dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Eight hundred and fifty-nine patients were enrolled in the study. Early intubation (i. e. , in the 8 h following vasopressor initiation) was performed in 209 patients (24%). At 24 h, 70 additional patients had been intubated and 44 were intubated between H24 and H72, and only one thereafter during the ICU stay. Cumulative intubation rate over the ICU stay was therefore 324/859 (38%, range among the centers with at least 20 patients included 14-65%; Q1-Q3 27-46%). The hazard of being intubated over the first 72 h is displayed in Fig. 1. The percentage of patients intubated early ranged from 4. 5 to 47%, [Q1-Q3: 17. 4-32. 6%] across the 18 centers with at least 20 patients included (these centers included 718 patients), see Fig. 2. Data of patients intubated early, late (after H8) and never intubated are displayed in Table  1 : parameters associated with acute severity (i.
Predictor variables used in the mixed-effects logistic regression model are detailed in Table  2  and its footnote. According to physicians, 71 (8. 3%) patients were intubated for emergent surgical procedures; these patients were excluded from the dataset for the analysis. Seven parameters were significantly associated with intubation by H8 and ranked in the model as follows by decreasing weight: Glasgow score, center effect, use of accessory respiratory muscles, lactate, vasopressor dose, pH and inability to clear tracheal secretions. However, the global R-square of the model was only 60% indicating that 40% of the variance of the decision to intubate or not was related to other variables than those entered in this analysis. Finally, mortality and other outcomes were significantly different between patients intubated early, late, and never intubated (see Table  3 ).
Missing data were observed in 13% of included patients, which may impact the accuracy of our results. We may have missed some important parameters that should have been considered to explain the decision to intubate. However, the prospective and dedicated design of our study allowed the monitoring of a wide range of baseline and clinical parameters. We may also have missed the adequate time frame during which parameters should have been monitored. In a future study, organizational factors (physician experience, workload, etc. ) may be registered. Another hypothesis is that this uncertainty represents clinical equipoise, opening the way for a randomized trial comparing early (8 h) vs. late (rescue) tracheal intubation.
Variant calling For paired-end sequencing, sequence reads were processed with  Cutadapt (v. 1. 15)  to remove low-quality (, Q10) bases, adapter sequences, and reads # 40 nt after trimming. The DM1-3 v4. 04 genome assembly, as well as DM1-3 chloroplast and mitochondrion sequences, were retrieved from (http:// solanaceae. plantbiology. msu. edu/pgsc_download. shtml), concatenated, and used as the reference sequence for pairedend read alignment with BWA MEM (v. 0. 7. 12-r1039)  (Li 2013) , with mismatch penalty six and all other parameters left at the program default. PCR duplicates were removed using Picard (v. 2. 14) MarkDuplicates, and only reads with mates mapping to the same chromosome were retained. For reads with overlapping mates, one of the two reads was soft-clipped in the overlap region using bamUtil::clipOverlap  (Jun et al.
Aneuploidy was then called if chromosomal standardized coverage exceeded the all-chromosome population by $ 3 SD. We identified local dosage variants by clustering standardized coverage values of nonoverlapping 250-kb bins using the R package MeanShift (CRAN -Package MeanShift). The clustering bandwidth parameter was set to the 50th percentile of interpoint distances in each 250-kb bin. Parental origin analyses of trisomy and dosage variants were carried out as previously described  (Henry et al. 2015). Briefly, reads with mapping quality $ Q20 and base calls $ Q20 were used to compute allele-specific read depth at the subset of 800,384 SNP loci identified above, located on chromosomes 1-12 of the DM1-3 v4. 04 assembly. The percentage of reads supporting the HI allele reads among all reads at loci within a nonoverlapping 1-Mb bin was then reported.
As a biological positive control was not available for SNP analysis, we empirically evaluated limitations of the SNP dosage assay by comparing dihaploids with simulated triploid hybrids expected to resemble an introgressed HI chromosome segment at all tested genomic loci. To construct triploid hybrids in silico, pseudorandom subsets of exactly 2,015,413 and 1,007,706 forward mates were drawn 100 times from raw sequencing reads of Alca Tarma (SRA ID SRR6123032) and IVP101 (SRA ID SRR6123183), respectively. The number of parental reads was chosen such that parental reads would be present in a 2:1 ratio expected for a triploid, and that the number of raw reads for each in silico hybrid would match the fifth percentile of raw read counts in the dihaploid sequencing data set. Similarly, 100 in silico hybrids were constructed using Alca Tarma and PL4 reads.
