dataesr/scientific-paragraphs-categorization huggingface dataset:
Issues covered included abortion, gun control, racial equality, and gender equality as well as a new issue: immigration. Experiment 10 was run in December 2022 and preregistered at https://osf. io/4xwha. A total of 1,063 US respondents were recruited on Proli c for Experiment 10, and 98 were removed from the data because they failed one of the two attention checks, leaving 965 participants (M age = 41, SD age = 14. 5, 49% women). In the intervention message condition of both studies, respondents read an intervention message about the reputational risks of a sharing politically congruent misinformation ("myside sharing") and then rated each item on dimensions that the message highlighted as potential sources of bias, before reporting their intentions to share. In the control condition, participants read a placebo message and then merely reported their intentions to share without rating them.
Pharmacokinetic models A literature review was performed in 2021 and 2022 to select articles to implement predictive models for the selected 19 drugs. These models can be of two types: population pharmacokinetics (popPK) models or models implemented with non-compartmental data. PopPK models are nonlinear mixed effect models whose kinetic parameters values, together with characteristics (also termed covariates) effects are estimated from a dataset using dedicated pharmacometric algorithms and software. The dataset contains patient covariates to be studied, posologies and exposure time series provided at some specific sample times. Thanks to popPK models it is possible to have access to an explanation of the kinetic parameters interindividual variability in terms of selected covariates (e. g.
, demographic, genetic, environmental data, etc. ). On the other hand, models implemented from non-compartmental analysis are based on the inference of kinetic parameter using exposure profile descriptors (e. g. , area under the curve, half-life of elimination, peak concentration, and time). The choice to implement a popPK model or a model based on noncompartmental data depended on several criteria. Initially, a popPK model is always preferred: for each molecule, a literature review was conducted to identify one or more popPK models. These models were subsequently compared and evaluated to choose the most suitable model that aligned with our requirements, using the following criteria: the general population is well represented in the model training dataset, with an important number of patients, samples, and low relative standard error.
Validation criteria of pharmacokinetic models The reference validation method for validating PK models involves the use of an external validation dataset, enabling the calculation of median prediction error (MDPE) and median absolute prediction error (MDAPE) validation criteria, used to quantify bias and inaccuracy, respectively. To be acceptable, MDPE must be between -20 % and 20 %, and MDAPE ≤ 30 %. This method is applicable to molecules subject to clinical therapeutic drug monitoring, and a dataset is not always available. In cases where this method is not applicable, due to lack of data or absence of therapeutic drug monitoring for a drug, we are implementing a simpler validation methodology, using therapeutic thresholds. This methodology was performed by assessing the correspondence of model simulation outputs with therapeutic thresholds from the pharmacological literature, to assess the PK behavior regarding efficacy and overexposure thresholds.
We generated various simulations to cover all the dosing regimens recommended by regulatory authorities for the various patients concerned by the treatment, by modulating the implemented covariates and recommended dosages. Outputs must be within therapeuticoverexposure or toxicity threshold to be validated. Clozapine PK model underwent validation with an external dataset, as described by colleagues in Lereclus et al. [31]. All other models were validated using thresholds method. The thresholds used for validation are available for each drug in Supplementary Material S2 Concen-tration_Time. xlsx.
To simulate the ECG signals, virtual electrodes were placed on the surface of the torso model to correspond to the standard 12 electrocardiographic leads. At the cellular level, we selected the ID6 male model (see Supplementary Material, Table  S2 ) as a representative of the male population and the ID35 female model (see Supplementary Material, Table  S2 ) as a representative of the female population. Transmural heterogeneity of the myocardium was considered, as mentioned above. For Purkinje cells, we used the model published by Stewart and colleagues  [46]. The electrical propagation through the ventricles was calculated by solving the monodomain equation (Eq. (  3 )) with ELVIRA software  [47] , which is based on a pseudo-adaptive finite element method in space and time to solve reaction-diffusion equations with highly nonlinear reactive terms.
Pharmacokinetic models validation The validation process for the 15 implemented PK models was performed successfully. Clozapine PK model was validated using an external database in Lereclus et al. [31]. Dataset contained 151 samples from 53 patients, MDPE was -19 % and MDAPE was 29. 4 %, showing absence of bias and inaccuracy. The other 14 PK models were evaluated using the therapeutic thresholds method. Models' outputs were evaluated regarding selected therapeutic thresholds from the literature. The tests assessed whether simulations with the parameters of the dosages recommended by the health authorities, as well as the patients concerned by these dosages (whose profiles were created by modulating the values of the covariates) were within the therapeutic threshold. Concentration over time series and the thresholds used to perform the validation are available for each drug in Supplementary Material S2 Concentration_Time.
