dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work was supported by grants from the  European Research Council  ( ERC AdGrant 322844  to R. S. ) and  Deutsche Forschungsgemeinschaft  ( SFB 992 ,  850 , and  746  to R. S. ). L. A. and T. M. received funding from the  German Epigenome Programme DEEP  ( BMBF 01KU1216G ). The authors thank  Ines Steinhauser ,  Jean-Luc Weickert , and  Josiane Hergueux  for providing excellent technical assistance and  Jin Rong Ow  for critical reading of the manuscript and helpful discussions. The authors are grateful to  Ulrike Bönisch  and members of the  Deep Sequencing Unit at the Max-Planck-Institute of Immunology and Epigenetics (Freiburg). The authors are indebted to  Hans-Henning Arnold  for providing Myf5-Cre mice. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
As U2OS cells have much longer and fairly heterogeneous telomeres, minor differences in telomere length could be masked by an insufficient resolution of their large DNA fragments. To further test functional consequences of ZBTB48 loss in U2OS cells, we quantified C-circles from WT and KO cells as a surrogate for ALT activity. In two out of five clones, we observed a significant reduction of C-circles compared to WT cells, whereas three other clones showed insignificant reductions (Fig  3C ). These data demonstrate that at least in telomerase-positive cells ZBTB48 is a negative regulator of telomere length and that even low amounts of ZBTB48 at telomeres are sufficient for suppression of telomere lengthening. FLAG-ZBTB48 ZnF1mut FLAG-ZBTB48 ZnF11mut FLAG-ZBTB48 ZnF8mut FLAG-ZBTB48 ZnF7mut FLAG-ZBTB48 ZnF6mut FLAG-ZBTB48 ZnF5mut FLAG-ZBTB48 ZnF4mut FLAG-ZBTB48 ZnF3mut FLAG-ZBTB48 ZnF10mut FLAG-ZBTB48 ZnF9mut FLAG-ZBTB48 ΔZnF1-10 in p u t ( 1 0 % ) ( T T A G G G ) n (G T G A G T ) in p u t ( 1 0 % ) ( T N N G G G ) n s c r a m b le in p u t ( 1 0 % ) ( T N N G G G ) n s c ZBTB48 acts as a transcriptional activator at a limited set of target genes To further dissect ZBTB48's cellular functions and to understand a putative crosstalk between telomere length regulation and other downstream effects of ZBTB48 deletion, we further analysed our ChIPseq data sets.
H Venn diagram of the overlap between ZBTB48 binding sites in U2OS and HeLa cells. I Feature distribution of ZBTB48 binding sites as in (G) for overlapping binding sites in U2OS and HeLa cells from (H). Its DNA-binding ability and the preferential binding to promoter regions suggest that ZBTB48 might carry out transcriptional regulatory functions. To test whether ZBTB48 indeed impacts on transcriptional levels, we performed next-generation RNA sequencing (RNAseq) analysis comparing each five WT and KO clones as biological replicates for U2OS and HeLa. While the vast majority of transcripts were not significantly changed, 23 and 11 transcripts were significantly downregulated in U2OS and HeLa cells, respectively, based on cut-offs of fold change > 2 and P-value < 0. 01 (Figs 5A and EV4A, Datasets EV3 and EV4).
Four out of the five protein-coding transcripts affected in HeLa were also depleted in U2OS cells. All transcripts in HeLa and all except two in U2OS, the antisense non-coding RNA LA16c-358B7. 4 and the lincRNA RP4-758J18. 13, were significantly downregulated in the ZBTB48 KO clones, suggesting that ZBTB48 acts as a transcriptional activator on most targets. In comparison, HeLa HOT1 KO cells only show significant downregulation of a single transcript (Fig EV4B  , Dataset EV5 ). To further confirm that ZBTB48 indeed impacts on expression levels, we performed label-free quantitative expression proteomics analysis in HeLa cells quantifying more than 6,000 proteins (Dataset EV6). While for technical reasons proteomes do not yet routinely have the same coverage as RNAseq data, we identified two downregulated proteins, MTFP1 and PXMP2, that actually overlap with the differentially expressed transcripts of the RNAseq experiments (Fig  5B ).
