dataesr/scientific-paragraphs-categorization huggingface dataset:
Multi-omics analysis identified a canonical pro-inflammatory signature in OACs To decipher the specific OAC signature in response to pro-inflammatory stimuli and to complete the description of OACs upon exposure to pro-inflammatory stimuli, in addition to the 3' SRP analysis, we performed tandem mass spectrometry analysis of the proteins extracted from OAC lysates (5 OA patients) treated the same way as the samples described previously (Fig. 1 ). This analysis validated and quantified 2688 proteins and, for the entire dataset, we were able to assign 794,053 MS/MS spectra, or an average of 26,468 per sample. The PCA analysis of proteomic samples revealed one significant outlier patient despite the absence of previously obvious differences with other samples (Fig. 3A , in black). Following the removal of this patient from our analysis, the variability between the different samples was drastically decreased, as illustrated by the percentage of variance Fig.
5A ). This analysis revealed an overall increase in the expression of glycolysis enzymes specifically in OACs treated with either IL-1b or TNF (Fig. 5A ). As the final product of glycolysis   is pyruvate, which is a metabolite that can initiate the tricarboxylic acid cycle (TCA, or citrate/Krebs cycle), we examined TCA-related genes using unsupervised heatmap analysis based on the TCA-related KEGG annotation (hsa00020). The TCA heatmap showed an overall decrease in TCA-related genes in OACs but not in NCs (Fig. 5B ). To strengthen our results, glycolytic and TCA gene dysregulation were also explored in two public datasets: GSE162510, which explored the transcriptomic changes upon IL-1b treatment, and E-MTAB-6266, which compared gene expression between the cartilage of OA knees and the cartilage of healthy knees from multi-organ donors as a control.
Interestingly, both datasets confirmed the expression profile highlighted in our study, with overexpression of the genes associated with glycolysis (Fig. 5C ), and downregulation of the genes related to TCA (Fig. 5D ) in OA conditions compared to the controls (untreated chondrocytes or chondrocytes from non-OA joints). To better understand the impact of this glycolytic increase on the overall cellular metabolism, we also explored other metabolic pathways directly related to glycolysis, such as the HBP and the pentose phosphate pathway. No regulation as clear and specific as the one observed for glycolysis was revealed for these pathways (Figure  S2A, B ). Moreover, in keeping with the increase observed in the expression of glycolysis enzymes and the decrease in the expression of TCA enzymes, our results revealed overexpression of LDHA and, as previously described, overexpression of MCT-4 (Slc16a3) at both the transcriptomic and proteomic levels in response to either IL-1b or TNF in OACs (Fig.
Experimental design Primary human chondrocytes, isolated from osteoarthritic knees of advanced OA patients (OACs) and vertebral transverse costal facet joints of non-OA patients (NCs). For histological validation, cartilage samples were paraffin-embedded and stained with Safranin-O. Isolated OA chondrocytes (OACs) and non-OA chondrocytes (NCs) were treated for 24 h with or without 1 ng/mL of IL-1b or 25 ng/mL of TNF or 50 ng/mL of IL-6/IL-6 soluble receptor (IL-6). Sequencing by 3' SRP was performed on OAC and NC samples. Moreover, mass-spectrometry was performed on OACs. The pro-inflammatory signature was defined with matched transcripts and proteins dysregulated by IL-1b or TNF treatments. NC transcriptomics data were then compared to the OAC pro-inflammatory signature (Fig. 1 ).
Sequencing and primary analysis Sequencing and primary analysis were performed according to Charpentier et al. , 2021. Briefly, the library was sequenced on a NovaSeq 6000 using NovaSeq 6000 SP Reagent Kit 100 cycles (#20,027,464, Illumina) with 17*-8-105* cycle reads (* addition of one cycle according to Illumina's recommendation). Bioinformatics steps were performed using a Snakemake (49) pipeline. Sample sequences were demultiplexed using a python script, and raw paired-end fastq files were transformed into a single-end fastq file for each sample and aligned on the Ensembl transcriptome hg38 using bwa.
Public datasets analysis Two public datasets, E-MTAB-6266 and GSE162510, were uploaded to explore the regulation of metabolism-associated genes. E-MTAB-6266 compared gene expression between the cartilage of 58 OA knees and 10 non-OA knees. GSE162510 explored the transcriptomic changes occurring in chondrocytes from 10 OA patients following 24 h of IL-1b treatment (0. 2 ng/ mL). A differential expression analysis table of the E-MTAB-6266 public dataset was uploaded from Soul et al. [33] , while GSE162510 was uploaded with the R package GEO query (2. 62. 2). Data visualization was then performed as previously described.
