dataesr/scientific-paragraphs-categorization huggingface dataset:
Observational data The total column O 3 , which mainly influences the O ( 1 D) photolysis rate, is constrained by the National Aeronautics and Space Administration (NASA) Solar Backscatter Ultraviolet (SBUV) Merged Ozone Data Set (MOD)  (Frith et al. , 2014). The SBUV MOD column O 3 data are derived by combining observations from nine SBUV-type instruments aboard NASA's Aura satellite. The monthly O 3 columns are available for 5 • zonal mean. Tropospheric O 3 is important in determining OH production. We constrain its spatial distributions using the tropospheric column O 3 data from the combined Aura Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) satellite observations, which are generated by subtracting the co-located MLS limb measurements (integrated over the stratosphere to derive stratospheric column ozone) from total column ozone retrieved by OMI, a UV-Vis nadir solar backscatter spectrometer  (Ziemke et al.
, 2011 Oman et al. , , 2013;; Nielsen et al. , 2017). The tropospheric chemistry of CESM1-CAM4Chem is based on MOZART-4 mechanisms with minor updates  (Emmons et al. , 2010; Lamarque et al. , 2012)  and the GEOSCCM is based on the Global Modeling Initiative (GMI) chemistry and transport model  (Duncan et al. , 2007) , which was originally developed for the GEOS-Chem model. The CO, NO 2 , O 3 , CH 4 , CH 2 O mixing ratios, total column O 3 , and meteorological conditions including T a and H 2 O (g) simulated by CESM1-CAM4Chem and GEOSCCM in 2010 are compared with the observational data described in Sect. 2. 1 in the Supplement (Figs. S1-S3 ). We chose the CESM1-CAM4Chem and GEOSCCM in this study since (1) their global mean OH concentrations and OH distributions (both horizontal and vertical) are around the multi-model mean values given by  Zhao et al.
, 2019). Including the chemical mechanism such as OH recycling by isoprene  (Lelieveld et al. , 2008)  would help to further reduce the N/S ratio for model-simulated OH fields. 2. The constraints brought on tropospheric OH are limited by quality of observations and time resolution of available model outputs. Data constraining the OH precursors come mainly from satellite observations and reanalysis data, the uncertainties of which are not considered in this study. For example, the MERRA-2 reanalysis data significantly overestimate H 2 O (g) in the upper troposphere  (Jiang et al. , 2015) ; The QA4ECV tropospheric NO 2 VCD is lower compared with surface observations under the extremely high-pollution case compared with surface observations  (Compernolle et al. , 2020). The performance of this method depends on the accuracy of observations used to constrain individual factors.
Data products regularly improve and, since the sensitivity of OH to each precursor is estimated by the chemical box model, we can easily improve the OH using the updated observational datasets. The OMI measures concentrations of chemical species around local time 13:30 LT, but most of the CCMI models only provide monthly means for 3D distribution of chemical concentrations. The monthly mean NO 2 and O 3 concentrations simulated by 3D models are therefore constrained only by such afternoon observations. For O 3 , of which the tropospheric mean lifetime is 23. 4 ± 2. 2 d  (Young et al. , 2013) , we assume that not considering diurnal variations only has a small influence. This is not the case for NO 2 with a much shorter lifetime  (∼ 1 d, Jaffe et al. , 2003). By comparing the tropospheric NO 2 VCDs observed by SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric Chartography; overpass time around local time 10:00 LT) with OMI, previous studies show that the tropospheric NO 2 VCDs have significant diurnal variations  (Boersma et al.
Code availability. The DSMACC model code and descriptions are available at http://wiki. seas. harvard. edu/geos-chem/index. php/ Main_Page (GEOS Chem, 2020). Data availability. The GEOSCCM OH fields are available at the Centre for Environmental Data Analysis (CEDA; http://data. ceda. ac. uk/badc/wcrp-ccmi/data/CCMI-1/output; CEDA Archive, 2019; Hegglin and Lamarque, 2015), the Natural Environment Research Council's Data Repository for Atmospheric Science and Earth Observation. The CESM1-CAM4Chem outputs for CCMI are available at http://www. earthsystemgrid. org (Climate Data Gateway at NCAR, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-23-789-2023-supplement.