In agreement with a role as a transcription factor, promoters of 15 out of the 24 protein-coding transcripts affected in ZBTB48 KO cells were bound in ZBTB48 ChIPseq experiments with 11 promoters (MTFP1, PXMP2, SNX15, PPP3CB, VWA5A, CCDC106, TMEM63C, TMEM100 MGAT5, MMP15, RNF150 and TUBA4A) bound in both cell lines (Fig 5C, Datasets EV1 and EV2). Notably, the promoters of MTFP1, PXMP2, SNX15, PP3CB and VWA5A were consistently bound in both U2OS and HeLa cells and their transcript levels were depleted in KO cells of both cell types (Fig  5C  and D ). Other examples highlight the presence of cell-type-specific differences. For instance, the CCDC106 promoter is clearly under ZBTB48dependent transcriptional control in U2OS cells, but is not bound by ZBTB48 in HeLa cells. Furthermore, TMEM63C only showed clear expression in U2OS WT cells, although the TMEM63C promoter was occupied by ZBTB48 in both cell lines, suggesting putative priming  [24].
Western blot Western blot samples were boiled in Laemmli buffer (Sigma-Aldrich) at 95°C for 5 min and run on a 4-12% Bis-Tris gel (NuPAGE, Thermo Scientific). Blotting to a nitrocellulose membrane (Protram; Schleicher&Schuell) was performed using a semi-dry transfer system for 90 min at 50 mA. The membrane was blocked in PBS containing 0. 1% Tween-20 (PBST) and 5% (w/v) non-fat milk for 1 h at RT and incubated with primary antibody overnight at 4°C. The following primary antibodies were used: rabbit anti-ZBTB48 (HPA030417, Atlas antibodies, Sigma-Aldrich, 1:1,000), mouse anti-FLAG M2 (F1804, Sigma-Aldrich, 1:5,000), rabbit anti-MTFP1 (SAB4301167, Sigma-Aldrich, 1:1,000), mouse anti-DM1alpha tubulin (MPI-CBG Antibody Facility, 1:50,000 dilution) and goat anti-GAPDH (AP16240PU-N, Acris Antibodies, 1:5,000).
EMBO reports ZBTB48 is a telomere-binder and transcription factor Arne Jahn et al 1× PBS) at RT, primary antibodies were diluted in blocking solution and incubated for 1 h at RT. The following antibodies were used: rabbit anti-ZBTB48 (HPA030417, Atlas antibodies, Sigma-Aldrich, 1:500), mouse anti-FLAG M2 (F1804, Sigma-Aldrich, 1:500), mouse anti-TRF2 (NB100-56506, Novus Biologicals, 1:250), mouse anti-PML (sc-966, Santa Cruz, 1:500) and mouse anti-HOT1 (MPI-CBG Antibody Facility, 1:1,000). Cells were washed three times for 3 min each in blocking solution. Next, samples were incubated with fluorescentlabelled donkey anti-rabbit-IgG and donkey anti-mouse-IgG (both from Invitrogen) with either Alexa488 or Alexa594 as secondary antibodies (1:500 in blocking solution) for 30 min at RT. After three additional washes in blocking solution, cells were either treated with 1 lg/ml DAPI and kept in 1× PBS (96-well plate) or mounted using DAPI Prolong Gold Antifade Reagent (Thermo Scientific).
The following antibodies were used: rabbit anti-ZBTB48 (HPA030417, Atlas antibodies, Sigma-Aldrich), rabbit anti-ZBTB48 (GTX118671, GeneTex), mouse anti-FLAG M2 (F1804, Sigma-Aldrich), rabbit anti-TRF2 (NB110-57130, Novus Biologicals), mouse anti-HOT1 (MPI-CBG Antibody Facility) and rabbit anti-HOT1 (MPI-CBG Antibody Facility) as well as rabbit and mouse IgG. The 75 ll of the remaining beads were added and incubated for 2 h. Beads were washed seven times with PBB (150 mM NaCl, without PI) and once with TE buffer (pH 8. 0). Samples were eluted twice with 100 ll elution buffer (1% SDS, 0. 1 M NaHCO 3 ) and de-cross-linked with 8 ll 5 M NaCl at 65°C. To remove RNAs and proteins, samples were digested with 100 lg RNase A and 40 lg proteinase K. For DNA purification, paramagnetic beads (Agencourt AMPure XP, Beckman Coulter) were used according to the manufacturer's instructions, and DNA concentrations were measured with Pico-Green (Thermo Scientific).
