dataesr/scientific-paragraphs-categorization huggingface dataset:
GEDI The Global Ecosystem Dynamics Investigation (GEDI) is the latest operational full waveform (FW) spaceborne LiDAR system, and has been acquiring data since April 2019 on board the International Space Station (ISS). The GEDI system is equipped with three identical 1064 nm wavelength lasers. Two of the laser beams are in full power, while the other one is split into two beams, thus generating in total four beams. Through dithering of each laser beam, GEDI acquires data along eight ground tracks. The GEDI tracks are 600 m across, and 60 m along track, with a footprint diameter of 25 m  [48]. GEDI data are processed and made available by the Land Processes Distributed Active Archive Center (LP DAAC). First, the received waveforms are smoothed in order to reduce the noise in the signal. Waveform smoothing is performed by means of a Gaussian filter with various widths.
As mentioned in the ATBD (Algorithm Theoretical Basis Document), currently a width of 6. 5 ns was used for the Gaussian filter (smooth width). The processed data are issued by six configurations of algorithms (a1 to a6), representing different threshold and smoothing settings. In the case of water surfaces, the location of the water surface peak in the waveform is determined using a second Gaussian filtering. The width of the second Gaussian filter (Smoothwidth_Zcross) is fixed to either 3. 5 or 6. 5 ns. Over our studied areas, there are between 3 and 13 acquisitions per lake during the first six months of available GEDI data (Table  3 ), corresponding to the time period between mid-April 2019 and end of October 2019. GEDI measures vertical structures using a 1064 nm laser pulse, and the echoed waveforms are digitized to a maximum of 1246 bins with a vertical resolution of 1 ns (15 cm).
CHGeo2004 Geoid Model The national geoid model of Switzerland CHGeo2004 was obtained by combining data from three different methods: gravity, vertical deflections, and GPS/leveling. Its accuracy was found to be about 2-3 cm through comparisons against independent data  [53]. The CHGeo2004 geoid model is made available by the Federal Office of Topography as a grid of 1 km of spatial resolution at  [54].
Generating the Time-Series of Water Levels from GEDI Lidar Data Over water, unimodal waveforms are observed in general and only two different sets of algorithms produce different elevations. Sets a1 and a4 are similar, and sets a2, a3, a5, and a6 are similar. Fayad et al. [70]  showed that the parameters used in algorithm a1 (Smoothwidth_zcross of 6. 5 ns) provide more precise elevations in comparison to algorithm a2 (Smoothwidth_zcross of 3. 5 ns). Therefore, in this study, only the elevations produced from algorithms a1 were analyzed. As such, we extracted for each GEDI shot from the L2A data product the latitude and longitude, as well as the elevation of the lowest peak or mode (for water surfaces only one peak is available). Not all GEDI acquisitions are viable, as atmospheric conditions and clouds can affect them.
Levelling of the Different Water Level Datasets Given that gauge station elevations are provided with respect to the Swiss height measurement system (LN02), and the water levels derived from AlTiS are given with reference to EGM2008 geoid model  [68] , and the geolocated GEDI footprints are relative to the WGS84 ellipsoid, a conversion of the different altimetry datasets is necessary for a consistent analysis. Two steps are necessary to perform a conversion between WGS84 and LN02 elevations. First, ellipsoidal elevations of GEDI footprints are converted to orthometric elevations with respect to the new Swiss height system LHN95 (Landeshöhennetz 1995) using the following equation: H LNH95 = h wgs84 -N CHGEO2004 (3) where H LHN95 is the converted altimetry elevation with respect to LHN05, and N CHGEO2004 the Swiss gravimetric geoid heights.
Validation of Altimetry-Based Lake Water Levels The lake water levels derived from radar and lidar altimetry were validated through comparisons against in situ gauge records made available by FOEN. All data were converted to the same reference. To limit systematic error due to the distance between in situ and satellite measurements, the more accurate geoid model available over the area (i. e. , CHGeo2004) was chosen as a common reference. Bias (computed as the difference between the in situ and altimetry-based water stages), root mean square error (RMSE), relative RMSE defined as the ratio between the RMSE and the difference between the maximum and minimum value of the in situ data during the observation period, and Pearson correlation coefficient (R) were estimated between in situ and satellite measurements.
