dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary and conclusion In this study, we have presented a novel method to quantify the SSH signature of a narrow slope current, the Northern Current in the North Western Mediterranean Sea, and to define its observability in altimetry data. It is based on a high resolution numerical model, intensively validated against in situ and HF radars data, and then considered as a reference for satellite altimetry data analysis. We consider the SSH and related surface geostrophic currents in parallel, using three nadir-looking radar altimeters that employ different technologies: Jason-2, SARAL and Sentinel-3. We investigate how the advances in performance of these altimeters  (Vergara et al. , 2019)  improve the observation of the NC. We show that in the HF radars covered region the NC has a clear signature in SSH, characterized by a sea level drop from offshore to the coast, generally centered at ~15-20 km to the coast with a mean value at 6.
14°E of 6. 9 ± 2. 2 cm and spreading over 18 ± 4 km. In winter, the SSH drops are generally stronger than in summer and then theoretically easier to detect for altimeters but in parallel, the NC width also tends to diminish, inducing opposite effects in terms of observability capability. Sammari et al. (1995)  have noticed a variability of the alongslope component of the NC between 10 and 20 days that altimetry does not allow to resolve. These results confirm that as a narrow, variable and close to the coast current, the NC monitoring is an issue for satellite altimetry and that the larger the temporal resolution of the mission, the greater the difficulty. This is particularly true for SARAL and Sentinel-3, regardless of the instrument performance. We then analyze the NC signature in altimetry data in comparison to the model reference.
Jason 2 and SARAL 1-Hz data stop at more than 10 km to the coast, sometimes preventing observation of the whole NC. Probably thanks to the SAR mode, it is resolved in Sentinel-3, with data at 1 km to the coast. In average, the SSH drops associated with the NC are always In summary, in terms of coastal circulation studies, the main advantages of Jason missions are the long time series (~30 years if we combine T/P and Jason-1,2,3 data) and its 10-day temporal resolution whereas its measurement noise, its large intertrack distance and its loss of coastal data are an issue. SARAL enables to strongly reduce the data noise and to have a much better spatial coverage but its temporal resolution and its loss of coastal data are negative points. Finally Sentinel-3 performs the best when approaching the coast and its SAR technique also allows to reduce the noise.
However, it has a too low temporal resolution. To ideally address the coastal observability question, future altimetry missions should combine instrumental improvements (Ka band and SAR altimetry as in SARAL and Sentinel-3) and the temporal resolution of Jason or better. But a single altimeter mission can clearly not observe the complex range of coastal ocean variability and we should certainly define an approach optimized for the coastal ocean in order to mix data from the 9 missions flying simultaneously in 2021. The method presented here can be easily transposed to other altimetry missions and other dynamical processes than the NC. As an example, we could also focus on eddy observability, studying the size, amplitude and spatial configuration of their signature in SSH, in comparison to the model reference.
Acknowledgements This study was done with the financial support of the  Region Occitanie  and the  CNES  through their PhD funding programs. Altimetry data used in this study were developed, validated and distributed by the  CTOH/LEGOS, France. Glider data were collected and made freely available by the  Coriolis  project (http://www. coriolis. eu. org) and programs that contribute. Support was provided by the  French Chantier Méditerranée MISTRALS program  (HyMeX and MERMeX components) and by the  EU  projects  FP7 GROOM  (grant agreement  284321 ),  FP7 PERSEUS  (grant agreement  287600 ),  FP7 JERICO  (grant agreement  262584 ) and the  COST Action ES0904 EGO (Everyone's Gliding Observatories ). The long-term monitoring of the Northern Current is part of the  Mediterranean Ocean Observation Service for the Environment (MOOSE)  with HF radars activities also supported by the  EU H2020  infrastructure project  JERICO-NEXT  ( 2015-2019 ) and actually by  EU   Interreg Marittimo program  SICOMAR-PLUS.
We thank the  Parc National de Port-Cros (PNPC) , " Association Syndicale des Propriétaires du Cap Bénat" (ASPCB)  and the  Group Military Conservation  and the  Marine Nationale  for hosting our radar installations. The simulation was performed using the HPC CALMIP platform under grant  P09115  and  GENCI  and  CINES (Grand Equipement National de Calcul Intensif , project  A0040110088 ). The SYMPHONIE model is distributed by the SIROCCO group (https://sirocco. obs-mip. fr).
