dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectroscopic observations MS 0451 was also observed with the Multi-Unit Spectroscopic Explorer (MUSE;  Bacon et al. 2010 ) at the VLT on January 10-11, 2016 (ID: 096. A-0105(A), PI: J. -P. Kneib), 1 https://github. com/davidharvey1986/pyHST in WFM-NOAO-N mode and good seeing of approximately 0. 8. The MUSE observations consist of two pointings of three exposures, slightly shifted to account for systematic variations in the detector response, and cover a field of view of ∼ 2. 2 arcmin 2. These data are used for the strong-lensing analysis. They were reduced using version 1. 6. 4 of the MUSE standard pipeline  (Weilbacher et al. 2012; Weilbacher et al. 2014) , which applies bias and illumination corrections; performs geometrical, astrometric and flux calibrations; and then combines the individual exposures for each pointing into a single data cube.
XMM-Newton X-ray Observations MS 0451 was observed by XMM-Newton (observation ID: 0205670101, PI: D. Worrall) on September 16-17, 2004 for a total of 44 ks. We reduced the XMM-Newton/EPIC data using the XMMSAS v16. 1 software package and a pipeline developed in the framework of the XMM-Newton Cluster Outskirts Project (X-COP,  Eckert et al. 2017). After performing the standard data reduction steps to extract calibrated event files, we used the XMMSAS tools mos-filter and pn-filter to automatically define good-time intervals (unaffected by soft proton flares) of 24 ks (MOS1), 24 ks (MOS2), and 19 ks (PN). For more details of this procedure, see  Ghirardini et al. (2019, Sect. 2 and Fig. 1). These data are used to measure the properties of the baryonic ICM. An independent, ∼ 50ks Chandra observation provides a high-resolution X-ray view of the cluster core but is not used by us here, since the covered area does not match the extended HST mosaic that is the focus of this paper.
Source detection We identify galaxies in the HST /ACS imaging mosaic from source properties determined with the SExtractor photometry package  (Bertin & Arnouts 1996). To maximise sensitivity to distant (small and faint) galaxies that contain most of the lensing signal, we adopt the 'Hot-Cold' technique  (Rix et al. 2004 ), i. e. , we first create a source catalogue using a 'cold' configuration, designed to detect only the brightest objects, and then a second one with a 'hot' configuration, optimised to detect faint objects. We then merge the two catalogues, removing any 'hot' sources that already have 'cold' detections. We also remove any objects located near saturated stars or saturated pixels, using polygonal mask regions defined by hand. Using SExtractor parameters, we assign each source a signal-to-noise ratio of detection, defined as S/N ≡ FLUX AUTO/FLUXERR AUTO, and classify objects as galaxies, stars or spurious features based on their overall brightness (MAG AUTO) and peak surface brightness (MU MAX).
X-ray imaging analysis We extract XMM-Newton images and exposure maps in the [0. 7-1. 2] keV band from the cleaned event files. To predict the spatial and spectral distribution of the particle-induced background, we analyse a collection of observations performed with a closed filter wheel (CFW). We compute model particle-background images by applying a scaling factor to the CFW data to match the count rates observed in the unexposed corners of the three EPIC cameras. The images, exposure maps and background maps for the three detectors are then summed to maximize the signal-to-noise ratio. To determine the thermodynamic properties of the cluster, we extract spectra for the three EPIC detectors in seven annular regions from the center of the source to its outskirts (radial range 0 -4 ). We also extract the spectra of a region located well outside the cluster to estimate the properties of the local X-ray background.
Baryonic-mass fraction To measure the gas-mass fraction f gas , we first integrate the non-parametric gas profiles (which do not assume hydrostatic equilibrium) and obtain a total gas mass of M gas,500 = (1. 29 ± 0. 15) × 10 14 M inside a sphere of radius R 500,HSE = 1. 28±0. 14 Mpc. Division by the total mass M 500 of the NFW model, which best fits the lensing data inside a sphere of radius R 500 = (1. 30±0. 06) Mpc, yields f gas,500 = (11. 6±2. 1)%, in good agreement with the result of f gas,500,HSE = (12. 2 ± 4. 3)% from our analysis assuming hydrostatic equilibrium. To measure the stellar-mass fraction, we use the ratio of stellar mass to light of quiescent galaxies log 10 (M * /L K ) = a z + b, (25) where a = -0. 18 ± 0. 04 and b = +0. 07 ± 0. 04  (Arnouts et al. 2007 ), assuming a Salpeter (1955) initial mass function (IMF) 6.
