGL (Figure 4). Maps are provided in the following Neurovault collection: https://neurovault. org/collections/13821/. Acknowledgements: This project was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Project ID: 771863 -FLEXSEM to EJ; Project ID: 818521 -DISCONNECTOME to MTS; Project ID: 866533 to DSM). Additionally, this work was conducted in the framework of the University of Bordeaux's IHU 'Precision & Global Vascular Brain Health Institute -VBHI', IdEx 'Investments for the Future' program RRI "IMPACT", which received financial support from the France 2030 program.
| Occurrence data We compiled a total of 10,720 occurrence records at 10-min resolution (~20 × 20 km) for all the Pliocene lineages of European vipers following the phylogenetic inference from  Freitas et al. (2020). Occurrence data were gathered from existing bibliography (Appendix S1), online repository (GBIF, 2020, www. gbif. org), museum collections and fieldwork campaigns carried out by the authors and collaborators. Due to their restricted distributions (see Figure  1 ) and low sample sizes (n < 2), three lineages (i. e. V. anatolica, V. monticola W-Atl and Vipera sakoi) were not further considered. To reduce spatial biases of occurrence data in ENM  (Merow et al. , 2013; Sillero et al. , 2021) , we spatially rarefied occurrence records per lineage considering the climatic heterogeneity of the whole distribution area of the genus (Appendix S2.
, 2007). Model parameterization was optimized for each lineage  (Merow et al. , 2013)  using the ENMeval R package  (Muscarella et al. , 2014) , which considered a combination of different 'feature class' and 'regularization multiplier' parameters, and models were evaluated using Akaike's information criterion (Table  S2. 3). Model replicates were developed in a training area under current conditions and then projected to the entire study area for current and past climatic conditions (LGM and LIG). All model replicates and respective projections were used to derive average models and projections. Average projections were assessed using standard deviation maps, and the environmental similarity of projecting variables in relation to training variables was assessed through multivariate environmental similarity surface analyses  (Elith et al.
| Phylogenetic signal analyses To test whether the climatic niche varied across lineages in accordance with their phylogenetic relatedness, we measured the strength of phylogenetic signal for each climatic variable considered. Specifically, we estimated Blomberg's K  (Blomberg et al. , 2003)  and evaluated its significance using a randomization procedure, as implemented in the phylosig function of the phytools R-package  (Revell, 2012 ). Blomberg's K has an expected value of 1 under a Brownian motion (BM) model of evolution, and it ranges from 0 to ∞. Values of K lower or higher than one imply that closely related species are, respectively, more different or more similar than expected under BM  (Blomberg et al. , 2003). A multivariate phylogenetic signal test, based on 1000 random permutations using the physignal function of the geomorph R-package  (Adams, 2014) , was also performed on the combined dataset including all environmental variables together.
| Ecological niche models All models exhibited high values of AUC for both training (avg. AUC > 0. 79) and test datasets (avg. AUC > 0. 75), and low standard deviations (avg. SD < 0. 03), indicating high model accuracy (Table  S2. 3). Overall, the probabilistic models predicted suitable areas consistent with the known distribution of each lineage (Appendix S3). Temperature annual range (TAR), precipitation of wettest month (PWM) and precipitation of driest quarter (PDQ) were the most important variables related to the distribution of most of the lineages, whereas mean temperature of coldest quarter (TCQ), mean temperature of warmest quarter (TWQ) and precipitation of warmest quarter (PWQ) resulted to be important only for some lineages (Table  S4. 1). Overall, TAR and PDQ were more important for most of the lineages of the Pelias clade, while PWM was more important for most of the lineages of Vipera 1 and Vipera 2 clades (Figure  2 ; Table  S4.
8) [50]. Briefly, raw sequences were denoised using the DADA2 pipeline, and sequences containing ambiguous bases, chloroplast, mitochondria, chimeras, and low-abundance sequences (quality score < 20) were removed  [51]. The obtained amplicon sequence variant (ASV) table summarized by the representative sequences was used for downstream analyses. The normalized sequences were taxonomically assigned with the SILVA 138 database  [52, 53].
Metagenomic and binning analyses Based on amplicon analysis, six BACs from B 1 and B 2 were selected for metagenomic analysis to investigate the metabolic potentials of the microbiota (Table  S2 ). Metagenomic libraries were constructed and sequenced using an Illumina NovaSeq 6000 platform (Table  S3 ). A total of 143,183,979,090 raw reads were generated from the six metagenomes. The paired-end raw data were filtered using Trimmomatic (v 0. 36) tool  [54] , and 381,352,622 high-quality clean tags were obtained. The clean reads were then de novo assembled using MEGAHIT (v 1. 1. 2) software to trim any contigs shorter than 300 bp. QUAST (v 2. 3) software was then employed for quality assessment of the metagenome assemblies, and 7,928,870 assembled contigs were obtained  [55]. MetaGeneMark (v 3.