Samples were randomly split into a training dataset containing 100 broilers of each line (pHu+: 43 males and 57 females, pHu-: 39 males and 61 females) and an external validation dataset of 34 birds per line (pHu+: 16 males and 18 females, pHu-: 15 males and 19 females). Average pHu values of breast meat were 6. 18 ± 0. 13 for the pHu+ and 5. 57 ± 0. 09 for the pHu-in the training data set and 6. 20 ± 0. 13 for the pHu+ and 5. 57 ± 0. 08 for the pHu-in the validation data set.
Data Processing and Spectral Assignment (Targeted Analysis) The spectral data acquired in positive and negative ionization modes were processed using XCMS R package implemented in Workflow4Metabolomics tools to extract detected signals and eliminate signal redundancies  (Smith et al. , 2006; Guitton et al. , 2017). The XCMS' parameters are provided in additional file 1, Supplementary Table  S1. The lipids assignment was targeted to an in-house database, with a precision of 5 ppm or 10 ppm for positive or negative ionization modes, respectively. This database was based on the lipidome of several biological matrices from different animals (serum, chicken egg yolk, cells, feces) and contained 542 and 265 lipids in positive and negative ionization mode, respectively. These lipids were annotated with SimLipid software (Premier Biosoft, San Francisco, Ca) according to their retention time, exact mass and MS/MS spectra.
The database contains 23 classes of lipids mainly phosphatidylcholine (PC; 279), phosphatidylethanolamine (PE; 91), diacylglycerol (DG; 80), and triglyceride (TG; 61) (additional file 1, Supplementary Figure  S1 ). This annotation reached level 1 on the scale of confidence in lipid identification  (Sumner et al. , 2007). Lipids were annotated according the LIPID MAPS Lipid Classification System  (Fahy et al. , 2009). Each peak area was normalized to the total peaks area of each chromatogram. The LC-HRMS analyses were performed in 2 batches. To overcome the signal drift between the 2 batches and be able to compare samples between them, the signal was corrected using the method "LOcally WEighted Scatter-plot Smoother" (LOWESS) available in Workflow4metabolomics tool  (Guitton et al. , 2017).
Orthogonal Projection to Latent Structures Discriminant Analysis and Orthogonal Projection to Latent Structures An orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed using the SIMCA 16 Software (version 16. 0. 2, Umetrics, Umea˚, Sweden) on the training dataset (100 pHu+, 100 pHu-). All data were scaled to unit variance. OPLS-DA is a method of supervised classification that predicts the categorical factor Y (pHu+ or pHu-group) by explanatory quantitative variables X (185 lipids). The minimum number of features needed for optimal classification of the OPLS-DA models was determined by iteratively excluding the variables with low regression coefficients and wide confidence intervals derived from jackknifing combined with low variable importance in the projection (VIP) until maximum improvement of the quality of the models.
The significance of the differences in number of carbons or unsaturation level between the acyl chains of the lipids contributing to the pHu+ and pHu-lines was tested through Welch's means equality t-test. Subsequently, the ability of the lipidome to predict the numerical value of the breast meat pHu on live animals was investigated using OPLS modeling, which differs from OPLS-DA models by the use of a quantitative Y. The pHu values from the chickens included in the training dataset (100 pHu+, 100 pHu-) were fitted to the minimum feature of the lipidome with OPLS. The quality of the model was evaluated by its explicative (R 2 Y) and predictive (Q 2 ) abilities, root meaned squared error of estimation (RMSEE), root meaned squared error by a 7-fold cross-validation (RMSEcv), and meaned squared error of prediction (RMSEP) for the external validation.
The OPLS-DA and OPLS models were externally validated on the validation dataset composed of 34 pHu+ and 34 pHuchickens randomly selected. The biochemical information about the lipids retained in the OPLS-DA and OPLS models that was used to develop the discussion was partly extracted from the HMDB database  (Wishart et al. , 2018) , Reactome  (Jassal et al. , 2020) , and KEGG  (Kanehisa and Goto, 2000).