ACKNOWLEDGEMENTS We would like to thank the referee for giving useful comments and improving our manuscript. We are grateful for constructive discussions of this work with  A. Edge ,  A. Niemiec , and  J. Nightingale. We thank  N. Martinet  for sharing unpublished results from his analysis, allowing a comparison between our two studies. SIT is supported by  Van Mildert College Trust PhD Scholarships. MJ is supported by the  United Kingdom Research and Innovation (UKRI)   Future Leaders Fellowship 'Using Cosmic Beasts  to uncover the  Nature of Dark Matter ' [grant number  MR/S017216/1 ]. RM is supported by a  Royal Society University Research Fellowship. DH acknowledges support by the  ITP Delta foundation. AR is supported by the  European Research Council  ( ERC-StG-716532-PUNCA ) and  STFC  ( ST/N001494/1 ).
This project was also supported by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ]. This work used the DiRAC Durham supercomputing facility, which is managed by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). Its equipment was funded by  BEIS  capital funding via STFC capital grants  ST/K00042X/1 ,  ST/P002293/1  and  ST/R002371/1 ,  Durham University  and  STFC  operations grant  ST/R000832/1. DiRAC is part of the  UK National e-Infrastructure.
Urban land surface processes need to be represented to inform future urban climate and building energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF-TEB. The coupling method is described generically, implemented into software, and the code and data are released with a Singularity image to address issues of scientific reproducibility. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities.
Journal of Advances in Modeling Earth Systems include support for WRF-Chem  (Grell et al. , 2005) , WRF-DA  (Huang et al. , 2009) , WRFPLUS  (Guerrette & Henze, 2015) , or WRF-Hydro  (Gochis et al. , 2018) , its benefits may outweigh these limitations to model developers, code maintainers, and end-users wishing to build WRF, as it includes: robust incremental rebuilds, dependency analysis of Fortran code, flexible library dependency discovery, integrated support for shared (Open Multi-Processing; OpenMP) and distributed (Message Passing Interface; MPI) memory, support for automated testing using continuous integration (CI), and availability of experimental prebuilt binary releases for Linux, macOS, and Windows from the project's GitHub page or through the integration with GIS4WRF  (Meyer & Riechert, 2019a) , a QGIS (QGIS Development Team, 2019) toolkit for preprocessing and postprocessing, visualizing, and running simulations in WRF.
To implement WRF-TEB, TEB version 4. 0. 1  (Masson et al. , 2020; Meyer et al. , 2020 ; Table  1 ) is used as it includes MinimalDX  (Meyer & Raustad, 2020)  to improve the modeling of air conditioners (AC), and Psychrolib  (Meyer & Thevenard, 2019)  to calculate psychrometric functions. Furthermore, support for Linux, macOS, and Windows with CMake allows direct integration in WRF-CMake. In TEB 4. 0. 1, Features 3 and 4 use a simplified vegetation scheme with a fixed albedo and Bowen ratio, whereas  Lemonsu et al. (2012)  treats the vegetation by coupling to ISBA (Interaction Soil Biosphere Atmosphere;  Noilhan and Planton (1989) ; see simplifications in section 3). Furthermore, feature 5 and 6 are not available in TEB 4. 0. 1.
F; Figure  2 ). Then, to minimize complexity and overheads involved in publishing the TEB library in package managers (e. g. , Ubuntu package repositories; Canonical Ltd, 2019), TEB is downloaded and compiled from source during the general WRF build process by relying on CMake's ExternalProject module  (Kitware Inc. , 2019b). Once coupled in this way, any improvements, bug fixes, or other changes to the ULSM are inherited from the model source code repository (e. g. , on GitHub) and included in WRF with a new commit (or version) identifier that is downloaded during the build process. This provides a central location for "issues," maintains the community around the model, and facilitates the creation of offline tests (e. g. , on units or individual components, or end-to-end integration tests of the whole model).
