dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The following data were collected at the initial clinical evaluation: sex, age at diagnosis, perinatal history including gestational age, delivery by breech and/or cesarean section, acute perinatal distress (Apgar score <7 at 5 min and/or neonatal resuscitation), aspect of the pituitary on MRI including the height of the anterior pituitary (1), presenting with symptoms, antecedence of hypoglycaemia, associated malformations (including ophthalmic), and micropenis or cryptorchidism in boys. Micropenis was defined as a penis length of <2 SD below the lower normal range  (23). Age and pubertal status, including clinical evaluation of testicular volume of the boys, were also collected at the last evaluation. The initial biological evaluation included the peak GH concentration after a stimulation test (ornithine, arginine-insulin and/or glucagon test), the plasma concentrations of insulinlike growth factor 1 (IGF1), cortisol at 0800 h, ACTH, TSH, and free thyroxin.
An investigation of the distribution, means and medians and application of the Shapiro-Wilk normality test confirmed that groups were not normally distributed. A p-value <0. 05 was considered significant. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of baseline inhibin B in predicting HH and determine the optimal cut-off values. The area under the ROC curve (AUC) was expressed as means. Descriptive and analytical statistics were conducted with RStudio software.
B. Calculation using PAI model The Garfield++ software  [17]  also has been used to compute the number of primary ionizations. This software is normally intended for the modelization of gaseous detectors. It allows to simulate the creation of electron/ion pairs due to the ionization of gas by an incident particle, the transport and amplification of these electrons in the gas and the signal induced on a readout plane. In our case, we simulated only the pair creation in the residual gas. For this step Garfield++ uses Heed ++  [18] , a C ++ code that implements the photoabsorption and ionization (PAI) model  [19]. Table  II  summarizes the results of the two previous calculations done for different IPM positions along the ESS linac, and the factor ratio between the methods.
This is done by placing conductors on each side. Each corrector is set to a certain potential in order to constrain the field. • Putting grounded conductors between the two IPMs  [10]  to protect against the IPM cross-interaction. COMSOL has been mainly used for the simulations of extraction fields, and a particle tracking algorithm has been implemented in C++. The code integrates the motion of particle due to the Lorentz force: F = m • ã = q • ( Ẽ(r, t) + ṽ ⇥ B(r, t)) (4) Fig. 3  shows an example of results given by the particle tracking. Without the corrections, the reconstructed profile is 35 % wider than reality. The shift in position is due to the cross interaction between the two IPMs. When corrections are enabled, the obtained transverse profile is much better: the error on the profile width is only 0.
B. Control System The whole system is almost fully compatible with the version 3. 16 of EPICS base. The PointGrey GigE cameras are well supported by the AreaDetector module. A custom plugin, developed by ESS, performs a gaussian fit on every image of the profile. Raw images are saved into HDF5 files. This format allows to combine various datasets together, for instance the raw IPM images with some beam information. Since all high voltage power supplies have their own SPCI Ethernet interface, a simple softIOC with StreamDevice is enough to control and monitor them. Three OPIs have been developed in order to control cameras, power supplies and a motor (geobrick) to move scintillating screens vertically to intercept the beam or to be safely moved far from it. These OPIs run under the BOY module of the ESS Control System Studio version 4.
Fig. 8  shows an image acquired from the optical IPM. One can see the shadow of a grid that is placed right in front of the MCP input. The grid has a pitch of 450 µm and a wire size of 50 µm. This gives an idea of the resolution that can be achieved with the optical IPM. The strips measure the profile in only one transverse direction. The data processing is different because the acquisition is done continuously. First, the pedestals are removed and a search is performed in the whole dataset to find events corresponding to a pulse. Fig. 9  shows the same profile measured by both IPMs.