Generation of Lipid Profiles The targeted lipidomics approach chosen for this study permitted the identification of 185 unique lipids (Figure  1 ) from a database of 731 unique lipids (Supplementary Figure  S1 ). The lipids identified in the sera belong to 14 classes, themselves classified into 4 categories (i. e. , glycerolipids, glycerophospholipids, sterols, and sphingolipids). The most represented in our analysis were phosphatidylcholines (PC, 20. 54%), phosphatidylethanolamines (PE, 18. 38%), triglycerides (TG, 12. 43%), sphingomyelins (SM, 10. 81%), and lysophosphatidylcholines (LPC, 9. 73%).
Variation of Lipid Profiles Between the Two pHu Lines An initial OPLS-DA (M1a) model was fitted to the 185 lipids identified in the sera of the 100 pHu+ and 100 pHu-that compose the training dataset to identify which lipids and acyl chain properties distinguish pHu lines. This model was composed of 1 predictive and 3 orthogonal components and had discriminant [R 2 Y (cum) ] and predictive [Q 2  (cum) ] abilities of 0. 74 and 0. 56, respectively, to separate pHu+ and pHu-lines (Supplementary Table  S2 ). The total number of carbons and unsaturation in the acyl chains of the lipids were plotted and overlaid with the values of the VIPs and the contributions extracted from the OPLS-DA model. Lipids belonging to the LPC, LPE, TG, DG, and Cer classes contributed exclusively or almost exclusively to the pHu-line, Cer containing 36 or 38 carbons in their acyl chain contributing the most (Supplementary Figure  S2 ).
Prediction of the pHu Phenotype An OPLS model (M2) was trained to predict breast meat pHu values from a set of serum lipids of the broilers from the training dataset composed of 100 pHu+ and 100 pHu-. This model composed of 1 predictive and 2 orthogonal components was fitted to 40 lipids with a descriptive and predictive abilities of R 2 Y (cum) = 0. 69 and Q 2 (cum) = 0. 62, respectively. The similarity of the R 2 Y (cum) and Q 2 (cum) as well as the permutation plot (Supplementary Figure  S5 ) indicated that the model was not over-fitted. The loadings and scores on the first predictive and  orthogonal components were presented in Figures  4A, B. The predictive performance of the model was further assessed by 1) the plotting of the value of the predicted pHu versus the value to the observed pHu and the fitting of a linear regression model to the data and 2) the computing of the RMSEE and RMSEcv.
By comparison to this untargeted lipidomics study, our lipid database had a similar coverage of most of the lipid classes except the FA, PA, and PI. However, further comparison between the lipidome of those two avian species is limited as their metabolism is most likely very different as it is, for example, the case between mice and rats  (Ishikawa et al. , 2015). In the same way, a comparison with egg yolk lipid composition, which had been characterized extensively  (Meng et al. , 2021; Wood et al. , 2021) , was not relevant.
16 pH units for the model based on the expression in the P. major of 20 genes while it was 0. 18 pH units in the present model based on 40 circulating lipids. Concretely, the model based on the lipidome allowed us correctly classifying 76% of the muscles exhibiting a normal value of pHu (between 5. 7 and 6. 1). The lower performance of the external validation (RMSEP = 0. 25 pH unit) may result from the lower representation of birds exhibiting a normal value of pHu in the validation dataset. The main interest of the model developed in the present study comes from the possibility of collecting lipidomic data from blood samples taken from live animals, which is especially crucial for selection, and also to obtain a good quality of prediction quite early during the life of the animal (17 days).
ACKNOWLEDGMENTS We thank the staff of the  Poultry Breeding Facilities (INRAE, UE 1295 Pôle d'Expérimentation Avicole de Tours, 37380 Nouzilly, France)  for producing the birds, and the  Avian Research Unit (INRAE, UMR Biologie des Oiseaux et Aviculture, 37380 Nouzilly, France) , and more particularly  Séverine Urvoix ,  Emilie Raynaud ,  Jérémy Bernard ,  Patrice Ganier , and  Marine Chahnamian.
Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genomewide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i. e. , case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses.