MS data acquisition For in-gel digestion, samples were reduced in 10 mM DTT (Sigma) for 1 h at 56°C followed by alkylation with 55 mM iodoacetamide (Sigma) for 45 min in the dark. Tryptic digest was performed in 50 mM ammonium bicarbonate buffer with 1 lg trypsin (Promega) at 37°C overnight. Peptides were desalted on self-made C18 Stage Tips and analysed with a Q Exactive Plus mass spectrometer (Thermo) coupled to an EASY-nLC 1000 system (Thermo) nanoflow liquid chromatography system. Peptides were separated on a C18 reversed-phase capillary (25 cm long, 75 lm inner diameter, packed in-house with ReproSil-Pur C18-AQ 1. 9 lm resin (Dr. Maisch) directly mounted on the electrospray ion source. We used a 4. 5 h gradient from 2 to 40% acetonitrile in 0. 5% formic acid at a flow of 225 nl/min. The Q Exactive Plus was operated in positive ion mode with a Top10 MS/MS spectra acquisition method per MS full scan.
MS data analysis The raw files were processed with MaxQuant  [49]  version 1. 5. 2. 8 against the UNIPROT annotated human protein database (81,194 entries). Carbamidomethylation on cysteines was set as fixed modification while methionine oxidation and protein N-acetylation were considered as variable modifications. Peptide and protein FDR were enforced at 0. 01 with match between runs option activated. LFQ quantitation  [50]  was made on unique peptides for protein groups with at least 2 ratio counts.
Bioinformatic analysis The protein group table was further processed with an in-house R script excluding contaminants (according to the contaminant list of MaxQuant), reverse hits and proteins only identified by a modified peptide. The biological and technical replicates were combined for statistical analysis and subjected to a two-tailed Welch's t-test to calculate P-values for each protein group. For scoring of differentially expressed proteins, an empirically determined S 0 and an adjusted P-value < 0. 05 (indicated by a dashed line in the volcano plot) were chosen. For the RNAseq experiments with HeLa cells, we obtained between 44 and 71 million of 51-bp paired-end reads in biological quintuplicates. For U2OS cells, we obtained between 30 and 33 million of 68-bp single-end reads, also in biological quintuplicates.
Reads were mapped to the human reference genome version GRCh38 together with the accompanying gene model v84 from Ensembl, using STAR version 2. 5. 1b [51]  and allowing up to 2 mismatches. Only uniquely mapped reads were used to quantify expression of genes, using featureCounts v1. 4. 6-p2  [52]  with default parameters and the same gene model used for mapping. Differential expression analysis was carried out using R (http://www. R-project. org/) and DESeq2  [53] , which were used to normalize and model the data with generalized linear models (GLM) with the negative binomial link function. For the identification of interstitial binding sites by ChIPseq experiments, we sequenced two biological replicates using two different antibodies, in duplicated technical conditions. The raw reads from the technical replicated experiments were pooled together, and we treated the different antibodies as different biological conditions.
org/packages/release/bioc/vignettes/ DiffBind/inst/doc/DiffBind. pdf)  [56]  between the KO and WT conditions, considering only peaks which were significantly different between the two conditions (FDR < 0. 01 and fold change > 8) and called consistently in all four WT replicates. The resulting peak set was annotated with the closest TSS using the gene model v84 from Ensembl. Identification of sequencing reads containing telomeric repeats was performed on unfiltered sequences, using a simple in-house developed Python script employing regular expressions matching. For motif discovery, we used MEME-ChIP  [57]  with the sequences from the peak regions and default parameters for the top 500 extratelomeric ChIPseq peaks.
05 and at least 100 reads per kilobase (rpk) within the peak region (Fig 4A-F, Datasets EV1 and EV2). These binding sites were highly enriched at gene promoters, with ~70 and ~55% of all peaks residing in the proximal promoter (≤ 1 kb from the transcriptional start site) in U2OS and HeLa, respectively (Fig 4G). In total, 127 binding sites (about 50% of the.
Acknowledgements TALENs were kindly provided by the  Seoul National University , South Korea, HT1080 super-telomerase cells by  Joachim Lingner  and HeLa 1. 3 cells by  Titia de Lange. We thank  Lígia Pina  for support with generating TALEN KO cells. Support by the  IMB Genomics Core Facility  is gratefully acknowledged. Research in the Kappei laboratory was supported by the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under its  Research Centres of Excellence initiative  and by the  RNA Biology Center at CSI Singapore ,  NUS , from funding by the  Singapore Ministry of Education 's Tier 3 grants, grant number  MOE2014-T3-1-006. Work in the Butter group was supported by the  DFG  ( Bu2996/1 ) and the  Rhineland Palatinate Forschungsschwerpunkt GeneRED (Gene Regulation in Evolution and Development).
