dataesr/scientific-paragraphs-categorization huggingface dataset:
6 ka, which has a very high September SIC (86 %) but a relatively low FCC relative abundance (3. 4 %). For this sample, only a single modern analogue could be identified, indicating that the fossil diatom assemblage is different from almost everything in the modern reference database. The single selected analogue is not chosen by the transfer function for any of the other MIS 5e samples from core TPC287, indicating that it is unlikely to be a truly representative modern analogue for the MIS 5e condition at this core site. The location of this single selected analogue, which is further south than any of the analogues chosen for the other MIS 5e samples from core TPC287, suggests that the fossil assemblage has been biased towards colder, heavier seaice conditions, probably due to dissolution or transport of the preserved assemblage.
Chadwick and Allen, 2021i). September SIC and SSST data for all samples, produced using the MAT transfer function, are available from PAN-GAEA (https://doi. org/10. 1594/PANGAEA. 936573, Chadwick et al. , 2021). Sample availability. Sediment sample material for core NBP9802-04 (http://igsn. org/DSR0003YW, last access: 18 January 2022;  Anderson et al. , 1998)  is available from the Lamont-Doherty Core Repository at Columbia University (corelab@ldeo. columbia. edu). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-129-2022-supplement. Author contributions. MC was responsible for data curation, investigation, visualization, and writing -original draft preparation. CSA was responsible for conceptualization, project administration, resources, supervision, and writing -review and editing.
Acknowledgements. The  British Ocean Sediment Core Research Facility (BOSCORF)  is thanked for supplying sediment samples for core TPC287 and multi-sensor core logging of core PC509. We thank the  Lamont-Doherty Core Repository of the Lamont-Doherty Earth Observatory  for providing sediment sample material for core  NBP9802-04 (IGSN -DSR0003YW ). The  International Ocean Discovery Program (IODP)  is thanked for providing the sample material for core U1361A. We also thank the  Oregon State University Marine and Geology Repository  for providing sediment samples for core  ELT17-9 , the  Sorting Centre of MNA-Trieste (Italy)  for providing sediment samples for core  ANTA91-8 , and  Simon J. Crowhurst  from the  Department of Earth Sciences, University of Cambridge (UK) , for X-ray fluorescence scanning of core.
Plant materials The WASAP was comprised of 845 accessions assembled by breeders, physiologists, and geneticists in the National Agricultural Research Organization from four West African countries (Senegal, Mali, Togo, and Niger) ( ), followed by guinea-caudatum (n = 25), durra-bicolor (n = 13), caudatum-bicolor (n = 8), guinea-durra (n = 5), and kafircaudatum (n = 1). Many accessions were not classified morphologically into botanical classes (n = 230). The Supplemental Data S1 contains more details about the panel. Genetic diversity and structure of the WASAP were compared with the GDP that consists of 692 worldwide sorghum accessions (excluding accessions from Americas) with available sequencing data including West African accessions in the GDP (hereafter named WAS-GDP)  (Lasky et al. , 2015; Morris et al.
Illumina single-end sequence reads of the WASAP and raw sequence data of the GDP were processed together using the TASSEL GBS v2 pipeline  (Glaubitz et al. , 2014). Sequence reads were trimmed to 64 bp and identical reads collapsed into tags. Tags were aligned to the sorghum reference genome v3. 1  (McCormick et al. , 2018; Paterson et al. , 2009)  using the Burrows-Wheeler alignment program  (Li & Durbin, 2009). Single nucleotide polymorphism markers were discovered using the DiscoverySNPCallerPluginV2 of the TASSEL GBS v2 pipeline with minimum locus coverage (mnLov) of 0. 1 and other parameters kept to default settings. A total of 546,133 SNPs were discovered. The SNPs with >20% missing data were excluded (n = 393,396 remaining SNPs). The SNPs with minor allele frequency (MAF) <. 01 were excluded (n = 201,193 remaining SNPs).
