dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods A detailed materials and methods section can be found in SI Appendix. RNA-Seq Library Preparation and Analysis. RNA was extracted and DNase treated and chemically fragmented. Strand-specific RNA-seq libraries were prepared and sequencing was performed using the Illumina NextSEq 500. Data were deposited in the European Nucleotide Database (ENA) under accession no. PRJEB25942. Sequencing reads were mapped to the NC_003210 L. monocytogenes EGD-e RefSeq genome. Minimum Inhibitory Concentration. Six to eight colonies were resuspended in BHI, diluted at OD 600 = 0. 001 in 96-well plates, and incubated in presence of increasing concentrations of antibiotics for 48 h at 37 °C without shaking. The MIC was determined as the lowest concentration that fully inhibits growth. Polysome Profiling.
In bacterial culture, erythromycin (0. 18 μg/mL) was added or not (untreated condition) at OD 600 = 0. 6 and the growth was pursued for 1 h. Chloramphenicol was quickly added to every culture (2 min at 5 mM final concentration) to stabilize polysomes, and bacterial pellet was washed and flash frozen. Cellular content were extracted using a FastPrep apparatus. An equal amount (15-35K OD 260 ) of cell lysates were loaded on sucrose gradient (5-50%), ultracentrifuged, and separated on a Biocomp instrument. Absorbance was read at 260 nm at a speed of 0. 12 mm/s. Homology Analysis. A total of 9,078 complete genomes retrieved from NCBI RefSeq representing 3,226 species of prokaryotes were analyzed. The composition of Lmo0762 and Lmo1296 in protein domains was analyzed using PFAM. We used these domains to search the database of genomes using hmmsearch.
Phylogenic Trees. First we reduced the redundancy of the dataset using mmseqs2 to cluster the proteins in clusters of 80% identity and 60% identity. When necessary, we added manually to the databases, the genomes that contained at least two copies of the protein to ensure that the phylogenetic reconstruction contained the two copies of each pair of duplicates. We made a phylogenetic reconstruction for each of the two datasets: we made multiple alignments with MAFFT v7. 407, we collected the informative sites in multiple alignment using trimAl v1. 2rev59, and made a phylogenetic reconstruction with IQtree v1. 6. 7. The dataset_60 reconstruction was used in the study because it had higher ultrafast bootstrap results and fewer taxa. However, the key results were common between the two reconstructions.
ACKNOWLEDGMENTS. We thank  Stefano Marzi  and  Pascale Romby  for advice;  Claire Poyart ,  Grégory Boël , and  Olivier Dussurget  for useful discussions;  Nora Vazquez-Laslop  for critical input into the article; Hugo Varet from the  C3BI-Hub (Institut Pasteur)  for his valuable help on data analysis and statistics;  Laurence Maranghi ,  Julienne Blondou , and  Youssouf Boina  for essential technical support; and our referees for their comments, which have significantly increased the quality of this work. This work was supported, in part, by grants (to P. C. ) [ European Research Council (ERC)   Advanced Grant BacCellEpi  ( 670823 ),  Agence Nationale de la Recherche Investissement d'Avenir Programme  ( 10-LABX-62-IBEID ), the  Fondation le Roch les Mousquetaires ]; and, in part, by grants (to R.
For these purposes, we use the detailed SURFEX/ISBA-Crocus snowpack model  (Vionnet et al. , 2012) , driven by SAFRAN meteorological reanalysis data  (Durand et al. , 2009) , to simulate the SMB of Saint-Sorlin Glacier (French Alps). We first assess the accuracy of SAFRAN meteorological reanalysis data at this high-elevation site using all available glaciological and meteorological measurements performed since 2005 on Saint-Sorlin Glacier. Then, the surface energy and mass balance model is calibrated using the measured energy balance to ensure that all the energy balance components are accurately represented. Next, the SMB model is evaluated using 20 years of seasonal SMB measurements (Sect. 4. 1. 1) and results are compared to those obtained with TIMs (Sect. 4. 1. 2). Section 4. 1. 3 focuses on annual SMB sensitivity to seasonal SMB.
