dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1978; Satow et al. , 2015; Vinci, 1985; Wulf et al. , 2020). The marine and onshore thickness data constrain a minimum area of 1. 4 × 10 4 km 2 (within the 1 cm isopach), giving an erupted tephra volume of ∼3. 1 km 3 , equivalent to 2. 2 km 3 DRE magma volume or ∼5. 3 × 10 12 kg magma mass and a magnitude of M ∼ 5. 7 (Figures  5a, 8 , Table  1  and Table  S3  in Data Set S1). The very widespread marine tephra layer, CA4, is the distal facies of the 22 ± 0. 3 ka old Cape Riva tephra (Bronk Ramsey,  Albert, et al. , 2015) , the rhyodacitic product of the third caldera-forming eruption of Santorini. On land, the Cape Riva tephra consists of a thick (∼4 m), welded and unwelded pumice fall deposit (Cape Riva A) overlain by tens of meters of ignimbrite  (Druitt, 1985; Druitt & Sparks, 1982). Marine tephra CA4 is found in 15 POS 513 cores.
Kolumbo The youngest tephra layer identified in our cores (CA1) was produced by the 1650AD eruption of Kolumbo submarine volcano. We found this tephra in 16 POS 513 cores mostly to the east of the volcano where it is 6-73 cm thick and comprises proximal submarine pyroclastic density current and fall deposits and distal fall deposits from subaerial ash transport. We used data from  Cantner et al. (2014)  and  Fuller et al. (2018)  to estimate the proximal 20 m isopach at the crater wall and 2 m isopach at the foot of the edifice. Combined with our marine data, the minimum area for the Kolumbo Tephra (within the 2 cm isopach; Figure  5 ) is 1. 8 × 10 4 km 2 and the total tephra volume is 4. 4 km 3 , corresponding to 2. 5 km 3 DRE magma volume or 5. 7 × 10 12 kg magma mass and a magnitude of M ∼ 5.
7 (Figures  5b, 8 , Table  1  and Table  S3  in Data Set S1). The rather limited lateral dispersal of Yali-1 ash seems to contrast with the impressive thickness of the proximal deposits. Where it is thickest (POS 513-52), the Yali-1 ash consists of four normally graded medium to fine ash layers with boundaries at low angles to each other, suggesting deposition from submarine density currents. As discussed above, the marine ash related to the Yali 2 eruption comprises two contemporaneous facies distinguished by glass composition and western and southern dispersal (Figure  4c ). The 3-to 11-cm-thick southern facies (Yali-2a) is massive, rather well-sorted, normally graded fine lapilli to coarse ash. The western facies (Yali-2b) is massive, 3-22 cm thick fine ash in three cores but in core POS513-50 closest to Yali island, it is an 86-cm-thick bed of multiply graded medium to fine lapilli overlain by stratified ash (Figure  4c ).
For the warm, relatively wet high-sealevel Holocene period all cores show elevated sedimentation rates between 6. 5 and 3. 6 ka, that is, from the top of sapropel S1 up to the Minoan tephra, similar to the rates below sapropel 1. Sedimentation rates within sapropel S1 (Figure  9 , Table  S2  in Data Set S1) are generally lower. Sapropel S3 is present in five cores but only two of these (cores POS513-37, -46) yield elevated sedimentation rates for the S3 interval. Although the sedimentation rates of the Aegean sapropel intervals roughly agree with those found at ODP967 south of Cyprus (S1 6. 6 cm/kyr, S3 5. 2 cm/kyr; Grant   9a ), yellow dashed line is the estimated coast line during the Middle/Late Pleistocene glacial maximum (∼140 ka; Figure  9a ), and gray lines are major faults, all from  Sakellariou and Galanidou (2016).