Genome-wide SNP variation The imputed dataset was functionally annotated to determine SNP effects on protein-coding genes using the snpEff program  (Cingolani et al. , 2012). The structural location and functional class (synonymous, missense, or nonsense) of each SNP were determined based on the sorghum reference sequence. Genome-wide SNP distribution along chromosomes, minor allele frequencies, and pairwise nucleotide diversity were estimated using the VCFtools program  (Danecek et al. , 2011). Linkage disequilibrium was determined for the entire WASAP and for each country's germplasm separately. Linkage disequilibrium was estimated based on the pair-wise correlation coefficient (r 2 ) among SNPs with MAF >. 05 (to reduce computational burden) in a window of 500 kb using the PopLDdecay package  (Zhang, Dong, Xu, He, & Yang, 2019).
Genetic structure analysis The genome-wide SNP variation of the collection was assessed based on the principal component analysis (PCA) using the snpgdsPCA function of the R package SNPRelate  (Zheng et al. , 2012). The accessions of WAS-GRIN and GDP were included in the analysis to robustly determine principal axes of genome-wide SNP variation and clustering of the WASAP along other germplasms. The combined dataset consisted of a subset of 103,871 (100K) high-quality SNP markers with a high level of polymorphism (MAF >. 1). The GDP accessions were used to determine the principal components (PCs) of genome-wide variation. These PCs were then used to estimate the genome-wide variation of the WASAP and WAS-GRIN accessions. The number of ancestral populations and ancestry fractions in the WASAP were determined using ADMIXTURE  (Alexander, Novembre, & Lange, 2009).
The imputed SNP dataset was LD pruned using PLINK 1. 9  (Purcell et al. , 2007)  to reduce redundancy of SNPs that are in high LD because such SNPs provide the same genetic information. Ancestral populations and ancestry fractions were determined using the pruned SNP dataset (60,749 independent SNPs) for K = 2-20 populations using default settings of ADMIXTURE. A fivefold cross-validation and 2,000 iterations were performed to identify the optimal K. We defined the optimal K as the minimum K-value where cross-validation error no longer decreased substantially. Accessions were assigned to subpopulations based on 0. 7 membership threshold. The neighborjoining distance-based clustering method was used to assess the genetic relationship among WASAP ancestral populations, WAS-GRIN, and GDP. The genetic distance matrix was generated based on the 100K SNP set using TASSEL 5  (Bradbury et al.
Field phenotyping Field phenotyping of a subset of 572 WASAP accessions (based on seed availability) was conducted at the Bambey Research Station, Centre National de Recherches Agronomiques (14. 42˚N, 16. 28˚W) in Senegal during the growing season of 2014. Two experiments, early-sown date at the beginning of the rainy season-normal planting date (Hiv1) and late-sown postflowering drought-stressed-planted 30 d later (Hiv2), were performed. A randomized incomplete block design (augmented block design) was performed with The Plant Genome 30 blocks of 24 entries each following a column-row field layout for spatial data analysis. Each block contained 19 genotypes and five check varieties. The five checks were randomly assigned into each block. Each genotype was assigned only once in each experiment.
Statistical analysis of phenotypic data Phenotypic variation was analyzed using the R program. Spatial analysis was performed to verify the effect of spatial variation across each experiment based on the check varieties within blocks using the SpATS package (Rodríguez-Álvarez, Boer,  van Eeuwijk, & Eilers, 2018). The genotype-adjusted mean value was determined after correcting for significant spatial variability. Variance components were estimated by fitting the mixed linear model with random effects for all genotypes (G), environment (E), and G × E interaction effects using the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015). The significance levels of variance components were determined using the RLRsim package  (Scheipl, Greven, & Küchenhoff, 2008). Broad-sense heritability (H 2 ) was estimated for each trait from the estimated variance components following the formula proposed by  (Piepho & Möhring, 2007)  for unbalanced data: 𝐻 2 = σ 2 𝐺 σ 2 𝐺 + ν∕2 where σ 2  is the genotypic variance, ν is the mean variance of a difference of two adjusted means.
The early and late planting date experiments were considered as two environments. Phenotypic correlations among traits were calculated using the Pearson correlation of the PerformanceAnalytics package  (Peterson et al. , 2014). BLUP values for each trait were calculated by combining data from both environments. Tukey's Honestly Significant Difference (TukeyHSD) in the Agricolae package  (Mendiburu, 2009)  was used to test the difference of genotype performance between environments.