Results Altimetry time-series of water levels were generated over the following Swiss lakes: Geneva, Neufchâtel, Thunersee, Vierwaldsee, Zurichsee, Walensee, and Zugersee using data from ERS-2, ENVISAT, JASON-1/2/3, SARAL, and SENTINEL-3A and B. Due to the coarse coverage of current altimetry missions on the Earth surface, not every mission ground-tracks cross every lake. However, the lakes chosen in this study are all covered by a SENTINEL-3 ground-track. Information on the radar altimetry crossings over the Swiss lakes considered in this study are presented in Table  5. In contrast, GEDI ground-tracks crossed several times each lake between 20 April 2019 and 25 October 2019. Note that when a lake has several in situ stations (Saint Prex and Sécheron for Lake Geneva; Lucerne and Brunnen for Lake Lucerne in this study), the results for only one of them are presented.
Validation of Radar Altimetry-Based Water Levels When considering radar altimetry data prior to the ones acquired by JASON-3 and SENTINEL-3 A and B missions, a lot of lake crossings do not provide any valid data or too few to build continuous time-series of water levels over the Swiss lakes. On the contrary, for all SENTINEL-3 crossings, time-series of water levels were derived, as well as for the only Jason-3 crossing over the Geneva Lake (Table  3 ). For all the altimetry-based time-series of water levels, comparisons were performed against in situ data. The statistical results are reported in Figures  2  and 3. A wide range of cross-section lengths are considered in this study, ranging from 0. 8 to 12. 9 km (Table  5 ). Due to the orbit characteristics of the 10-day repeat orbit missions, only Geneva Lake is crossed by one ground-track from the JASON-1/2/3 missions.
If no valid observations from JASON-1 are available, it is frequently sampled by JASON-2 and -3 with observability greater than 78 and 88% respectively (Figure  2a ). Half of the lakes are crossed by one or two ground-tracks from the missions with a 35-day repeat orbit (Table  5 ). On the seven crossings, valid data were found for 3, 1, and 3 of them using ERS-2, ENVISAT, and SARAL, respectively. In these cases, the lake surface was observed ~70% of the time with ERS-2 and ENVISAT missions whereas using SARAL observations, this percentage ranges from 50% for Lucerne Lake, to ~60% and even over 85% over Geneva Lake (Figure  2a ). All the eight lakes are observed with the missions on the 27-day repeat orbit, with 9(3) crossings over 5(3) lakes for SENTINEL-3A(3B), respectively (Table  4 ). An excellent observability of the lakes is reached with these missions (>95% and even equals to 100% for SENTINEL-3B).
The results obtained using data from the missions operating in LRM acquisition mode are outperformed by the ones obtained by SENTINEL-3 operating in SAR acquisition mode and OL or DEM tracking mode. The observability is greater than 95% in the case of the nine cross-sections between a lake and a SENTINEL-3A ground-track and equals to 100% in the case of the three cross-sections between a lake and a SENTINEL-3B ground-track (Figure  2a ). The bias is almost constant (-0. 17 ± 0. 04) m over the 12 VS. RMSE is lower or equal to 0. 07 m and R higher than 0. 85 when the cross-section between the ground-track and the lake surfaces is longer than 1 km (Figure  3  and Table  5 ). It is, for instance, the case for SENTINEL-3A ground-track 0741 crossing Geneva Lake on a shorter distance than JASON ground-track 044 and closer to the shore.
Conclusions In this study, the performances of both RA and lidar missions were assessed over monitored lakes located in a mountainous environment. To do so, the recently developed AlTiS software was used as it is able to process the data from the different RA missions presented in the manuscript. It will be maintained and regularly updated to improve it, integrate GDR from other missions, and take into account suggestions from the users. For instance, it would be possible to read and process the ENVISAT (v3), Cryosat-2 (baselines C and D), and SENTINEL-6/Jason-CS GDR in the future versions of AlTiS. The presence of hills and mountains in the surrounding of lakes has a strong impact on the capability of RA to detect the lakes surfaces. Classical LRM RA missions operating in open loop mode at Kuband (i.
Acknowledgments: We thank  Michèle Oberhänsli  from  BAFU  for her help with the description of the water stage of the Swiss lakes. We also thank two anonymous Reviewers for their comments which helped us to improve the quality of the manuscript. AlTiS is an  Open Source  project under CeCill license (IDDN certification:  IDDN. FR. 010. 0121234. 000. R. X. 2020. 041. 30000-https://www. iddn. org/cgi-iddn/certificat. cgi?IDDN. FR. 010. 0121234. 000. R. X. 2020. 041. 30000 ,accessed on 3 June 2021).