26) software was used for gene prediction with default parameters, and 11,340,914 genes were identified  [56]. The cd-hit (v 4. 6. 6) software was used to build a non-redundant gene set (5,834,387 genes) with thresholds of 95% similarity and 90% coverage  [57]. In addition, the metabat2 (v 2. 12) software was employed for contig binning with ≥ 90% bin completeness and ≤ 5% redundancy to clarify the metabolic potentials of microbiota in the BAC  [58]. After being assessed by CheckM (v 1. 0. 6) software, a total of 109 metagenome-assembled genomes (MAGs) with high quality were obtained for detailed downstream analysis (Table  S4 ). The relative abundance of MAGs was quantified using BWA software  [59]. The phylogenomic tree was constructed with reference to the method described in previous study  [60].
Phylogenetic analyses of the concatenated ribosomal protein genes were performed using Anvi'o (v 6. 1) software  [61]  based on the hidden Markov Model profile  [62]. The best-fit model and 1000 ultrafast bootstrap tests were implemented using the IQ-TREE (v 1. 6. 12) software  [63]  to construct maximum-likelihood trees. Phylogenetic trees constructed based on these MAGs were visualized using iTOL tool  [64]. The taxonomic annotation of MAGs was blasted against the GTDB database  [65]. Prodigal software (v 2. 6. 3) was used to predict the protein-coding regions  [66]. The protein-coding regions were annotated using a BLAST search against the NCBI nr database, KEGG server (BlastKOALA)  [67] , eggNOG-mapper (v 5. 0. 0) [68] , and InterProScan tool (v 5. 44-79. 0) [69]  to reconstruct the metabolic pathways of selected MAGs in the BAC.
Conservation analysis of miR-202/tead3b pairing geometry Orthologs for O. latipes tead3b ENSORLG00000006370 were identified in Teleostei using NCBI's "Homologene" database (https://www. ncbi. nlm. nih. gov/homologene) and the list was manually supplemented with a Holostei sequence (Lepisosteus oculatus tead3 ENSLOCG00000009736, identified as an ortholog of O. latipes tead3b by sequence homology and conserved synteny with the smpd2a/b gene). For each of these 96 species, the longest 3´ UTR isoform among RefSeq mRNA sequences was extracted, resulting in a list of 95 UTR sequences (for species Chanos chanos, the only annotated tead3b mRNA sequence contains an empty 3´ UTR). UTRs were aligned using ClustalW, and sequences orthologous to the O. latipes miR-202 binding site were extracted from that alignment.
Mature miR-202 sequence in each species was inferred from aligned mir-202 pre-miRNA hairpins found by a genome-wide homology search or from the FishmiRNA database  (24). Sequences orthologous to the O. latipes miR-202 binding site in tead3b, and species-specific mature miR-202 sequences, were then conceptually annealed using RNAduplex in order to identify their most stable pairing geometry.
Script and data availability Scripts, raw and intermediate data used for the identification of candidate targets exhibiting higher WT-to-KO variability than intra-WT variability, for the comparative analysis of miR-202/target site duplex stability, and for the conservation analysis of miR-202/tead3b pairing geometry, are available at https://github. com/HKeyHKey/Janati-Idrissi_et_al_2023 and at https://www. igh. cnrs. fr/en/research/departments/geneticsdevelopment/systemic-impact-of-small-regulatory-rnas#programmes-informatiques.
, significant wave height (SWH) and mean wave direction, etc. ) instead of surface wave spectra. Subsequently, the wave-induced Doppler shifts simulated by the original and simplified DPDop models are systematically validated with the collocated Sentinel-1B SAR WV mode observations. Finally, we retrieve the radial current speeds of the ocean surface from Sentinel-1B WV data and compare them with high-frequency (HF) radar measurements. The remainder of this article is organized as follows. Section II describes the data set. In Section III, we present the simulation and validation results of the original and simplified DPDop models. The conclusion is given in Section IV.
II. DATASET A. Sentinel-1 Data In this study, Sentinel-1B Level 2 ocean surface radial velocity (RVL) data are used to validate the original and simplified DPDop models. The RVL data are derived from 78 190 SAR global WV scenes from December 2017 to January 2018 [see Fig. 1(a)  and (b) ]. The WV mode alternately acquires data in 20 × 20 km vignettes with a 5 × 5 m spatial resolution every 100 km along the orbit, with two different incidence angles (24 • for WV1 and 37 • for WV2). In addition to RVL, the Sentinel-1B Level 2 data contain auxiliary information, including the collocated wind and wave fields from ECMWF and WW3, respectively. To obtain an accurate RVL, the Doppler shift due to the relative velocity of the SAR and rotating earth ( f geo dc ) and the Doppler shift induced by an antenna electronic mispointing ( f elec dc ) need to be estimated and subtracted from the SAR-measured Doppler shift.
B. HF Radar Data HF radars are shore-based microwave instruments and can measure OSCs over a large region (∼300 km) of the coastal ocean under most weather conditions. The HF radar transmits electromagnetic waves of 3-50 MHz (10-100 m wavelengths), which travel along the ocean surface beyond the horizon and are scattered from the surface waves of half radar wavelength. The Doppler shift of the backscattered signal is used to measure the radial current speed of the ocean surface  [23]. The accuracies of the HF radar current measurements typically range from 5 to 20 cm/s  [24] ,  [25] ,  [26]. In this study, we use hourly HF radar current measurements with a spatial resolution of 6 km to validate SAR-derived radial current speeds. These data are acquired in the east and west coastal areas of the USA and can be accessed on the HF Radar Network (HFRNet) website 1.