Genome-wide association studies Genome-wide association studies were performed using the general-linear model (GLM) in the GAPIT R package  (Lipka et al. , 2012)  and multi-locus mixed model (MLMM) in the mlmm package  (Segura et al. , 2012). The MLMM stepwise regression model was used to account for background effects. The GLM does not account for background effects (population structure and kinship) but shows most significant associations. In contrast, the mixed-model method implemented in MLMM accounts for background effects but increases falsenegative associations. These different methods were used as complementary. We did not consider looking at nominal p-value, as we are aware that the GLM results are generally inflated. Our goal was to test the hypothesis that some of the top associations would colocalize with known candidate genes.
Bayesian information criterion and LD iteratively nested keyway (BLINK)  (Huang, Liu, Zhou, Summers, & Zhang, 2019)  was also used to verify overlapping associations with GLM and MLMM methods. A total of 130,709 SNPs with MAF >. 02 were used for the GWAS analysis. We use SNPs with MAF >. 02 because moderately rare variants can contribute to phenotypic variation  (Hernandez et al. , 2019; Peloso et al. , 2016). The first three PCs generated from TASSEL 5 and kinship matrix from GAPIT were used to account for polygenic background effects in the MLMM analysis. The GWAS were performed using BLUP values of DFLo and PH across two environments and for each environment. Linkage disequilibrium heatmaps of genomic regions surrounding GWAS QTL were constructed using the LD heatmap 0. 99-4 package  (Shin, Blay, McNeney, & Graham, 2006).
Genome-wide association studies for flowering time and plant height We assessed the effectiveness of the genome-wide SNP data for genetic dissection of complex quantitative traits using GWAS. For DFLo BLUPs, the GLM model identified many significant associations at Bonferroni correction. 05 (Figure  5a ). A leading QTL was identified near Ma6/Ghd7 candidate gene between SNPs S6_651847 (top association in the region, ∼45 kb away) and S6_699843 (within gene). A second leading QTL was identified between S9_54917833 and S9_54968379 at 43 kb and 4 kb from SbCN8 candidate gene, respectively (Supplemental Table  S4 ). The GLM showed inflation of p-values with many significantly associated SNPs. The MLMM model identified eight QTL at Bonferroni threshold (3. 8 × 10 -7 ). Some of these QTL colocalized with known candidate genes Ma6 at 45 kb (S6_651847) and SbCN8 at 381 kb (S9_55345348) (Table  3 ).
Developing a high-quality regional genomic resource In the present study, we assembled 756 sorghum accessions from West African sorghum germplasm and characterized the genome-wide SNP variation of the panel. We demonstrated that this genome-wide SNP dataset is of sufficient quality for genomic and quantitative genetic analyses suitable for crop improvement through genomics-assisted breeding. The strict data filtering criteria used before and after genotype imputation provided a final dataset with reduced number of SNPs (n = 159,101) many of which have impacts on protein-coding sequences (Supplemental Figure  S1A ). The quality control of the SNP dataset matched our expectations, as the F ST , PCA, and neighbor-joining analyses (Figures  2  and 4 ; Supplemental Figure  S2 ) confirmed the expected structure of sorghum by botanical type and geographic region  (Bouchet et al.
, 2017; Lasky et al. , 2015; Morris et al. , 2013; Wang, Hu, Upadhyaya, & Morris, 2019). The validity of the SNP dataset was further confirmed based on GWAS with the identification of QTL (Figures  5  and 6 ) that colocalized with known candidate loci: Ma6 and SbCN8  (Murphy et al. , 2014; Yang et al. , 2014)  for days to flowering and qHT7. 1 and Dw3  (Li et al. , 2011 (Li et al. , , 2015;; Multani et al. , 2003)  for plant height. Although regional diversity panels could limit confounding effects as a result of population structure in GWAS, in this case, the strong population structure in the WASAP appeared to increase false-positive associations in the GLM  (Brachi, Morris, & Borevitz, 2011). However, using the stepwise regression in MLMM though helped to control the inflation of p-values observed in the GLM (Figure  5  and 6 ).
