dataesr/scientific-paragraphs-categorization huggingface dataset:
Primer and Probe sequences are detailed in the Key Resources Table. High-throughput sequencing and analysis ChIP-seq and RNA-seq libraries were loaded on E-gel and a band corresponding to $300 bp (DNA + Adaptor) was collected. After quality assessment, the eligible library was sequenced on the Illumina HiSeq 2000 machine and generated 42 to 52 bp tags. After read mapping to the hg19 reference genome using BWA  (Li and Durbin, 2009)  and removal of PCR duplicates by Picard MarkDuplicates option (http://broadinstitute. github. io/picard/), peak calling was conducted using MACS1. 3. 3  (Zhang et al. , 2008)  at a p-value cutoff of 10 À6. Read counts for each putative region were enumerated and then normalized to RPKM (reads per kilobase of gene length per million reads) for visualization in heatmaps or boxplots.
Software BWA  (Li and Durbin, 2009) , Picard MarkDuplicates (http://broadinstitute. github. io/picard/), MACS1. 3. 3  (Zhang et al. , 2008)  Genomic Regions Enrichment of Annotations Tool  (GREAT [McLean et al. , 2010] ), as detailed in the methods section (high-throughput sequencing and analysis) and Key Resources Table.
21 amplification, also can be recurrent  (Lefort et al. , 2008). The biological significance of such recurrent abnormalities is still discussed, but they might result in a strong selective growth advantage for cultured cells, as already demonstrated for the 20q11. 21 amplification  (Zhang et al. , 2019). Therefore, it is crucial to carefully catalog all genetic alterations found in hPSCs and identify the recurrent ones. To this aim, we carried out a meta-analysis of published genetic abnormalities found in hPSCs. We could give a precise definition of recurrent genetic abnormality and then listed all of them in a large dataset. As these recurrent genetic abnormalities are found in specific genomic regions, we developed a focused droplet digital PCR (ddPCR) approach that allows screening more than 90% of these recurrent ab-normalities in DNA isolated from cell culture supernatant.
Meta-Analysis of hPSC Genetic Abnormalities and Identification of a Recurrence Pattern To catalog all genetic abnormalities previously detected in hPSCs using various techniques (karyotyping, fluorescence in situ hybridization, comparative genomic hybridization, microarray-based comparative genomic hybridization, and next-generation sequencing [NGS]), we selected primary research articles that reported genetic abnormalities in hESCs and human induced PSCs (hiPSCs), and extracted the DNA abnormality genomic coordinates as well as the experimental data to characterize these abnormalities. We collected data on 942 cell samples and on 415,750 variants and abnormalities from 107 different studies published between  2004 and 2016 (Figures 1A and 1B ; Table  S1 ). The dataset included the major publications on genetic abnormalities in hPSCs during culture and also articles that identified one or several abnormal clones in new hPSC lines.
A first global analysis of all listed mutations allowed identifying genome locations where these abnormalities were more frequently localized: trisomy 12 and 12p amplification, 20q11. 21 amplification, trisomy 17 and 17q amplification, chromosome 1 amplification, and trisomy X (female cell lines) (Figure  S1A ). Abnormalities that accumulated at a specific genome location (i. e. , recurrent abnormalities) were mostly aneuploidy or copy-number variations (CNV), in agreement with previous reports. No abnormality smaller than 10 base pairs (bp) displayed a recurrent profile in this large dataset (Figure  S1B ). We also reported 93 translocations and 20 inversions, involving all chromosomes with the exception of chromosome 12. They were mainly at chromosomes 1 and 17, but without a clear recurrent pattern (Figure  1C ).
Noteworthy, with the exception of the BCL2L1 locus, we did not find convergence on any specific chromosome locus, even by using the large dataset collected for our meta-analysis. As illustrated by Figure  2C , most recurrent zones are large parts of chromosome arms. Although regions with more recurrences than others can be identified, only the BCL2L1 locus has a pattern pointing to a precise zone. This suggests that recurrence is the consequence of several genes dispersed on a large chromosome section that must be simultaneously upregulated through copy-number gains (or downregulated for the rare cases of recurrent deletions) to promote in vitro culture fitness. For example, the most recurrent part of chromosome X (X:1, 60,600,000) contains several genes that are annotated as involved in regulation of cell cycle, DNA repair, apoptosis, stemness, or cancer.
