dataesr/scientific-paragraphs-categorization huggingface dataset:
RIMS-seq identifies the correct methylase specificity de novo in E. coli K12. In order for RIMS-seq to identify methylase specificities de novo, we devised an analysis pipeline based on MoSDi  (22)  to find sequence motif(s) that are over-represented around C to T transitions in R1 reads (Figure  3A , analysis pipeline available at https: //github. com/Ettwiller/RIMS-seq). In brief, the pipeline extracts the sequence context at each C to T read variant in R1 (foreground) and R2 (background). MoSDi identifies the highest over-represented motif in the foreground sequences compared to the background sequences. To accommodate the presence of multiple methylases in the same host, the first motif is subsequently masked in both the foreground and background sequences and the pipeline is run again to find the second highest over-represented motif and so on until no significant motifs can be found (see Materials and Methods for details).
RIMS-seq can be complemented with SMRT sequencing to obtain a comprehensive overview of methylase specificities. RIMS-seq performed in parallel with SMRT sequencing has the advantage of comprehensively identifying all methylase specificities (m5C, m4C and m6A methylations) and results in an assembly of higher quality than with short reads illumina data. We applied this hybrid approach to Acinetobacter calcoaceticus ATCC 49823 for which a SMRT sequencing and assembly had been done previously  (4). RIMS-seq was performed as described above and the reads were mapped to the genome assembly obtained from SMRT-sequencing. We again found the two m5C motifs: CGCG (P-value = 1. 84e -1535 ) and GATC (Pvalue = 4. 93e -6856 ) from the RIMS-seq data in addition to the 13 m6A motifs described previously using SMRT sequencing (4).
Taken together, these results indicate that the heat alkaline treatments in the measured ranges are not expected to notably affect the sequencing performance QC in bacteria. Once a motif is found, the pipeline is repeated until no more motifs are found, enabling identification of multiple methylase specificities in an organism. (B) Assembly statistics obtained using the sequence from the standard DNA-seq (+3H, left) and RIMS-seq (right). Visualization using assembly-stats program (https://github. com/rjchallis/ assembly-stats). The corresponding table with the statistical values is available in the supplementary material (Supplementary Table  S2 ). (C) Fractions of C to T read variants in CGCG (yellow) or GATC (green) contexts compared to other contexts for R1 and R2 in Acinetobacter calcoaceticus ATCC 49823 using the assembled or the reference genome.
Results obtained with RIMS-seq were also validated using bisulfite sequencing. RIMS-seq identified two motifs in Helicobacter pylori from the ATCC synthetic gut microbiome: GCGC as well as an additional non-palindromic motif CCTC that was identified by the bisulfite analysis pipeline as CYTC with Y being either C or T. The CCTC motif is very common in Helicobacter pyloris species, it has been described to be modified at m5C on one strand, while modified at m6A on the other strand  (4). In order to confirm the RIMS-seq motif, we investigated the bisulfite-seq data and compared the methylation level in cytosines present in the CCTC context versus cytosines in any other context. We see a methylation level above 90% at the cytosines in the CCTC context confirming the existence of this methylated motif in Helicobacter pylori (Figure  5D ).
e113 19 PAGE 8 OF 12 Figure 3. 198123 Figure 3. De novo discovery of methylase specificity using RIMS-seq. (A) Description of the RIMS-seq motif analysis pipeline. First, C to T read variants are identified in both Read 1 and Read 2 separately. Then, the MosDI program searches for overrepresented motifs. Once a motif is found, the pipeline is repeated until no more motifs are found, enabling identification of multiple methylase specificities in an organism. (B) Assembly statistics obtained using the sequence from the standard DNA-seq (+3H, left) and RIMS-seq (right). Visualization using assembly-stats program (https://github. com/rjchallis/ assembly-stats). The corresponding table with the statistical values is available in the supplementary material (Supplementary TableS2). (C) Fractions of C to T read variants in CGCG (yellow) or GATC (green) contexts compared to other contexts for R1 and R2 in Acinetobacter calcoaceticus ATCC 49823 using the assembled or the reference genome.