Raw SAFRAN reanalysis data Since AWS records on glaciers are limited in time and scarcely distributed, the near-surface meteorological forcing data are estimated by meteorological reanalyses. In this study, we used the SAFRAN meteorological re-analysis system  (Durand et al. , 2009). SAFRAN data are provided using atmospheric vertical profiles simulated by an atmospheric model  (ERA-40 reanalysis until 2001 and ARPEGE operational model after 2002). Results are then corrected by optimal interpolation with observed meteorological data from various sources (automatic weather stations, manual observations carried out in the climatological network or at ski resorts, remotely sensed cloudiness, atmospheric upper-level sounding). Note that surface observations that could be used to correct data are scarce at very high altitudes (i.
Adjusted SAFRAN data SAFRAN data were compared to the AWS m measurements over 10 years  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  and to the available AWS g measurements. Biases were adjusted and the influences of all corrections mentioned below on the simulated SMB are discussed in Sect. 4. 2. SAFRAN and AWS m hourly air temperatures over the ablation and accumulation seasons are well correlated -R 2 = 0. 98 (summer) and 0. 99 (winter), both significant at the 99 % confidence level (Student's t test), and the root-mean-square errors (RMSEs) are 0. 7 • C (summer) and 0. 76 • C (winter). Hourly SAFRAN relative humidity is also in good agreement with the AWS m data (R 2 = 0. 74, significant at the 95 % confidence level, and RMSE = 13. 6 %). The comparison between SAFRAN and AWS m incoming longwave radiation indicates an overestimation of SAFRAN data for low cloudiness conditions.
, 2012). As mentioned in  Litt et al. (2017) , when large-scale atmospheric forcing was strong, intense downslope winds were observed, aligned with the main glacier flow (i. e. coming from the south; see Fig. 1b ). The wind speed measured at AWS m (glacier foreland) were first compared to the wind measured at AWS g06 and AWS g09. Since the correlation between the measured wind speed on the foreland and on the glacier is high (R 2 = 0. 97, RMSE = 1. 7 m s -1 ), we assumed the wind speed measured at AWS m to be representative at the glacier scale and used it to replace SAFRAN wind speed estimates in this study. However, data are limited to the 2006-2015 period. Outside this period  (over 1996-2005) , the SAFRAN wind speed was corrected using a quantilemapping method  (Déqué, 2007; Gobiet et al.
, 2015). This method was chosen because it is considered to be one of the most efficient bias adjustment methods available (e. g. Gobiet et al. , 2015). Percentiles of the observed distribution (AWS m measurements) and the SAFRAN distribution are calculated using every data of a given month and for each month over the 2006-2015 period. A linear method was used for mapping and extrapolated data over the minimum/maximum observed quantile were estimated with a linear function. The resulting mapping function of the quantile-quantile plot was  used to adjust the SAFRAN wind speed distribution over the 1996-2005 period. Finally, SAFRAN-cumulated winter precipitation over each winter was compared to the winter SMBs measured at each accumulation measurement site. As already mentioned in previous studies  (Gerbaux et al.
, 2005; Dumont et al. , 2012) , using SAFRAN raw data leads to a significant underestimation of the winter SMB. The accumulation amount was adjusted based on the methodology developed in previous studies  (Vincent, 2002; Gerbaux et al. , 2005; Dumont et al. , 2012; Réveillet et al. , 2017). For each winter, individual winter SMB measurements were first used to compute multiplication factors for SAFRAN precipitation. The multiplication factors were then spatially interpolated over the entire glacier surface area (kriging method) to obtain an annual map of multiplicative factors. These factors were then used to correct solid and liquid precipitation. The factors varied from 1. 2 to 2. 1 depending on both the year and the site. Applying these factors led to an increase in winter SMB ranging from 0.
Meteorological inputs An important question is whether the Crocus model forced with SAFRAN reanalysis data could be used on a large set of glaciers or over a long time period without in situ meteorological measurements available to evaluate or correct the atmospheric forcing. The sensitivity of the model to the corrections made on the meteorological forcing described in Sect. 2. 3. 4 and summarized in Fig. 2  is presented below. Uncertainties are calculated over the 2006-2010 period, at each measurement point of the glacier.