(I) CNV characterization of chromosome 20q, 12, X, 17, 1, and 5 in four CCDC40_KO HY03 clones obtained using CRISPR/Cas9 technology. No abnormality was detected in any of the four clones. analysis is that samples used in our work were analyzed using methods which have biases, and this could have influenced the recurrence score. Whole-genome NGS would provide the unbiased data necessary for the comprehensive identification of anomaly recurrence in PSC. Finally, the comparison with parental samples (in the case of iPSC lines), and the comparison with data from a large number of healthy volunteer DNA samples is necessary to rigorously distinguish recurrence from polymorphisms because the databases routinely used to identify polymorphisms are neither comprehensive nor error-free. Nonetheless, the recurrence distribution is biased, and this allows studying common abnormal regions by targeted PCR.
In conclusion, we used a large dataset of hPSC genomic abnormalities based on more than 100 publications to define recurrent genetic abnormalities, including a set of common abnormal genomic regions that involve more than 90% of all recurrent abnormalities. This offered the opportunity to develop a targeted ddPCR approach that could be performed on culture supernatant. Therefore, we propose a simple test based on supernatant-DNA (iCS-digital test) that could be used to routinely screen cultured hPSC lines for excluding the presence of common CNVs.
Data Collection Clinical data was collected as follows: age, ethnicity, sex, date of diagnosis, disease duration, and presence of skin, lung, heart, ocular, and neurological involvements. Articular assessment, which was collected at diagnosis and during follow-up, included: tender joint count, swollen joint count, localization of joint involvement and DAS44-CRP. Synovitis was diagnosed by a clinical examination and/or joint ultrasonography: among 39 included patients with clinical synovitis, 31 have undergone joint ultrasonography and have all ultrasound-confirmed synovitis. Synovitis was defined as synovial hypertrophy ≥2, or synovial hypertrophy ≥1+PD signal ≥1, according to the OMERACT definitions  (12). Joint activity was quantified using DAS28-CRP at diagnosis and during the follow up.
The use of non-steroidal anti-inflammatory drugs (NSAIDs), steroids and/or synthetic disease-modifying antirheumatic drugs (DMARDs), hydroxychloroquine and TNF antagonists was collected. Laboratory data included serum hemoglobin, platelet, neutrophil and lymphocyte counts, calcium, creatinine, alanine aminotransferase, aspartate aminotransferase, C-reactive protein (CRP), erythrocyte sedimentation rates (ESR), gammaglobulin, angiotensin converting enzyme (ACE), rheumatoid factor, antinuclear and anti-citrullinated protein antibodies (ACPA). Sarcoidosis was considered as acute if remission was completely reached within 12 weeks. Löfgren's syndrome was defined in patients with acute sarcoidosis in the presence of arthritis, bilateral hilar adenopathy and erythema nodosum, and fever  (6, 13).
Acknowledgements The authors would like to thank  Prof. Rolf Müller  and his group from the  Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)  for providing the E. coli K12 strain used in the wet plating experiments, as well as  Shardul Bhusari  from the  Leibniz Institute for New Materials (INM)  for his help in cultivating the fluorescent E. coli bacteria culture. The authors also want to acknowledge the help of the INM by enabling access to their LSM facility. This work was funded by the  German Research Foundation (DFG)  within the project " Controlled bacterial interaction to increase the antimicrobial efficiency of copper surfaces"  (project number  415956642 ) and SFB1027 Collective Research Center as well as the  German Aerospace Center -Space Administration (DLR)  within the project " Investigation of antimicrobial metal surfaces under space conditions-An effective strategy to prevent microbial biofilm formation"  (project number  50WB1930 ).
We report final analysis outcomes from the phase 3 HELIOS study (NCT01611090). Patients with relapsed/refractory chronic lymphocytic leukemia/small lymphocytic lymphoma without deletion 17p (n ¼ 578) were randomized 1:1 to 420 mg daily ibrutinib or placebo plus 6 cycles of bendamustine plus rituximab (BR), followed by ibrutinib or placebo alone. Median follow-up was 63. 7 months. Median investigator-assessed progression-free survival was longer with ibrutinib plus BR (65. 1 months) than placebo plus BR (14. 3 months; hazard ratio [HR] 0. 229 [95% confidence interval (CI) 0. 183-0. 286]; p <. 0001). Despite crossover of 63. 3% of patients from the placebo plus BR arm to ibrutinib treatment upon disease progression, ibrutinib plus BR versus placebo plus BR demonstrated an overall survival benefit (HR 0.