(6) t1-enhanced SMRT sequencing(6) Organism Accession numbers (biosample) RIMS-seq motif(s) Validated motif(s) Escherichia coli K12 SAMN02604091 CCWGG CCWGG (1,2,4) Acinetobacter calcoaceticus ATCC 49823 SAMN14530202 GATC G A T C (4) CGCG CGCG (2,4) Bacillus fusiformis 1083 SAMN17843035 ACCTGC ACCTGC (2,3) GCAGGT GCAGGT (2,3) Bacillus amyloliquefaciens H ATCC 49763 SAMN12284742 GCWGC GCWGC (3) Clostridium acetobutylicum ABKn8 SAMN17843114 GCNNGC GCNNGC (3) Aeromonas hydrophila NEB724 SAMN14533640 GCCGGC GCCGGC (3) Haemophilus influenzae Rd ATCC 51907 SAMN02603991 GRCGYC* GRCGYC (5) ACCGCACT AGTGCGGT Haemophilus parahaemoltyicus ATCC 10014 SAMN11345835 GCGC GCGC (2) M. HhaI clone (E. coli) N A R CGC GCGC (4) CCWGG.
Instruments and Data Regular observations of ozone in the free troposphere are sparse: Only around 50 ozone sounding stations worldwide (e. g. Tarasick et al. , 2019) , a handful of tropospheric LIDARs  (Gaudel et al. , 2015; Leblanc et al. , 2018) , and about twenty Fourier Transform Infrared Spectrometers (FTIRs,  Vigouroux et al. , 2015). In-Service Aircraft for a Global Observing System (IAGOS,  Nédélec et al. , 2015)   10. 1029/2020GL091987 2 of 11 mildly polluted air, reducing nitrogen oxides and/or VOCs will reduce the photochemical production of ozone and result in less ozone. In heavily polluted air, in contrast, reducing nitrogen oxides can increase ozone concentrations, because less nitrogen oxide is available to destroy ozone. In this study, we use data from three types of ozone instruments, but mostly from ozonesondes on weather balloons.
Data Availability Statement Most of the ozonesonde data used in this study are freely available from the World Ozone and UV Data Center (https://woudc. org) at Environment Canada (https://exp-studies. tor. ec. gc. ca/), and are downloadable at https://woudc. org/archive/Archive-NewFormat/OzoneSonde_1. 0_1/). Some ozonesonde data for 2020 were not yet available at the WOUDC. Instead, rapid delivery data were obtained from ftp://zardoz. nilu. no/nadir/projects/vintersol/data/o3sondes (requires registration), at the Nadir database of the Norwegian Institute for Air Quality (NILU, https://projects. nilu. no/nadir/obs. html). Registration information, and the same data in a different format, are available from the European Space Agency Validation Data Center (https://evdc. esa. int/). For Boulder, Trinidad Head, Hilo, Fiji, and Samoa, stations operated by the US National Oceanic and Atmospheric Administration, Global Monitoring Laboratory (https://www.
esrl. noaa. gov/gmd/ozwv/), data can be obtained freely from ftp://aftp. cmdl. noaa. gov/data/ozwv/Ozonesonde/. FTIR and lidar data, as well as some ozonesonde data, are from the Network for the Detection of Atmospheric Composition Change (https://ndacc. org), and are freely available at ftp://ftp. cpc. ncep. noaa. gov/ ndacc/station/ and ftp://ftp. cpc. ncep. noaa. gov/ndacc/RD/. Copernicus Atmosphere Monitoring Service (CAMS) global chemical weather EAC4 re-analyses are available at https://atmosphere. copernicus. eu/data. CAMS operational global analyses and forecasts are available at https://apps. ecmwf. int/datasets/data/cams-nrealtime/.
1c  and Supplementary Fig. 1 ). Thanks to the large dataset (74,114 NCFs in total), the recovered reflected phases exhibit high signal-to-noise ratio. The arrival times of reflected body waves in the NCFs have a good agreement with the theoretical traveltime curves (Supplementary Fig. 1 ). However, the reflected body waves from the 410-and 660-km interfaces interfere with the Rayleigh wave trains within some distance ranges. We remove this interference with a curvelet filtering technique  19, 20. All the NCFs were filtered into two period bands (2-5 s, 5-10 s) and stacked within a series of overlapped distance bins before curvelet filtering, to investigate discontinuity sharpness and frequency dependence of reflected body waves. The reflected body waves show different features in two different period bands (Fig.