Participants The sample included 84 adults with ADHD aged 18-57 (M = 32. 48 years; SD = 10. 23; 57. 12% female). Participants were recruited from the outpatient psychiatry clinics of the University Hospital of Strasbourg. ADHD and comorbidities diagnoses were established by senior psychiatrists based on the DSM-5 criteria  (American Psychiatric Association, 2013). No exclusion criteria were applied. Demographic data, comorbidities and treatment were recorded at the diagnostic assessment. Detailed demographic data are provided in Table  1. Subjects provided written informed consent prior to inclusion in the study in accordance with the Declaration of Helsinki. This study was approved by the regional ethics committee of the South of France (CPP SUD MEDITERRANEE II; February 19, 2018; reference number: 2017-A01618-45).
Enrichment of LoF in genes with vision-related GO-terms In the set of 512 pseudogenes identified above, we searched for enrichment in some GOterms, using human and zebrafish orthologous gene names. Using orthologous zebrafish genes, we found significant enrichments in biological processes linked to eye and vision. The three most significant terms were "eye photoreceptor cell differentiation", "peripheral nervous system neuron development" and "cellular response to UV" (Additional file 2: Table  S1A ). Significantly enriched terms, almost all related to eye and vision, were also found with the Phenotype GeneRIF Predicted Z-score database (Additional file 2: Table  S1B ). We found that the most enriched biological process was "visual perception" with human orthologous genes using the DAVID database, but the FDR-adjusted p-value was not significant.
Under the hypothesis of a recent origin, we would expect that cave-specific pseudogenes would accumulate few LoF mutations. Using 47 cavefish genomes from four different caves, our results supported this idea, we only discovered three new vision genes with segregating non-functional alleles, each with only one LoF mutation, and that were present in one or two caves. In addition, one of these LoF mutations was previously identified in the Pachón genome assembly  [9]  and is only found in this population. While many vision pseudogenes can be found in other cavefish species, A. mexicanus data show that few vision genes are pseudogenized, thus supporting the recent evolution of blindness in this species  [9, 18, 19]. Moreover, at a genome-wide scale and without a priori selection of genes involved in vision, we found an enrichment of vision-related GO-terms among 512 pseudogenes, corroborating previous studies comparing the accumulation of conservative versus radical mutations in transcripts  [26].
Removal of artifactual LoF variants As inferred LoF mutations heavily depend on the gene annotation quality, we used MAFFT v7. 467  [47]  to align A. mexicanus protein sequences (keeping the longest transcript of each gene) with orthologous sequences of 9 other teleost species retrieved using Broccoli v1. Conserved regions of these alignments, i. e. , gene parts that are consistently annotated in the different genome assemblies  [48, 49] , were defined using trimAl v1. 4. 1  [50]. We kept 933 LoF variants located in these conserved regions in A. mexicanus and removed genes for which the ensemble annotation lacked a methionine start codon. We also removed premature stop codons and frameshifts located in the last 5% of the genes and consecutive frameshifts restoring the coding frame, for a final dataset of 533 cavespecific LoF variants located on 512 genes.
GO-term enrichment of cave pseudogenes For each pseudogene, we retrieved Danio rerio and Homo sapiens orthologous ensembl gene name and Ensembl gene ID using the R package "biomaRt" v3. 15  [51]. A total of 243 orthologous genes found in humans were used as input in the DAVID database (https://david. ncifcrf. gov/,  [52] ) and in EnrichR  [53]  to investigate diseases enrichment in the OMIM and ClinVar 2019 databases. In zebrafish, we found 335 orthologous genes, and we looked for enriched terms in the "GO Biological Process GeneRIF Predicted Z-score" and "Phenotype GeneRIF Predicted Z-score" databases implemented in FishEnrichR  [53].
The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.
Plant material Two-hundred and thirteen (213) breeding lines from nine African countries that were part of the African core collection were used in this study (Supplementary Table  1 ; Fig. 1 ). The African core collection was constructed from a nucleus of 116 non-redundant breeders-preferred genotypes and expanded to 300 genotypes using genotyping data and the core hunter software  (De Beukelaer et al. 2018). The 213 genotypes of the subspecies fastigiata (32 "hybrid" (combinations between botanical types), 97 Spanish, 10 Valencia) and the hypogaea subspecies (74 Virginia) used in this study were selected based on availability of seed for multi-location trials. Each trial contained a maximum of 200 genotypes per season depending on seed availability.