ACKNOWLEDGMENT The authors are grateful to  James Storey  and his colleagues ( CERN, Geneva, Switzerland)  for their help with the TimePix detector. We would like to thank  Cyril Bachelet  and his team ( CSNSM, Orsay, France ) for their support during the tests at IRMA. We are thankful to our colleagues at  IPHI  ( CEA, Saclay, France ) for their assistance and availability during two beam test campaigns. Finally, we thank all people from the  ESS  collaboration for their fruitful exchanges and support that help us to design the monitors.
lead to error-prone inference that has the potential to misdirect conservation interventions and undermine decision-making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarizing and visualizing extrapolation in multivariate environmental space. 2. dsmextra automates the process of conducting quantitative, spatially explicit assessments of extrapolation on the basis of two established metrics: the Extrapolation Detection (ExDet) tool and the percentage of data nearby  (%N). The package provides user-friendly functions to (a) calculate these metrics, (b) create tabular and graphical summaries, (c) explore combinations of covariate sets as a means of informing covariate selection and (d) produce visual displays in the form of interactive html maps.
3. dsmextra implements a model-agnostic approach to extrapolation detection that is applicable across taxonomic groups, modelling techniques and datasets. We present a case study fitting a density surface model to visual detections of pantropical spotted dolphins Stenella attenuata in the Gulf of Mexico. 4. Predictive modelling seeks to deliver actionable information about the states and trajectories of ecological systems, yet model performance can be strongly impaired out of sample. By assessing conditions under which models are likely to fail or succeed in extrapolating, ecologists may gain a better understanding of biological patterns and their underlying drivers. Critical to this is a concerted effort to standardize best practice in model evaluation, with an emphasis on extrapolative capacity.
Although several extrapolation metrics have been proposed to address this (Table  S1 ), there is little consensus on which works best for a given ecological scenario, and scant consideration of how results are sensitive to metric choice  (Grenier, Parent, Huard, Anctil, & Chaumont, 2013). Standard protocols for supporting consistent assessments of extrapolation are therefore lacking  (Sequeira et al. , 2018; Yates et al. , 2018) , leaving end users deprived of both the guidelines and the tools they require to objectively audit models projected into novel and/or data-deficient contexts. We introduce the dsmextra R package, a user-friendly toolkit for quantitatively assessing extrapolation. The package was built as a companion to dsm, a sibling package focused on the analysis of distance sampling survey data using generalized additive models  (Miller et al.
, 2013). However, dsmextra only requires knowledge of covariate values at sampled and prediction locations, meaning that it is not restricted to line/strip-transect data and that its functions are readily applicable to a wide variety of datasets, irrespective of how they were collected or of the statistical frameworks in which they are analysed. Here, we describe the package, explain the concepts underpinning its use and illustrate some of its features using a survey dataset on pantropical spotted dolphins Stenella attenuata in the Gulf of Mexico.
| SOF T WARE AND E X AMPLE dsmextra provides functions (Table  S2 ) designed to assist users in measuring, summarizing and visualizing extrapolation, as precursory steps in the traditional modelling workflow (Figure  1 ). A number of these functions are adapted from existing-but scattered-code released on CRAN (https://cran. r-proje ct. org/) or available as ad hoc scripts in the supplemental materials of peer-reviewed articles (e. g. Mannocci et al. , 2018). dsmextra represents the first attempt to consolidate these methods while extending their usability and functionality. Attempts at characterizing extrapolation are typically made following model calibration, as a test of model adequacy, realism and predictive performance  (Robinson, Nelson, Costello, Sutherland, & Lundquist, 2017). We argue that this is unproductive, in part because model fitting can be time-consuming (especially for large datasets), meaning that there is little to gain from spending hours (or more) building a model that is unlikely to return credible outputs in the first place.
Additionally, a number of comparative studies have demonstrated mixed extrapolation success among different models (or model types) fitted to the same datasets (e. g. Shabani, Kumar, & Ahmadi, 2016) , suggesting that idiosyncrasies in model structures and parameterizations may result in inconsistent out-of-sample behaviour that may compromise the interpretation of model outputs. Lastly, the independent data required for rigorous model validation are often lacking, precluding any opportunities to objectively measure the quality of model predictions under novel conditions  (Sequeira et al. , 2018). For these reasons, we instead suggest that extrapolation assessments take place a priori, ensuring that models that do perform poorly due to extrapolative issues outside the sampled covariate space can be identified early on and avoided (Figure  1 ).
grid cells subject to either (Figure  2 ). n. covariates can be supplied as any vector of integers from 1 to p, where p is the total number of covariates available. For instance, Figure  2  was produced with the command: compare_covariates(extrapolation. type = "both", samples = survey. segs, n. covariates = 5, covariate. names = stenella. covariates, prediction. grid = pred. grid, coordinate. system = GulfMexico_proj, create. plots = TRUE) Map production is the final step in the dsmextra workflow. map_ extrapolation generates interactive visualizations of extrapolation using leaflet (https://rstud io. github. io/leafl et/), giving users the capacity to pan/zoom in on areas of interest and toggle map layers on/ off as desired (Figure  3 ). Species sightings and survey tracks can also be overlaid, if available.