Conclusions Using time marks provided by dated marine tephras, we have determined apparent hemipelagic sedimentation rates for the Aegean Sea between the islands of Milos in the west and Nisyros in the east. Some of these data reach back to ∼160 ka but most are <80 ka. We have discussed these data in the context of large sealevel and coastline changes, source material provision by large explosive eruptions, and paleomorphologic position as well as relation to tectonic uplift and subsidence. We have determined the ages of seven previously undated tephras using the sedimentation rate at the respective core intervals. Most of these ages are well constrained by measurements in several cores. The seven new ages are 37. 0 ± 1. 3 ka for the Firiplaka Tephra from Milos, 34. 0 ± 0. 9 ka for Yali 2 fall deposit (which roughly agrees with the 31 ka age based on oxygen isotope stratigraphy;  Federman & Carey, 1980) , ∼54.
The different petrological characteristics of the Nisyros and Yali volcanic rocks have been interpreted   Mason et al. , 2004; Pyle, 2000)  bins. Bins are defined as, for example, M6 including all magnitudes 5. 5 ≤ M < 6. 5. (b) Cumulative erupted magma mass versus age for Santorini and the Kos-Nisyros-Yali volcanic complex. Santorini data ignores minor tephras and lavas emplaced during the last 200 kyrs (cf. Wulf et al. , 2020). Orange dotted line indicates how adding the masses of the Skaros shield (Sk) and the Therasia dome complex (TD) would shift younger data slightly to the right. For Kos-Nisyros-Yali, the tephras shown were erupted after the construction of the Nisyros and Yali edifices on the floor of the KPT caldera. The green dashed line indicates the Nisyros-Yali combined edifice volume of ∼39 km 3 , equivalent to an erupted magma mass of ∼4.
2 × 10 13 kg  (Nomikou et al. , 2021)  as a temporal change in fractionation paths within a large crustal magma reservoir governed by input from depth of magmas changing from lower to higher volatile contents  (Bachmann et al. , 2012; Francalanci et al. , 1995). The almost contemporaneous eruption of the Upper Pumice from Nisyros and the Yali-1 pumice breccia leaves no time for a gradual change but implies coexistence of two petrologically different magma reservoirs at Nisyros and Yali despite their close proximity. Isopach maps of 20 tephras have been constructed; 15 are well constrained by numerous thickness data but 5 are based on limited data. Tephra volumes, magma volumes and magma masses have been determined, based on these maps. In the case of Santorini, these data cover almost the complete succession of major tephras known on land, and add up to a total magma DRE volume of at least 130 km 3.
Acknowledgments The RV POSEIDON cruise POS 513 in 2017 and the subsequent analytical work were financed by  GEOMAR Helmholtz Centre for Ocean Research Kiel. We would like to thank  R. Dettbarn ,  F. Hampel ,  C. Sievers ,  C. Wittig ,  K. -L. Wang , and  H. -Y. Lee  for their contributions to our reference data base. This is Laboratory of Excellence ClerVolc Contribution Number  503. We gratefully acknowledge constructive reviews by  Ralf Gertisser ,  Sabine Wulf  and an anonymous reviewer.
| Messinian Salinity Crisis (MSC) stratigraphy The distribution of the MSC sedimentary sequences is used to define paleoshoreline indicators that constrain our model. They have been identified and widely studied and described mainly from seismic datasets by several authors  (Camerlenghi et al. , 2009; Cameselle & Urgeles, 2017; Dal Cin et al. , 2016; Driussi et al. , 2015; Lofi, Sage, et al. , 2011; Maillard et al. , 2006; Ochoa et al. , 2015; Urgeles et al. , 2011; Lofi, 2018 and references therein; Pellen et al. , 2019; Raad et al. , 2021). The deep basins of the Western Mediterranean (i. e. Provençal and Algerian Basins) contain the full MSC trilogy (e. g. Lofi, Sage, et al. , 2011  see also Figure  1b ), identified mainly through seismic reflection profiles with its components listed below: • Lower unit (LU): age, origin and lithology remain unclear.