2007; Okello et al. 2014 Okello et al. , 2017;; Mukoye et al. 2020; Mabele et al. 2021). Percentage GRD incidence  (Waliyar et al. 2007 ) was recorded at 30, 60 and 90 days after planting (DAP). Percentage disease incidence (PDI) was calculated as: The PDI data at 30, 60 and 90 days were used to calculate the Area Under Disease Progress Curve (AUDPC) using the formula: where y i is the PDI at the ith observation; t i is time (in days) at the ith observation and n is the total number of observations  (Simko and Piepho 2012).
Statistical analysis of phenotypic data Best linear unbiased predictions (BLUPs) and thereafter variance components within environments were estimated in the lme4 package  (Bates et al. 2015)  in R (R core team 2021) by manipulating the REstricted Maximum Likelihood (REML) method using the model: where Y ijk is the kth observation for the ith genotype; µ is the overall mean; G i is the Genotype effect, Rj is the replication effect while R/B jk is the effect of blocks nested in replicates, respectively; ε ijk is the error term associated with Y ijk. BLUP variance components estimated within environments were appropriated to calculate Broad-sense heritability (H 2 bs) for GRD using the formula: where σ 2 g is the genetic variance component and σ 2 e is the residual (error) component and nr is the number of replications.
DNA isolation, genotyping and SNP calling Three seeds per genotype were planted per pot in the screen house at the Regional Center for Drought Adaptation Improvement (CERAAS) in Senegal West Africa. Thinning was done to retain one plant per genotype. Twenty mg of oven-dried young leaves from a single plant were collected 21 days after planting. DNA was isolated using the MATAB protocol  (Gawel and Jarret 1991)  and purified using the Zymo DNA purification Kit (ZYMO Research USA). A final concentration of 100 ng/µl was obtained for genotyping. Genotyping was done using the Thermofisher SNP array Axiom Arachis2 with 48 K SNPs  (Clevenger et al. 2018; Korani et al. 2019). SNP data were extracted from raw files and filtered using Axiom Arachis Suite Version 4. 0. 3 from Thermofisher scientific (https:// www.
Genetic diversity, population structure and linkage disequilibrium (LD) Filtered SNPs were used to draw a Neighbor-Joining dendrogram in TASSEL 5. 2. 67  (Bradbury et al. 2007 ). Principal Component Analysis (PCA) was done in the SNP & Variation Suite (SVS version 8. 9. 0). Ten principal components (PCs) and the additive model were used to generate Eigen values. The first three principal components of the variation were plotted and visualized in R software using the scatterplot3d 0. 3-41 package  (Ligges and Machler 2003). The Discriminant Analysis of Principal Components (DAPC) was done using the adegenet v. 2. 1. 5 package in R software by retaining fifty principal components and clustering the genotypes into four groups  (Jombart 2008). LD decay was estimated using the software Pop-LDdecay v.
GWAS analysis Marker trait associations (MTAs) were calculated by combining the filtered SNP dataset of the genotypes with the corresponding BLUPs in R software using the Genome Association and Prediction Integrated Tool (GAPIT) version 2 package  (Tang et al. 2016). The enriched Compressed Mixed Linear Model (ECMLM) method which builds on the Compressed/Mixed linear model factors by grouping individuals into clusters and stipulates the relationship among groups to correct for population structure  (Li et al. 2014 ) was used as below; where y is a vector of the phenotype (disease levels); β represents unknown fixed effects, as well as population structure and marker effects; u is a vector of size s (number of groups) for unknown random polygenic effects following a distribution with mean of zero and covariance matrix of G = 2K 2 a and K is the group kinship matrix with element K ij (i, j = 1, 2,.
s) representing the relationship between group i and j, and 2 a is an unknown genetic variance. X and Z are matrices for β and u while e is a vector of random residual effects that are normally distributed with zero mean and covariance R = I 2 e. where I is the identity matrix and 2 e is the unknown residual variance. The resulting associations were displayed as Manhattan plots alongside quantile-quantile (Q-Q) plots to demonstrate the model fitness using qqman package in R  (Turner 2018). The P values for each marker were adjusted for false discovery rate (FDR)  (Benjamini and Hochberg 1995)  and used to select significant associations (P < 0. 05). Candidate genes were identified within 250 kbp distance of the significant marker using Arachis duranensis and Arachis ipaensis reference genomes.