Suitability of genomic resources for GWAS To establish the utility of our genome-wide SNP dataset for GWAS, we carried out GWAS for DFLo and PH and demonstrated colocalization of QTL with known genes from previous studies. While flowering time genes and natural variants have been characterized in the U. S. sorghum  (Casto et al. , 2019; Murphy et al. , 2014) , the genetic basis of the substantial photoperiodic flowering time variation in West African sorghum is not yet known  (Bhosale et al. , 2012). The QTL S6_651847 near Ma6 (Gdh7)  (Murphy et al. , 2014)  highly contributed to the proportion of phenotypic variation of flowering time. This QTL was mapped by GLM, MLMM, and BLINK methods across environments (Figure  5c  and 5d ; Supplemental Figure  S6A ), indicating that Ma6 might be a major flowering time gene in the WA sorghum germplasm.
9%, respectively, of height variation (Supplemental Table  S6 ) but were not colocalized (within 500 kb) with known height genes. The major-effect QTL from this study were mapped using a diversity panel but their effects on breeding populations and different elite backgrounds across West African environments are unknown. These major-effect QTL would need to be validated with multi-year phenotypic data and other approaches, such as multi-parent linkage mapping and near-isogenic lines. Following validation, these height and flowering QTL could be useful for marker-assisted selection to recover locally adaptive plant types across botanical types within WA or during prebreeding with exotic trait donors  (Yohannes et al. , 2015). Given the positive findings for height and flowering time, we anticipate this resource will be suitable for characterization and mapping of other complex traits of interest to West African breeding programs.
For instance, Togo materials grown in common garden experiments in Senegal were able to mature on time in both sowing dates. This panel is suitable for assessing agronomic traits such as grain yield from caudatum cultivars, drought tolerance from durra and durra-caudatum cultivars, or grain quality and disease resistance from guinea cultivars. This SNP dataset could be used by the genetics community in genomic prediction, genetic diversity and haplotype analyses, and GWAS to serve global sorghum breeding especially in combination with other regional genomic resources. Historically, West African sorghum germplasm has been useful for global sorghum breeding including durra and caudatum accessions that were sources of yellow endosperm and.
, 2011; Robert et al. , 2017). Substrate properties (e. g. , grain size and stability) are important features affecting cold-water coral and sponge community composition and density  (Wilborn et al. , 2018; De Clippele et al. , 2019). MBES offers the opportunity to quantify the backscatter echo intensity as a proxy for substrate roughness, composition and texture. The backscatter corresponds to the overall "inner and micro-scale" material properties of the seabed  (Jackson and Briggs, 1992; Brown and Blondel, 2009; Micallef et al. , 2012). So far, this aspect of acoustic vertical mapping has not yet been investigated, nor has it been used for the study of substrate characteristics and their distribution at vertical geomorphological features. This study uses acoustic data collected with a MBES frontmounted onto a ROV at a deep-sea wall located offshore Western Greenland with the aim to (i) improve the workflow to obtain well-navigated vertical bathymetry and retrieve substrate information, (ii) map out the habitat diversity by applying an unsupervised habitat mapping method based on abiotic terrain variables and (iii) test if the unsupervised abiotic classes contain different benthic communities characterized with ROV photography.
Digital Terrain Model Acquisition A portion of the underwater wall was mapped using a Reson7125 multibeam echosounder (MBES; 400 kHz, max. 140 • swath angle, 512 beams) front-mounted onto the ROV Isis piloted from the RRS Discovery (Dive D334). Over 2h40, the ROV performed 7 survey lines at a constant distance (25 m) parallel to the wall. Horizontal survey lines were achieved at three depths, 25 m apart (Figure  1C ). They were undertaken with two different headings (i. e. , 137 and 214 • ) to ensure better coverage of the different parts of the wall, as the terrain displayed different orientations (Figure  1C ). In total, seven survey lines were carried out by keeping Isis' attitude as constant as possible. The MBES system was operated through the Seabat7K software, while the data were recorded with PDS2000, v.
, 2013; Huvenne et al. , 2018). Corrected navigation was acquired by adding the coordinates recorded by the DVL to the average offset between the DVL and the USBL recordings, calculated using a 180-s interval rolling average (e. g. , Supplementary Figure  2 ). Acoustic data files were converted from the. pds format to. s7k files using PDS2000 (version 3. 7), and then transferred into the CARAIBES software (Ifremer) for computing of the terrain point cloud of the wall. As established by  Huvenne et al. (2016)  and  Robert et al. (2017) , smoothed navigation coordinates were transformed to a metric coordinate reference system (UTM Mercator) for rotation in R (version 3. 2. 3; R Core Team, 2013)  in order to simulate a conventional downwardlooking configuration for processing the acoustic data, as to date no acoustic processing software offers the option of processing forward-looking MBES data.