3c-e. As our study region is outside the cold slab and no significant high wave speed anomaly resolved beneath the seismic array, a normal adiabat (1600 K) is assumed. At each location, synthetic waveform was calculated for epicenter distance at 100 km. The spectral amplitude of the synthetic seismogram was subsequently normalized and multiplied by the corresponding spectral amplitude of real NCF waveform data  17. The reflected body waves also were normalized according to our target time window and only the relative amplitude information was retained. Overall, the synthetic waveforms show an excellent first-order agreement with our observation (Fig. 3c, d ). Even the 660-km discontinuity is much broader due to the enrichment of MORB and associated with week-to-invisible P660P at longer periods (5-10 s), the small sudden increase of wave speed ~660 km can also cause obviously P wave reflection at shorter periods (2-5 s) in the southeastern part of the profile.
That is probably why high-frequency reflected body waves were nearly invisible in the NCFs section in our previous study  16  , in which the total number of used NCFs is about one-fourth of those adopted in this study. It should be noted that the absolute amplitudes of different phases have been lost after time-domain normalization. So, we focus solely on the remarkable relative amplitude features that seem very stable and reliable for different sub-datasets (Supplementary Figs. 8-10). Separation of the reflected body waves and Rayleigh waves. As shown in Fig. 1c , the predominant Rayleigh waves interfere with the weak reflected body waves from 410-and 660-km interfaces in time-space domain. It is difficult to separate these overlapped signals in time-space domain. A curvelet transform technique was implemented to separate these signals in curvelet domain according to the differences in their slopes  20.
Acknowledgements  Jikun Feng  is grateful to  Chunquan Yu  for his help on curvelet filtering and  Ling Chen  and  Wenbo Wu  for useful discussions. This research is funded by  Youth Fund of National Natural Science Foundation of China (41904048) , the  Strategic Priority Research Program of Chinese Academy of Sciences  (Grant No. XDB 41000000 ), and  National Natural Science Foundation of China  ( 41574034 ).
Data availability Waveform data of permanent stations for this study are collected through Data Management Centre of China National Seismic Network at Institute of Geophysics, China Earthquake Administration (https://doi. org/10. 11998/SeisDmc/SN, http://www. seisdmc. ac. cn). Waveform data of portable stations are archived at China Seismic Array Data Management Center at Institute of Geophysics, China Earthquake Administration (https://doi. org/10. 12001/ChinArray. Data, http://www. chinarraydmc. cn). Restrictions apply to the availability of the raw waveform data; request for the original data needs to be sent directly to the management centers. Ambient noise cross-correlations generated and analyzed during the current study are available in the Mendeley Data repository  51.
Introduction Recent advances in cancer genomics and adoption of electronic health records together provide opportunities for development of precision oncology  [1] [2] [3]. However, it is not known what clinical data are essential for reaching this goal. To expand upon recent clinical data collection recommendations  [4]  and to hasten improvement of outcome predictors  [5] , we performed an exploratory analysis of all available clinical, histological, and autopsy data including neuroendocrine marker data in 33 men enrolled in a rapid autopsy study of metastatic prostate cancer (mPC). We also analyzed relevant literature-reported biomarker associations between clinical findings and features predicting disease trajectory. We combined these results to propose a framework for future data recording that encompasses both clinical observational studies and intervention trials in the context of recent advanced prostate cancer (PC) trial data recommendations  [4, 6]  and guidelines  [7] [8] [9].
Patients We curated all available clinical records from a series of 33 men who provided informed consent to participate in the Johns Hopkins Medicine Institutional Review Board-approved (NA_00003925) Project to ELIminate lethal CANcer (PELICAN) integrated clinical-molecular autopsy Outcome measurements and statistical analysis: Data exploration with correction for multiple testing and survival analysis from the time of diagnosis to time to death and time to first occurrence of severe pain as outcomes were carried out. The effect of seven complications on the modeled probability of dying within 2 yr after presenting with the complication was evaluated using logistic regression. Results and limitations: Feature exploration revealed novel phenotypes related to mPC outcome. Four complications (pleural effusion, severe anemia, severe or controlled pain, and bone fracture) predict the likelihood of death within 2 yr.
Cohort description The study data included 207. Metastatic PC was listed in the autopsy report as the underlying cause of death in all cases except for patient A13, whose proximate cause of death was sepsis and peritonitis caused by a perforated sigmoid colon due to corticosteroid treatment of mPC, and therefore his underlying cause of death is listed as mPC. In 16/33 (48%) of cases, mPC was the sole listed cause of death. Additionally, more proximate causes of death were listed in 17/33 (52%) cases, including pneumonia (various types; 11/33, 33%) and single cases (1/ 33, 3%) of sepsis and peritonitis, pericardial tamponade, terminal gastric aspiration, pulmonary embolism (PE), diffuse alveolar damage, pyelonephritis, pleural effusion, renal failure, perforated sigmoid colon, and malignant pericardial effusion.