Median read depth in nonoverlapping 10-kb windows was then determined using custom Python software available from (https://github. com/kramundson/ LOP_manuscript). We observed a positive correlation of window median read depth and GC content, suggesting that PCR amplification bias was introduced during sequencing library construction  (Benjamini and Speed 2012). For each 10-kb bin, this bias was corrected by dividing median read depth by GC content for that bin. The resulting values were clustered using the MeanShift package in R  Ciollaro and Wang (2016). The centroid of the largest cluster was designated as the copy number corresponding to the expected ploidy (four for Alca Tarma and two for dihaploids), and multiples of the centroid were used to designate the remaining copy number states.
Data availability Sequence data have been deposited in the National Center for Biotechnology Information Sequence Read Archive: Bio-Project identifier PRJNA408137. Analysis code has been deposited at https://github. com/kramundson/LOP_manuscript. Supplemental material available at figshare: https://figshare. com/projects/Genomic_Outcomes_of_Haploid_Induction_ Crosses_in_Potato_Solanum_tuberosum_L_/70793.
Sequencing reads were aligned to the DM1-3 reference genome and read counts per chromosome were normalized to those of the tetraploid parent, such that values near 2. 0 were obtained for chromosomes present in two copies and values deviating from 2. 0 indicated aneuploidy. In 19 individuals, standardized coverage was significantly elevated for a single chromosome, suggesting a primary trisomy (Figure  2A ). Root tip chromosome spreads were evaluated for 15 of the 19 putative trisomics, confirming the 2n = 2x + 1 = 25 karyotype in all cases (Figure  2 , B and C and Figure  S1 ). In total, six trisomics of chromosome 2, two of chromosomes 4, 5, 7, and 8, and one of chromosomes 1, 3, 6, 10, and 12 were detected in the population (Figure  2A  and Figure  S1 ). To determine the parental origin of each trisomy, 382,967 SNPs homozygous for the same allele in both HIs, but homozygous for an alternate allele in Alca Tarma, were identified from sequencing the parental genomes (Supplemental Data set S1).
Acknowledgments The authors thank  Awais Khan  for encouraging and coordinating the early phase of this collaboration;  Michell Feldmann ,  Jordan Weibel ,  Jeanine Montano , and  Helen Tsai  for constructive comments on writing and data analysis; and the  CGIAR Collaborative Program on Roots Tubers  and  Bananas  for supporting the development of the LOP population used here. This work was supported by the  National Science Foundation Plant Genome Integrative Organismal Systems (IOS)  Grant  1444612  ( Rapid  and  Targeted Introgression of Traits via Genome Elimination ) to L. C.
1°, i. e. about 10 km) from the French Meteorological Office Météo-France. Harmonic components of tide at the sea boundaries and bathymetry have been provided by the Service Hydrographique et Océanographique de la Marine (SHOM: The French Navy Oceanographic department). Daily freshwater discharges from the Aulne and Elorn rivers which drive salinity gradients were given by the French water office database. The exit time step of model results was 15 min. The duration of the spin-up period was set to two weeks. In addition, in the Bay of Brest, vertical profiles of current velocities were measured by an Acoustic Doppler Current Profiler (ADCP; 600 kHz WorkHorse Sentinel) mounted at a surface floating platform during the 16-17 July, 2014. Current velocities were recorded every 2 s throughout the water column in 0.
The maximum likelihood estimates and the B statistics were obtained using the MATLAB script written by Beet et al. (2003)   89. To separate day and night hours, the use of PAR values was preferred to the official hours of sunrise and sunset. The nighttime was defined when PAR values in surface, mid-depth and bottom waters were close to zero, i. e. lower than 0. 15 µmol m -2 s -1 (Fig. 1a, b ). Thus in the bay of Douarnenez, the nighttime was from 23:00 to 06:00, and in the bay of Brest from 23:00 to 05:00. Hours of day-night transition (22:00 and 07:00 for Douarnenez, and 22:00 and 06:00 for Brest) were excluded from the analyses following Tapia et al. (2010)  88. To compare the vertical distribution between developmental stages, daytime and nighttime were separated in order to minimize the effects of a potential diel migration.