Furthermore, attitude data were transformed to comply with the new downwardlooking configuration of the navigation (see in  Huvenne et al. , 2016). The datasets of the survey lines recorded with similar heading were merged after aberrant soundings were manually removed in CARAIBES. This resulted in two 0. 3 m-resolution point clouds, one for each part of the wall, which were exported as point clouds in. txt and back-rotated to their initial reference system in R  (Robert et al. , 2017). The software  CloudCompare (v. 2. 11; 2019)  was used to spatially combine the point clouds collected with different ROV headings. Small lateral adjustments (<10 m) had to be made as slight offsets of latitude and longitude arose between both point clouds, possibly as a result of the smoothing operations of the navigation.
Backscatter intensity was corrected in the Seabat7k software for spherical spreading and absorption losses based on the water temperature and salinity at depth. The acoustic signal amplitudes recorded in the. s7k files did not represent the actual reflectivity in dB, but nominal values (i. e. , no unit). The backscatter extraction with the function Epremo of CARAIBES simply relays that information while the function Ereamo performs the projection in the 3D space. No correction accounting for the true incidence angle on the seafloor was applied. A mosaic was created using the smoothed and rotated navigation coordinates, and further exported in a point cloud with a resolution of 0. 3 m. The backscatter was also back-rotated in R, and merged with the bathymetry point cloud by averaging the four nearest backscatter values based on the X,Y,Z coordinates of the bathymetric points.
relative topographic position (Bathymetric Position Index, BPI) of the terrain in addition to the backscatter values which were used as a proxy for substrate physical characteristics  (Wilson et al. , 2007; Brown et al. , 2011). Normal vectors were computed with a quadric function to derive multi-scale topographic variables (Table  1 ) and were transformed to "dip/dip direction" for computing the slope and the aspect from which the roughness, the mean and the Gaussian curvatures were derived in CloudCompare following  Robert et al. (2017). Terrain Ruggedness Index (TRI), Orientation and Topographic Position Index (TPI) were computed in R [R Core Team, 2013; code provided from  Robert et al. (2017) ]. Abiotic descriptors were calculated for each point of the point cloud. This produced the input dataset for the subsequent clustering: a matrix where each point (i.
Acquisition of Seabed Imagery and Biological Data The biological data were extracted from seabed images collected during DY081, Dive D333  (Hendry, 2017; Culwick et al. , 2020). In total, 159 images were extracted across a depth gradient (715 to 807 m) explored during two vertical transects that crossed the flank of the cross-shelf glacial trough (Figure  2 ;  Broad, 2020 ; see methodology within). The position of the images corresponded to the area mapped using the forward-facing MBES during Dive D334. Images of the wall were collected every 30 s using the ROV Isis at an approximate horizontal distance from the wall of 2. 5 m. The ROV is equipped with a forward-facing camera "Scorpio" (Insite Pacific Inc. ) which is offset from the ROV frame at a downfacing angle of 22. 5 • and carries parallel lasers spaced 0.
e. , environmental proximity) rather than spatial proximity  (Benoist et al. , 2019; Broad, 2020). Morphospecies abundances were summed in each composite sample. To test for differences in the biological assemblages characterizing each majority cluster, morphospecies abundance data within composite samples were Hellinger transformed and investigated with nonmetric multidimensional scaling plots (nMDS) using a Bray-Curtis dissimilarity matrix calculated in R with the vegan package  (Oksanen et al. , 2013 ). An Analysis of Similarities (ANOSIM) and Similarity Percentages (SIMPER) were calculated in PRIMER Version 7 to identify significant differences between majority clusters and the morphospecies responsible for pairwise dissimilarity  (Anderson et al. , 2008).
DISCUSSION This study successfully mapped and characterized the fine-scale topography of a vertical wall located in deep Greenland waters (760 m). Additionally, our study also extracted the backscatter information from the MBES data and used it together with terrain variables calculated in the 3D space, to create the first habitat map of a deep-sea vertical wall. The subsequent comparison with the faunal communities identified in groundtruthing images indicated that the initial unsupervised classification resulted in habitat categories holding ecological relevance.