dataesr/scientific-paragraphs-categorization huggingface dataset:
baumannii strain D36 (GenBank:NZ_CP012952. 1) (Fig. S7 ). However, the wzy gene (a polymerase required for capsular polysaccharide biosynthesis) within the capsule locus was interrupted by an ISAba125 insertion sequence in all CR-Ab isolates. Further comparative analysis found a portion of the capsule locus in MS14413 to share 99% nucleotide identity to the capsule from A. baumannii strain BAL_097 (GenBank: KX712116), which carries a wzy gene at the beginning of the capsule region. This unusual gene placement also appears in MS14413, and likely complements the loss of the internal wzy     2 ). The high nucleotide identity at this region also indicates possible recombination. MER R R R R R TIM R R R R R TAZ R R R R R CRO R R R R R CAZ R R R R R FEP R R R R R KZ R R R R R Azt 6 mm GENT R R R R R TOB R R R R R Aph(3′)-Ic-1 + + + + + aadA1 + + + + + armA + + + + + Quinolones CIP R R R R R NOR R R R R R Trimmethoprim/ Sulphonamide TMP R R R R R SXT R R R R Overlapping the capsule (K) region in MS14413 is a large 41 375 kb tandem duplication, encompassing two copies of Tn2006 (Fig.
Whole-genome shotgun metagenomics detects CR-Ab in hospital environment Ongoing surveillance was conducted using WGS following the initial outbreak. Despite continual environmental cleaning and routine swabbing, the outbreak CR-Ab strain persisted through to September 2018 (Fig. 3 ). Swabs collected from surfaces within the ICU and Burns Unit (e. g. handles, tables, shelves, computer equipment) in 2016 and 2017 were unable to detect CR-Ab in the environment and did not yield enough DNA for direct metagenomic sequencing (data not shown). Due to 11 new cases of CR-Ab detected between May to September 2018, additional environmental sampling was carried out in the Burns ward environment. Between July to October 2018, areas of presumed high bacterial load (such as floor drains, plumbing, inside burns bath drains etc) were targeted for environmental sampling (Fig.
CONCLUSION By using WGS to assist in a large outbreak of CR-Ab (and other MDR Gram-negative bacilli) we show how genomics can be used to improve rapid respond measures and outbreak management, as well as provide in-depth characterization of the outbreak strains to establish a historical database that can be used to guide responses to future outbreaks. We also show how direct sequencing of environmental samples was able to detect evidence of the outbreak strain leading to key changes in infection control policy.
Acknowledgements We acknowledge all staff at the  Royal Brisbane and Women's Hospital and Pathology Queensland  who were involved in patient care and the clinical response to the outbreak described in this study. We acknowledge the facilities, and the scientific and technical assistance of staff at the  Australian Centre for Ecogenomics Sequencing Facility [The University of Queensland (UQ) ] and the  Public Health Microbiology Laboratory at Queensland Forensic and Scientific Services (Queensland Health ). We thank  Thomas Cuddihy  ( QFAB Bioinformatics and Research Computing Centre, UQ ) for high-performance computing support. We thank  Kate Peters  and  Minh-Duy Phan  ( Schembri lab, UQ ) For technical assistance preparing genomic DNA. We also thank  Nguyen Thi Khanh Nhu  ( Schembri lab, UQ ) and  Chelsie Smith [The University of Queensland Centre for Clinical Research (UQCCR) ] for their assistance with additional colistin testing of A.
The experimental dataset presented was collected in an 18 m long and 1 m wide laboratory flume. Low to high flood flows through an urbanized floodplain were modelled. The floodplain bed is rough, modelled with dense artificial grass. A square cylinder array, representing house models, was set on the rough bed. The cylinder immersion rate was varied: cylinders are emerged for three flow cases and slightly submerged for one case. The experimental dataset comprises water levels, measured using an ultrasonic transit time probe, velocities across the channel measured using an Acoustic Doppler Velocimetry with a side looking probe, and velocities in longitudinal-vertical planes measured using Particle Image Velocimetry. These data could help understanding the physical processes associated with high flood flows through urbanized floodplains, with a focus on the transition from emerged to submerged obstacles.