Because the statistical test applies only when the same number of replicates is available for each condition  89  , and to avoid any potential autocorrelation in larval vertical distribution between successive hours, statistical comparisons were carried out for groups of 5 daytime and 5 nighttime plankton profiles selected at random and without replacement from the complete datasets. This random draw procedure was repeated 10 times for each comparison, and ranges of values obtained for the test B and their associated probability (p-values) were retained, following the procedure used by Tapia et al. (2010)  88. Differences were considered significant when 6 or more individual p-values (out of 10) were significant at the 0. 05 significance level. The same procedure was used to compare the vertical distribution between developmental stages, except that the significance level for individual p-values was set as 0.
Acknowledgements We are grateful to the crew of the  R/V Neomysis  ( INSU-CNRS ), and to  Alexandre Robert  and  Gwendoline Traisnel  for their valuable help at sea. We also acknowledge the help of  Fabián Tapia (University of Concepción, Chile)  who provided us with the script of the Beet test. We thank  Céline Cordier  for providing us with the map of our study area. Romaric Verney  and  David Le Berre  ( IFREMER, Brest ) helped us with ADCP data. This study is part of the project  DEXPO "Compréhension de la Dynamique d'Expansion de l' ophiure Ophiocomina  nigra" which was supported by the  EC2CO National Programme on Coastal Environments.
github. io/S-RIPChapter2E. pdf, last access: 15 July 2020). Further details on assimilated observations and model treatments have been provided by  Long et al. (2017)  for temperature,  Davis et al. (2017)  for water vapour, and  Tegtmeier et al. (2020)  for the structure of the tropical tropopause layer (TTL), among others. The full intercomparison period covers January 1980 through December 2014 and includes all five reanalyses. We also conduct a more detailed intercomparison of daily covariations among selected variables from January 2001 to December 2010. Results for the full intercomparison are presented in Sects. 3, 4, and 6, while results based on daily co-variability are presented in Sects. 4 and 5. Our intercomparison period includes the CFSR-CFSv2 transition in January 2011 and the intermediate year 2010 (as discussed by  Fujiwara et al.
42 %). However, as discussed at the beginning of Sect. 2, direct comparisons between cloud variables derived from satellite observations and those derived from models can be misleading. MERRA-2 provides outputs from the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP;  Bodas-Salcedo et al. , 2015)  as an ancillary product in the reanalysis. Included in this product are estimates emulating HCC as observed by MODIS. Whereas MERRA-2 produces a tropical-mean HCC of 43. 35 % during 2001-2014, the MERRA-2 COSP product indicates that MODIS would observe a tropical-mean HCC of only 24. 71 %. This latter estimate is in good agreement not only with ISCCP HGM but also with Terra MODIS (26. 04 %) and CERES SYN1Deg (23. 89 %) gridded products (Fig. 2 ) and extends to the spatial distribution of HCC.
12e  and j ). Water vapour and cloud fields from CFSR should be avoided at these levels, although these issues appear to be improved in CFSv2 (Fig. B1 ). We have also reported evident discontinuities at production stream transitions in CFSR (Fig. 14 ), indicating that this data set should be used with caution, especially in analyses that span the 2010 bridge year and/or the 2011 transition to CFSv2 (see also Appendix B). Taking all factors into account (an absence of major drifts or jumps, consistently high correlations, and standard deviations quite close to those found in observationally based analyses), ERA5 appears to provide a better representation of temporal variability in HCC, OLR, and LWCRE within the tropics than other recent reanalyses. However, it is important to note that the current version of ERA5 contains known discontinuities in some variables in the early 2000s (e.
3f ). Third, a pronounced anvil maximum in CWC is present in the tropical upper troposphere in ERA5 but not in ERA-Interim (Fig. 4a  and b ). The distribution in ERA5 is more consistent with CloudSat observations but still shows substantial discrepancies (Fig. 4f ). Fourth, as a consequence of the increased CWC in the upper troposphere, the positive bias in OLR and negative bias in LWCRE in ERA-Interim are both reduced in ERA5 (Fig. 5 ). Distributions of LWCRE, SWCRE, and total CRE based on ERA5 are more consistent with those inferred from CERES data (Fig. 7 ). However, in both ERA5 and ERA-Interim, low biases in LWCRE relative to CERES EBAF are twice as large in absolute magnitude as high biases in OLR because clear-sky OLR is underestimated, while all-sky OLR is overestimated. This may indicate issues with composition, emissivity, or other aspects of the LW radiation scheme in addition to clouds.