Identification of haplotypes Stable markers within identified significant QTL regions were used as references for building the haplotype blocks. y = X + Zu + e All markers that were within the LD decay distance of 250 kbp made up a haplotype block. Individuals with ambiguous nucleotide calls were excluded from analysis. Phenotypic data were categorized based on the identified haplotypes and used to test for association. One way ANOVA with Duncan's test as a post hoc test was used to identify significant associations and measure specific differences between pairs of means in R using package  DescTools ( et al. 2021). Only haplotypes that were present in at least five or more genotypes were considered for the statistical analysis. Further, Haploview v4. 2  (Barrett et al. 2005)  was used to visualize the presence of LD between the SNP markers within the significant haplotypes.
Genomic regions associated with GRD resistance Due to low disease pressure in Serere that resulted in the lack of significant genetic variation in the response of genotypes to GRD for season 2021B, this dataset was not included in the GWAS analysis. Both the genotypic and phenotypic datasets that were used for GWAS have been made available at this link (https:// figsh are. com/s/ ebf60 2b52e a2c55 07f26). GWAS for Serere 2020A and 2020B yielded no significant markers (Supplementary Fig. 2 ) and for that reason, were not used for any further analysis or data interpretation. All the results presented below are for Nakabango. Thirty-two significant marker-trait associations (MTAs) (FDR P < 0. 05) were detected in at least one season and/or from combined seasons in Nakabango (Table  2 ; Supplementary Table  2 ).
Manhattan and QQ plots supporting the GWAS results are provided in Fig. 6. Of the 32 markers detected, 21 were from chromosome A04 while 10 were from chromosome B04, which is syntenic to A04 (Fig. 6 ). One marker was detected on chromosome B08 (Table  2 ) and was supported with two seasons data as well as the combined dataset (Fig. 6 ). Eleven markers (AX-147219783, AX-147219785, AX-147219808, AX-147219820, AX-147219834, AX-147219906, AX-147219910, AX-147219925, AX-147247475, AX-147247493, AX-147247508) were common in all the four (2020A, 2020B, 2021B and combined) datasets (Table  2 ). The season 2020B reported the highest number of MTAs (30 SNPs) followed by the combined dataset with 26 MTAs (Table  2 ). The Percent Variation Explained (PVE) that was estimated based on R 2 ranged from 0.
25 to 0. 29 (2020A), 0. 34-0. 42 (2020B), 0. 32-0. 38 (2021B) and 0. 40-0. 49 (combined dataset) (Supplementary Table  2 ). The combined dataset picked up an association with one additional SNP that was not detected with any of the single season's data (AX-147219934).
Haplotype-based association analysis We identified five haplotypes from the respective QTL regions that were associated with resistance to GRD (Table  3 ; Fig. 7 ). All the haplotypes were located on chromosome A04 except one that was located on chromosome B08. Tests of significance for all the possible allelic combinations at each haplotype block is given in Supplementary Table  3. Box plots drawn using each season and combined data confirmed the differences in performance between the favorable haplotypes and the alternative allelic combinations (Supplementary Fig. 3 ). One of the haplotype blocks (TGAA), was just 1 Mbp away from a major disease resistance gene (TIR-NBS-LRR) (Supplementary Table  4 ). Using combined datasets, we identified 39, 25, 13, 9 and 9 genotypes that harbored favorable haplotypes 1, 2, 3, 4 and 5, respectively (Table  3 ).
There were 46 non-redundant genotypes from the combined dataset that harbored at least one favorable haplotype (Supplementary Fig. 4 ). Two genotypes (Ug-5_Oug-SERENUT_9T_UG and Ug-164_Oug-ICGV_SM_06518) harbored all the favorable haplotypes (Supplementary Fig. 4 ). Most of the 46 genotypes were Virginia (39) types with only 5 being Spanish and one each as Valencia and Hybrid types. A majority of the 46 genotypes were from Uganda (Table  3 ) and were genetically similar, forming a cluster within the predominantly Virginia market class group (Fig. 8 ). There were hardly any GRD resistant genotypes among the Spanish market class cluster (Fig. 8 ).
The focus of this initial study was to determine broadly, resistance versus susceptibility under Ugandan GRD hotspots. The Area Under Disease Progress Curve (AUDPC) tool was useful in providing a quantitative summary of GRD intensity over time for each of the genotypes observed. No doubt, GRD will not be fully understood unless the specific agents and forms of satRNA are fully characterised with the corresponding reactions that they elicit from different genotypes. The lack of consistent disease pressure in Serere resulted in no significant marker-trait associations. Accurate phenotypic datasets are critical for successful GWAS results  (Gage et al. 2018). Earlier studies in GRD also reported significant and positive correlation between broad sense heritability (H 2 bs ) and increased disease pressure (Van der  Merwe et al.