Statistical analysis The statistical analyses used in the transcriptomic analysis were performed using the R software package DESeq2 (52) with a Benjamini & Hochberg correction. The PCA and biplots were created using the prcomp function from the stats package within R statistics (version 4. 1. 1) and the factoextra R package (1. 0. 7). Differences in continuous variables were assessed using the non-parametric Mann-Whitney test (n < 30), and p-values less than 0. 05 were considered to be statistically significant. The statistical analyses were performed using GraphPad Prism 8. 0v software.
Thus, a special focus on energetic metabolism pathways such as glycolysis and TCA at the transcriptomic level revealed that the expression of glycolytic enzymes and TCA enzymes are inversely regulated in OACs. Indeed, our study, showed, that the expression of most genes involved in glycolysis is increased whereas the expression of those related to TCA is decreased in OACs upon exposure to IL-1b or TNF. This increased expression of glycolytic genes such as Glut-1, HK2, or LDHA has been previously described in the pro-inflammatory response of OA and non-OA chondrocytes  [34] [35] [36] [37]. However, in our study, no significant regulation of metabolism-related genes upon exposure to IL-1b or TNF was observed in non-OA chondrocytes (NCs). This finding was confirmed by a public data set obtained from chondrocytes isolated from knees with OA and healthy knees from age-matched multi-organ donors (E-MTAB-6266)  [33].
However, all our observations were supported by additional studies using publicly available datasets. Nevertheless, since accessing a healthy knee joint is challenging, supplementation of this study with other animal models such as dogs or horses would be substantial. These future studies should also complement the proinflammatory signature with data on secretome and metabolome dysregulations. Despite these limitations, the main interest of our study lies in the multi-omics approach completed by a functional approach to investigate the action of two different pro-inflammatory treatments. Our study identified a canonical pro-inflammatory signature and its specific signaling pathways in chondrocytes from OA joints compared to chondrocytes from non-OA joints. Although IL-1b or TNF treatments are widely used to mimic the pro-inflammatory component of OA, the specificity of the metabolic alterations observed only in OACs strengthens the need for a thorough study of the link between inflammation and metabolism.
Acknowledgements We are most grateful to the  Genomics Core Facility GenoA , member of Biogenouest and  France Genomique  and to the  Bioinformatics Core Facility BiRD , member of Biogenouest and the  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ) for the use of their resources and their technical support. We thank  Jean-Charles Gaillard  ( ProGénomix Platform, CEA-Marcoule ) for help with the mass spectrometry analysis.
Funding This work was supported by the  Agence Nationale de la Recherche (ANR)  through the project  KLOTHOA  ( ANR-18-CE14-0024-01 ; CV), by the region " Pays de la Loire " ( RFI Bioregate, GenOA, CV, AD ), and by research grants from the  Arthritis Foundation  and the  "Société Française de Rhumatologie" (CV).
SNP calling, quality control and ltering The analysis pipeline is shown in Figure  S1. Scripts were deposited in the forgeMIA repository: https://forgemia. inra. fr/amandine. cornille/dianthus_3sp_project. Raw sequence reads were demultiplexed and cleaned using default settings with the process_radtags function in Stacks v2. 0b. Reads were mapped against the D. carthusianorum reference genome available at ETH Zurich using BWA v0. 7. 17 (default settings, (Li, 2013)), and only reads with a mapping quality ≥ 1 were retained. Biallelic SNPs and indels were retained. Variants were then called using FreeBayes v. 1. 2. 0 (Garrison and Marth, 2012) using the default settings, except that the use-best-n-alleles were set to 4, and the population prior to the species level was used. We retained SNPs with <10% missingness and individuals with <15% missing SNPs.
We retained SNPs with MAF > 0. 01. We computed missingness statistics and estimated linkage disequilibrium (LD) in windows of 1Mb using PLINK v1. 90 software  (Chang et al. , 2015). We then removed SNPs under strong LD using the PLINK LD-based variant pruner, which computes correlations between genotype allele counts. SNPs with a correlation coe cient r LD ² > 0. 99 were removed.