The vertical profile of moist static energy (Fig. 11 ) is an instructive example. MSE is calculated solely using variables targeted by data assimilation. However, our results reveal important differences in the vertical profile of MSE, especially in convective regions. Nor are these differences attributable solely to discrepancies in https://doi. org/10. 5194/acp-20-8989-2020 Atmos. Chem. Phys. , 20, 8989-9030, 2020 the water vapour fields, as illustrated by the stabilizing effects of upper-tropospheric warm biases in MERRA-2. Biases in upper-tropospheric temperatures imply differences in the vertical location and spatiotemporal variability of isentropic surfaces as well. Such differences may impact the results of reanalysis-driven transport model simulations, regardless of whether those studies assume isentropic, diabatic, or kinematic representations of vertical motion.
Such biases may also influence the spatiotemporal distribution of convective source regions of air entering the stratosphere as inferred from transport model simulations. A systematic intercomparison of cloud top heights and temperatures may be useful for revealing further deficiencies or idiosyncrasies of the convective parameterizations used in the reanalysis models. Further investigation along these lines may also consider how these features can imprint upon more widely used reanalysis products and model simulations that use reanalysis fields to drive atmospheric transport. In this appendix, we briefly document selected aspects of the cloud, convection, and radiation parameterizations in the reanalysis atmospheric models. Additional information on the models, data assimilation schemes, and other elements of the reanalysis systems has been provided by  Fujiwara et al.
6 ). It is only when we evaluate the changes at the transition that these inconsistencies crop up, implicating changes in the model. Precisely which changes to the model are responsible is unclear at present; however, both the existence of these discrepancies and the scale of the associated changes underscore our summary recommendation that users of CFSR/CFSv2 should approach any analysis spanning the transition with extreme care. https://doi. org/10. 5194/acp-20-8989-2020 Atmos. Chem. Phys. , 20, 8989-9030, 2020 https://doi. org/10. 5194/acp-20-8989-2020 Atmos. Chem. Phys. , 20, 8989-9030, 2020 Data availability. ERA-Interim products were acquired from the public archive maintained by ECMWF (http://apps. ecmwf. int/ datasets, ECMWF, 2011a, b) using the Python API (https:// confluence. ecmwf.
int/display/WEBAPI, ECMWF, 2018d). Dailyaverage heating rates and TOA fluxes were constructed from 12 h forecasts. Daily averages of all other data are from instantaneous 6-hourly products. ERA5 heating rates were also acquired from the ECMWF archive (ECMWF, 2018c) using the Python API; all other ERA5 products were acquired from the Copernicus Climate Data Store (https://cds. climate. copernicus. eu, ECMWF, 2018a ECMWF, , b, 2019a, b), b). Due to bandwidth and storage limitations, vertically resolved data from ERA5 were obtained at 3-hourly resolution, while two-dimensional data (cloud fractions and OLR) were obtained at the full hourly resolution. Daily-average heating rates and TOA fluxes were constructed from time-averaged data, while daily averages of all other variables were constructed from instantaneous outputs.
JRA-55 heating rates were obtained from the NCAR Research Data Archive (RDA; https://rda. ucar. edu, JMA, 2013b); all other JRA-55 products were obtained from archives maintained by the Japan Meteorological Agency (http://jra. kishou. go. jp/JRA-55, JMA, 2013a). Daily means of heating rates and TOA fluxes were calculated from time-averaged diagnostic fields, while daily means of other variables were calculated from instantaneous outputs at the standard temporal resolution (6-hourly for vertically resolved fields; 3-hourly for high-cloud cover). CFSR and CFSv2 products were acquired exclusively through the NCAR RDA  (Saha et al. , 2010a (Saha et al. , , b, 2012)). Daily averages of heating rates and TOA fluxes were calculated from time-averaged forecast fields; daily averages of all other variables were calculated from 6-hourly instantaneous fields.
MERRA and MERRA-2 products were obtained from the NASA Goddard Earth Sciences Data and Information Services Center (https://daac. gsfc. nasa. gov, GES DISC, 2020). For MERRA we exclusively used monthly-mean IAU products  (GMAO, 2008a, b). For MERRA-2 daily means we use the 3hourly time-averaged heating rates (GMAO, 2015c) and 3-hourly instantaneous profile fields (GMAO, 2015a), both from the IAU (ASM) product set. Hourly time-averaged fields were used for high-cloud fraction and TOA radiative fluxes (GMAO, 2015b   , 1996). The NASA/GEWEX SRB data were ac-quired from the NASA Langley Atmospheric Science Data Center (https://gewex-srb. larc. nasa. gov, SRB Science Team, 2012). Author contributions. JSW and XS conducted the analysis and wrote the initial draft. BL and XZ provided data and helped to interpret the results.
