dataesr/scientific-paragraphs-categorization huggingface dataset:
Near the MAR axis, water depths along the active transform fault exceed 4800 m within the nodal basin, whereas in the fracture zone valley water depth varies from 3400 m close to the MAR to 3750 m further east (Fig. 2 , Supplementary Fig. 5 ). East of the TdC island group, the fracture zone inner valley deepens to 4000 mbsl, shoaling again eastwards to 3750 mbsl. Differences between the satellite-derived depths in the global dataset GMRT v3. 242 and those in our high-resolution survey reach up to 200-250 m (Supplementary Fig. 5 ). Volcanic edifices within the TTFZ rise up to 400 m above the neighboring seafloor. The presence of the Rockhopper sedimentary fan makes the seafloor to the south of the islands ~350 m shallower than in the surrounding areas of the fracture zone (Supplementary Fig.
5 ). Sub-bottom profiler data (Teledyne ATLAS Parasound P70). Sub-bottom profiler data are used to characterize the acoustic fabric of the seafloor and shallow subseafloor. They can be used to distinguish between buried and exposed seafloor fabric related to seafloor spreading, the presence and internal structure of pelagic sediment cover, and areas covered by relatively young volcanic products. Parasound profile P1 (Fig. 9a ) is oriented SW-NE about 70 km west of Inaccessible Island (Fig. 5b ). The profile shows a row of relatively old ridges (abyssal hills) formed before ~15Ma by seafloor spreading at the MAR  21, 22. The ridge flanks and the valleys and plains between them are partly covered by thin (<50m) sediments that are characterized by low seabed backscatter values (<-35dB). At its northeastern end, profile P1 images relatively young volcanic overprint of the seafloor (compare to Figs.
We mapped volcanic cones in our bathymetric dataset, noting a westward decrease in the frequency of their occurrence (Fig. 7 ). From the available bathymetry, cones appear to be absent westward of a point 250 km WSW of TdC and 130 km east of the MAR (Fig. 7 ). Global 52 and regional  32  seismicity data show local earthquake activity close to the westernmost mapped volcanic cones. Humphris et al. 53  reported a geochemical anomaly amongst sparsely sampled data along the MAR west of TdC, reporting enriched basalts but no clearly plume type basalt. Humphris et al. 53  interpreted these results as an indication of poor and waning communication between TdC plume mantle and normal mantle beneath the MAR in a region where the two were previously better mixed. Without dense sampling of rocks along this portion of the MAR, including the mapped OCC, we cannot provide any new constraints to this problem.
However, there were no signs of increased onshore thermal activity or ground deformation, such as increased rock fall activity or disruption of man-made structures, on the island8. Subsequently, pumice was discovered by cray fishers on Sandy Point Beach on the island's east coast8. Taking the direction of prevailing currents into consideration, the locations of the pumice discoveries suggest its source lay south or southwest of TdC. Subsequent analysis of seismological data from two stations on the island showed a dramatic increase in earthquake activity beginning on 29 July 2004, with more than 2000 local earth-quakes detected between July and December 20049. The initial earthquakes showed a consistent increase in amplitude with time, culminating in four earthquakes with magnitudes of 4. 7-4.
5b ). Schloemer et al. 32  located two earthquakes in 2012 in the close vicinity of Isolde Seamount, at focal depths of 5-15 km. These events suggest that the plumbing system of the volcano may still be active. Deeper earthquakes (up to 30 km depth) were located closer to the TTFZ by Schloemer et al. 32. Conclusions. MBES bathymetry, seabed backscatter, and sub-bottom profiler data around TdC show evidence for relatively recent submarine volcanic activity. The GIS based BTM and backscatter analysis were used for mapping volcanically altered oceanic terrain based on objective criteria. Combining our findings with seismological results, we interpret that these relatively young volcanic cones are being fed by TdC mantle plume material, but this will have to be confirmed by future sampling. Our data further challenge the assumption that the present-day Walvis Ridge-TdC hotspot is located directly below TdC.