A local genetic map could thus be established (Additional file 1: Table  S4 ). (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. To likely identify candidate genes belonging to novel highlighted Ocu1 and Ocu14 intervals, we analysed publicly available RNA-seq data extracted from skin of rabbits including a generic sample (accession number SAMN00013655), Rex black rabbit (accession number SAMN02693835), Rex white rabbit (accession number SAMN02693836) and Rex chinchilla rabbit (accession number SAMN02693834). Only 5 annotated genes (2 and 3 in Ocu1 and Ocu14 genomic regions, respectively) were quantified in skin (Fig. 2b ). It occurred that the three RPS pseudogenes (RPS14 -ENSOCUG00000026323, RPS27 -ENSOCUG00000024168 and RPS20 -ENSOCUG00000025838) looked like processed pseudogenes since they all carried both START and STOP codons in the OryCun2.
All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted October 9, 2021. ; https://doi. org/10. 1101/2021. 10. 07. 463146 doi: bioRxiv preprint 12 publicly available RNA-seq data obtained from skin including a generic sample, a Rex black, a Rex white and a Rex chinchilla rabbit highlighted quantification of messengers from both RPS14 and RPS20 processed pseudogenes. The study of Tonner et al. detected transcription of ribosomal protein pseudogenes in diverse human tissues from RNA-seq data  [51]. Unlike RPS genes that are constitutively expressed in almost all tissues, RPS pseudogenes are differentially expressed, suggesting that they may contribute to tissue-specific biological processes  [50]. Two studies carried in mice  [31]  and zebrafish  [53]  showed coloration defects when mutations in Rps20 and Rps14, respectively, were induced.
Genotyping data and quality control The DNA samples were genotyped at the Centro Nacional de Genotipado (CeGen) platform (Santiago de Compostela, Spain) using the Affymetrix® AxiomOrcun™ SNP Array as recommended by the manufacturer. The SNP array contains 199,692 molecular markers spanning both chromosomes and scaffolds. The order of the SNPs was based on the Rabbit OryCun2. 0 assembly released by the Broad Institute of MIT and Harvard  [81]. Missing data imputation and haplotype phasing were performed with the software FImpute  [82]. The SNP data were then filtered based on minor allele frequencies ≥ 0. 005 leading to a final SNP dataset of 162,070 markers for association analyses. The other standard filtering were not applied in primo-analyses to not eliminate markers that could pinpoint structural variants.
5 U of Taq polymerase and 12 μM for allele-specific primers and 30 μM for the common primer in a final volume of 5 μL. We followed provider recommendation for the PCR program, fluorescent reading was made on a Quant Studio 6 (Thermo Fisher Scientific, USA) and results were analysed with the software Quant studio Real Time PCR (Thermo Fisher Scientific, USA). Finally, genotyping using capillary electrophoresis were perfomed on a ABI3730™ (Applied Biosystems, USA). The PCR were (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted October 9, 2021. ; https://doi. org/10. 1101/2021. 10. 07. 463146 doi: bioRxiv preprint performed with 0. 1 mM of the extended primer, 0. 15 mM of the hybridization primer carrying the dye and 0.
15 mM of the reverse primer. The other conditions of PCR and cycle are similar to the RFLP PCR. PCR products were loaded on the ABI3730™ after a first step of 1/20 dilution and 2 µL of the dilution were mixed with formamide and size standard GeneScan-600Liz Size Standard (Applied Biosystems, USA) before a denaturation step at 94°C for 5 min. Analyses were perfomed with GeneMapper™ Software (Applied Biosystems, USA).
RNA-seq alignments Publicly available RNA-seq raw data from back skin of rabbits were uploaded to perform alignment, quantification and transcript discovery with statistics. Quality controls, alignments and analyses were performed with the open-source nf-core/rnaseq workflow (https://nf-co. re/rnaseq) using the 3. 0 version that implemented fastqc 0. 11. 9 and qualimap 2. 2. 2 for quality controls and STAR 2. 6. 1 for the mapping. Paired-end reads were aligned on the reference OryCun2. 0 genome and annotation version Oryctolagus_cuniculus. OryCun2. 0. 104. gtf was used for analyses.