Funding This project received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement No. 101016496  (SimCardioTest). This work was also partially supported by the  Dirección General de Política Científica de la Generalitat Valenciana  ( PROMETEO/2020/043 ). The authors also thankfully acknowledge the computer resources at MareNostrum and the technical support provided by  Barcelona Supercomputing Center  ( IM-2021-1-0001 ,  IM-2021-1-0003 ,  IM-2022-1-0002 ,  IM-2023-1-0002 ). JL is being funded by the  Ministerio de Ciencia, Innovación y Universidades for the Formación de Profesorado Universitario  (grant reference:  FPU18/01659 ). Funding for open access charge:  Universitat Politècnica de València.
In doing so, we attempt to reconcile the different views highlighted above on the persistence of blocked and zonal flows and explore the strengths and limitations of the dynamical systems view of atmospheric persistence. The remainder of the paper is structured as follows: Sect. 2 details the datasets and methods used for the analysis, and Sect. 3 shows composites of the atmospheric configurations associated with the warm-temperature extremes and an analysis of the persistence of these configurations and of possible driving mechanisms for heatwaves, namely temperature advection and radiative forcing. Sec-tion 4 discusses these results, and finally Sect. 5 presents the conclusion of this paper.
Data We use the ERA5 reanalysis dataset from the European Centre for Medium Range Weather Forecasts (ECMWF)  (Hersbach et al. , 2020 (Hersbach et al. , ) over 1979 (Hersbach et al. , -2018. The specific variables used were temperature at 2 m (t 2 m ), meridional and zonal wind components at 10 m (v surf and u surf , respectively), geopotential at 500 hPa (Z500), temperature at 500 hPa (T 500 ), meridional and zonal wind components at 500 hPa (v 500 and u 500 , respectively), surface sensible heat flux (SSHF), surface pressure (p surf ), and SLP, all at a horizontal spatial resolution of 0. 5 •. Daily means were calculated from hourly data for surface variables and from 6-hourly data for variables considered at 500 hPa due to the much larger size of pressure-level variables. Our Euro-Atlantic domain spans 25-75 • N, 15 • W-50 • E, similar to that of  Messori et al.
The surface potential temperature advection, T potential surf, adv , is defined as follows: T potential surf, adv = -v surf × ∇T potential , (1) with units of kelvins per day and where v surf = (u surf , v surf ) is the vector surface wind, and the potential temperature, T potential , is defined as T potential = t 2 m × (p surf /p 0 ) -R/C p , (2) where p surf is surface pressure, p 0 = 1000 hPa, and R/C p ≈ 2/7. T 500,adv = v 500 × ∇T 500 , (3) with units of kelvins per day and where v 500 denotes the vector form of u 500 and v 500. The function gradient from the Python package NumPy  (Harris et al. , 2020)  was used to compute the gradients in Eqs. (  1 ) and (  3 ). Statistical significance at a level of p = 0. 05 was tested for each grid box in the composite plots in Sect. 3. 1 and 3. 2 using a two-sided test to assess the uncertainty in the location of the true mean using a cluster bootstrap method  (Cameron et al.
Computation of atmospheric persistence We calculate atmospheric persistence separately for the SLP and Z500 fields over the full Euro-Atlantic domain considered here. This enables us to capture both surface-level and mid-level flow patterns. The computation of persistence relies on the dataset being analysed including good analogues for all its time steps. In Fig. A4  in Appendix A we show analogue quality tests which demonstrate that the analogues we find in our data are of sufficient quality to perform this analysis and are not severely impacted by the non-stationarity of the atmosphere due to the atmospheric-warming trend. We define persistence as θ -1 , where θ is the extremal index  (Moloney et al. , 2019) , and persistence is in units of time steps. Persistence is an estimate of the average number of consecutive recurrences, or analogues, for a given atmospheric state.