| Flexural-isostatic backstripping Pseudo-3D (planform) flexural-isostatic modelling of vertical motions due to surface loading was performed using TISC software  (Garcia-Castellanos et al. , 2002)  allowing for a basin-wide evaluation of the topographic evolution during the MSC. The current basin state with the depth of bounding surfaces and the thickness of the various stratigraphic units is defined in grids of 200 × 200 resolution spanning an area of 860 by 890 km corresponding to the area in Figure  1. We perform backstripping accounting for the subsidence caused by sedimentation and rebound due to the removal of a water load during periods of low water level, as well as compaction of the pre-Messinian sediment unit (Figure  3 ). The flexural calculations adopt an elastic thin plate, assuming that loads are supported only by a strong lithosphere laying on a low-viscosity asthenosphere which behaves like a fluid.
We test for a range of lithospheric strength parameters by using activation energy values from  Govers and Wortel (1995)  and  Cloetingh and Burov (1996). Using the tAo code  (Garcia-Castellanos et al. , 1997)  we calculate the effect of curvature due to sediment loading along a 2-D profile crossing the main crustal blocks (Figure  6 , see Figure  1  for position profile A). Bathymetry of the target region was derived from the GEBCO_2014 (IOC-IHO) grid. The thickness of the offshore Miocene to Quaternary deposits in the Western F I G U R E 4 Thickness in metres of sedimentary units used in the reconstruction, as interpolated from the seismic dataset compilation in Figure  1 , using velocities presented in Table  Mediterranean (Figure  4 ) was determined from compilations of extensive seismic surveys (Figure  1 ) including partially reinterpreted 2D seismic lines  (Acosta et al.
, 2001; Gallart et al. , 1995; Just et al. , 2011; Leroux et al. , 2019; Maillard & Mauffret, 1993; Maillard et al. , 1992 Maillard et al. , , 2014;; Mauffret et al. , 1995; Raad et al. , 2021; Roca & Guimerà, 1992; Sàbat et al. , 1997)  and a 3D cube in the Ebro delta region  (Urgeles et al. , 2011). The seismic derived bathymetry, base PQ and the acoustic basement are available online as part of a wider dataset in the Western Mediterranean  (Bellucci et al. , 2021). Although some sediments were deposited onshore their limited thickness and lateral distribution make for small effects when considering the regional scale, so we limit our investigation to offshore regions. In the northeastern corner of our region data was not available, so grids were extended manually to be consistent with the deep basin thicknesses and prevent artefact shorelines in the Ligurian and Provençal basins.
, 2009)  point F I G U R E 5 (a) Effective elastic thickness (EET) variation from tAo model along a NE Iberia Geo-Transect (see Figure  1  for location). Crustal units with different density, heat production and thermal conductivity used for constructing geotherms derived from a compilation of crustal structural data (colour filled bodies) and thermal lithosphere-asthenosphere boundary (LAB, red-dark line) from  Carballo et al. (2015). EET values determined for weak and strong rheological parameters for lower crust and upper mantle from  Govers and Wortel (1995)  and  Cloetingh and Burov (1996)  and for coupled versus decoupled crust and mantle. EET values are determined using tAo code by constructing Yield Strength Envelopes along transect based on rheology, geotherms and induced bending stresses by sediment loading since onset MSC.