DATA AVA I L A B I L I T Y S TAT E M E N T The package website (https://densi tymod elling. github. io/dsmex tra/) provides full documentation, including a comprehensive tutorial for novice users. Package files and code are hosted on Github at https:// github. com/densi tymod ellin g/dsmextra and archived on Zenodo at https://doi. org/10. 5281/zenodo. 3529465 (Bouchet et al. , 2020). The dolphin dataset used in the case study is shipped with dsm and described at http://seamap. env. duke. edu/datas et/25.
ACKNOWLEDGEMENTS We thank  Luke B. Fletcher ,  Martin French ,  Dirk O. Gericke ,  Clemens Kellermann ,  Laurent Masse , and  Martin Preising  for helpful discussions. M. B. , B. B. L. W. , M. S. , and R. R. acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG)  within the FOR 2440. B. B. L. W. , M. S. , and S. H. were supported by the  DOE Office of Science, Fusion Energy Science  under  FWP 100182. The work of T. D. and P. A. S. was performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344  and supported by  Laboratory Directed Research and Development (LDRD)  Grant No. 18-ERD-033. D. K. was supported by the  Helmholtz Association  under  VH-NG-1141. The DFT-MD calculations were performed at the  North-German Supercomputing Alliance (HLRN)  facilities and the computing cluster Titan hosted at the  ITMZ at University of Rostock.
EC Function Assays Cell proliferation and death were evaluated using BrdU incorporation assay and anti-DNA/histone ELISA, respectively. In a 96-well culture format, 1 3 10 4 ECs were seeded per well, followed by serum starvation for at least 6 h. Growth media was then added, and proliferation and death were independently evaluated after 24 h according to manufacturer's instructions (Roche, Indianapolis, IN) using a multiwell spectrophotometer at 450 nm as previously described  (27, 28). Each assay was repeated in triplicate. EC migration was evaluated using a monolayer EC migration assay as previously described  (27, 28). Monolayer scratches were serially evaluated at 0, 3, 6, and 16 h, and percentage of wound area closure was measured using ImageJ software. Each condition was repeated in triplicate.
Proteins were detected with rabbit anti-CEPT1 (20496-1-AP; Proteintech Group), anti-PPARa, rabbit antiphospho-PPARa (Ser 12; PA1820; Life Technologies Corp. ), rabbit anti-ACOX1 (10957-1-AP; Proteintech), and mouse anti-CPT1a (66039-1-Ig; Proteintech) antibodies. Rabbit anti-caveolin-1 (ab2910; Abcam Biotechnology) was used for Western blot loading controls. Band densitometry analysis was performed using ImageJ software as previously described  (27, 28). Band densities were averaged across triplicate blots and expressed as ratios relative to protein loading control or nonphosphorylated total protein. For immunostaining, HUVECs were fixed with 100% methanol and serially washed with PBS; 2% BSA blocking agent was added for 1 h at room temperature. Immunostaining was then performed using primary antiphospho-PPARa (Ser12) 1:200 or rabbit anti-CEPT1 antibody 1:50.
Real-time PCR RNA was purified from HUVECs, MLECs, and human arterial intima tissue as previously described  (24). Target mRNA was quantified by real-time PCR using specified primer sets (designed to span introns, not react with genomic DNA, and validated with quantitative PCR melting curve analysis) (Supplementary Table  1 ) and Applied Biosystems PowerUp SYBR Green Master Mix (A25742; Thermo Fischer Scientific). Samples were evaluated using the 7500 Fast Real-Time PCR System (Applied Biosystems) and analyzed using Fast System Software (Applied Biosystems). Threshold cycle (Ct) values were normalized to either RPL32 mRNA (for human EC and tissue analysis) or RPL34 mRNA (for murine EC analysis) and expressed as fold change relative to the mean Ct value of the control group using the D Ct method  (31).