Univariate linear mixed models for association analyses We used the GEMMA (Genome-wide Efficient Mixed Model Association) software to perform association analyses. Briefly, GEMMA fits a univariate linear mixed model (LMM)  [84]  or a Bayesian sparse linear mixed model using Markov chain Monte Carlo (BSLMM)  [85] ; both methods control for population structure. SNP effects were tested with the following univariate animal mixed model LMM  [84] :  =  +  +  +  with ~MVN  (0,  -1 ) and ~MVN  (0, τ -1   ) where  is the vector of phenotypes for a given trait,  is the incidence matrix of covariates corresponding to fixed effects and  stands for the effects of these covariates,  is the vector of allelic dosages of the genotypes (0, 1 or 2) and β stands for marker size effect,  is the random polygenic effect and  is the random residual effect.
Acknowledgements We thank all people of the animal facility, who carefully looked after the animals and for their help with the skin biopsies. We warmly thank  Laurence Drouilhet ,  Bertrand Servin  and  Alain Vignal  and all those who have contributed directly or indirectly to this work for discussion and support. We also thank both  Centro Nacional de Genotipado (CeGen)  (http://www. usc. es/cegen/) and GenoToul bioinformatics (http://bioinfo. genotoul. fr/)facilities.
Three Rex rabbits with black or white or chinchilla back skin were considered. Accession numbers for BioSample (https://www. ncbi. nlm. nih. gov/biosample/) are SAMN02693835, SAMN02693836 and SAMN02693834 for black, white and chinchilla, respectively. Accession number for raw data (https://trace. ncbi. nlm. nih. gov/Traces/sra/) are SRR1201255, SRR1201256 and SRR1201257 for black, white and chinchilla, respectively.
To this end, we processed all rs-fMRI data using the same pipeline in n=32,988 individuals. We performed 36 connectome-wide association studies (CWAS, Figure  1B ) on 1) 1,003 carriers of one among 7 neuropsychiatric and 9 non-psychiatric CNVs identified from 4 clinical cohorts and the UK Biobank; 2) 30,185 and 174 non-CNV carriers from the UK Biobank and clinical cohorts respectively, 3) 778 individuals with idiopathic ASD, SZ, BIP or ADHD from 4 datasets and their respective 848 controls (Table  1 ). The following data have never been published: 876 CNV carriers, all PGS, all brain morphometry traits, and two out of the four genetics-first cohorts.
The 'neuropsychiatric' cluster was significantly correlated with transcriptional modules involving the thalamus (ventral, dorsal, and thalamocortical), striatum, and neocortex, but negatively correlated with modules involving cerebellar cortex (M17, M24) (Figure  6B ). To further refine our analysis, we sought to predict which cell types could represent key drivers of positive correlation between AHBA transcriptomic modules and thalamic FC profiles. We took advantage of a multi-brain region single nucleus RNA sequencing dataset generated for the normal adult human cerebral cortex  (29) , which provides key cell type specific marker genes for glutamatergic (excitatory), GABAergic (inhibitory), and nonneuronal cells. Thalamic FC profiles of the neuropsychiatric cluster were significantly associated with marker genes of 4 glutamatergic cell types, oligodendrocytes, and somatostatin interneurons.
Sample We analyzed 32,988 individuals from nine datasets (Table  1 , Figure  1B , Supplementary Materials and Methods). CNVs were also identified in an unselected population (UK Biobank) (see Supplementary Materials and Methods for the CNV calling procedure and final sample description). The 9 non-psychiatric CNVs (light grey font) were defined as variants without any previous association with psychiatric conditions in large cases control studies  (24) (25) (26) 41).
Idiopathic psychiatric conditions and respective controls Individuals with idiopathic ASD, SZ, ADHD, BIP and their respective controls were sampled from 4 multicenter datasets  (42) (43) (44) (45)  (Supplementary Materials and Methods). Individuals with idiopathic ASD and their respective controls were sampled from the ABIDE1 multicenter dataset  (43). Individuals with idiopathic SZ and their respective controls were obtained from aggregated fMRI data of 10 studies, and from the UCLA Consortium for Neuropsychiatric Phenomics (CNP)  (42). Individuals diagnosed with ADHD (DSM-IV) and their respective controls were obtained from the ADHD-200 dataset  (44)  and the CNP. Individuals with idiopathic BIP and their respective controls were also sampled from the CNP dataset (Supplementary Materials and Methods).
Computing connectomes We segmented the brain into 64 functional seed-based regions defined by the multi-resolution MIST brain parcellation  (47). FC was computed as the temporal pairwise Pearson's correlation between the average time series of the 64 seed-based regions, and then Fisher-z transformed. The connectome of each individual encompassed 2,080 connectivity values: (63x64)/2 = 2016 region-to-region connectivity + 64 within seed-based region connectivity. We chose the 64 parcel atlas of the multi-resolution MIST parcellation as it falls within the range of network resolution previously identified to be maximally sensitive to FC alterations in neurodevelopmental disorders such as ASD  (48). We corrected for multiple comparisons using a false discovery rate strategy  (49). Statistical analyses were performed in Python using the scikit-learn library  (50).