Recently, more information can be found in a review of the data available  19. As extreme flood data are very scarce or even non-existent in the field, the present dataset could help understanding the physical processes associated with extreme flood flows in a large simplified urban district area. Moreover, these data could be used as benchmark data to assess the ability of numerical models from one-dimension (1D) to three-dimensions (3D) to estimate the flood hazard over a wide range of flood event magnitudes  2, 20. The experiments were carried out in an 18 m long and 1 m wide open-channel flume (Fig. 1a ), which is located in the Hydraulic and Hydro-morphology Laboratory (HHLab) at INRAE Lyon-Villeurbanne, France. The channel bed slope in the longitudinal direction, S 0 , is equal to 1.
Four streamwise uniform flows on average were investigated, varying the immersion rate H/k, where H is water depth and k is square cylinder height, with H/k = 0. 42; 0. 93; 0. 98; 1. 48. The hydraulic parameters are reported in Table  1 , in which = Fr H U gH ( ) / U Q Q is the Froude number, ν = Re H U H ( ) / U Q Q is the Reynolds num- ber, = U Q A / Q is bulk velocity, A = BH being the wet area and B the channel width. Note that, the data with immersion rate H/k = 0. 93 and 1. 48 are more exhaustive than the other two flow cases because the velocity measurement is more detailed (see section Methods subsection ADV and PIV velocity measurements positions).
Methods A right-handed Cartesian coordinate system was used (Fig. 1 ), in which the x-axis is aligned with the longitudinal direction, parallel to the flume bottom, the y-axis is aligned with the spanwise direction, and the vertical z-axis is aligned with the vertical direction (normal to channel bed). The origin x = 0 is located at the beginning of working length (Fig. 1c ), y = 0 at the right-hand sidewall, and z = 0 at the top of the grass blades (Fig. 1b ). Determining the flow rate. The inlet flow rate is controlled by a control valve Samson 3310 with servomotor PSQ, and is monitored with an electromagnetic flow meter (Krohne Waterux 3000 IFC 100). The discharge measurement uncertainty is 0. 3% of the maximum range set either here ± 0. 16 L. s -1 , calculated with manufacturer data for a maximum flow rate of 50 L.
For each flow case, water depth is measured independently for a duration of t = 10, 20, 30, 40, 60, 90 and 100 s. The convergence time found for each flow case is equal to 40 s. Once the convergence time has been established, the measurement is then carried out. It is ensured that all measurements are within the  PIV measurements were performed in the vertical-longitudinal plane while ADV measurements were performed across the channel (vertical-lateral plane). They thus complement each other to explore the physical processes involved and can be compared. The convergence time of the time-averaged velocities and turbulence statistics was estimated for the ADV data at positions y/(L/2) = 6; 6. 55; 7 (Table  2 ), and at 3 elevations z: near the channel bed, at z/H = 0. 40 and near the free surface (Table  3 ).
Data records The experimental data have been uploaded on the Zenodo website  29  , under the DOI number https://doi. org/10. 5281/zenodo. 3871773. Four Excel files termed according to the value of immersion rate H/k can be found. In each files, several sheets present the water depth, the ADV time-averaged velocities and turbulence statistics across the channel at x = 12 m (described in Table  3 ), the ADV velocities at y = 6 for 4 different positions x = 4,8,12,16 m. The last sheet describes the central pattern ADV velocities and turbulence statistics for the two flows with H/k = 93% and 148% flows.
Technical Validation Flow sensor and displacement system. The inlet flow rate is controlled by a control valve Samson 3310 with servomotor PSQ, and is monitored with an electromagnetic flow meter (Krohne Waterux 3000 IFC 100). The discharge measurement uncertainty is 0. 3% of the maximum range set either here ± 0. 16 L. s -1 , calculated with manufacturer data for a maximum flow rate of 50 L. s -1. The flow sensor was used throughout the measurement campaign showing that the flow rate used for a given water level was replicated and reproducible within the device's margin of error. The channel is equipped with a motorized displacement system (Siemens S7-1200), carrying the measuring devices. The accuracy along the three space directions is of 0. 1 mm. These measuring devices are an ultrasonic probe with transit time and an Acoustic Doppler Velocimetry (ADV) with a 3D side looking probe.