, 2017). This approch remains architecture dependent but provides full control over OS (i. e. , Ubuntu 18. 04 in our case), compiler vendor (i. e. , GNU in our case), source of data sets, processing steps for data, model version and configuration, and plotting routines. Although this level of reproducibility may be deemed sufficient, the container would still rely on the Ubuntu package repository to install the compiler and external libraries without defining exact versions, or to download the "latest" (un-versioned) WPS high-resolution geographical data set (from the UCAR website). This can therefore lead to downloading newer compiler, library, or data set versions when re-building the Singularity image and ultimately alter results included in this paper. Here, we achieve a reasonable level of scientific reproducibility by archiving all tools, data (including our results for reference, as equal results can only be guranteed on the same hardware), and software together with a Singularity image containing OS and external libraries to Zenodo  (Meyer, 2020; Figure 3).
Model Evaluation A fundamental aspect of any software development is testing. Although neither WRF nor TEB have been developed with testing in mind, in this section we outline tests for: the coupling (integration test, section 6. 3) and meteorological evaluation (section 6. 4). The former assesses the coupling technically, while the latter is used to explore the scientific benefit of the coupling. Both tests use similar model configurations (section 6. 2) with meteorological and geographical data for Toulouse, France (section 6. 1). 10. 1029/2019MS001961.
Any difference larger than machine precision is attributed to coupling implementation errors. Results are evaluated graphically and statistically (metrics Appendix A). The implemented test detects errors that cause incorrect 1. loading of parameters from the urban parameter table file; 2. passing of parameters to TEB; 3. conversion of date/time from WRF to TEB conventions; 4. passing of geographical and date/time coordinates to TEB; 5. passing of TEB-internal state variables; 6. storing of TEB-specific output diagnostics; 7. updating of WRF state variables from TEB outputs; 8. grid cell looping; and 9. activation of TEB based on grid cell vegetation type and global scheme selector. These errors can lead to software crashes or nonidentical results between TEB offline and WRF-TEB. With offline TEB forced with data from the WRF dynamical core quantities converted prior to use in TEB are not assessed.
Scientific reproducibility is addressed by providing model source code, configurations, data, and scripts with a Singularity image deposited on Zenodo. The coupled WRF-TEB model has been integrated into WRF and WRF-CMake and released as a free, open-source software on GitHub at https://github. com/tebmodel/wrf-teb. We encourage future versions of WRF to include the implementation of a flexible number (i. e. , beyond three) of urban classes to allow for a greater heterogeneity of urban form and function to be represented.
Specifically, household wealth Z-scores were centered within each community (see Methods), thus reflecting relative wealth within a community (van de  Pol and Wright, 2009). Absolute wealth associations were calculated as the combined posterior effects of community-level mean wealth and within-community wealth Zscores. Results reported in the main text focus mostly on relative wealth and inequality as the pertinent tests of our predictions, and are reported as standardized coefficients (β) or odds ratios (OR), both represented by the posterior mean, as well as the proportion of the posterior on one side of zero (P>0 or P<0), i. e. the probability of a positive or negative association. Means and credible intervals for all parameters are reported in supplementary tables S2-S14. In most models, the predictors and random effects jointly explained about 20-40% of the variance in the data (R 2 range: 0.
Wealth and wealth inequality. Wealth data were collected in 2006/2007 and 2013/2014. Household wealth was assessed through an inventory of commonly owned items including traditional goods, i. e. items manufactured from local organic materials (e. g. canoes, bows and arrows), market goods, i. e. industrially produced items obtained through trade or purchase (e. g. bicycles, motorbikes), and livestock (e. g. pigs, cows), which were subsequently converted into their local market value in Bolivianos and summed (Fig. 1 ). Objective household wealth arguably provides only an indirect measure of people's subjective wealth and status  (Norton, 2013)  but these data were most widely available for this study. Furthermore, household wealth correlated significantly, albeit weakly, with subjective status  (Amir et al.