In other words, it quantifies how long a given state of the atmospheric circulation is expected to remain similar to itself. The computation of θ depends on the definition of recurrences, which we identify here as follows: 1. The Euclidean distance between the SLP or Z500 maps for each day and all others in the dataset is computed. Thus, for any given day we can quantify how "similar" it is to any other day. 2. Of these distances, the smallest 5 % (q = 0. 95), as recommended by  Süveges (2007) , are selected as analogues, that is the days that are most similar to the given day. 3. A generalised Pareto distribution is fitted to the negative log of the analogue distances to ensure that the distribution of negative-log distances converges  (Ferro and Segers, 2003). By applying this requirement we ensure that we have a sufficiently large dataset and analogues of sufficient quality.
Code availability. The package CDSK used to compute theta is available at https://github. com/yrobink/CDSK (last access: 18 July 2023)  (Robin, 2021). Data availability. The ERA5 reanalysis data analysed in this study can be downloaded from https://cds. climate. copernicus. eu/ cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form (last access: 18 July 2023) (Hersbach et al. , 2020).
In Sect. 2, we describe the GOODS-ALMA survey and the multiwavelength data used. In Sect. 3, we present our selection criterion for optically dark or faint galaxies at z > 3. In Sect. 4, we study the properties of individual sources in our sample, such as the redshift, stellar mass, star formation rate (SFR), molecular gas mass, and dust temperature. In Sect. 5, we present and discuss the properties of optically dark or faint galaxies mainly based on our optical to millimeter stacking analysis. In Sect. 6, we calculate the cosmic SFRD contributed by optically dark or faint galaxies and discuss the level of the incompleteness of our understanding of dust-obscured star formation in the z > 3 Universe. Finally, we summarize our main conclusions in Sect. 7. Throughout this paper, we adopt a Chabrier initial mass function (IMF;  Chabrier 2003)  to estimate SFR and stellar mass.
GOODS-ALMA survey GOODS-ALMA is an ALMA 1. 13 mm survey in the deepest part of the Great Observatories Origins Deep Survey South field (GOODS-South;  Dickinson et al. 2003; Giavalisco et al. 2004 ). It covers a continuous area of 72. 42 arcmin 2 (effective The calibration was processed using the Common Astronomy Software Application package (CASA;  McMullin et al. 2007)  with the standard pipeline. We systematically inspected calibrated visibilities and added a few additional flags to the original calibration scripts. The calibrated visibilities were then time-and frequency-averaged over 120 s and 8 channels, respectively, to reduce the computational time for subsequent continuum imaging. Given the excellent coverage of the uv plane and the absence of very bright sources, we used the task TCLEAN in CASA version 5.
6. 1-8 to produce a dirty map with 0. 05 pixels and a natural weighting scheme to avoid potential biases from the CLEAN algorithm. The resulting high-and low-resolution 1. 13 mm continuum maps have similar root mean square (rms) sensitivities, that is, σ 89. 0 and 95. 2 µJy beam -1 , with spatial resolutions of full width at half maximum (FWHM) 0. 251 × 0. 232 and 1. 330 × 0. 935, respectively. To improve the sensitivity, we concatenated these two data configurations in the uv plane with visibility weights proportional to 1:1. The combined map achieves an rms sensitivity of σ 68. 4 µJy beam -1 with a spatial resolution of 0. 447 × 0. 418 (see Fig. 1 ). For more details on the same data reduction, we refer to  Franco et al. (2018)  for the high-resolution dataset (GOODS-ALMA 1. 0) and A18, page 2 of 26  Gómez-Guijarro et al.
0, which is a combination of high-and lowresolution 1. 13 mm continuum maps (see details in Sect. 2. 1); and (vi) radio data: radio image at 3 GHz (10 cm) from the VLA (PI: W. Rujopakarn, private communication), which covers the entire GOODS-ALMA field  (Rujopakarn et al. 2016, and in prep. ). In Table  1 , we summarize the multiwavelength dataset used in this work from UV to mm.
2019). The catalog includes all UV, optical, and NIR data (13 filters in total) taken by HST over 18 years across the field. The 5σ point-source depth in the H-band is approximately 27. 0-29. 8 mag. We note that the Notes. These bands are used for the UV-to-MIR and MIR-to-mm SED fitting. From left to right: Telescope and/or instrument, the filter name, the central wavelength of the filters, and the reference for the survey, including the images and catalogs we used. (a)  Nonino et al. (2009). (b) HLF program  (Whitaker et al. 2019 ). (c)  GOODS-South field includes the HUDF, which is much deeper than other parts of the HST data, but covers only a small portion of the field. 2. IRAC catalog: We built the IRAC catalog in the GOODS-ALMA field using the deepest IRAC 3. 6 and 4. 5 µm images from the GREATS program  (Stefanon et al.