Mass Spectrometry Phospholipidomic Data Analysis The mass spectrometry-derived lipid mass spectrum for arterial intima and EC samples was performed as diabetes. diabetesjournals. org Zayed and Associates previously described  (24). This analysis included 54 PCrelated species (including 23 PCs), 16 alkyl ether PCs (aPCs), and 11 sphingomyelins (Supplementary Table  2 ) and 22 PE-related species, including 12 PEs and 11 plasmenyl PEs (pPEs) (Supplementary Table  3 ). Unilateral Femoral Artery Ligation, Laser Doppler Perfusion Imaging, and Appearance and Use Score Assessments Ligation, imaging, and use scores were determined as described with some modifications  (27). Limb appearance was independently graded on a scale of 0-4 (4 5 digit/foot autoamputation, 3 5 severe discoloration/gangrene, 2 5 moderate discoloration, 1 5 mild discoloration, 0 5 normal appearance).
Acknowledgments. The authors thank  Amanda Penrose ,  Section of Vascular Surgery at Washington University , for assistance with collection, preparation, and processing of  Biobank Core Facility  research samples and  Susan Grathwohl ,  Section of Vascular Surgery at Washington University , for mouse colony maintenance and independent review of peripheral murine angiograms. The authors thank  Dr. Chenglong Li ,  Section of Vascular Surgery at Washington University , assistance with Ppara 2/2 murine HLI operations. VE-Cadherin-CreERT2 mice were a gift from the laboratory of  Dr. Ralf Adams ,  Max Planck Institute for Molecular Biomedicine, Germany. The authors also thank  Dr. Xiaochao Wei ,  Division of Endocrinology, Metabolism, and Lipid Research at Washington University , and  Dr. Rithwick Rajagopal ,  Division of Endocrinology, Metabolism, and Lipid Research at Washington University , for their critical suggestions and critiques.
Funding. This work was supported by the  Washington University School of Medicine Vascular Surgery Biobank Core Facility  and by the  Vascular Cures   Foundation Wylie Scholar Award  (M. A. Z. ), the  American Surgical Association Research Fellowship Award  (M. A. Z. ), the  Society for Vascular Surgery Foundation Research Investigator Award  (M. A. Z. ),  Washington University School of Medicine Diabetes Research Center National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)  grants  P30 DK020589  (M. A. Z. ),  NIH/National Heart, Lung, and Blood Institute  grant  K08 HL132060  (M. A. Z. ),  Nutrition Obesity Research Center NIH/NIDDK  grant  P30 DK056341  (B. W. P. ), and  NIH/NIDDK  grant  R01 DK101392  (C. F. S. ). The  Washington University School of Medicine Mass Spectrometry Facility  is supported by  U.
The incoming neutron intensity was obtained from the Neutron Monitor Data Base (NMDB) of Castilla-La Mancha station (CALM), and the number of neutrons was in addition corrected to fluctuations in neutron intensity: N pi (t) = N p × Inn avg Inn (10) where N pi is the number of neutrons corrected by the incoming neutron intensity, Inn avg is the average neutron intensity over the measurement period and Inn is the day specific neutron intensity. Finally, the neutron count intensity is corrected for atmospheric humidity: N pih (t) =N pi (t) × [1+0. 0054× (H-H 0 )] (11) N pih is the number of neutrons corrected for air humidity variations, where H 0 is the average air humidity (g cm -3 ) over the measurement period and H is the day-specific air humidity value (g cm -3 ). Once the incoming neutrons were corrected, these were converted into soil moisture (O (t) ) by using the following equation and calibration with help of measured soil moisture contents by each sampling campaign (O g ): O (t) = a 0 × N pih (t) N 0 -a 1 -1 -a 2 × ρ bd -W L -(SOM + B R ) (12) where ρ db is bulk density (g cm -3 ),W L is latice water (W L , m 3 m -3 ), SOM is the water equivalent of soil organic matter content (m 3 m -3 ), B R is root biomass (B R , m 3 m -3 ), N pih (t) is the corrected count neutrons; parameters a 0 , a 1 , and a 2 are 0.