Are the families of stakeholders largely the same, or are they distributed in a very different fashion? Do the recurrent arguments resonate with other socio-technical controversies or do they open entirely new perspectives? It is worth bearing in mind that my intention is not to provide readers with an exhaustive analysis of all the publications on SG in the four countries mentioned since 2006. Instead, I will try to bring to light the main trends in this debate. Paul Oldham & al. have already produced a wideranging list of publications about both solar geoengineering and carbon capture  (Oldham & al. , 2014) , and Jesse Reynolds has drawn a list of the multiple policy proposals on SG which have been put forward so far  (Reynolds, 2019). Considerable resources on geoengineering are also available at the Kiel Earth Institute's Climate Engineering database and at the University of Montana's ethics of geoengineering online resource center.
Antarctic Bottom Water (AABW) is known as a long-term sink for anthropogenic CO 2 (C ant ), but the sink is hardly quantified because of the scarcity of observations, specifically at an interannual scale. We present in this paper an original dataset combining 40 years of carbonate system observations in the Indian sector of the Southern Ocean (Enderby Basin) to evaluate and interpret the interannual variability of C ant in the AABW. This investigation is based on regular observations collected at the same location (63 • E-56. 5 • S) in the framework of the French observatory OISO from 1998 to 2018 extended by GEOSECS and INDIGO observations (1978, 1985 and 1987). At this location the main sources of AABW sampled is the low-salinity Cape Darnley Bottom Water (CDBW) and the Weddell Sea Deep Water (WSDW).
, 2007; van Heuven et al. , 2011 van Heuven et al. , , 2014;; Shadwick et al. , 2014; Roden et al. , 2016). However, little is known about the variability and evolution of the CO 2 fluxes in AABW formation regions, and since biological and physical processes are strongly impacted by seasonal and interannual climatic variations  (Fukamachi et al. , 2000; Gordon et al. , 2010; McKee et al. , 2011; Gordon et al. , 2015; Gruber et al. , 2019b) , the amount of C ant stored in the AABW may be very variable, which could bias the estimates of C ant trends derived from datasets collected several years apart (e. g. ,  Williams et al. , 2015; Pardo et al. , 2017; Murata et al. , 2019). In this context of potentially high variability in C ant uptake at AABW formation sites, as well as in AABW export, circulation and mixing, we used repeated observations collected in the Indian sector of the Southern Ocean to explore the variability in C ant and C T in the AABW and evaluate their evolution over the last 40 years.
AABW sampling during the last 40 years Most of the data used in this study were obtained in the framework of the long-term observational project OISO (Ocean Indien Service d'Observations) conducted since 1998 onboard the R. S. V. Marion Dufresne (IPEV/TAAF). During these cruises, several stations are visited, but only one station is sampled down to the bottom (4800 m) south of the Polar Front, at 63. 0 • E and 56. 5 • S (hereafter denoted OISO-ST11). This station is located in the Enderby Basin on the western side of the Kerguelen Plateau (Fig. 1 ) and coincides with station 75 of the  INDIGO-3 cruise (1987). In our analysis, we included all the data available for the OISO-ST11 location (which has not been sampled during each cruise for logistic reasons). We also included data from station 14 (deepest sample taken at 5109 m) of the INDIGO-1 cruise  (1985)  and station 430 (deepest sample taken at 4710 m) of the GEOSECS cruise (1978) located near the OISO-ST11 sampling site (405 and 465 km away from it, respectively; Fig.
Validation of the data For 1998-2004, the OISO data were quality-controlled in CARINA  (Lo Monaco et al. , 2010)  and for  2005 and 2009 -2011 in GLODAPv2 (Key et al. , 2015; Olsen et   (Key et al. , 2004)  and used for the first C ant estimates in the Indian Ocean  (Sabine et al. , 1999). The adjustments recommended for these historical datasets have been revisited in CARINA and GLODAPv2. In this paper we used the revised adjustments applied to the GLODAPv2 data product, with one exception for the total alkalinity (A T ) data from INDIGO-3 for which we applied an intermediate adjustment between the recommendation from GLODAPv1 (confirmed in CARINA) for no adjustment (due to a lack of available observations in this region for robust comparison) and the adjustment by -8 µmol kg -1 applied to the GLODAPv2 data product (justification in the Supplement).
(2009)  since 2009. Based on replicate measurements for deep samples we estimate an error of about 0. 3 % for both nutrients. NO 3 data are not available for all the cruises used in this analysis. The mean NO 3 concentration in the LAABW at OISO-ST11 is 32. 8 ± 1. 2 µmol kg -1 , while the average value derived from the GLODAP-v2 database in bottom water south of 50 • S in the southern Indian Ocean is 32. 4 ± 0. 6 µmol kg -1. The lack of NO 3 data for few cruises has been palliated by using a climatological value of 32. 4 µmol kg -1 with a limited impact on C ant determined by the C • method (<2 µmol kg -1 for estimates based on the differences observed between NO 3 measurements and the climatological value).