In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arcticwide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0. 7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes.
The smallest bias of 0. 26 m over thin ice has been found when using the SMOS product. Considering the complementarity of CS2 and SMOS retrievals and the need for a better representation of thin ice regimes in global-scale sea-ice thickness data products, the goal of this study is to provide a merged product of CS2 and SMOS sea-ice thickness retrievals, which has the capability to provide Arctic sea-ice thickness distributions over the entire thickness range with reduced uncertainties. We also aim for a weekly update rate of the merged product. This ensures that we obtain a sufficient coverage of CS2 observations over perennial sea ice, while, at the same time, we benefit from the daily update rates of SMOS observations in order to capture ice growth rates in thin ice regions during the freeze-up.
We apply two different merging schemes. The first is represented by a weighted mean (WM), based on the individual uncertainties, which only provides estimates at grid cells where weekly observations are available. The second approach uses an optimal interpolation (OI) scheme for Arctic-wide estimates. Table  1  summarizes the input thickness products and the merged products, their temporal and spatial resolution, and coverage and applicability depending on study purposes. In order to assess the improvement of the merged products, we use airborne sea-ice thickness data and compare them with co-located data of the merged products. This paper is outlined as follows: in Sect. 2, we first present the individual sea-ice thickness products derived from CS2 and SMOS measurements, including a detailed description of input data and highlighting the complementarity of both thickness products.
Data and methods This section is structured as follows: first, the input data (Sect. 2. 1) are presented, and then the merging of weekly CS2 and SMOS data by applying a weighted mean based on the individual uncertainties with the product referred to as WM is described (Sect. 2. 2). Finally, the merging of weekly CS2 and SMOS data by applying an OI scheme with the product referred to as CS2SMOS is explained (Sect. 2. 3).
Input data We use the Alfred Wegener Institute CS2 product (processor version 1. 2;  Ricker et al. , 2014; Hendricks et al. , 2016)  and the SMOS sea-ice thickness retrieval from the University of Hamburg (processor version 3. 1;  Tian-Kunze et al. , 2014; Kaleschke et al. , 2016)  as input ice thickness data. Auxiliary data of ice concentration and ice type were obtained from the Ocean and Sea Ice Satellite Application Facility (OSI SAF).
CryoSat-2 weekly sea-ice thickness retrieval In the first step we use CS2 SIRAL level-1b orbit data files that are provided by ESA. They contain geolocation information and time of the Doppler beam formed radar echoes. SIRAL is operated in two different modes over sea ice. The synthetic aperture radar (SAR) mode covers major parts of the ice-covered area, while the interferometric mode (SIN) is applied mostly in coastal areas. Both modes serve for retrieving ice thickness but must be processed separately, as we discard the phase information of SIN waveforms  (Kurtz et al. , 2014). The radar echoes (waveforms) are processed for each CS2 orbit according to  Hendricks et al. (2016)  and  Ricker et al. (2014). A 50 % threshold-first-maximum retracker  (Ricker et al. , 2014; Helm et al. , 2014)  is used to obtain ellipsoidal surface elevations (L), which are corrected for geophysical perturbations like tides and atmospheric effects  (Ricker et al.
SMOS weekly sea-ice thickness retrieval Thin sea-ice thickness has been retrieved from the 1. 4 GHz (L-band) brightness temperatures measured by SMOS for the winter seasons (15 October-15 April) from 2010 to present  (Mecklenburg et al. , 2016). The retrieval method consists of a thermodynamic sea-ice model and a one-ice-layer radiative transfer model  (Tian-Kunze et al. , 2014). The resulting plane layer thickness is multiplied by a correction factor assuming a log-normal thickness distribution. The algorithm has been used for the operational production of an SMOS-based seaice thickness data set from 2010 on  (Tian-Kunze et al. , 2014). In this study we use the most up-to-date version (v3. 1) of the ice thickness data set, which has been produced operationally since October 2016. The v3. 1 data for the previous winter seasons had been reprocessed using the same algorithm.
Complementarity of CryoSat-2 and SMOS sea-ice thickness products The two main factors that drive the complementarity between the CryoSat-2 and SMOS sea-ice thickness products are the data coverage on the one hand and the sea-ice thickness uncertainties on the other hand. Figure  2  shows typical uncertainty maps and the relative uncertainties of CS2 and SMOS monthly mean thickness retrievals from November 2015 and March 2016. While with SMOS relative uncertainties are lowest for thin ice (< 1 m), CS2 relative thickness uncertainties are smaller over thick ice and rise asymptotically towards thinner ice less than 1 m thick. This is due to the fact that CS2 thickness estimates over thin ice rely on the retrieval of small surface elevations slightly higher than sea level, while freeboard of thicker ice is much larger  (Ricker et al.