, 2020 (Hong et al. , , 2021) )  derived from European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample  (Bos et al. , 2018). Specifically, in two previous GWAS we set out to identify variants underlying variation in several cerebrospinal fluid (CSF) phenotypes, such as levels of CSF Aβ and tau protein  (Hong et al. , 2020) , or neurofilament light (NfL) chain, chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng), which reflect axonal damage, astroglial activation, and synaptic degeneration, respectively  (Hong et al. , 2021). However, the EMIF-AD MBD dataset features several other quantitative phenotypes, including cross-sectional MRI measurements and cross-sectional and longitudinal neuropsychological tests, which are used as outcome traits in the current study.
Sample Description Analyses were based on the EMIF-AD MBD dataset which was collected across 11 different European study centers  (Bos et al. , 2018). In total, this dataset included 1,221 [563 (46%) female; mean age = 67. 9 years, SD = 8. 3] individuals from three diagnostic stages: normal controls (NC), subjects with mild cognitive impairment (MCI) and subjects with a clinical diagnosis of AD. A diagnosis of AD was based on National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association criteria (NINCDS-ADRDA)  (McKhann et al. , 1984)  while MCI was diagnosed using criteria of  Petersen (2004)  in nine centers, while two centers used the criteria by  Winblad et al. (2004). Individuals showing normal performance on neuropsychological assessment (within 1.
Magnetic Resonance Imaging Phenotypes Description The five MRI phenotypes were collected for 862 subjects. Brain MRIs were used to assess hippocampal volume (mm 3 , left and right hemisphere, and sum of both; all adjusted for intracranial volume), whole brain cortical thickness (in mm), and white matter lesions (WML; using the Fazekas scale)  (Ten Kate et al. , 2018). The Fazekas scale categorizes WMLs into 4 categories: Level 0 (no or almost no lesion), level 1 (multiple punctate lesions), level 2 (early confluent WML), and level 3 (presence of large confluent WML). Details on the scanning procedures and data harmonization across centers can be found in  Bos et al. (2018)  and Ten  Kate et al. (2018).
Neuropsychological Phenotypes Description Cross-sectional (and follow-up) data were available for the following seven neuropsychological domains within the EMIF-AD MBD dataset: global cognition (Mini Mental State Examination, MMSE), attention, executive function, language, memory (immediate and delayed) and visuoconstruction (for a detailed description of all neuropsychological tests see Supplementary Material). For each cognitive domain, a primary.
DNA Extraction, Genotype Imputation and Quality Control A detailed description of the genotyping procedures, quality control (QC) and subsequent data processing can be found in  Hong et al. (2020)  (Supplementary Material). Here, the same genotype data were used for the GWAS analyses. Briefly, 936 DNA samples were subjected to genome-wide SNP genotyping using the Infinium Global Screening Array (GSA) with Shared Custom Content (Illumina Inc. ). Imputation was then performed using Minimac3  (Das et al. , 2016). Extensive post-imputation QC resulted in 7,464,105 autosomal SNPs with a minor allele frequency (MAF) ≥ 0. 01 in 888 individuals of European ancestry. More details can be found in Supplementary Material. For the X chromosome, QC was performed separately for male and female subjects for non-pseudoautosomal regions, using slightly different criteria compared to the autosomes (see Supplementary Material).
04) to prevent inflation of test statistics owing to low frequency SNPs (see Table  1  for more details). Diagnostic status was coded with two dummy variables as follows: NC = (0, 0), MCI = (0, 1), AD = (1, 1). For four longitudinal cognitive phenotypes an additional dummy variable was introduced to code for the neuropsychological test used, in cases where two different tests were used for generating these phenotypes. Details can be found in Supplementary Material. To explore associations on the X chromosome that were potentially driven by genetic sex, we additionally conducted the analyses separately in females and males. We then combined these two additional sets of results in a meta-analysis using Stouffer's method as implemented in METAL  (Willer et al. , 2010). As we found no noteworthy differences in the results using Stouffer's method, only the results from the linear regression analysis in the combined sample are shown.