Sensitivity to precipitation correction SAFRAN precipitation was corrected annually using an extensive data set of winter SMBs on Saint-Sorlin Glacier. Here we test different approaches to correct SAFRAN precipitation to consider the case when such extensive measurements are not available. First, as already mentioned in previous papers (e. g. Gerbaux et al. , 2005; Dumont et al. , 2012) , using raw SAFRAN precipitation leads to an underestimation of the winter SMB due to the lack of observations in high-altitude areas and the complexity of considering local effects such as wind transport. Using raw SAFRAN precipitation data leads to a very low NS coefficient for simulated winter SMBs with respect to observed values. This difference in terms of winter SMB also strongly impacts the performance in simulating summer SMB and annual SMB (Table  3 ).
Our results emphasize the important role of wind speed in energy balance exchanges and its impact on the SMB (Fig. 7 ). Indeed, wind impacts the snow surface density through snow compaction (e. g. Vionnet et al. , 2012)  and the turbulent fluxes (e. g. Litt et al. , 2017). In fact, each component of the turbulent fluxes (H and LE) simulated with original SAFRAN wind data is lower than those simulated with the measured wind. For instance, the mean value of H computed over summer 2006 is equal to 7. 2 W m -2 (with a SD of 10. 7 W m -2 ) when simulated with SAFRAN wind data, compared to 22. 2 W m -2 (with a SD of 37. 8 W m -2 ) when using measured wind speed. Considering wind speed data from AWS m leads to an increase in the snow density of about 50 kg m -3 for the upper layers of the snowpack when density is lower than 300 kg m -3.
Nevertheless, despite these limitations for future simulations, this physical model remains crucial to study and understand physical processes and interactions between atmospheric variables and ablation. Otherwise, although empirical approaches based on simple meteorological variables also have serious drawbacks, they could be more appropriate for simulations of glaciers in the future, especially to simulate summer SMB in ablation areas, bearing in mind the lack of availability of reliable information on future meteorological variables and surface roughness. Data availability. Part of the data used in this paper (seasonal mass balances and automatic weather station data) are from the Alpine GLACIOCLIM observatory and can be accessed at https: //glacioclim. osug. fr. For access to Safran data, please contact Samuel Morin or Marie Dumont.
Purpose: While the use of transthoracic echocardiography (TTE) in the ICU is rapidly expanding, the contribution of TTE to altering patient outcomes among ICU patients with sepsis has not been examined. This study was designed to examine the association of TTE with 28-day mortality specifically in that population. Methods and Results: The MIMIC-III database was employed to identify patients with sepsis who had and had not received transthoracic echocardiography. The statistical approaches utilized included multivariate regression, propensity score analysis, doubly robust estimation, the gradient boosted model and an inverse probability-weighting model to ensure the robustness of our findings. Significant benefit in terms of 28day mortality was observed among the TTE patients compared to the control ('no TTE') group (Odds Ratio = 0.
Covariates Instead, we utilized the presence or absence of CVP values as the covariate. Thus, a flag indicating whether CVP was recorded was included as a co-variate in our models. Similarly, laboratory tests for BNP, troponin and creatinine kinase were not ordered in more than half of the cohort. Therefore, flags indicating whether these tests were obtained were used as covariates. (Details around missing values can be found in the Appendix). The source codes for all analyses can be found at https://github. com/nus-mornin-lab/echo-mimiciii.
Our findings raise the possibility that TTE provides information to physicians that may aid in the management of critically ill septic patients. We fully realize that observational, database studies of this kind require careful, multifaceted, and rigorous statistical approaches in order to produce valid, reliable, and actionable results. We believe that we have done so in this regard for the subject at hand, and intend to pursue further such analyses in the future in order to minimize the ambiguity of clinical decision-making in the confounding and complex environment posed by the ICU. The findings should in no way be taken as the final and definitive word with regard to the value of TTE in the management of sepsis in the ICU. As an observational single center study retrospectively performed on electronic health record data, the potential issues of residual confounding by variables not captured in the EHR, as well as generalizability of the findings to other institutions, require additional investigation.
This last strain also was revealed to possess one of the smallest genomes (ϳ1 Mb) ever sequenced for a lactic acid bacterium. Details of the draft genomes are given in Table  1. Whole-genome sequencing of these strains was carried out by Eurofins MWG Operon Laboratories (Ebersberg, Germany) using Illumina MiSeq 2 ϫ 150-bp paired-end libraries. Reads were assembled de novo by the Velvet software  (10). All contigs were aligned against a relevant complete genome of closely related strains or species using progressiveMauve  (11) , and annotation was performed with the MicroScope platform  (12). Accession number(s). The sequence data have been deposited in DDBJ/ENA/ GenBank under the accession numbers cited in Table  1.