Photometry We visually inspected all the candidates and noted that blending was common in the IRAC images due to their relatively worse spatial resolution (e. g. , ∼2 at 4. 5 µm). To obtain photometric values in different bands without contamination from neighboring galaxies, we simultaneously de-blended sources in the multi-wavelength images (from UV to 8 µm) by applying the de-blending code 1 described in  Schreiber et al. (2018b). The de-blending method is briefly summarized in the following three steps. Step 1: for each OF candidate, to save computational time, we first cut the stacked HST image (four bands 1 qdeblend: https://github. com/cschreib/qdeblend of HST/F105W, F125W, F140W, and F160W) into a 10 × 10 area around the IRAC position of the candidate. Then, we detected all sources in the clipped stacked HST image.
Redshifts and stellar masses We fit the SED from the UV to MIR (rest-frame UV to NIR) to measure the photometric redshifts (z phot ) and stellar masses (M ). The photometric redshifts were determined with the code EAzY 2   (Brammer et al. 2008 ). Then we fixed the redshift and derived M with the code FAST++ 3. The setup is described below. Photometric redshifts were obtained with the galaxy template set "eazy_v1. 3", which includes, in particular, a dusty starburst model to account for extremely dusty galaxies. We did not apply the redshift prior based on K-band magnitudes, as this prior is based on models that do not reproduce high-redshift mass functions (see discussion in  Schreiber et al. 2018a). Two sources, OFG15 and OFG27, have spectroscopic redshifts (z spec ) confirmed by one-line detections.
Infrared SED fitting The infrared luminosities (8-1000 µm; L IR ) and dust mass (M dust ) are derived from the FIR SED-fitting. We used two methodologies in the fit, depending on whether the galaxies have a Herschel counterpart or not (see Figs. C. 1 and C. 2 for the entire sample). For galaxies with a Herschel counterpart (3/27), we performed the FIR SED-fitting with CIGALE 4 (Code Investigating GALaxy Emission;  Burgarella et al. 2005; Noll et al. 2009; Boquien et al. 2019). We fit data from 24 µm up to millimeter wavelengths from the catalogs of  Wang et al. (in prep. ) ,  Elbaz et al. (2011) , and GOODS-ALMA v2. 0 1. 13 mm  (Gómez-Guijarro et al. 2022a ). The dust infrared emission model is the one of  Draine et al. (2014). The parameters used in CIGALE are shown in Table  4. For galaxies without a Herschel counterpart but with ALMA detections at 1.
g. ,  Jin et al. 2019). The MBB fit was performed using a Markov Chain Monte Carlo (MCMC) approach with 12000 iterations using the Python package PyMC3 6. The derived T dust for individual galaxies with a Herschel counterpart are listed in Table  5. For individual galaxies (three galaxies with Herschel counterparts were fitted with MBB), their T dust exhibit a large dispersion, that is, T dust = 37-69 K, and only one (T dust = 51 ± 2 K) is in agreement with the expected value from the redshift evolution in MS galaxies from the literature  (Schreiber et al. 2018c). The fitting results are also shown in Figs. C. 1 and C. 2. Considering that only three OFGs have T dust values, we do not go deeper into the discussion of individual galaxies. Instead, we further discuss the stacked OFG samples in Sect.
Fitting of the stacked SEDs We obtained the stacked full-wavelength SEDs in the same way as for the individual galaxies (see Sects. 4. 1, 4. 2, Tables  3, 4 ). In brief, first, we fitted the broad photometry at OPT to MIR with the EAzY code to obtain photometric redshifts. Then, we independently performed the OPT to MIR SED fitting with FAST++ and the MIR to mm SED fitting with CIGALE, respectively, at the previously obtained redshifts of the stacked sources. This approach helps to 1) disentangle the degeneracy between redshift and other parameters, such as stellar age and dust temperature; and 2) break the energy balance principle (the total energy emitted in the MIR and FIR is determined by the attenuation of observed starlight in the UV and optical) used in CIGALE. For dusty star-forming galaxies, especially for H-dropouts and K sdropouts with strong dust obscuration, there could exist regions with strong UV extinction due to strong dust obscuration, which may not participate in the UV to optical part, but emit FIR light (e.