Polygenic Scores via Bayesian regression and continuous shrinkage priors (PRS-CS) PRS-CS (51) was used to infer posterior effects using only those SNPs for individuals of European ancestry (EUR) from the UKBB dataset that were also present in both the discovery GWAS summary statistics and an external 1000 Genomes EUR-ancestry linkage disequilibrium (LD) panel. Posterior effects were computed using discovery GWAS for MDD  (52) , SZ  (53) , ASD (3), total cortical SA,(5) average CT,(5) inflammatory bowel disease (IBD),(54) Low-Density Lipoprotein (LDL),  (55)  and Chronic Kidney Disease (CKD) (56)   (Supplementary method). Note that the discovery GWAS used for IQ, MDD, and CKD included individuals from the UKBB. To ensure convergence of the underlying Gibbs sampler algorithm, we ran 25,000 Markov chain Monte Carlo iterations and designated the first 10,000 MCMC iterations as burn-in.
Concordance between functional, genetic, and transcriptomic correlation We computed correlations of whole-brain connectome profiles across pairs of conditions and traits (Pearson correlation) using the 2,080 beta values of each CWAS. We obtained genetic correlation (rG) values across pairs of conditions and traits (neuroticism  (61) , intelligence (6), cross-disorder (2), brain morphological traits (5)) from previously published GWAS and the database of genetic correlations across traits (ldsc. broadinstitute. org). We also obtained correlation values of transcriptomic profiles between 10 pairs of conditions  (28). We performed concordance analyses between correlation at the genetic (rG), and functional connectivity (rFC) levels as well as the transcriptomic (rT) and the FC (rFC) levels using DescTools R package  (58).
Cluster extraction and Principal Component analysis We defined 2 clusters using hierarchical clustering (hclust function from stats R package) on the 20 FC-profiles. To identify the FC networks driving the 'neuropsychiatric' cluster 1, we conducted a PCA on the 14 scaled FC-Profiles within cluster 1 using the prcomp function from stats R package. Functional connections with top decile loadings for principal components 1 and 2 (PC1, PC2) were represented on chord diagrams using the circlize R package (code available on Github).
AHBA co-expression modules and eigengenes We tested the association between gene co-expression modules and FC-profiles by computing spatial correlation (Pearson r) between expression patterns of eigengenes of co-expression modules and FC-profiles. We used eigengenes for the 18 major AHBA co-expression modules  (17). The significance of the spatial correlation was tested using label-shuffling (permutation test by performing similar contrasts in 5000 randomly sampled groups) and BrainSMASH  (64). K-means clustering was used for grouping co-expression modules and FC-profiles (ComplexHeatmap package in R, consensus k-means clusters with 1000 iterations are reported).
CNVs carriers and controls'Genetics-first' cohorts were recruited based on the presence of a CNV, regardless of symptomatology, four consortia (two out of four have never been published before): the Simons Variation in Individuals Project (VIP) consortium data (16p11. 2 and 1q21. 1 CNVs carriers) (40), the University of California, Los Angeles (22q11. 2 CNVs carriers), the Montreal rare genomic disorder family project (MRG, CHU Sainte-Justine, Montreal, Canada), and the Define neuropsychiatric-CNVs Project (Cardiff, UK) (see Supplementary Materials and Methods for individual dataset descriptions).
Data and materials availability Data from UK Biobank was downloaded under the application 40980, and can be accessed via their standard data access procedure (see http://www. ukbiobank. ac. uk/register-apply). UK Biobank CNVs were called using the pipeline developed in Jacquemont Lab, and described in https://github. com/labjacquemont/MIND-GENESPARALLELCNV. The final CNV calls are available from UK Biobank returned datasets (Return ID: 3104, https://biobank. ndph. ox. ac. uk/ukb/dset. cgi?id=3104 ). ABIDE1, COBRE, ADHD200, CNP, 16p11. 2 SVIP data are publicly available: http://fcon_1000. projects. nitrc. org/indi/abide/abide_I. html , http://schizconnect. org/queries/new , http://fcon_1000. projects. nitrc. org/indi/adhd200/ , https://www. openfmri. org/dataset/ds000030/ , https://www. sfari.