By adopting some or all of the biomarkers and data recommendations, and fully sharing trial and molecular data, we can make far more rapid progress. The PELICAN33 data used for the current study, which are deposited in the European Genome Archive, represent an important first step in that process. Although the current study is limited to 33 patients, it nonetheless demonstrates that accrual of high-quality longitudinal cohorts including autopsy analysis will be central to progress advancing precision oncology. The fact that the patients in PELICAN33 underwent autopsy between 1995 and 2004, prior to introduction of abiraterone and enzalutamide, is a positive attribute because the current study can provide a basis for comparison of phenotypic outcomes in future autopsy cohorts including patients treated with abiraterone, enzalutamide, and other more recently developed therapies.
Acknowledgments: We thank the men and their families who participated in the PELICAN integrated clinical-molecular autopsy study of prostate cancer. We acknowledge technical support and input from  Tapio Visakorpi ,  Heini Kallio ,  Hanna Rauhala ,  Maria Juujärvi ,  Anna-Mari Aitolahti ,  Arja Jukkola ,  John T. Isaacs ,  Victoria Sinibaldi ,  Tom Smyth ,  G. Mamo ,  Miika Sainio ,  Olavi Yli-Harja  and  Norris Heintzelman.
g. , De  Rydt and Gudmundsson, 2016; Seroussi et al. , 2017; Favier et al. , 2019). Thus, the timing and magnitude of simulated ice sheet retreat are imprecise, but we can identify responses that are robust across simulations, and also draw attention to the largest sources of uncertainty. Section 2 gives an overview of CISM and summarizes the protocol and ocean data sets for ISMIP6 Antarctic projections. We describe the model initialization technique and evaluate the spun-up state in Sect. 3, and we present the results of ocean-forced simulations in Sect. 4. Section 5 gives conclusions and suggests directions for future research. 2 Model and experimental description.
Nudging procedure When spinning up the model, we aim to to reach a stable state with minimal drift under modern forcing, while also simulating ice sheet properties that agree with observations. It is challenging to achieve both goals at once. Long spin-ups typically yield a steady-state ice sheet with large biases in thickness, velocity, and/or ice extent, while initialization methods that assimilate data to match present-day conditions often have a large initial transient. We compromised by using a hybrid method similar to that of  Pollard and DeConto (2012) , running a long spin-up while continually nudging the ice sheet toward the observed thickness. Beneath grounded ice, we adjust C p (x, y), a poorly constrained, spatially varying friction coefficient in the basal sliding law (Eq. 1).
Spun-up model state We carried out six Antarctic spin-ups, each with a different pairing of the three basal melt parameterizations (local, nonlocal, and nonlocal-slope) and the two calibrations (MeanAnt and PIGL). For each spin-up, the ice sheet is initialized to presentday thickness using the BedMachineAntarctica data set  (Morlighem et al. , 2019)  with analytic vertical temperature profiles. The surface mass balance and surface temperature are provided by a regional climate model,  RACMO2. 3 (van Wessem et al. , 2018) , and the geothermal heat flux is from  Shapiro and Ritzwoller (2004). During each spin-up, the simulated ice thickness is nudged toward observations by adjusting C p (x, y) and δT sector as described in Sect. 3. 1. The spin-ups were run for 20,000 model years, allowing the ice sheet to come very close to steady state; at this time the total mass is changing by < 1 Gt y -1.
-Extending more CMIP AOGCM simulations beyond 2100, to generate forcing for multi-century ice sheet simulations. -Forcing ISMs with a greater variety of scenarios, including overshoot scenarios to study whether WAIS retreat, once begun, could be stopped or reversed. -Adding more realistic physical processes and feedbacks, such as hydrofracture and solid-Earth/sea-level effects. Some ISMs already simulate these processes, and a subset of the ISMIP6 Antartic projections included hydrofracture. -Continuing to develop comprehensive data sets of sub-ice-shelf melt rates and Southern Ocean temperature and salinity, to better force models and validate parameterizations.