, 16, 1559-1576, 2020 https://doi. org/10. 5194/os-16-1559-2020 where TrOCA • is evaluated as a function of and A T (Eq. 3) as TrOCA 0 = e b-(c) -d A 2 T. (3) In these expressions, the coefficients a, b, c and d were adjusted by  Touratier et al. (2007)  from deep water free of anthropogenic CO 2 using the tracers 14 C and CFC-11 from the GLODAPv1 database  (Key et al. , 2004). The final expression used to calculate C ant is C ant = O 2 + 1. 279 C T -1 2 A T -e 7. 511-1. 087. 10 -2 -7. 81. 10 5 A 2 T 1. 279. ( 4 ) The consideration of the errors on the different parameters involved in the TrOCA method results in an uncertainty of ± 6. 25 µmol kg -1 (mostly due to the parameter a, leading to ± 3. 31 µmol kg -1 ). As this error is relatively large compared to the expected C ant concentrations in deep and bottom SO water  (Pardo et al.
We did not detect any significant trend in A T , and S over the full period, but over shorter periods our data show a significant decrease in A T. The low A T values observed over 2000-2004 (Fig. 3d ) could suggest reduced calcification in the upper ocean, leading to less sinking of calcium carbonate tests and a decrease in A T in deep and bottom water over this period (Fig. 2d ). For this period the increase in C T was lower than the accumulation of C ant , but such a feature is disputable in view of the uncertainty on the C ant calculation. This event is followed by an increase in the "natural" component of C T (C nat , calculated as the difference Ocean Sci. , 16, 1559-1576, 2020 https://doi. org/10. 5194/os-16-1559-2020 Table  2. Trends (per decade) of observed and calculated properties in the LAABW estimated over different periods (in bold: significant trends at the 95 % confidence level).
, 2014; Smith and Treguer, 1994) , as well as from the OISO-ST11 dataset for the OISO-ST11 LAABW and OISO-ST11 LCDW. Error bars are calculated from the individual annual averaged values for the OISO-ST11 LAABW and from all data for the OISO-ST11 LCDW. For the OISO-ST11 LAABW, the grey cross are the GEOSECS (lowest ) and INDIGO-1 (highest ) values. Williams et al. , 2015; Pardo et al. , 2017; Murata et al. , 2019). For example, we found relatively higher C ant concentrations in 1985 (10 µmol kg -1 ) compared to 1978 (5 µmol kg -1 ) and 1987 (7 µmol kg -1 ). This is linked to a signal of low S in 1985 (Fig. 3f ) that could be due to a larger contribution of fresher water such as the WSDW or CDBW. This could also be related to the different sampling locations. Over the last decade  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)   tween 1994 and 2007) , suggesting an acceleration of AABW freshening in recent years.
0 ± 0. 5 µmol kg -1 per decade) is slightly faster than the C T trends estimated in the WSBW in the Weddell gyre: https://doi. org/10. 5194/os-16-1559-2020 Ocean Sci. , 16, 1559-1576, 2020 Table  3. Compilation of C ant sequestration investigations in the AABW (γ n ≥ 28. 25 kg m -3 ) using the TrOCA method. The C ant estimation of  Pardo et al. (2014)  is calculated using theoretical AABW mean composition (with 3 % of ALBW) and the carbon data from the GLODAPv1 and CARINA databases. Sandrini et al. (2007)  values have been measured at the bottom of the Ross Sea and correspond to recently sinking high-salinity shelf water (HSSW). The mean values published by  Roden et al. (2016)  for the AABW present WSDW characteristics but can be a mix of CDBW and LCDW. +1. 2 ± 0. 5 µmol kg -1 per decade over the period 1973-2011 and +1.
, 2018) , a change in the ventilation of C T -rich deep water, and reduced CO 2 uptake  (Lenton et al. , 2009). The reconstructed pCO 2 fields by  Landschützer et al. (2015)  suggest that the reduced CO 2 sink in the 1990s is identified at high latitudes in the SO (see  Figs. 2a and S9 in Landschützer et al. , 2015). However, as opposed to the circumpolar open ocean zone (e. g. ,  Metzl, 2009; Takahashi et al. , 2009 Takahashi et al. , , 2012;; Munro et al. , 2015; Fay et al. , 2018) , the long-term trend of surface f CO 2 and carbon uptake deduced from direct observations is not clearly identified in the seasonal ice zone (SIZ), the shelves around Antarctica, and thus in the AABW formation regions of interest to interpret our results  (Laruelle et al. , 2018). There, surface f CO 2 data are sparse, especially before 1990, and cruises were mainly conducted in austral summer when the spatiotemporal f CO 2 variability is very large and driven by multiple processes at regional or small scales, such as primary production, sea ice formation and retreat, and water circulation and mixing.