Instead, the hydroacoustic data support a hotspot location west of the island, consistent with recently published geophysical data. The newly discovered Isolde Seamount south of TdC was likely the location of a submarine eruption in 2004. An OCC mapped at the ridge-transform intersection west of TdC indicates a relatively recent reduction in melt supply via plumeridge interaction south of the TTFZ. Future rock sampling of the newly discovered submarine volcanoes will be required to test these ideas and better characterize the geochemical composition of the Tristan mantle plume. Combining such geochemical information with the existing data base along the Walvis Ridge will improve hotspot and mantle melting models for the Walvis Ridge hotspot track and mantle plume interaction with the MAR.
Methods Multibeam echosounder data. Hydroacoustic data were acquired by a hull-mounted deepwater KONGSBERG EM120 MBES with 191 beams during cruise MSM20/236 and by the upgraded KONGSBERG EM122 with up to 864 soundings in high-density and dual-swath mode during cruise MSM2435. The ship's speed varied from 13 kn during the transits to 8 kn for dedicated bathymetry surveys around TdC, the TTFZ and the MAR. Data quality varied considerably depending on the ship's speed. To correct the depth-measurements for a varying sound velocity (SV) in the water column, SV-profiles were taken regularly by using an SVPlus probe from Applied Microsystems, which directly measures the sound speed every 1 s during its deployment  35, 36. Shallow seafloor (<200 mbsl) in the vicinity of Nightingale Island was mapped using the KONGSBERG EM1002 MBES with 111 beams, mounted temporarily in the moon-pool of the vessel during the recovery of land stations  35.
Beside the depth information, the EM120/122 and EM1002 systems also provide seabed and water column backscatter data. Rather than using the per-beam backscatter data, we extracted the higherresolution seabed image reflectivity (also referred to as "beam time series" or "pseudo sidescan" data) from the raw MBES files. To avoid confusion within this non-standardized terminology, we have opted to use the term "seabed backscatter intensity". Due to the different transducer geometry and frequency, EM1002 backscatter has not been considered for the combined mosaic. Data were edited and processed using the open source software package MB-System  62. Due to the sometimes high noise level of the multibeam data, we decided not to apply automatic filters and to edit the data manually using mbeditviz.
Both results are available in the PANGAEA data repository (see below). Backscatter intensity values for volcanic cones or less sediment-draped lava flows are significantly increased compared to the overall values (see Supplementary Fig. 6 ). Further details about backscatter validation and mosaicking are given in the Supplemental information (Supplementary Fig. 7 , Supplementary Note 1). Rugosity raster & Benthic Terrain Modeler. To investigate the structural complexity of the study area and to assure a level of confidence when interpreting other derivates, a rugosity (surface roughness) raster was created using the arc-chord ratio (ACR) index  63. The ACR method is defined as the contoured area of the surface divided by the area of the surface orthogonally projected onto a plane of best fit.
In this way, it effectively decouples rugosity from the slope. To reduce artefacts in the rugosity raster caused by the underlying bathymetry data, the ACR rugosity raster was calculated from the threefold resampled bathymetry data. The benthic terrain of TdC was analyzed using ESRI's ArcGISTM with the aid of the application's BTM v. 3. 034,  64  which classifies bathymetry data and analyses seafloor characteristics. The most important derivative for this analysis is the Bathymetric Position Index (BPI). The BPI is modified from the Topographic Position Index, which is used in analyses of terrestrial environments  65. It compares the elevation of each cell in the bathymetry raster with the mean elevation of a defined neighborhood around that cell. In this analysis, an annular neighborhood with an inner and outer radius is used.
MERIAN cruises until the shutdown of the system due to technical reasons on cruise MSM24. The system's beam width is 4. 5° along-track and 5. 0° across-track. The primary high frequency signal (18 kHz) was recorded for water-column investigations, while the secondary low frequency signal (SLF, 4 kHz) is used to image the shallow subseafloor. The Parasound files were converted into SEG-Y data by using the PS32segy tool (Hanno Keil, University of Bremen, Germany). Afterwards, the ReflexW software67 was used to low-pass filter the data and to mute the water column. The vertical resolution of reflections limits the sediment layer separation and depends on the pulse length, which is defined by its frequency and number of periods. Parasound beams are characterized by 4 kHz SLF frequency and a periodic time of 1⁄4 kHz = 0.