CC BY 4. 0 License. NCAR. NJ is funded by the French National Research Agency (ANR) through the TROIS-AS project (ANR-15-CE01-0005-01) and by the European Commission through the TiPACCs project (grant 820575, call H2020-LC-CLA-2018-2). Support for XAD was provided through the Scientific Discovery through Advanced Computing (SciDAC) program funded by the US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research Programs. HS and SN were supported by grants from the NASA Cryospheric Science and Modeling, Analysis and Prediction Programs, and the NASA Sea Level Change Team. This is ISMIP6 contribution No X. 22 https://doi. org/10. 5194/tc-2019-334 Preprint. Discussion started: 14 January 2020 c Author(s) 2020. CC BY 4. 0 License.
Acknowledgements. We thank the  Climate and Cryosphere (CliC)  project, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoting ISMIP6. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the CMIP data and providing access, the  University at Buffalo  for ISMIP6 data distribution and upload, and the multiple funding agencies who support  CMIP5  and  CMIP6  and  ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6.
Code availability. CISM is an open-source code developed on the Earth System Community Model Portal (ESCOMP) git repository at https://github. com/ESCOMP/CISM. The version used for these runs is tagged as ISMIP6_Antarctica_TC2019. Data availability. The model configuration and output files for the spin-ups described in Sect. 3 and the projection experiments in Sect. 4 will be archived on the NCAR Climate Data Gateway at https://www. earthsystemgrid. org/.
related with the RV  (Aurière et al. 2009). Following this first result, it is important to determine if the magnetic variations are periodic, possibly resulting from rotation and if the RV variations could be impacted by, or even reflect, this weak activity. Not only can stellar activity induce jitter, which complicates planet detection, but the planetary hypothesis has been discarded in some cases of red giant hosts (e. g. ,  Hatzes et al. 2018; Delgado Mena et al. 2018; Reichert et al. 2019). We therefore performed a Zeeman long-term monitoring of Pollux to characterize its magnetic field and to investigate its contribution to the RV measurements. We collected spectropolarimetric data of Pollux using Narval at TBL and ESPaDOnS at CFHT for a duration of 4. 25 years, that is, for more than two periods of the RV variations.
Spectropolarimetric survey of Pollux with Narval and ESPaDOnS We observed Pollux on 41 epochs and acquired 265 Stokes V series, first using ESPaDOnS  (Donati et al. , 2006a)  at CFHT in a snapshot program and then its twin Narval at the TBL in a long-term monitoring program, from 2007 September to 2012 February, as described below. All data obtained as part of this project are available through the PolarBase database  (Petit et al. 2014). Each instrument consists of a Cassegrain polarimetric module connected by optical fibers to an echelle spectrometer. In polarimetric mode, the instrument simultaneously acquires two orthogonally polarized spectra covering the spectral range from 370 nm to 1000 nm in a single exposure, with a resolving power of about 65000. The observational properties of the instruments, as well as reduction procedures, are the same as described by  Aurière et al.
(2009)  and AKC. A standard circular polarization observation consists of a series of four sub-exposures between which the half-wave retarders (Fresnel rhombs) are rotated in order to exchange the paths of the orthogonally polarized beams within the whole instrument (and therefore the positions of the two spectra on the CCD), thereby reducing spurious polarization signatures. The extraction of the spectra, including wavelength calibration, correction to the heliocentric frame, and continuum normalization, was performed using Libre-ESpRIT  (Donati et al. 1997) , which is a dedicated and automatic reduction package installed at CFHT and at TBL. The extracted spectra consist of the normalized Stokes I (I/I c ) and Stokes V (V/I c ) parameters as a function of wavelength, along with their associated Stokes V uncertainty σ V (where I c represents the continuum intensity).
We computed the S -index (defined from the Mount Wilson survey;  Duncan et al. 1991)  for the chromospheric Ca ii H&K cores to monitor the spectral line activity indicators. These com- putations are described in detail by AKC. We also computed the heliocentric RV of Pollux from the averaged LSD Stokes I profiles using a Gaussian fit. The RV stability of ESPaDOnS and Narval is about 20-30 m s -1  (Moutou et al. 2007; AKC). The averaged value for each date of S -index and RV are presented in Table  1. The data up to 18 March 2009 were already presented by  Aurière et al. (2009). All the new observations were made with Narval. The S -index measurements were updated to use the procedure described by AKC and those of B and RV to use the method of  Mathias et al. (2018).