We attempted to detect long-term changes in CO 2 uptake in this region using the qualified f CO 2 data available in the SOCAT database  (Bakker et al. , 2016) , but our estimates (not shown) were highly uncertain due to very large spatial and temporal variability. To conclude, all previous studies conducted near or in AABW formation sites clearly reveal that these regions are potentially strong carbon sinks, but how the sink changed over the last decades is not yet evaluated, and thus we are not able to certify that the recent C ant stability that we observed in the LAABW at our location is directly linked to the weakening of the carbon sink that was recognized at the large scale in the SO from the 1980s to mid-2000s (Le  Quéré et al. , 2007; Landschützer et al. , 2015). Changes in the accumulation of C ant in AABW could also be directly related to changes in physical processes occurring in AABW formation regions.
Conclusions The distribution and evolution of C ant in the bottom layer of the SO are related to complex interactions between clihttps://doi. org/10. 5194/os-16-1559-2020 Ocean Sci. , 16, 1559-1576, 2020 matic forcing, air-sea CO 2 exchange at formation sites, and biological and physical processes during AABW circulation. The dataset that we collected regularly in the Enderby Basin over the last 20 years  in the framework of the OISO project, together with historical observations obtained in  1978 , 1985 and 1987 (GEOSECS and INDIGO cruises) , allows for the investigation of C ant changes in AABW over 40 years in this region. The focus on AABW variability is made by defining Lower Antarctic Bottom Water (LAABW) as described in Sect. 2. 3. Our results suggest that the accumulation of C ant explains most, but not all, of the observed increase in C T.
We also detected a decrease in O 2 that is consistent with the large-scale signal reported by  Schmidtko et al. (2017) , possibly due to a decrease in AABW formation  (Purkey and Johnson, 2012). Our data further indicate rapid anomalies in some periods, suggesting that for decadal to long-term estimates care has to be taken when analyzing the change in C ant from datasets collected 10 or 20 years apart (e. g. ,  Williams et al. , 2015; Murata et al. , 2019). Our results also show different C ant trends over short periods, with a maximum increase of 5. 2 µmol kg -1 per decade between 1987 and 2004 and apparent stability in the last 20 years (despite an increase in C T ). This suggests that AABW has stored less C ant in the last decade, but our understanding of the processes that explain this signal is not clear.
, 2018) , to offer a solid database to validate ocean biogeochemical models and coupled climatecarbon models  (Russell et al. , 2018)  and ultimately reduce uncertainties in future climate projections. Financial support. This research has been supported by the European Integrated Projects CARBOOCEAN (511176) and CAR-BOCHANGE (264879). Review statement. This paper was edited by Mario Hoppema and reviewed by two anonymous referees.
Acknowledgements. We thank the captains and crew of the R. S. V. Marion Dufresne  and the staff at the  French Polar Institute (IPEV)  for their important contribution to the success of the cruises since 1998. We are also very grateful to all colleagues, students and technicians who helped to obtain the data. We extend our gratitude to  Paula Conde Pardo ,  Steve Rintoul ,  Benoit Legresy  for the discussions during the preparation of the paper and to  Megan Kay Shipton  for the valuable comments. We thank two anonymous reviewers and the editor  Mario Hoppema  for their comments and constructive suggestions that helped improve the paper. The OISO program was and is supported by the  French institutes INSU, IPEV  and  OSU Ecce-Terra  as well as the  French program SOERE/Great-Gases. Ocean Sci.
Data availability. GEOSECS, INDIGO and OISO 1998-2011   data are publicly available via the Ocean Carbon Data System (OCADS; https://www. nodc. noaa. gov/ocads/oceans/GLODAPv2_ 2019, Olsen et al. , 2019). OISO original data are available at https://www. nodc. noaa. gov/ocads/oceans/RepeatSections/clivar_ oiso. html (Olsen et al. , 2019). OISO 2012-2018 will be available in GLODAPv2. 2021. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-16-1559-2020-supplement. Author contributions. LM, CLM, NM, JF and CM performed the sampling and carried out the measurements of the OISO data. LM prepared the paper with contributions from CLM and NM.