Psychosocial and health variables. A variety of variables recorded during roughly annual examinations by THLHP physicians and anthropologists were merged onto the wealth data; all data collected within two years of the wealth data were considered. Body mass index (BMI) Z-scores were calculated using Tsimane-specific growth curves  (Blackwell et al. , 2016b)  (www. github. com/adblackwell/localgrowth) as well as the total distribution of Tsimane adult BMIs, representing deviations from the local population average for a given age and sex. Diastolic and systolic blood pressure were measured by THLHP physicians during routine medical exams. Cortisol was measured in first-morning urine, corrected for specific gravity using enzyme-linked immunosorbent assays (see von  Rueden et al. , 2014 for details).
Full model summaries with means and 95% CIs, as well as (conditional) Bayesian R 2  (Gelman et al. , 2019)  as a goodness of fit measure are given in the supplementary materials (Tables  S2-S14 ). All data and R code will be made available at https://github. com/adrianjaeggi/tsimanewealthandhealth. Woolard K, Garcia AR, Stieglitz J, Kaplan HS, Gurven M, Trumble BC. 2019. Do perceptions of social status and material wealth associate with biomarkers of health in a relatively egalitarian population? Am J Hum Biol 31.
Funding and conflicts of interest The Tsimane Health and Life History Project has been funded by  NSF  ( BCS0136274 ,  BCS0422690 ) and  NIH/NIA  ( R01AG054442 ,  R01AG024119 ,  R56AG024119 ). AJ was supported by the  Swiss NSF  ( PBZHP3-133443 ) and by the  SAGE Center for the Study of the Mind. JS acknowledges IAST  funding from the  French National Research Agency (ANR)  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ). The authors report no conflicts of interests.
Implementation, simulation runs, and variables measured The model was implemented in C, with the code freely available (https://github. com/popgenomics/ quantiSex). We initially tested our simulations by comparing the results for neutral variation with predictions of population genetic models  [31, [45] [46] [47]. We also established run times necessary for equilibration of the metapopulation sex allocation. We explored parameter space by choosing domains that seemed most interesting and revealing, with all runs replicated multiple times (see Results). We computed statistics for neutral population diversity and differentiation, and for sex allocation, after reproduction. Population genetics statistics F ST , F IS , F IT , G ST , G ST , G ST and D jost were computed internally and by calling the R library 'diveRsity'  [48].
Acknowledgements CR was funded by grants awarded to JRP by the grant  Swiss National Science Foundation  ( 31003A_163384 ) and by the  University of Lausanne. CM was supported by  Swiss National Science Foundation  grant  PCEFP3181243. SN was supported by  Swiss National Science Foundation  grant  31003A_138180. We are grateful to  Jérôme Goudet  for helpful discussions. The simulations were performed on the Core Cluster of the  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ).
0 V2 (Digital CMOS camera C11440-22CU) camera and Dual OptoLED power supply (Cairn Research Ltd) as a source of light. Fluorescence images were acquired at room temperature (RT) with a ×10 objective (20 frames/s). Neurons were recorded during 10 min for stabilization, afterward for induction of epileptiform activity, 100 μM 4-aminopirimidine or vehicle was added to the EM. After 5 min allowing the diffusion of the drug, activity was recorded during additional 10 min. Analysis of calcium imaging recordings was performed with NETCAL  64  , a custom-made software package developed in Matlab (MathWorks). Recordings were first time-averaged across the whole recording to obtain a clear picture of the cell bodies. Cells were automatically detected as regions of interest (ROIs) using the timeaveraged image.
02% sodium azide. After washed with TBS-T, membranes were incubated with anti-rabbit or anti-mouse horseradish peroxidase-conjugated secondary antibodies (1:2000; Promega). Secondary antibody binding was detected by the enhanced chemiluminescence substrate kit (Santa Cruz Biotechnology, Santa Cruz, Ca, USA). For loading control a mouse monoclonal antibody for α-tubulin was used (Sigma-Aldrich Cat# T9026, RRID:AB_477593). ImageJ software was used to quantify the different densitometry immunoreactive bands relative to the intensity of the α-tubulin in the same membranes. Data are expressed as the mean ± SEM of band density.