Considering SV for the TWT in water, the sample rate resolution results to ~6 cm ((1500 m s -1 /2)/(12,200 s -1 )). Note: the sample rate resolution is always smaller than half the vertical acoustic resolution, which in our case is 0. 19 m, as mentioned above. Satellite data investigation. We tried to back-track pumice of the 2004 eruption in MODIS/Terra + Aqua satellite data using the technique of Jutzeler et al. 61. However, the 2004 dataset does not cover all of the spectral bands necessary for that technique, and cloud cover prevented observations in the visible light bands (surface reflectance). volcanic cones and lava fields west of Tristan da Cunha (TdC) in the less-studied South Atlantic. TdC and the associated Walvis Ridge are key hotspot expressions whose source motion and melting history can contribute to the understanding of fundamental processes of mantle plume dynamics and planetary evolution.
Results Data coverage. The regional distribution of our new MBES bathymetry and backscatter data at 30 m grid resolution is shown in Fig. 2. All but the very shallowest seafloor is covered within a radius of ~45 km around the archipelago. Further afield, nearcomplete coverage was also achieved at the intersection of the TTFZ with the MAR. More scattered data were collected along regional tracks between seismological and electromagnetic oceanbottom stations and on transits to the area  35, 36. We describe features of the seafloor in three-specific areas (see Figs. 2 3 4 5 6 7 8 ). Central area. On the main island's submarine flanks, the contour at 2000 meters below sea level (mbsl) is almost circular with a diameter of ~20 km (Fig. 3a ). At this depth, a saddle to the southwest of the island connects the edifice to a plateau bearing the neighboring islands.
The shallow shelf around Inaccessible Island connects southeastwards to a ~25 km 2 shelf around Nightingale Island, where water depths are less than 150 m. The edifice of Nightingale Island (Fig. 3 ; 37°26′S 12°29′W, ~2. 6 km 2 ) has a basal diameter of ~17 km, encompassing steep slopes with angles at >15°very similar to those of TdC and Inaccessible Island. The new datasets reveal previously unknown features, including two submarine volcanoes to the southwest and southeast/east of Nightingale Island, named Rockhopper and Isolde Seamounts (Fig. 3 ). Rockhopper Seamount (37°34′S 12°45′W) has a basal diameter of about 20-22 km and rises to a depth of 470 mbsl at its summit, almost 2500 m above the adjacent seafloor (Fig. 4a, b ). The  summit forms an NNW-SSE elongated ridge that is almost parallel to the region's abyssal hill fabric.
A second, less prominent WSW-ENE striking ridge runs almost parallel to the TTFZ (Fig. 5 ). With >20°, the seamounts' slopes are considerably steeper than the slopes of the islands, but still within the range of oceanic shield volcanoes  16. A sedimentary fan, characterized by low backscatter values (<-30 dB), is imaged to the southeast of Rockhopper Seamount (Fig. 3 ). Isolde Seamount (37°26′S 12°20′W) has a basal Nightingale Island (Fig. 4c, d ). The summit lies on a ridge that is elongated ENE, almost parallel to the TTFZ. The data reveal the presence of a ~500 m-wide depression whose floor lies at ~550 mbsl, which we interpret as a summit crater. A more detailed survey would be necessary to test this interpretation. In addition to the new named seamounts, a small eroded plateau (4 × 4 km, with a basal diameter of 6 km) is mapped in water depths of 200-220 mbsl to the north of Nightingale Island and east of Inaccessible Island (37° The seafloor to the west of Inaccessible Island is imaged in profile P2 (Fig.
Acknowledgements This work was supported by the  German Research Foundation (DFG)  grant  GE 1783/4-1/2  as part of the  DFG Priority Program SPP1375 SAMPLE  ( JE 296/9-1, 9-2 ). Additional support was provided by the  Alfred Wegener Institute Bremerhaven. We thank the captain  Ralf Schmidt , the crew of R/V MARIA S. MERIAN and the Scientific Parties of cruises  MSM20/2  and  MSM24. Bathymetric data acquisition during cruise MSM24 was supported by  Irena Schulten  and  Gesa Barkawitz. This work contains public sector information, licensed under the Open Government Licence v2. 0, from  UKHO. Julia Sigwart  and  Joachim Reuter  also greatly contributed to the improvement of this paper.
Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors' ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important.
Another way around selection is to study passenger nonproductive sequences, which are unsuccessful products of VDJ recombination and thus unaffected by selection  [5, 17, 22]. These sequences make up a minority of DNA sequences, and are rarely found in mRNA sequences because of allelic exclusion, which limits their use to very large datasets. Another confounding factor arises from phylogenetic biases due to the complex multi-lineage structure of the repertoire. While methods have been developed to infer substitution rates from lineages in a lineage-specific  [21]  or repertoire-wide way  [23] , they do not aim to correct for selection and do not address the question of hypermutation targeting. Here we propose a new framework for quantifying and predicting immunoglobulin mutability. The model is trained on the reconstructed phylogenies of nonproductive lineages from very large published B cell repertoires totalling around half a million nonproductive sequences  [7] , allowing us to overcome previous limitations of dataset sizes.
Synthetic data was designed to mimic as closely as possible the features of the real repertoire data to be analyzed. We used tree topologies inferred on out-offrame lineages from 9 individuals of Ref. [7]. The sequence at the root of each tree was replaced by a random sequence drawn using IGoR, a model of stochastic VDJ recombination  [22]. Random mutations were then introduced along the tree structure, following the same number of mutations on each branch as in the original lineage, and according to the SHM model (Eq. 2). Contextdependent parameters γ w were set to the previously published S5F model  [16]  , and three variants of the position dependent β x were tested: flat, and two sinusoidal profiles (see Methods). Finally we collected sequences at the leaves of the trees into a synthetic dataset.
Starting from this dataset, we performed alignment, clonal family inference, tree reconstruction and finally model inference using the exact same procedure as for real data. We compared parameters inferred this way to the true values of γ w and β x (Fig. 2 ). We were able to recover these rates with excellent accuracy (Pearson's r 2 = 97% for both γ and β). The fact that the procedure recovers the correct position-dependent profile β x , including a flat one (Fig. 2B ), shows that the framework successfully cor-rects for the two following confounding factors. First, sequence conservation across the different V, D, and J segments means that context and position are often intertwined, making the extraction of each dependence difficult. Second, high variability in the CDR3 may cause errors in the assignment of sequences into clonal families, and makes it harder to reliably call mutations than in the germline regions.
Mutabilities depend on both sequence context and position Confident that our procedure is able to infer rates reliably, we next applied it to real data, consisting of the out-of-frame lineages from Ref. [7]. The inferred dependencies of mutability with context and position are presented in Fig. 3. We represent context dependence using a flat variant of the "hedgehog" plots used in Ref. [16] , for A-, T-, C-, and G-centered motifs (Fig. 3A-D ). Full parameter tables are available at https://github. com/ statbiophys/shmoof. Context dependent rates for A-centered motifs correspond well to the standard WA classification  [26] : 76% of A-centered 5-mers with γ w > 1 are of the WA type, and only 7 of 128 WA 5-mers have γ w 1. T-centered motifs are dominated by coldspots and their mutabilities do not align well with their corresponding reverse complement counterparts.
Model is consistent across individuals and explains data better than previous approaches To check the model's generality, we estimated its variability across individuals by computing Pearson's correlation coefficient between the context (γ w , Fig. 4A ) and position (β x , Fig. 4B ) mutability profiles of different donors. The precision with which we can estimate model parameters depends on the number of sequences used for inference, particularly for rare 5-mer contexts. Because two individuals had many more reads than the others 7, we pooled together these seven individuals to make com- parisons with similar dataset sizes (Fig. 4C ). We then compared the 2 individuals and 1 meta-individual with each other and with a model learned on data from all individuals. For the 2 individuals with the largest repertoire datasets, the results are highly reproducible with Pearson's r 2 = 78% for context and r 2 = 70% for po-sition parameters (Fig.