Cosmic-Ray Neutron Probe Validation The validation was carried out by comparing the CRNP and capacitance soil moisture estimations using two methodologies, the RMSE and the Kling-Gupta efficiency (KGE)  (Gupta et al. , 2009; Kling et al. , 2012). This validation was independently developed for T and C plots, not only for the whole time series, but also for three different sub-periods. The semi-arid climate conditions of the study site are expected to provide soil moisture values within a wide range, which includes wet and very dry periods. Schreiner-McGraw et al. (2016)  also studied the performance of CRNP in a semi-arid environment, and in spite of the good results, the worst performance was observed in wet and dry periods. Hence, with the aim to assess the CSR performance under very different soil moisture conditions, this study divides the soil moisture dataset according to the capacitance soil moisture values.
Statistical Analyses The assumption of a Gaussian distribution of the different datasets was studied using a Kolmogorov-Smirnov (K-S) test with the Lilliefors correction. To analyze the correlations between soil moisture (capacitance and CRNP) and the environmental variables, a Pearson correlation test was done for variables for which the null hypothesis that these variables are Gaussian distributed was not rejected. On the other hand, a Spearman non-parametric correlation test was done for the variables for which the null hypothesis that they are Gaussian distributed was rejected. Both tests, Pearson and Sperman, were carried out at p < 0. 05 level. Subsequently, the temporal lag between O and the environmental variables was studied by daily misplacing the environmental variables until the time lag with the greatest correlation and significance was found.
FUNDING This study is a component of the research projects:  E-HIDROMED  ( CGL2014-58127-C3 ) and  CEHYRFO-MED  ( CGL2017-86839-C3-2-R ) funded by the  Spanish Ministry of Science and Innovation and FEDER funds , and  LIFE17 CCA/ES/000063  RESILIENTFORESTS. AM is beneficiary of an  APOSTD fellowship  ( APOSTD/2019/111 ) funded by the  Generalitat Valenciana.
The authors have previously used highspeed X-ray CT to produce consecutive datasets of batteries during discharge for quantitative displacement measurements via digital volume correlation (DVC)  25. The DVC of consecutive tomograms was used to explore the evolution of electrode structures in Li primary cells, which are widely used across consumer electronics, backup-power and aerospace applications. In the current manuscript, we extend our previous study to evaluate the Li distribution in the MnO 2 cathode of a commercial CR2 Li-ion primary cell from Duracell  26  using both X-ray and neutron CT. The experiments were performed at the ID15A beamline at the European Synchrotron Radiation Facility (ESRF), known for high-speed and high-resolution imaging capability  6, 27  , and at the V7 CONRAD-2 neutron imaging beamline at the BER II research reactor at Helmholtz Centre Berlin, known for its combination of high-resolution neutron imaging with step-wise sample evolution  28, 29.
5 µm. Virtual unrolling of tomography. A comprehensive spatial analysis of the spiral-wound electrode assembly is challenging. Therefore, to facilitate spatial quantification along the length of the spiral electrode, virtual unrolling of the datasets was applied. To determine the electrode thickness change during the discharging process, an arc region out of the electrodes was selected, which covers about 360°of the battery circumference and four electrode windings, as shown in Fig. 1d  for the unrolling of the neutron tomogram. Subsequently, the high-intensity nickel mesh was removed from the unrolled X-ray data by a thresholding process, before the volumes were averaged over the electrode's height to get a mean value of the cathode expansion and the intensity change. Because of the similarity between the greyscale values of the Li x MnO 2 and the current collector in the neutron data, it was not possible to remove the mesh by a threshold.
Hence, the mesh appears as a constant value in the further processed neutron images. Figure  3  shows cut-outs of the processed data for the X-ray and neutron images at different SoC. The images display the radial outer electrode windings in the upper region. The X-ray image of the cathode reveals a rapid volume expansion over the discharging period from the pristine state (CR2-000) to the partly discharged state to -604. 90 mAh (CR2-096). The average cathode volume increased by about +27%, from 389 to 495 µm thickness, while the intensity decreases due to the Li insertion. The neutron images show a similar volume expansion and electrode movement during discharging. The volume change for the discharged cell to -745. 08 mAh is +30%, from 386 to 501 µm in thickness, and +26. 5% to -580. 55 mAh, which corresponds to the +27% measured in the X-ray datasets by a slightly higher capacity.