Usage Notes This dataset is intended to bring insight into the transition from emergence to slight submergence of house models focusing on their impact on the velocity field in a dense urban area. As extreme flood data are very scarce or non-existent in the field, this dataset could be used as a benchmark data for other experimental studies and numerical modelling of flows through a simplified urban area. This is relevant in the flood risk management. Another use of this dataset is to compare the data with other configurations and to conclude upon the physical processes involved.
50b). 27 The XRF data were dead time corrected. The XRF maps were quantied using the sensitivity values obtained via the measurements of IAEA Animal Bone (H-5) certied reference material, the details can be found in the Master's thesis of V. Pichler. 28 The sensitivity values of the elements for which data was not availablesulphur and potassium, were derived by interpolation. The thickness of the cuts was not considered in quantication. The elemental maps, showing the whole scanned areasource images, are included in the ESI. † For easier comparison and correlation of XRF and LA-ICP-MS data, further analysis by ImageLab (v. 2. 99 Epina GmbH, Pressbaum, Austria) was performed (see also below in (3) LA-ICP-MS measurements and data analysis)and the resultant elemental maps, showing the very same area scanned by both techniques are included in the current manuscript.
The original mXRF maps can be found in the ESI. † Fluorescence sum spectra were plotted with Origin (version OriginPro 2015, OriginLab Corporation, Northampton, MA, USA). (3) LA-ICP-MS measurements and data analysis A NWR213 laser ablation system (ESI, Fremont, CA, USA) equipped with a frequency quintupled 213 nm Nd:YAG laser and a fast-washout ablation cell, always held above the actual ablation site, was used for all shown LA-ICP-MS experiments. The device was coupled to an iCAP Q quadrupole ICP-MS instrument (Thermo Fisher Scientic, Bremen, Germany) using PTFE tubing (inner diameter 2 mm). A schematic representation of a general LA-ICP-MS setup is shown on Fig. S1  in ESI. † Helium was used as carrier gas for cell washout, which was mixed with Ar make-up gas upon introduction into the plasma.
Acknowledgements Authors thank  Prof. Heinrich W Thaler  for providing the samples. Authors would like to thank  Dr Stéphane Blouin  for support and EDX analysis of sample preparation tools, and technicians of LBIO for careful sample preparation. Authors gratefully acknowledge the help provided by  Angelica de Leon  (for cartilage preparation and characterization by XRF),  Lukas Fenninger  (for XRF measurements of cartilage pellets),  Alexander Kleeweiss  (for XRF measurements of cartilage pellets, fused beads), and  Maximilian Bonta  (for running trial test on a sample with LA-ICP-MS). AT and MR were supported by the  FWF (The Austrian Science Fund)  Project  P 27715. AT also received support within  MEIBio (Molecular and Elemental Imaging in Biosciences) PhD program  of  TU Wien. The authors acknowledge  TU Wien Bibliothek  for nancial support through its  Open Access Funding Programme.
The SAMIR tool (Satellite Monitoring of Irrigation)  [42] , whose application is presented in this paper, was developed within the framework of the joint international laboratory (TREMA) and is designed to compute spatialized estimates of ET and crop water budget assessment at regional scale based on the FAO56 method and the NDVI time series. In this study, we evaluated the reliability of an approach based on a soil water balance model driven by remote sensing data, implemented using SAMIR software, for assessing crop irrigation requirements and monitoring spatialized irrigation water use. After calibration and validation of the model at plot scale, the approach was used to compare remotely sensed estimates of crop irrigation requirements and in situ observations of irrigation water use in the Haouz irrigated plain (Morocco).