4I ). Correlation rises to r 2 = 44% for contexts appearing in synonymous mutations, versus r 2 = 18% for the other contexts for which S5F recourses to extrapolation, emphasizing the limitations of that extrapolation. A summary of the performances of the different modeling approaches on the mutabilities in the different regions of the IgH gene is shown in Fig. 4J. We also checked for overfitting by dividing the dataset into a training and a testing, finding similar results (Fig. S6 ). The full position and context dependent model (µ s,x = γ w β x ) performs better than models with context or position alone (µ s,x = γ w and µ s,x = β x ). While the context explains the bulk of the mutation profile, adding positional effects substantially boosts performance. Our model clearly outperforms the S5F model, although it should be reminded that S5F was trained on a distinct dataset.
We then computed the correlation function f (r), and compared it to true profiles (Fig. 5B ). We obtain a good agreement for ξ = 10 and = 5% corresponding to an average of ∼ 1 follow-up mutation per targeting event. This result suggests that as many as 50% of observed mutations are follow-up mutations. We asked whether this large number of non-targetted mutations may bias the inference of the targeting model, which assumes no follow-up mutations. To assess this effect, we re-inferred the rates {β x } and {γ w } from synthetic datasets simulated with ξ = 10, = 5%, with data-inferred context profile γ w , and with data-inferred or flat position profiles β x (Fig. S9 ). We find that the re-inferred mutabilities mostly agree with the true ones, with a slight shrinkage of values and enhanced mutabilities of cold spots, owing to the equalizing effect colocalization.
Our framework enables direct measurement of the mutability γ w of a wide range of 5-mer contexts, recovering the known classifications of hot and cold spots  [16, 28]. We show that our model outperforms existing methods as well as purely context or position dependent models in terms of explaining the data. The introduction of an explicit and universal position dependence, β x , allows us to unveil an excess of mutations in the CDR1 and CDR2 regions. This enrichment of mutations cannot be simply explained by their harboring more hotspot contexts. We cannot exclude that this residual position dependence is due to more complex context effects missed by our model (based e. g. on 7-mers, which would be impractical to infer from the present dataset). Alternatively, SHM may preferentially target these regions independently of their sequence context, possibly through epigenetic mechanisms.
For further analysis we chose to focus exclusively on the IgG class. Reads were filtered for quality and paired using default presto parameters. Pre-processed data was then aligned to V, D and J templates from IMGT  [38]  database using IgBlast  [39]. In total there were 3. 6 × 10 6 IgG sequences per person (average 3. 6×10 6 , median 1. 8×10 6 ), of which up to 2% were unproductive (average 5. 7 × 10 4 , median 2. 9 × 10 4 ).
Synthetic datasets We created synthetic datasets using the S5F model of mutability (downloaded from clip. med. yale. edu/shm) for γ w. We used a flat profile, β x = 1 as well as sinusoidal profiles ln β 1 x = 2 sin(x/δ) -1 and ln β 2 x = 2 cos(x/δ) -1 with δ = 50. For each branch (i, j), we compute the mutability µ si,x as a function of x, and then introduce mutations at n random positions picked without replacement according to µ x / x µ x , where n is the number of mutations on the branch (fixed by the lineage structure taken from the real data). FIG. S3 : Alternative position definition. We compare the models based on different position definitions: x, the distance from the 5 end of the V gene (A, red) and xr, distance from the 3 end of the J gene (B, green). Error bars correspond to 95% confidence intervals.
Annotation and functional relevance Significant CpG sites were annotated by GREAT v3. 0. 0  [18]. We correlated the significant CpG sites we identified to the expression level of gene nearby (with a region of ± 250 kb) by cis expression quantitative trait DNA methylation (cis-eQTM) analysis. Blood eQTM was assessed in the BIOS consortium dataset  [19]. Matched nasal RNA sequencing data and DNA methylation data was used to perform eQTM analysis for significant nasal CpG sites and these results were additionally replicated in the larger collection of nasal brushes obtained in the PIAMA study  [20]. The genes that were identified in eQTM analysis were used for pathway analysis by Con-sensusPathDB  [21]. To check if the results of replicated CpG sites were affected by inhaled corticosteroids (ICS), we stratified asthma subjects by ICS usage, and compared the DNA-methylation in the remission group to asthma with ICS and asthma without ICS group, respectively, by t-test.