dataesr/scientific-paragraphs-categorization huggingface dataset:
Chandra The Chandra X-ray Visionary Project (XVP; PI B. Benson) published X-ray data of 90 SPT systems, at 0. 35 < z < 1. 2, and their properties have been characterized in several papers (e. g,  Semler et al. 2012; McDonald et al. 2014) , including coolcore properties  (McDonald et al. 2013 ) and a morphological classification  (Nurgaliev et al. 2017) , which are relevant for this study. The matching of the  Nurgaliev et al. (2017)  sample with DESY3-SPT renders 41 clusters within our redshift-mass limits (DESY3-XVP sample hereafter). We also make use of Chandra public data by using the Mass Analysis Tool for Chandra (MATCha) pipeline  (Hollowood et al. 2019). Given an input cluster catalog, this code queries the Chandra archive and reduces all public data for clusters in the catalog. The output of MATCha includes measurements of cluster T X , L X , and X-ray centroid within a set of apertures, 500 kpc, r 2500 , r 500 , and core-cropped r 500.
The position of the X-ray peak is estimated as the brightest pixel after smoothing with a Gaussian of 50 kpc width (see  Hollowood et al. 2019, for details). Matching the MATCha program results to our DESY3-SPT sample we obtain 39 matches, 1 of them a new addition to our sample. In total, we find 42 systems with Chandra data in the DESY3-SPT sample.
XMM Newton We also searched for counterparts in the XMM-Newton (XMM) space telescope archive. The XMM analysis was performed using an adaptation of the X-ray Automated Pipeline Algorithm (XAPA;  Freeman et al. 2002)  developed for the XMM Cluster Survey (XCS, e. g,  Romer et al. 2000). XCS uses all available X-ray data in the XMM Science Archive to search for galaxy clusters that were detected first in XMM images. X-ray sources are detected in XMM images using an algorithm based on wavelet transformations (e. g. ,  Lloyd-Davies et al. 2011) , and then compared to the position dependent point spread function (PSF) to classify them as extended, PSF-sized, or point like. Most of the extended X-ray sources are clusters, therefore all extended sources are flagged as cluster candidates, but in some cases these can be low-redshift galaxies, supernova remnants or multiple blended point sources.
BCG-X-ray centroid/peak offset The X-ray centroid/peak to BCG offset is a well known predictor of the dynamical state of a cluster. We use the X-ray centroid and peak to BCG distance to complement the the morphology and coolcore samples. To select relaxed systems we follow  Mann & Ebeling (2012) ; we use D BCG-X < 42 kpc if we use the X-ray peak, and D BCG-X < 71 kpc if we use the X-ray centroid. We add 12 clusters from the XMM data and 11 from Chandra data to this sample. We note that the conditions described in § 3. 2. 2 and § 3. 2. 3 also include clusters classified as disturbed; SPT-CLJ0102-4603, SPT-CLJ0252-4828, SPT-CLJ0346-5439, and SPT-CLJ2218-4519. These systems are excluded from both the relaxed and disturbed samples. SPT-CLJ0102-4603 is identified as a cool-core cluster with K 0 = 10.
3, is shown in Fig. 3. Images for a sub sample of 12 disturbed systems and 12 relaxed systems can be see in Fig. 13. 41 ) and disturbed (  43 ) cluster sample mass-redshift plane. The mass and redshift cuts described in section § 2. 3 have been applied. It can be seen that the average mass of the relaxed clusters are higher than the average mass of the disturbed clusters. We also create an intermediate sample of clusters, numbering 204, consisting of all clusters that remain. Note that such sample will contain disturbed and relaxed clusters (i. e. , our disturbed and relaxed samples are by no means complete). We also tested an optical proxy for the cluster dynamical state. We used the  Wen & Han (2015)  recipe, but we were unable to find consistent results, when comparing to samples created using X-ray data for dynamical state estimation.