ACKNOWLEDGMENTS This work and S. P. 's postdoctoral fellowship were supported by the  ANR RedLosses project , grant  ANR-16-CE21-0006 , operated by the  French Agence Nationale de la Recherche. We are grateful for the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr) for providing computational resources and data storage. The LABGeM ( CEA/ IG/Genoscope & CNRS UMR8030 ) and the  France Génomique National Infrastructure  (funded as part of the  Programme d'Investissements d'Avenir  managed by  Agence Nationale pour la Recherche , contract  ANR-10-INBS-09 ) are acknowledged for support within the MicroScope annotation platform.
The PCASP was calibrated before and after the campaign, and the CDP was calibrated and cleaned before most flights during the campaign as described in  Rosenberg et al. (2012). The PCASP and CDP employ light scattering in order to determine particle size; thus, the nominal size bins have been adjusted for a refractive index (RI) of dust of 1. 53-0. 001i, informed by the refractive index closure results from Sect. 2. 5, using the CStoDConverter software  (Rosenberg et al. , 2012). This results in an increase in diameter of the largest size bins due to the more absorbing imaginary part of the refractive index as compared to PSL (polystyrene latex) used in the manufacturer's calibrations. The software also provides uncertainties in the bin centres and bin widths due to oscillations in the Mie scattering curve, which can cause ambiguities in the scattering-size relationship.
This propagation of errors allows us to utilize data from the full size range while still maintaining a measure of the sizing uncertainty and can be seen by horizontal error bars in Fig. 2. The larger errors around diameters of 3. 5 and 20 µm, for example, relate to inflection points on the Mie scattering curves. These horizontal sizing errors are propagated through to calculations of number and volume size distribution errors. The first size bins are removed for both the PCASP and CDP since the lower edges are not well defined. In a similar manner to Fennec, custom bin widths were set for the CDP. The smallest bin of the CDP was set much wider than standard, with a lower minimum detection threshold in order to increase sensitivity to smaller particles, and subsequent processing of the particle-by-particle data allowed the smallest size bin to be split into four sub-bins.
An "all-in" method was applied where particles are only counted when they fall completely within the photodiode array. The particle size was defined as the diameter of the smallest circle enclosing the particle (denoted "CC" -circumscribing circle) and thus for non-spherical particles may lead to an overestimation of particle size. Secondly, data from the 2-DS instrument were analysed using the Optical Array Shadow Imaging Software (OASIS), developed by the National Centre for Atmospheric Science (NCAS) and Droplet Measurement Technologies. Particle sizes were calculated using the mean of the x and y dimensions of each particle image (denoted "XY") using a "centre-in" method where particles are only counted when the centre of the particle falls completely within the photodiode array. The x and y dimensions are measured along the probe array; i.
Some CIP15 AER-D data were also processed using a centre-in, mean XY metric, but unfortunately it was not possible to process data for all the SLRs with this method. Therefore, these data were used to provide information on instrumental differences between the 2-DS and CIP15 when processed with the same size metric (XY mean). It was found that the impact on the FULL particle size distribution (PSD) was very small (d eff differed by under 1 %), but that d max was up to 6 % larger with the CIP15 XY compared to the 2-DS XY. The upper uncertainty of 6 % in d max was therefore propagated in combination with the other uncertainties in d max.
006 but with an additional smallest value of 0. 0001 which was required for the MBL SLRs. The value of k 550 which produced best agreement with the SSA from the nephelometer and PSAP was selected for each SLR. The resulting values of k 550 are shown in Fig. 3 , with modal values of 0. 001i for SAL SLRs and 0. 0001i for MBL SLRs. This set of refractive index data is referred to hereafter as the "iterated refractive indices". The derived iterated refractive indices were then used in two ways. Firstly, a value of 1. 53-0. 001i was used to correct all size distributions from the PCASP and CDP. Secondly, iterated refractive indices derived for each SLR were selected to generate optical properties for the FULL PSD and ACC PSD described in Sect. 2. 6.