, 2014). As a consequence, the relative uncertainty increases over thin ice, as measurement uncertainties do not decrease over thinner ice. Note that the CS2 uncertainties shown here represent observational uncertainties only. Systematic errors as associated with the usage of a snow climatology or due to variable snow penetration will increase the uncertainty of altimetry-based thicknesses  (Ricker et al. , 2014 (Ricker et al. , , 2015;; Kwok, 2014; Armitage and Ridout, 2015). Due to the different update rates of sea-ice thickness observations, CS2 grids are usually based on data composites from 1 month, while SMOS-based retrievals provide daily complete coverage of the ice-covered ocean up to about 85 • N. Figure  1  compares weekly means of CS2 and SMOS for November 2015 and March 2016. While valid SMOS ice thickness estimates are found mostly in the marginal ice zones, the CS2 ice thickness retrieval covers major parts of the Arctic MYI.
In November, during the freeze-up, SMOS retrievals cover major parts of the Beaufort Sea, Chuckchi Sea and East Siberian Sea. Towards spring, due to continued ice growth in these regions, the regions with SMOS retrievals retreat southwards, covering major parts of the Bering Sea and the Sea of Okhotsk (Fig. 1b ). Figure  3  illustrates the number of valid grid cells of the weekly means as shown in Fig. 1. The number of grid cells with co-located SMOS and CS2 estimates is less than 2000, while the number of grid cells that contain thickness estimates from CS2 or SMOS only is about 5000, highlighting the complementary data coverage of both sensors.
Weighted mean We compute the weighted mean sea-ice thickness Z using weekly CS2 and SMOS ice thickness grids during the target week: Z = Z cs2 /σ 2 cs2 + Z smos /σ 2 smos 1/σ 2 cs2 + 1/σ 2 smos , (4) where σ represents the observational uncertainty of the individual products. Figure  4  shows the weighted means for weeks in November 2015 and March 2016. In contrast to the OI approach, presented in the next section, the weighted mean only provides thickness estimates where observations are available during the target week, leaving data gaps in the CS2 domain. In the following we refer to the weekly weighted mean product as WM.
e. , each element represents a grid cell of the merged CS2SMOS ice thickness retrieval to be produced. Z b is a background field vector, and Z o is the vector that contains all SMOS and CS2 observations. Here we use already gridded, weekly mean CS2 and SMOS thickness estimates as observations, as shown in Fig. 1  and as described above. Using gridded data as observations reduces their observational uncertainties and provides equally distributed observations, which improves the performance of the OI. In addition, gridding of raw data reduces the number of available observations used for the OI, increasing the efficiency of the OI routine. We assume that the observations are static, i. e. , remain temporally coherent within a week and do not change due to ice deformation and motion. Therefore, we neglect any temporal correlations.
The background field The weekly CS2 ice thickness composite possesses large gaps resulting from the limited orbital coverage (Fig. 1a ). But for the OI approach, an Arctic-wide coverage is required for the background field. Therefore, we use a composite of retrievals from adjacent weeks to create a background field with nearly complete coverage for the Central Arctic at a certain target week (Fig. 6a ). Here we combine data from the 2 weeks before and after the target week. Therefore, in con-trast to CS2 near real-time sea-ice thickness retrievals  (Tilling et al. , 2016) , products can only be released 2 weeks after data acquisition. In order to ensure independence between the observations and background field, CS2 data from the target week are not included in the background field. For the same reason, we use an SMOS weekly mean from 1 week before and after the target week.
The initial background field is computed by a weighted mean using Eq. (  4 ). Gaps in the weighted average are interpolated by using a nearest neighbor scheme. In order to reduce noise, the background field is low-pass filtered with a smoothing radius of 25 km before it is applied in the OI algorithm (Fig. 6b ). Since we use CS2 and SMOS retrievals for the background field beyond the target week and because the SMOS composite contains artifacts in coastal regions, we additionally use a weekly mean of the daily OSI SAF ice concentration product to determine the ice coverage during the target week. Here, we apply a threshold of 15 % and only grid cells that exceed this value will be considered as ice covered, which corresponds to the ice extent products provided by OSI SAF and the National Snow and Ice Data Center (NSIDC).