(2011)  showed the absence of a UU in their 3D dataset. Rather, they interpret the Messinian 'unit C' as a shallow water detrital fan. A water level of -1,300 m is required to expose the Ebro margin in this region. However, the water level cannot have been much lower as connectivity must have been maintained between the eastern Valencia Basin and the southwest Valencia Basin where the UU limit is clearly identified and mapped by  Cameselle and Urgeles (2017)  varying around a reconstructed depth of -1,100 m, although this connection is obscured by post-Messinian volcanic activity in the Columbretes. In the Gulf of Lions, the Upper unit limit lies considerably deeper, close to the reconstructed shoreline for a -1,500 m water level. In the steep Algerian Margin and Emile Baudot Escarpment, the UU limit lies further basinward than even the -2,000 m isobath.
to constrain this component of post-Messinian vertical motions. For the Provençal and Algerian Basins a MOR cooling model (Stein & Stein, 1992) is used, whereas for the Valencia Basin which consists of extended continental crust we use the McKenzie (1978) model. Dannowski et al. (2020) propose a failed rift and extended continental crust rather than full oceanic crust underlying the Ligurian Basin, which is the northeastern continuation of the Provençal Basin. However, as this region is not covered by our dataset and the Provençal Basin represents the wider and older part of this extensional domain we see no strong motivation to apply a continental crustal model to the Provençal Basin. The application of such a model would yield slightly smaller thermal subsidence values and deeper estimates of Messinian bathymetry in the basin.
Simple Summary: PRC2 (Polycomb repressive complex 2) is a catalytic multi-subunit complex involved in transcriptional repression through the methylation of lysine 27 at histone 3 (H3K27me1/2/3). Dysregulation of PRC2 has been linked to tumor development and progression. Here, we performed a comprehensive analysis of data in the genomic and transcriptomic (cBioPortal, KMplot) database portals of clinical tumor samples and evaluated clinical correlations of EZH2, SUZ12, and EED. Next, we developed an original Python application enabling the identification of genes cooperating with PRC2 in oncogenic processes for the analysis of the DepMap CRISPR knockout database. Our study identified cancer types that are most likely to be responsive to PRC2 inhibitors. By analyzing co-dependencies with other genes, this analysis also provides indications of prognostic biomarkers and new therapeutic regimens.
Abstract: PRC2 (Polycomb repressive complex 2) is an evolutionarily conserved protein complex required to maintain transcriptional repression. The core PRC2 complex includes EZH2, SUZ12, and EED proteins and methylates histone H3K27. PRC2 is known to contribute to carcinogenesis and several small molecule inhibitors targeting PRC2 have been developed. The present study aimed to identify the cancer types in which PRC2 targeting drugs could be beneficial. We queried genomic and transcriptomic (cBioPortal, KMplot) database portals of clinical tumor samples to evaluate clinical correlations of PRC2 subunit genes. EZH2, SUZ12, and EED gene amplification was most frequently found in prostate cancer, whereas lymphoid malignancies (DLBCL) frequently showed EZH2 mutations. In both cases, PRC2 alterations were associated with poor prognosis.
Are SUZ12 and EED implicated in various tumor types? What are the clinical implications of different PRC2 subunits? What other mutations, besides for the SWI/SNF members, could confer tumor sensitivity to inhibitors of PRC2 subunits and could be used as clinically relevant markers? In the present study we analyzed publicly available genomic and transcriptomic database portals to identify cancers in which the PRC2 subunits are likely to play essential roles.
Materials and Methods The cBioPortal for cancer genomics (https://www. cbioportal. org/, accessed on 2 November 2020)  [30]  was used to query alterations in EZH2/SUZ12/EED genes in clinical samples. DNA analysis was carried out using a set of 185 studies (TCGA-and non-TCGA) that were manually curated with no overlapping samples (total 48,045 samples). Cancers 2021,  13, 3155 3 of 26  In the analysis of the frequency of a specific alteration, only samples tested with available information (e. g. , the copy number or missense/gain-of-function (GOF) mutations) were taken into account. For multiple myeloma survival analysis, we used RNAseq data of 674 newly diagnosed MM patients with longitudinal follow-up from the Multiple Myeloma Research Foundation CoMMpass trial (NCT01454297; version IA11a), termed in the following CoMMpass cohort  [31].