Acknowledgements. We thank  Peter Bechtold  from  ECMWF ,  Yayoi Harada  and  Ryoji Nagasawa  from the  JMA Meteorological Research Institute , and  Kris Wargan  from  NASA GMAO  for help in clarifying aspects of the reanalysis data and  Yi Huang  and an anonymous reviewer for comments and suggestions that have greatly improved the paper. We are also grateful to  Xianglei Huang  for advice on OLR data and radiative transfer models, to  Jennifer Kay  for providing the CloudSat-CALIPSO data, and to  Yi Qin  and  Xiaolu Yan  for helpful discussions. A condensed version of this material has been prepared for Section 8 of the S-RIP report. We thank  Masatomo Fujiwara ,  Gloria Manney , and  Lesley Gray  for their leadership of this activity and all S-RIP participants for their contributions. Financial support.
Radiographic analysis All patients underwent standing full spine films. Radiographs were measured using validated software (SpineView, Laboratory of Biomechanics ENSAM ParisTech, France)  [8] , and the following sagittal radiographic parameters were obtained: PI, PI-LL, PT, SVA. After calculating the PI-LL/ PT ratio, patients were stratified based on PI-LL/PT as two groups using 40% and 60% percentile as threshold to distinguish different pelvic compensation recruitment. The PI-LL/ PT ratio stands for the level of pelvic compensation involvement in the setting of lumbosacral malalignment.
3D Muscle Reconstruction The 3D reconstruction of individual muscles, listed in Table  1 , was performed using Muscl'X software (ENSAM, Laboratory of Biomechanics, Paris, France), a validated software  [9]  already employed in previous literature  [10]. Some muscles were combined, since the low contrast made an accurate separation of the individual muscles difficult. The lumbar part of the psoas was reconstructed separately, but at a point where the distinction with the iliacus was not possible, it was then integrated into the iliacus. The external obturator, adductor longus, brevis and magnus and pectineus were reconstructed into a single group named "Adductor. " The vastus lateralis and intermedius were reconstructed together. The muscle reconstructions were done on the fat images. Figure  1  (reprinted with permission from the authors of previous literature  [10] ) presents the 3D reconstruction of the left muscles for one patient.
The hamstring tightness in Scheuermann kyphosis was also reported and defined as a definite sign or etiological factor. Hosman et al. found that tight hamstrings were linked to limit lumbar and pelvic range of motion and risk of postoperative sagittal negative imbalance indicating overretroverted pelvis  [26]. In the current study, the volume of B. F. L was correlated with and could be used to predict PT, while larger SVA was correlated with more fat infiltration of B. F. L, which implied less contribution to compensatory mechanisms such as hip extension and knee flexion. The current study also reported the feasibility to predict sagittal malalignment with only G. Med, G. Min and B. F. L. Actually, predicting sagittal alignment from muscle parameters is challenging in common clinical settings that are typically not equipped to obtain the appropriate advanced imaging and subsequently analyze the data rapidly.
Statistical analysis The forefoot plantar pressure distribution was expressed as a percentage (from 0% to 100%); hence, a non-bounded distribution (such as a normal distribution) could not be assumed. Accordingly, we transformed the data into a beta distribution by dividing the percentage by 100 (yielding values of between 0 and 1, corresponding to the support of a beta distribution). We used a Bayesian statistical approach based on Markov chains and Monte Carlo integration techniques (using OpenBUGS software)  (Lunn et al 2000). After 5,000 burn-in iterations, the application of three chains with 50,000 iterations and a thinning factor of 10 yielded a total of 15,000 iteration samples for a posteriori estimates. The model with respect to the study's main objective was a mixed beta regression model with covariates that explained the mean of the beta distribution.
Acknowledgements The authors thank the  Centre d'Investigation Clinique CIC-1434 (INSERM and CHRU de Strasbourg, Strasbourg, France) , the management staff and the students at the  Institut de Formation en Masso-Kinésithérapie d'Alsace (IFMK, France)  for their assistance with and participation in this research. We also thank  Alexandre Kubicki  (head of physiotherapy at the  IFMK, Montbeliard France ) for valuable advice, and  Medicapteurs SA  ( Toulouse, France ) for loaning us the baropodometric platform.