6 (using reference data from the 1,000 Genomes Project). Subsequently, the top SNPs, i. e. , those with the smallest P-values per respective phenotype, were examined in more detail using additional tools. First, the Variant Effect Predictor on Ensembl (VEP, 3 McLaren et al. , 2016) was used to determine a possibly functional effect due to changes in the coding sequence, e. g. , missense variants. Second, SNPs were examined using data from the RegulomeDB database 4   (Boyle et al. , 2012)  to assess possible effects on gene expression. Third, we used data from the Genotype-Tissue Expression (GTEx, V8) project portal 5   (Lonsdale et al. , 2013)  to assess whether SNPs represent expression / splicing quantitative trait loci (eQTLs/sQTLs). While GTEx provides data on gene expression in 54 tissues, we laid particular emphasis in genes expressed in brain.
In the GTEx database  (Lonsdale et al. , 2013) , the lead-SNP identified here (rs16829761) is not listed as eQTL or sQTL, which may be due to the comparatively low MAF (0. 01). The CADD score, i. e. , the in silico predicted deleteriousness, of rs16829761 is also low at approximately 0. 074. In addition, none of the gene-based GWAS analyses using MAGMA revealed any genome-wide significant signals (P < 2. 566E-06) using the MRI traits analyzed. The genomic inflation factor λ ranged between 0. 984 and 1. 060 in these five gene-based analyses.
Role of APOE in Genome-Wide Association Studies on Magnetic Resonance Imaging and Neuropsychological Performance Given the substantial role that variants in APOE play in the genetic architecture of AD, we present findings for this locus separately, i. e. , the results for SNP rs429358 (which defines the ε4 allele) and rs7412 (which defines the ε2 allele). In relation to the common genotype ε3/ε3, the risk to develop AD is increased by a factor of ∼3. 2 for genotype ε3/ε4, while two ε4 alleles (genotype ε4/ε4) show ORs around 10-12 when compared to normal controls  (Neu et al. , 2017). The minor allele at rs429358 (ε4) is overrepresented in the EMIF-AD MBD dataset with an MAF ∼29% (the MAF in the general Northern European population is ∼16%), which is due to the special design of participant recruitment (see  Bos et al.
, 2020). In the AD context, it is noteworthy that SMOC2 was recently found to be altered in CSF samples of early AD in a proteomics profiling study  (Whelan et al. , 2019). Interestingly, there is similar evidence on a potential link to AD for a SMOC2 isoform, i. e. , SMOC1 (gene: SMOC1, located on chromosome 14q24. 2). While variants in this gene did not show strong evidence of genetic association with the traits analyzed here, it is noteworthy that SMOC1 was recently nominated as a novel AD biomarker in proteomic screens of AD CSF and brain samples in various studies  (Bai et al. , 2020; Wang et al. , 2020; Sathe et al. , 2021). In summary, our finding of genome-wide significant association between SMOC2 and memory performance in the EMIF-AD MBD datasets extends the emerging literature on the role of SPARC protein family members in AD and related traits.
g. , insufficient power, aspects of sample ascertainment, it is in general agreement with the literature: In the GWAS on cognitive function by  Davies et al. (2018) , APOE ε4 also showed only marginal association (P = 2. 2E-04), and it was not reported to show genome-wide significant evidence of association (P = 4. 1E-07) with hippocampal volume in the GWAS by  Hibar et al. (2017). Lastly, while rs429358 did show evidence of genomewide significant association with several imaging traits in the more recent GWAS on brain imaging phenotypes in the United Kingdom biobank, none of the hippocampus-based measurements exceeded that study's multiple testing threshold  (Smith et al. , 2021). We note, that our current findings are different from our earlier GWAS analyses in the EMIF-AD MBD dataset, where the ε4 allele showed very pronounced evidence of association in CSF and imaging markers related to Aβ42  (Hong et al.
Thus, independent replication-ideally in larger datasets-is needed to confirm the main findings of our GWAS before any furtherreaching conclusions can be reached. Third, we note that the phenotype data used as outcome traits in our GWAS analyses were collected at different participating centers at times using different types of examinations (e. g. , different tests to study the same overarching neuropsychological domain). To alleviate potential bias resulting from this inherent phenotypic heterogeneity, all clinical data were processed, quality-controlled and harmonized (e. g. , by normalizing most variables within centers) centrally by an experienced team of researchers (see  Bos et al. , 2018 for more details). We emphasize that this potential heterogeneity does not apply to the genetic data as these were generated in one laboratory experiment and subsequently processed jointly in one analytical framework, minimizing the emergence of potential batch effects.