Species and population subdivision, admixture, and isolation by distance patterns We investigated population structure and admixture using the TESS3R package  (Caye et al. , 2016). TESS3R considers the geographical coordinates of each individual such that genotypes from geographically closer locations are considered more likely to belong to the same cluster. We used the conditional autoregressive (CAR) Gaussian model of admixture with a linear trend surface, setting the spatial interaction parameter (q) to 0. 6. When clustering the genotypes, these parameters (q and trend) affect the weighting assigned to spatial proximity. We simulated K values ranging from 2 to 8. The resulting matrices of the estimated cluster membership coe cient (Q) were visualized using the DISTRUCT software  (Rosenberg, 2004).
We determined the nest level of genetic structure by controlling the bar plots and using the cross-validation procedure implemented in TESS3R. Once the best K value was chosen, an individual was assigned to a given cluster when its assignment probability to this cluster was > 75%. We chose this threshold based on the distribution of membership coe cients inferred using TESS3R for each cluster (see the results below). We further explored the genetic subdivision among individuals with a neighbor-joining tree (NJ), principal component analysis (PCA), and SVDquartets tree. The NJ tree was drawn using Nei's distance  (Saitou and Nei 1987)  in the ape R package  (Paradis and Schliep 2019). We ran a PCA using the SNPRelate R package  (Zheng et al. 2012 ). We used SVDquartets software as implemented in PAUP* v.
Genomic diversity and differentiation estimates We computed Watterson's θ  (Watterson, 1975 ) and Tajima's D  (Tajima, 1989 ) values using the PopGenome R package  (Pfeifer et al. 2014; Pfeifer et al. 2018)  for each geographical location and population (i. e. , clusters inferred with TESS3R, excluding admixed individuals with a membership coe cient <0. 75 in the focal cluster). We also computed Nei's nucleotide diversity π(Nei and Li 1979), observed (H O )and expected (H E ) heterozygosity  (Nei 1973) , and the inbreeding coe cient F IS for each site and each population withStacks v2. 52  (Catchen et al. , 2013). We used kernel-smoothed diversity estimates because they are less susceptible to random biological or sequencing errors and are more likely to re ect actual genome-wide intraspeci c diversity  (Catchen et al.
Estimates of gene ow The extent of gene ow among sites was assessed by testing for isolation-by-distance (IBD) patterns; that is, we assessed whether genetic differentiation (F ST )/(1-F ST ) among geographical sites was correlated with geographical distance. We estimated the genetic distance F ST /(1-F ST ) using the PopGenome R package  (Rousset 1997; Pfeifer et al. 2018). A total of 1,000 Mantel tests  (Mantel, 1967)  were performed between the two distance matrices using the adegenet R package  (Jombart, 2008). D-statistics were also used to detect gene ow among populations. The D-statistic, or the ABBA-BABA statistic, is a practical and widely applied parsimony-like method for disentangling gene ows from incomplete lineage sorting. We used the Dtrios command in Dsuite  (Malinsky, 2019)  to compute the four-taxon D-statistics for 236,964 unlinked SNPs.
We tested whether gene ow occurred among the D. carthusianorum populations identi ed with TESS3R, and among the subset of populations used for ABC inferences (see below). D-statistic signi cance was assessed using a jackknife  (Green et al. , 2010 ) on 20 blocks. The p. adjust function in R v. 3. 5. 3 (R Core Team, 2019) was used to apply a Benjamini-Yekutieli correction  (Benjamini and Yekutieli 2001). Results were visualized with the Ruby script 'plot_d. rb' available from M. Matschiner (https://github. com/mmatschiner).
SNP dataset The 172 sequenced libraries yielded 209,140,709 million single-end reads. After mapping onto the D. carthusianorum reference genome, 3, 334, 211 SNPs were obtained. After ltering for missingness (Figure  S4 , Table  S1 ), 468,705 SNPs for 160 individuals were retained (i. e. , 12 out of the initial 172 D. carthusianorum individualsdid not pass the ltering control for SNP missingness). After removing the SNPs in LD, our nal dataset consisted of 236,964 SNPs in 160 D. carthusianorum individuals.
Therefore, we simulated four populations in the ABC-RF analyses (Romanian, Czech, Pyrenean, and Eastern French). To reduce the computational requirements of the simulations, we randomly selected 20% of the SNP dataset to run ABC. This corresponded to 39,346 SNPs out of the initial 236,964. Note that we recovered the same population genetic structure with the 39,346 SNPs as that inferred from the 236,964 SNP dataset (Figures  S12  and S13 ). The tested scenarios were designed based on the patterns of spatial genetic diversity and GSSA analyses, suggesting an origin of colonization in the East. The Romanian population had the highest genetic diversity and was the most differentiated, suggesting that this population diverged the earliest. The gene ow modalities tested in the different scenarios were as follows: no gene ow among populations, gene ow among all populations, and gene ow among populations with signi cant Dstatistics (round 3, Figures  S2  and S3 ).