The statistical significance of differences in overall survival between patients' groups was calculated using the log-rank test and survival curves were plotted using the Kaplan-Meier method. For other tumor types, the survival analysis was carried out using the Pan-Cancer datasets of the online tool www. kmplot. com (accessed on 15 January 2021)  [32]. The Pan-Cancer dataset is based on TCGA data generated using the Illumina HiSeq 2000 platform with survival information derived from the published sources  [33]. In the survival analysis, each cutoff between the lower and upper quartiles was analyzed by Cox proportional hazards regression; the best performing cutoff was used in the final analysis. The Kaplan-Meier survival plots were generated, and the hazard rates with 95% confidence intervals were computed to numerically assess the difference between the two cohorts.
DepMap (https://depmap. org/portal/, accessed on 6 October 2020) analysis of the dependency of a panel of tumor cell lines on individual genes was conducted using the CRISPR (Avana) Public 20Q3 (DepMap, Broad (2020): DepMap 20Q3 Public. figshare. Dataset doi:10. 6084/m9. figshare. 12931238. v1. )  [34, 35]. The following files of the Public 20Q3 release were downloaded for the analysis: "Achilles_gene_effect. csv" (Genetic Dependency CRISPR (Avana) Public 20Q3, Genes 18119, 789 Cell lines, 30 Primary Diseases, 27 lineages); "CCLE_mutations. csv" (Cellular Models Mutation Public 20Q3, 18802 Genes, 1741 Cell Lines, 35 Primary Diseases, 37 Lineages); "sample_info. csv" (Cellular Models Cell Line Sample Info, 1804 Cell lines, 35 primary diseases, 38 lineages). For gene ontology data the Uniprot (https://www.
uniprot. org/, accessed on 22 February 2021) database was used. The Power Query Microsoft Excel Tool was used to combine the data from different files. In total, the DepMap 20Q3 release contains the results of the experimental analyses of the effects of EZH2, SUZ12 or EED gene knockout by the CRISPR/Cas9 method on the proliferation of 777 cancer cell lines (Supplementary File S1 "CRISPR (AVANA) Public 20Q3 PRC2 cell lines info"). To calculate p-values for the dependence hypotheses between the sensitivity of cell lines to EZH2, SUZ12, or EED knockouts and the presence of each documented non-silent mutation, we developed an original application termed Genes. py. Genes. py is written in Python and uses the Scipy, Numpy, and Pandas libraries. The silent mutations were excluded from the analysis using the attribute 'Variant_Classification' in 'Cellular Models Mutation Public 20Q3 file.
To characterize the amplification of EZH2, EED, and SUZ12 in different tumor types we analyzed clinical samples for the amplified PRC2 subunit genes using the cBioPortal database (https://www. cbioportal. org/, accessed on 2 November 2020)  [30]. The samples were grouped by tissue origin and only cohorts with at least 500 samples were analyzed. We found that the amplification of EZH2 as well as EED and SUZ12 subunits was not limited to a specific tumor type (Figure  1A, B ). Furthermore, the amplification frequency of specific subunits differed significantly among tumor types (Figure  1A, B ). Interestingly, simultaneous amplification of several PRC2 subunit genes can be detected in the same clinical sample (Supplementary File S2). Cancers 2021,  13  4 of 28 To characterize the amplification of EZH2, EED, and SUZ12 in different tumor types we analyzed clinical samples for the amplified PRC2 subunit genes using the cBioPortal database (https://www.
Next, we queried the cBioPortal database to identify the prognostic role of PRC2 impairment in prostate cancer and in DLBCL lymphoma. Figure  3A ,B show that amplifications of both EZH2 (logrank p = 1. 8 × 10 -7 ) and EED (logrank p = 1. 4 × 10 -10 ) in prostate cancer were associated with poor prognosis (data from  [33, 42, 43] ). For the SUZ12 gene, only two samples with amplifications and survival data are available, making the results uninterpretable. In GCB-DLBCL, up to 21. 7% of samples contained GOF missense mutations in the EZH2 SET domain  [44, 45]  leading to an increased PRC2 catalytic activity  [46, 47]. In a previous study, a high GOF frequency in GCB-DLBCL was correlated with longer patient survival  [48]. However, using the available prognostic data for the DLBCL lymphoma patients from three independent studies  [33, 49, 50] , we found that GOF mutations correlate with negative overall survival (logrank p =3.