C. Validation of the Simplified DPDop Model The inputs of the simplified DPDop model are the wind fields and the wave characteristic parameters. Thus, we can use the collocated wind and wave data from ECMWF and WW3 to estimate the wave-induced Doppler shift and compare it with Sentinel-1B observations. Before the comparison, we assess the wind and wave data from ECMWF and WW3 using the collocated buoy observations. Fig. 6 (a) shows that the ECMWF wind speeds are in good agreement with the buoy observations when wind speeds range from 0 to 15 m/s. The bias and RMSE are -0. 31 and 1. 24 m/s, respectively. We also observed small deviations between the WW3 SWHs and buoy measurements, with a bias of 0. 07 m and an RMSE of 0. 38 m [see Fig. 6(b) ]. The range of SWH for the comparison is between 1 and 6 m.
IV. CONCLUSION The SAR-measured geophysical Doppler shifts from the ocean surface comprise various contributions from wind waves, swell, and currents. To derive reliable OSC information from SAR observations, wave-induced Doppler shifts must be precisely estimated and eliminated. Thus, a semiempirical model, termed the original DPDop, has been developed to calculate Doppler shifts associated with wind waves and swell  [15]. However, this model has not yet been comprehensively evaluated. In this study, we use the original DPDop model to simulate wave-induced Doppler shifts using buoy wind and wave spectra data and compare them with the collocated Sentinel-1B SAR WV mode observations. The bias and RMSE are -0. 24 and 5. 55 Hz, respectively. A key input of the original DPDop model is the surface wave spectra, which are not always available over the global ocean.
This work was supported in part by the  Joint Project  between the  National Science Foundation of China  under Grant  42061134016  and the  Russian Science Foundation  under Grant  21-47-00038 ; in part by the  National Science Foundation  under Grant  42076181 ; in part by the  China Scholarship Council (CSC)  Ph. D. Joint Training Program  under Grant  202008320521 ; in part by the  Government Research Initiative Program (GRIP)  of the  Canadian Space Agency ; in part by the  Ocean Frontier Institute of Dalhousie University ; and in part by the  Fisheries and Oceans Canada SWOT program , the  Ministry of Science and Education of the Russian Federation  under State Assignment  0763-2020-0005.
Methods: We performed a genome-wide Association Study comparing 543 HIV Elite Controllers with 3,272 uninfected controls of European descent. Using the latest database for imputation, we analyzed 35,552 Single Nucleotide Polymorphisms (SNPs) within the Major Histocompatibility Complex (MHC) region. Results: Our analysis identified 2,626 SNPs significantly associated (p<5. 10-8) with elite control of HIV-1 infection, including well-established MHC signals such as the rs2395029-G allele which tags HLA-B*57:01. A thorough investigation of SNPs in linkage disequilibrium with rs2395029 revealed an extensive haploblock spanning 1. 9 megabases in the MHC region tagging HLA-B*57:01, comprising 379 SNP alleles impacting 72 genes. This haploblock contains damaging variations in proteins like NOTCH4 and DXO and is also associated with a strong differential pattern of expression of multiple MHC genes such as HLA-B, MICB, and ZBTB12.
Leveraging the latest bioinformatics databases, we imputed 35,552 Single Nucleotide Polymorphisms (SNPs) within the MHC region, and identified 2,626 significant signals surpassing the genome-wide significance threshold  (5. 10-8). The MHC region spans approximately 5 million base pairs (genomic coordinates 28,477,797 to 33,448,354) on chromosome 6, according to the latest GENCODE gene annotation for the GRCh38 reference genome  (14). Within this region, a total of 373 protein-coding genes, 18 pseudogenes, and 12 non-coding RNA genes were identified by the GENCODE annotation. To translate these 2,626 SNP associations into function, we then conducted a comprehensive analysis of these positive signals with the goal of addressing the following key biological questions, namely, a. the reason for the massive genetic impact of the MHC region in AIDS, b.
e. uninfected subjects) to deal with the level of mRNA expression in the PBMCs of ECs. Supplementary Table  2A  presents the strongest differential expression patterns in PBMCs found for the 54 representative SNPs of the haploblock according to the GTEx database  (19). As shown in Supplementary Figure  1 , the ratio of the mean level of mRNA expression for the minor allele (which corresponds to the haploblock) over that of the major allele may reach extreme values under 0. 5 or higher than 3, demonstrating the high impact of the haploblock on mRNA -and thus protein-expression. Twenty-one genes for which the differential mRNA expression linked to the SNP alleles of the haploblock yield strongly significant p values according to GTEx, are presented in Figure  3A. Among the overexpressed mRNAs, we observe Mir6891 (p=10 -81 ), ZBTB12 (p=10 -64 ), NOTCH4 (p=10 -22 ), HLA-S (p=10 -34 ), HLA-B (p=10 -31 ), HLA-L (p=10 -31 ), and C4A (p=10 -22 ).