2000001621 to the CGIAR Africa Rice Center (AfricaRice) and the Integrated Breeding Platform (IBP) as management entity for the EBCA (Enhancing institutional breeding capacity in Ghana, Senegal and Uganda to develop climate resilient crops for African smallholder farmers). Author EA was supported by Makerere University Regional Center for Crop Improvement, an East and Southern African Centre of Excellence, funded by the world bank through the International Development Association Report No. PAD 1436, Project ID P151847.
Acknowledgements We acknowledge  Essohouna Modom Banla ,  Dramane Sako ,  Issa Faye ,  Amade Muitia ,  Justus Chintu ,  Lutangu Makweti ,  Richard Oteng-Frimpong ,  James Asibuo  and  Adama Mamadou Coulibaly  for contributing germplasm to the African core collection. We are also grateful to  Erick Owuor Mikwa  and  Millicent Avosa  for data and laboratory support, respectively.
All the reference databases have been published for most of quarries known to be exploited during Antiquity: Greece, Italy  [11] , Spain  [12] , Portugal  [13] , France  [14] , and so on. To these two unavoidable methods, a third one has been added by some authors such as cathodoluminescence  [15] , fluid inclusion  [16] , elemental analyses  [17] , and electronic paramagnetic resonance  [18]. The second strategy processes all the data together through principal component analysis to determine the marble source. For this purpose, it is necessary to collect all the measures on the same set of samples (quarries and artefacts). Therefore, to test this holistic approach, other techniques may be added to the panel of the complementary techniques usually used, without necessarily having a database on the techniques developed over several decades.
6 MHz a 90 • pulse of 18. 9 µs, an interpulse delay of 7 s, 12,000 repetitions corresponding to an acquisition time of around 24 h, and a rotation rate of 4 KHz. 25 Mg NMR spectra of one sample of each quarry and two archaeological items (29 MM and BL) were recorded on a 18. 8 T Avance Neo (Bruker, Wissembourg, France) equipped with a 4 mm HX low gamma probe  [24]  using the following parameters as proposed in  [25] : a spectral width of 20,400 ppm (1 MHz) at 48. 98 MHz, a 90 • pulse of 3. 5 µs, an interpulse delay of 17 s, 2048 repetitions of corresponding to an acquisition time of 10 h, and a rotation speed of 10 kHz. The spectra were simulated as follows: 13 C line shapes were simulated with Bruker Biospin software (version 4. 2. 0, 2022, Bruker, Wissembourg, France). 43 Ca line shapes were accounted for assuming a central-transition second-order quadrupolar broadening defined by a quadrupolar coupling constant C Q and an asymmetry parameter η Q , on top of the position (isotropic chemical shift δ iso ) and a convolution by a Gaussian broadening rendering a distribution of δ iso using DMFIT software  [26].
Statistical Software All collected data were analyzed with R version 4. 2. 0 (2022-04-22,)  [30]  and the following packages: dimensio 0. 2. 2  [31] , dplyr 1. 0. 9  [32] , ggplot 2 3. 3. 6 [33] , ggrepel 0. 9. 1  [34] , khroma 1. 9. 0  [35] , patchwork 1. 1. 1 [36] , robCompositions 2. 3. 1  [37] , and tidyr 1. 2. 0  [32].
Unfortunately, the simulation of each spectrum does not give reliable physicochemical parameters in order to use them as provenance markers (C Q around 1. 7 MHz, η Q between 0 and 0. 3 and a LB/GB ratio of approximately 40/40; see table inserted in Figure  4 ) owing to a low signal-to-noise ratio. 4 Ca NMR spectra of dolomitic marbles (black) and their best fit (sola software Bruker Biospin, pink) using the parameters displayed in the table. STB7: Lez-Saint-Béat quarry, THV7: Thasos Cap Vathy-Quarry, CN1, Coin Quarry, 29 MM, male figure from the Museum of Malaga, BL, molding from the Caesar Theatre of Zaragoza. 25 Mg NMR spectra were also recorded for the same five samples as those analysed for 43 Ca NMR (three quarry samples and two artifacts): this isotope has a 5/2 spin and a magnetogyric ratio (-1639 × 10 7 rad s -1 T -1 ) lower than 43 Ca.