Correlations between the EZH2, SUZ12, EED Transcription and Patient Survival We next investigated the correlation of transcription of the EZH2, SUZ12 and EED genes with patient prognosis. Investigating the survival prognosis in multiple myeloma (MM) patients, we have shown that PRC2 core genes, EZH2, SUZ12 and EED, were significantly overexpressed in MM cells compared to normal plasma cells, making these cells sensitive to EPZ-6438, an inhibitor of EZH2  [51]. Moreover, high EZH2 expression correlated with poor prognosis in MM  [52]. Herein, we studied the correlation of the elevated transcription of PRC2 core genes with the survival rate. RNAseq data were obtained from 674 newly diagnosed MM patients with longitudinal follow-up (Multiple Myeloma Research Foundation CoMMpass trial; NCT01454297; version IA11a).
Patients were divided into two cohorts based on high (Figure  4A , red lines) or low (Figure  4A , green lines) mRNA levels of PRC2 subunits. The life expectancy (overall survival; OS) was analyzed for these two groups. Figure  4A  demonstrates that the increased EZH2 transcription was significantly (logrank p = 2. 98 × 10 -13 ) correlated with poor prognosis. At the same time, high abundance of SUZ12 and EED transcripts showed no significant correlation with survival, suggesting that EZH2 expression is the strongest PRC2 prognostic marker in MM. To analyze the correlations of EZH2, SUZ12 and EED expression with the survival of patients with other tumors, we used RNAseq data in the Pan-Cancer dataset of the Kaplan-Maier plotter website (www. kmplot. com, accessed on 15 January 2021). The results are summarized in Table  1.
This paralog has much lower methyltransferase activity  [79]  and is less important for development  [80]. However, EZH1 can partially replace EZH2 in cells where EZH2 expression is impaired  [81] [82] [83]. Consistent with this possibility, there are no homologs for the SUZ12 and EED proteins in humans. Due to a higher sensitivity of cell lines upon gene knockout, further analysis was based on CRISPR/Cas9 data. We thus queried the DepMap CRISPR EZH2, SUZ12, or EED knockout data for Top Co-dependency Pearson correlations. In the case of each gene knockout (EZH2, SUZ12, or EED), two other subunits were identified as Top co-dependent (Table  2 ). This is in accordance with their cooperative function in regulation of transcription. Thus, DepMap corroborates biochemical data indicating the validity of this analysis.
This suggests that GOF mutations in the SET domain of EZH2 can augment the sensitivity of lymphoma cells to the inhibition of either EZH2, SUZ12, or EED. We thus queried the DepMap CRISPR EZH2, SUZ12, or EED knockout data for Top Co-dependency Pearson correlations. In the case of each gene knockout (EZH2, SUZ12, or EED), two other subunits were identified as Top co-dependent (Table  2 ). This is in accordance with their cooperative function in regulation of transcription. Thus, DepMap corroborates biochemical data indicating the validity of this analysis. Along with lymphoma, the cell lines of other tissue origins were among the most sensitive to PRC2 knockouts (Table  3 , Supplementary File S1). These included TUHR10TKB kidney cancer cell line sensitive to depletion of either of the PRC2 subunits (Table  3 ); the OC316 ovarian cancer line that was in the Top10 for EZH2 and EED (Table  3 ) and in the Top40 for SUZ12 (Supplementary File S1) and, finally, the VCAP prostate cancer line-in the Top10 for EZH2 and SUZ12 (Table  3 ) and in the Top40 for EED (Supplementary File S1).