Acknowledgments We thank the  Center for Adaptation  to a changing environment (ACE,  ETH Zürich) ,  andATIP-Avenir. We are grateful to the  INRAE MIGALE bioinformatics facility  (MIGALE; INRAE, 2020). Migale Bioinformatics Facility, doi:10. 15454/1. 5572390655343293E12) for providing assistance, computing, and storage resources. We thankthe informatic team at the  GQE-Le Moulon , particularly  Adrien Falce ,  Benoit Johannet , and  Olivier Langella , and the  Functional Genomics Center Zurich (FGCZ)  for the sequencing facilities. We thank  Niklaus Zemp  and the  Genetic Diversity Centre (GDC), ETH Zurich , which helped analyze the data. AR and TU were supported by the  Ministry of Research, Innovation , and Digitization through the  BIORESGREEN/BioClimpact /23020401  and  SPECTRAVEG-PN-III-P2-2.
, 2022). While studies to date suggest that ACM-CAP products will likely be EarthCARE's default "best estimates"  (Mason et al. , 2022a) , this will not be known for sure until EarthCARE's "commissioning phase". Should ACM-CAP fail and only (some) L2a retrievals remain usable by RT Regardless of whether ACM-CAP or alternate L2a-composite profiles are to be used by ACM-RT's RT models, they 85 need to be readied for use there. Hence, the last steps of ACM-COM take profiles of meteorological variables and surface conditions, passed in respectively from the X-MET processor  (Eisinger et al, 2022)  and databases, and merge them with ACM-CAP or L2a-composite products. Following previous satellite missions (e. g. ,  L'Ecuyer et al. 2008; Kato et al. , 2013) , ACM-RT computes SW and LW BB flux and HR profiles by applying 1D RT models to each admissible JSG profile along the L2-plane.
Gases Molecular optical depths are computed by the CKD method in RRTMG_LW v4. 85 and RRTMG_SW_v3. 9 and used by both 1D and 3D RT models. Molecular absorption coefficients for RRTMG's k-distributions were obtained from the line-by-line RT model (LBLRTM), which has been evaluated against surface and laboratory observations 170  (Clough et al. 2005; Shephard et al. 2009; Alvarado et al. 2012 ). LBLRTM's spectroscopic line parameters are essentially equivalent to  HITRAN 2000 and HITRAN 1996 (SW)  databases. Algorithmic accuracy of LBLRTM is 0. 5%  (Clough et al 2005)  with limiting errors generally attributed to line shape and spectroscopic input parameters. Wavenumbers for RRTMG 's LW bands are: 10-350, 350-500, 500-630, 630-700, 700-820, 820-980, 980-1080,   1080-1180, 1180-1390, 1390-1480, 1480-1800, 1800-2080, 2080-2250, 2250-2380, 2380-2600, and 2600-3250 cm -1.
2008)     1 ( ) exp , N r n r r r r                      (     1 1 , 2 s s R p d       (9) where s  is cosine of scattering angle, with   5 plates, large aggregates composed of 10 plates, solid bullet rosettes, and hollow bullet rosettes. Maximum dimen-240 sion for each habit ranges from 2 μm to 10,000 μm for 189 discrete sizes. Three surface roughness conditions were considered for each ice habit: smooth, moderate, and severe. Each constituent has volume, projected area, effective size, extinction efficiency, 0  , and g. Their scattering phase functions are tabulated at 498 unequal angles, but were transformed into 1,800 equal angular bins for use in (9). 10 s R   (forescatter) and   11 s R     (backscatter To make this dataset's size suitable for operational use, optical properties were averaged over  and assumed distri-245 butions of habit, size, and roughness that were derived from CALIPSO observations  (Baum et al.
Data availability 610 The EarthCARE Level-2 demonstration products the ACM-COM products discussed in this paper are available from https://doi. org/10. 5281/zenodo. 7117115  (van Zadelhoff et al. , 2022)  as are "operational" ACM-RT output. Specialized ACM-RT calculations presented in this paper, e. g. , with increased photon count, and radiative transfer calculations are available from https://doi. org/10. 5281/zenodo. 7272662  (Cole et al. , 2022). ACM-RT and performed independent 3D solar radiative transfer calculations. MS integrated the 1D radiative transfer model into ACM-RT. All authors were involved in development of ACM-RT and ACM-COM and contributed material and/or test to the manuscript.