2. 5) to the variability of SSA, we show both d eff and k 550 as a function of SSA in Fig. 13 , separated into the ACC PSD and FULL PSD size ranges. It is clear that during AER-D, it was the variability of dust composition that controlled the variability of SSA rather than the variability of size distribution. In Fig. 13a  for the SAL (orange) points, it can be seen that the direct observations for Circles represent calculations using the RI derived from Mie iterations; diamonds represent calculations using RI derived from composition data from filter samples assuming internal mixing (four samples). For the FULL PSD with the iterated RI, the same accumulation mode RI is extended to the coarse mode. For the filter RI, a size-specific RI is used for the accumulation mode and the FULL PSD (as given in Table  7 ).
the accumulation mode (orange asterisks, where nephelometer and PSAP scattering and absorption are used to calculate SSA) are in agreement with Mie simulations using the ACC PSD (small orange circles). When the coarse-mode size distribution is then included (large orange circles), effective diameter increases and there is a slight decrease in SSA. Although d eff does impact the magnitude of the SSA, within AER-D the shape of the PSD was relatively stable, with a relatively constant d eff. This meant that the small variation in size distribution shape had minimal impact in determining the variability of SSA. Figure  13b  shows the variation in SSA as a function of composition, represented by k 550. Here we show the relationship between these two variables calculated with different RI datasets: firstly for RI from the Mie scattering iterations (circles, Sect.
2. 5. 1) and secondly for RI calculated for filter sample composition (diamonds, Sect. 2. 5. 2). Optical properties are shown for the accumulation mode only (small data points), and for the FULL PSD (large data points). For dust in the SAL, Fig. 13b  shows a consistent decrease in SSA with the imaginary part of the refractive index. This relationship becomes more negative when the full coarse-mode PSD (large symbols) are included because for a fixed RI, the larger particles exert more absorption. Although there is some variability between the results from different RI datasets, overall they show the same trend. Contrastingly to Fig. 13a , Fig. 13b  shows that the SSA variability was strongly influenced by the variability in composition. This is the case for both accumulation mode observations of SSA and for the full size distribution.
This is consistent with our findings that the calculated refractive index from the filter samples is strongly influenced by the iron content and its absorbing properties. It appears that over the Sahara, variations in the PSD (affected by dust age) have an important impact on SSA, while over the ocean the impacts of composition (perhaps either by chemical aging or by sampling dust from different sources) become more important. Finally, we compare optical properties calculated with composition data from the filter samples using both internal and external mixing assumptions against observations in Fig. 14  for the ACC PSD (a) and the FULL PSD (b). Observations are available only for the ACC PSD size range. Figure  14a  shows that compared to the observations of SSA, the external mixing assumption provides closer agreement with the observations, confirming that the internal mixing assumption used to derive refractive indices shown in Sect.
Full spectral complex refractive indices were calculated from the filter samples. At 550 nm, the imaginary part of the refractive index k 550 for the FULL PSD was 0. 0012 to 0. 0030 in the SAL, strongly influenced by the volumetric iron content. Real parts were relatively low at 1. 48 due to the low real values contributed by the literature kaolinite data. In general, refractive indices at 550 nm calculated from two different methods agreed well. For the full spectrum of data, iterated k 550 values representing the accumulation mode covered 0. 0005 to 0. 0025 with a modal value of 0. 001. Refractive indices calculated from composition data and internal mixing assumptions were found to overestimate absorption, while external mixing assumptions provided the best agreement with observations. SSAs for the accumulation mode were measured, and SSAs for the full size distribution were calculated using measured size distributions and derived refractive indices at 550 nm.
Figure13. Contribution to single scattering albedo from particle size (a SSA vs. d eff ) and from composition (b SSA vs. k 550 ), separated by SLRs in the SAL (orange and yellow) and MBL (blue). Small data points represent the ACC PSD only; large data points represent the FULL PSD. Asterisks in (a) are direct observations behind inlets. Circles represent calculations using the RI derived from Mie iterations; diamonds represent calculations using RI derived from composition data from filter samples assuming internal mixing (four samples). For the FULL PSD with the iterated RI, the same accumulation mode RI is extended to the coarse mode. For the filter RI, a size-specific RI is used for the accumulation mode and the FULL PSD (as given in Table7).