Model Calibration and Evaluation The actual ET measured at the eight flux sites was used to calibrate and validate the model at plot scale. The model calibration procedure consisted of maximizing the Nash-Sutcliffe efficiency (NSE) criterion computed between observed and simulated ET using a steepest descent algorithm: NSE = 1 - ∑ n i=1 (y iobs -y isim ) 2 ∑ n i=1 (y iobs -y iobs ) 2 ( 11 ) where n is the number of observations, y isim and y iobs are the values of simulated and observed variables for day i respectively and y iobs is the average of observations over the study period. In order to consider different climatic conditions and irrigation practices, the calibration was done using the data from the fields (C1, C2, C3) for wheat and the field C4 for olive trees, whereas the validation was achieved using the data from the fields (V1, V2, V3) for wheat and the field V4 for olive trees.
Model Application for Spatialized Estimates of RS-IWR The model implemented in the SAMIR software was run over the whole R3 irrigated district using the image time series for the seasons  2002-2003, 2005-2006 and 2008-2009  and the calibrated parameters (Table  2 ). The soil in the district was considered almost homogeneous and we used the median values obtained by  Boulet et al. (2009)  for θfc and θwp. The initial soil water content (Init_RU) for annual crops was estimated considering previous precipitation and was set to 10% of the soil available water (between θfc and θwp) because no significant precipitation was observed before the starting dates of the simulation. For olive orchards, the initial soil water content was set to 50% because most of the olive farms had wells and were frequently irrigated regardless of surface water availability.
Conclusions Evaluating the spatial and temporal distribution of the crop irrigation water requirement at field level using remote sensing-based soil water balance models provides interesting insights regarding irrigation water use when compared with in-situ monitoring of observed irrigation water supplied at the irrigation district scale. The evaluation at plot scale of the remote sensing-based FAO56 soil water balance model implemented in the SAMIR software, under different weather and water management conditions, provided acceptable estimates of actual ET, indicated by low values of RMSE obtained for wheat (0. 56 mm/day) and olive trees (0. 54 mm/day), which were the main crops in the region. The model also showed its ability to provide good estimates of irrigation water requirements and could be applied for evaluating irrigation water use in large-scale irrigated areas.
In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github. com/migariane/Tutorial_Computational_Causal_ Inference_Estimators.
4, 5 n this tutorial, we introduce the estimators mentioned above and show their computational implementation in regards to their chronological development (ie, the methods were developed to address the limitations of the previous approaches). However, these methods are also introduced from a practical computational perspective, allowing readers to learn by using the replicable code. We use the Stata statistical software (StataCorp, 2020. StataCorp LLC, College Station, TX), R statistical software (R Development Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria), and Python software (Python Software Foundation, 2020). All materials are available at a GitHub repository for reuse and replication of our examples at https://github.
com/migariane/ TutorialComputationalCausalInferenceEstimators. All examples in this article use Stata code, but examples using R and Python are provided on the GitHub repository. In the following sections, we will illustrate the computational implementation of different estimators that are computing the same estimand (ie, the average treatment effect (ATE), a. k. a risk difference for a binary treatment and outcome). We will not focus on the assessment of heterogeneous treatment effects. In Section 2, we briefly introduce the setting to estimate the ATE using Connors' study. In Section 3, we introduce the g-computation based on the g-formula; and in Section 4, we introduce the methods based on the inverse probability of treatment weights (IPTW). Afterwards, in Section 5, we describe the computation of double-robust methods including the augmented inverse probability of treatment weighting (AIPTW), and in Section 6, we present targeted maximum likelihood estimation (TMLE).
SETTING TO ESTIMATE THE ATE To illustrate the implementation of the most common causal inference estimators we use an empirical data set from the prospective cohort study of Connors et al. 6  We use the data within the aforementioned GitHub repository; the original data are available at: https://hbiostat. org/data/, however some variables will need to be recoded (see Box 1). The study was set within intensive care units (ICUs) of five US teaching hospitals between 1989 and 1994, and evaluated the effectiveness of right heart catheterization (RHC) on short-term mortality (30 days) of 5735 critically ill adult patients (2184 treated and 3551 untreated) receiving care for 1 of 9 prespecified disease categories. A common estimand in causal inference is the ATE. The ATE is defined by an average of the difference of two random variables (ie, the potential outcomes Y (1) and Y (0)).