6. 1); 3) Massive OFGs that are not LBGs: there are 16 OFGs not classified as LBGs with log(M /M ) > 10. 3. To compare with the results of  Wang et al. (2019)  on the SFRD, here we A18, page 12 of 26 Notes. ( * ) Uncertainties are the 16-84th percentile ranges of the probability distribution function given by the SED fitting. ( * * ) Uncertainties on derived quantities were calculated from the propagation of the errors in the parameter values. (a) Photometric redshift, determined with the code EAzY. (b) M and A V , derived from the UV to MIR SED fitting with the code FAST++. (c) Given by IR SED fitting with CIGALE. (d) Measured by MBB model fit (see Sect. 4. 5). (e) Derived from S 3 GHz assuming a radio spectral index α = -0. 75 (see Eq. (  10 )). ( f ) Calculated from the IRradio correlation (see Eq.
In the GOODS-ALMA region, we have a total of 28 OFGs (including a candidate IRAC 4. 5 µm dropout). We analyzed the properties of individual and stacked OFGs, respectively. We calculated their SFRD and quantified the incompleteness of our understanding of the cosmic star-formation history in the z > 3 Universe. Here are the main results of this work: 1. After performing SED analyses with the code EAzY and FAST++, we find that the OFGs cover a redshift of z phot > 3, as indicated by the theoretical galaxy templates. The median redshift of individual OFGs is z med = 4. 1, with a wide distribution (z = 3. 2 -7. 0), consistent with z med,stacked = 4. 5 ± 0. 2 derived from the median stacked SED. The OFGs have a broad stellar mass distribution with log(M /M ) = 9. 4-11. 1, with a median of log(M med /M ) = 10.
We injected ten sources each time on the IRAC 3. 6um and 4. 5um images to avoid excessively artificial source confusion caused by bright fake sources. After each injection, we performed the same blind source detection procedure using SE. In From 24 µm up to millimeter wavelengths, we fitted galaxies with a Herschel counterpart using the CIGALE code (orange solid line) and galaxies without a Herschel counterpart but with the ALMA 1. 13 mm detection using the dust template libraries (green line;  Schreiber et al. 2018c , see §4. 2). A18, page 25 of 26.
Acknowledgements. We are very grateful to the anonymous referee for instructive comments, which helped to improve the overall quality and strengthened the analyses of this work. We thank  Alain Omont ,  Boris Sindhu Kalita ,  Dangning Hu ,  Shuowen Jin ,  Pascal Oesch , and  Bing Luo  for valuable discussions and suggestions that improved this paper. This paper makes use of the following ALMA data: ADS/JAO. ALMA #2015. 1. 00543. S and  ADS/JAO. ALMA # 2017. 1. 00755. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  NSC ,  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO , and  NAOJ. This work is supported by the  National Key Research and Development Program of China  (No.
2017YFA0402703 ), and by the  National Natural Science Foundation of China  (No. 11733002 ,  12121003 ,  12192220 ,  12192222 ,  12173017 , and  12141301 ). This work is also supported by the  Programme National Cosmology et Galaxies (PNCG) of CNRS/INSU  with INP and  IN2P3 , co-funded by  CEA  and  CNES. M. -Y. X. acknowledges the support by  China Scholarship Council (CSC). M. F. acknowledges  NSF  grant  AST-2009577  and  NASA   JWST GO Program   1727. G. E. M. acknowledges the  Villum Fonden  research grants  13160  and  37440  and the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under the grant No. 140. R. D. gratefully acknowledges support by the  ANID BASAL  projects  ACE210002  and  FB210003.
Whole genome sequencing Whole genome sequencing was performed at the HiSeq platform (Illumina). DNA libraries were prepared using ultrasound DNA fragmentation and NEBNext Ultra DNA Library Prep Kit for Illumina (New England Biolabs). Data for the M. tuberculosis sequenced genomes were deposited in the NCBI Sequence Read Archive (project number PRJNA822891). TB Profiler database (http://tbdr. lshtm. ac. uk/) was used for genotypic detection of drug resistance. MDR, pre-XDR and XDR phenotypes were defined according to the updated World Health Organization definitions: MDR are strains resistant to isoniazid and rifampicin; pre-XDR -resistant to isoniazid, rifampicin, fluoroquinolone; XDR -resistant to isoniazid, rifampicin, fluoroquinolone plus bedaquiline and/or linezolid  [16].