To that end, we used the data of a longitudinal cohort study, which aimed to find biomarkers to predict motor recovery by means of repetitive assessments using diffusion tensor imaging and clinical evaluation. Materials and methods. Patients A total of 22 patients were recruited from the stroke unit and the neurorehabilitation department of La Pitié-Salpêtrière Hospital (November 2018 to December 2020) to participate in a study that aimed to find biomarkers to predict motor recovery (ClinicalTrials. gov Identifier: NCT03739892). The inclusion criteria were as follows: (i) first-ever stroke event (ischemic or hemorrhagic); (ii) impaired upper limb function defined on the Fugl-Meyer Assessment score; and (iii) aged ≥ 18 years old. The exclusion criteria were as follows: (i) suffering from a life-threatening disease during the follow-up period; (ii) contraindications to MRI; (iii) severe aphasia hampering the ability of the patients to follow a rehabilitation program; and (iv) aged > 85 years old.
Preprocessing The data were first denoised, and Gibbs artifacts were removed with the MRtrix 3 software  (13). The b = 0 volumes were then extracted and combined with the opposite phase direction volumes to perform EPI distortion correction with the topup tool from FSL (5. 0)  (14). We then corrected the data for motion and eddy current distortions with an eddy tool from FSL  (15). Fractional anisotropy (FA) maps were generated using the diffusion tensor model.
Data computation Stroke lesions were manually outlined by a neurologist on the FLAIR images. For each tract of interest (CST, SLF I, and II), template tracts were obtained from a healthy whole-brain tractography using MRtrix3 [details in  Moulton et al. (16)  and Supplementary material]. Individual FA maps were registered to the healthy whole-brain tractography space first linearly using the FSL flirt tool with 6 degrees of freedom and then nonlinearly using the FSL-fnirt tool with 12 degrees of freedom. During the registration, the lesioned voxels were masked to avoid distortions due to the lesion. We then extracted densityweighted FA values from the affected and unaffected entire fasciculi  (17) (18) (19) (20) (21).
This approach therefore provides a means of assessing the influences of specific drivers that is complementary to trend analysis, as influences are estimated for each species and can then be related to species traits. In the present study, we assess the effects of four major anthropogenic pressures, including agricultural intensification measured as the cover of farms with high input of pesticides and fertilizers, land use with change in forest cover, urbanization, and climate change measured as temperature change, on the large-scale spatiotemporal dynamics of European bird populations. We use the largest dataset available  (39)  resulting from the combination of standardized Breeding Bird Surveys conducted in 28 countries from 1980 to 2016 (representing 170 common bird species monitored at more than 20,000 sites with standardized protocols) to: 1) state the large-scale spatiotemporal dynamics of European bird populations over 37 years; 2) relate them to global spatiotemporal dynamics of the four pressures over the last decades, based on the analyses of trends and time series; and 3) investigate whether and which combinations of species traits were more prone to be positively or negatively affected.
We chose this measure as it can account for price differences among products and countries and can discriminate between farm types  (53). The percentage of forest cover was calculated as the number of hectares covered by forest and other wooded land [from the FAO (54)], divided by the country's surface area, between 1996 and 2016. We used data from Eurostat  (55)  to assess urbanization values for each country in 2009, 2012, 2015, and 2018. These values are obtained from Land Use and Cover Area frame Survey and correspond to sealed areas, i. e. , roofed builtup areas, artificial nonbuiltup areas, and other artificial areas. To obtain annual values between 2009 and 2016, data have been linearly interpolated following the protocol used by the FAO for land-use data  (54). Temperature data were extracted from the E-OBS v20.
0e database  (56)  which contains daily mean temperature between 1950 and 2018 over a regular grid of Europe at a scale of 0. 1 degree (ca. 111 × 70 km 2 at European latitudes). For each grid cell, daily temperatures were averaged between August from the year preceding the monitoring and June of the monitoring year as both breeding and nonbreeding season temperatures could have an effect on birds  (57). Averaging across cells within a country yielded an annual mean temperature for each country, for each year from 1996 to 2016. We chose to use a homogenized dataset for each pressure (i. e. , not coalescing data from different sources for a given pressure). This implied that no high-input farm cover and urbanization data were available for Norway and Switzerland. At country scale, trends in high-input farm cover, forest cover, urbanization, and temperature corresponded to the relative change calculated as the slope of a linear regression between annual pressure values (the response variable), and years (the explanatory variable) scaled by the first pressure value.