In the following set of boxes, we show how to estimate the marginal causal effect (ie, effect of RHC on short-term mortality) using the nonparametric and parametric g-formula in Stata. The Stata code, and the implementation of the same computational approach using R and Python, is provided in a GitHub repository: https://github. com/migariane/ Tutorial_Computational_Causal_Inference_Estimators. For now, in the first nine boxes, we use sex as the sole confounder, namely "c" (sex: 0 female, 1 male). It is an oversimplification for pedagogical purposes, which allows readers to readily appreciate the implementation of the computation of the parametric and nonparametric g-formula using G-computation methods. In Boxes 10 to 13, we extend the methods by including multiple confounders. In contrast to classical methods (regression-based methods), the way we adjust for confounding based on the generalization of standardization (g-formula) is more coherent as we assume that the effect of RHC on short-term mortality can differ by sex.
11 Typically, for procedures that use parametric models, the bootstrap is a reliable estimator of the variance (ie, the bootstrap uses the standard deviation of the bootstrap estimates of the ATE as a plug-in for the SE and the computation of the confidence intervals). However, note, it does not account for the bias engendered by model misspecification, so it only provides sampling variability for whatever the estimator is estimating. 11 The accuracy with which the bootstrap distribution estimates the sampling distribution depends on the number of observations in the original sample and the number of replications in the bootstrap. To implement the bootstrap procedure in Stata we need to define a program that estimates the nonparametric g-formula and then samples (with replacement) the ATE to derive the confidence intervals for the ATE.
Although IPTW with regression adjustment (IPTW-RA) is usually more efficient than IPTW, it also relies on different parametric modeling assumptions: (i) a parametric G-computation regression adjustment model, and (ii) a model for the propensity score of binary treatments. The G-computation weighted model uses the weights calculated from the predictions of the propensity score logistic model. An estimated propensity score that is close to 0 or 1 is problematic, since it implies that some individuals will receive a very large weight leading to imprecise and unstable estimates (ie, near positivity assumption violation). Therefore, the use of stabilized weights is suggested (see code from Box 20), and the bootstrap for statistical inference.
4 However, using the IF for statistical inference may require larger sample sizes to avoid finite-sample issues. Recent research and theoretical developments support the use of double-robust cross-fit estimators to retain valid statistical inference when using machine learning algorithms that are non-Donsker. 44 he computation of the IF is provided in Box 25 (Step 6: rows 38-53). In Box 26, we outline how to compute the ATE using data-adaptive procedures implemented in the eltmle user-written Stata command. 45 This command implements the TMLE framework for the ATE of the marginal risk ratio and odds ratio for a binary or continuous outcome and a binary treatment. It also includes the use of data-adaptive estimation of the propensity score g 0 and regression outcome Q 0 models via ensemble learning,  46  which is implemented by calling the SuperLearner package v.
2. 0-21 from R. 46, 47  The super-learner uses 5-fold cross-validation by default to assess the performance of prediction regarding the potential outcomes and the propensity score as weighted averages of a set of machine learning algorithms. The SuperLearner has default algorithms implemented in the base installation of the tmle-R package v. 1. 2. 0-5. 35 The default algorithms include the following: (i) stepwise selection, (ii) generalized linear modeling (GLM), (iii) a GLM variant that includes second order polynomials and two-by-two interactions of the main terms included in the model. Additionally, eltmle has an option to include Bayes generalized linear models and generalized additive models as additional algorithms.
SIMULATION The motivation of this section is to compare all of the different methods provided in the tutorial under a simple Monte Carlo simulated experiment. For simplicity and pedagogical purposes, we only simulate one sample. However, we provide the results and code in R of a Monte Carlo experiment with 1000 samples based on the same template as the one presented here and available at https://github. com/migariane/TutorialComputationalCausalInferenceEstimators. In Box 27, we outline the data generation process to create random variables including the confounders, the treatment, and the outcome. Afterward, we estimate the simulated value for the ATE, and compute the ATE using all the aforementioned different estimators under a scenario of forced near-positivity violation and model misspecification.