The viewing jar and the testing cage were cleaned with 70°ethanol between animals to avoid the possibility of olfactory cues affecting behavior. The data from individual components of the modified SHIRPA were pulled to provide a general SHIRPA index as previously described  72. For further information about the SHIRPA procedure, more detailed information is provided in the Mouse Phenome Database at the Jackson Laboratory's website: https:// phenome. jax. org/projects/Jaxpheno7/protocol#SHIRPA. To check spontaneous locomotor activity, anxiety-like parameters, and navigation we used the OF. Briefly, the apparatus consisted of a white square arena measuring 40 × 40 × 40 cm in length, width, and height, respectively. Dim light intensity was 60 lux throughout the arena. Animals were placed on the arena center and allowed to explore freely for 30 min.
Spontaneous activity was measured. At the end of each trial, any defecation was removed, and the apparatus was wiped with 30% ethanol. Animals were tracked and recorded with SMART junior software (Panlab, Spain). The NOL memory task evaluates spatial memory and is based on the ability of mice to recognize when a familiar object has been relocated. Exploration took place in an open-top arena with quadrangular form (45 × 45 cm). The light intensity was 40 lux throughout the arena. Mice were first habituated to the arena in the absence of objects (1 day, 30 min). Some distal cues were placed throughout the procedure. On the third day during the acquisition phase mice were allowed to explore two duplicate objects (A1 and A2), which were placed close to the far corners of the arena for 10 min. After a delay of 24 h, one object was placed in the corner diagonally opposite.
Acknowledgements The authors want to thank  Ms. María Sánchez Enciso  and  Mr. José M. González Martín  for their help in animal handling and care during in vivo experiments. We thank  Ana López  ( María de Maeztu Unit of Excellence, Institute of Neurosciences, University of Barcelona ,  MDM-2017-0729 ,  Ministry of Science, Innovation and Universities)  for technical support. The authors want to thank also to professor  Baoji Xu  for the kindly donation of the TrkB f/f mice. A. Giralt  was supported by a grant from  Ministerio de Ciencia, Innovación y Universidades  ( RTI2018-094678-A-I00 ). A. Giralt  is a Ramón y Cajal fellow ( RYC-2016-19466 ). J. A. research was supported by grants  SAF2017-88076  and Marató TV3 Foundation. S. F. -G. was supported by grant  Marató TV3 Foundation. A. Gruart and J.
MRi acquisition procedure. Imaging scans were all acquired on 3. 0 T Achieva MRI system (Philips). All functional data and localizer scans were collected in 3-mm thick oblique axial slices with echo planar imaging (EPI) sequence (repetition time (TR) = 2000 ms, echo time (TE) = 28 ms; 80 × 78 × 3. 7 matrix, slice thickness = 3 mm; 0. 3 mm gap). Slices were angulated in an oblique axial manner to reach whole-brain coverage. To ensure reaching a steady state condition, the first two scans were discarded. This resulted in approximately 1320 (on average 700 and 620) scans for encoding and 300 (2 × 150) scans for the localizer. At the end of the experiment, a T1-weighted structural image (256 × 256 × 172 matrix, 1 × 1 × 1. 3 mm voxels) was also collected. This structural MRI image was used in data analyses for coregistration and segmentation.
fMRi data preprocessing. Raw fMRI data from the encoding and localizer tasks were preprocessed and analyzed using statistical parametric mapping (SPM12; http://www. fil. ion. ucl. ac. uk/spm) within Matlab2014b). First, we performed motion correction using iterative rigid body realignment to minimize the residual sum of squares between the first and all further functional scans. Second, we performed rigid body coregistration to the corresponding T1 image with mutual information optimization. Subsequently, the T1 image was segmented into grey matter, white matter and Cerebrospinal Fluid (CSF), air and skull. All data were then spatially normalized through unified segmentation with bias regularisation to the Montreal Neurological Institute (MNI) 152 T1 image. For the univariate analyses, all scans were then spatially smoothed with a full-width at half maximum (FWHM) of 8 mm.
For the MultiVariate Pattern Analyses (MVPA), all scans were spatially smoothed with a FWHM of 5 mm  24. Subsequently, because raw scans were used instead of beta values, the scans were manually temporally filtered with a high-pass filter (to remove scanner drift) and demeaned by computing a deviation score of the mean for each non-zero voxel in the brain and dividing by the standard deviation (feature scarling  25  ). MVPA-analyses were performed using Python17 as well as Nilearn and scikit-learn libraries  26.