6. At that redshift range, our work shows agreement on the Schechter parameters between the relaxed and disturbed clusters. When compared to literature work, the largest discrepancy comes from a study on cluster samples built using the velocity distribution as the dynamical state proxy. To understand such differences we explore the velocity dispersion method over the 42 clusters in this sample with Gemini/GMOS and VLT/FORS2 spectroscopy. We use the Anderson-Darling test to classify the cluster dynamical state, finding 33 relaxed and 9 disturbed clusters. We found that the disturbed stacked LF shows a steeper faint end than the relaxed stacked LF, albeit with large error bars. We will explore this further with recently acquired Gemini/GMOS spectroscopic data for 14 disturbed clusters from this paper.
As we mentioned above, the BCG luminosity at z > 0. 55 in relaxed systems are indeed brighter than BCGs in the disturbed and the intermediate clusters. Furthermore, at > 1 magnitude brighter than the 10σ depth across the cluster fields, BCG photometry is unaffected by the survey photometric limits. In a follow up paper, we will expand this work using deeper iz data from DES Y6, as well as dedicated pointed observations of z > 0. 7 clusters. Furthermore, in the near future, eROSITA will not only provide confirmation of the dynamical state of these clusters, but will render a sample of thousands of clusters that will allow us to test to a higher significance the findings shown here, when combined with a BCG selection in a systematic way. Figure  13. The top 12 clusters correspond to the highest redshift relaxed systems.
Disturbed DESY3-SPT cluster sample. Notes. a Clusters with a BCG candidate in McDonald et al. (2016). Due to the McDonald et al. (2016) selection procedure, the BCGs presented for these 5 cases are different than what is reported here. See § 2. 4 for details. b The offset reported corresponds to the distance between the BCG and X-ray centroid. of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171.
The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Unionâ ȂŹ s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.
ACKNOWLEDGEMENTS TJ is pleased to acknowledge funding support from  DE-SC0010107. AS is supported by the  ERC-StG  ' ClustersX-Cosmo ' grant agreement  716762 , and by the  FARE-MIUR  grant ' ClustersXEuclid '  R165SBKTMA. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
Documentation Data were recorded on a standardized case report form at admission and at discharge from the hospital, as well as after 120 ± 15 days during a telephone interview; they were subsequently entered into a central online database. Type 2 diabetes mellitus was defined as current treatment for diabetes, a previous diagnosis of type 2 diabetes or a fasting plasma glucose concentration ≥ 126 mg/dL. Hypertension was defined as current blood pressure-lowering treatment, a previous diagnosis of hypertension or having blood pressure > 140/90 mm Hg. A sedentary lifestyle was defined as < 20-30 minutes of walking on < 3-4 days per week. A blood test was carried out within 24 hours of admission to hospital, and a full lipid profile was documented.
Statistics Patients were divided into groups depending on whether or not they had type 2 diabetes. Data are presented as means ± standard deviations, medians (interquartile ranges) or percentages with absolute values. Statistical significance was determined using the χ 2 test or the Mann-Whitney-Wilcoxon test. Kaplan-Meier analysis was used to provide estimates of mortality and non-fatal events after ACS, with log-rank tests used to determine statistically significant differences between the groups of patients with versus without diabetes. All data analysis was carried out using SAS software, version 9. 2 (SAS Institute, Cary, NC, USA).
The GPS and audio and electrical stimuli data were recorded. To quantify social facilitation, individual VF interactions were grouped into 179 "events" across 3 days; starting from when the first animal (leader) approached the VF. The responses of other animals were categorized as  (1)  followed the leader to move into the exclusion zone (followers, F),  (2)  accompanied the leader back into the inclusion zone (facilitated, Fa), (3) did not show any reaction (non-facilitated, NFa). A social facilitation score (SFaS) was calculated as SFaS (%) = (F/(Fa+NFa+F)) * 100. A single leader animal led on average 37% of events with 76. 2% of all reactions categorized as facilitated by other individuals. Animals responded to the behavior of conspecifics more during the VF implementation compared with facilitated movement during natural grazing patterns when no VF was present (P < 0.
Experimental Design For this study, the data collected across 3 days each from 8 groups of eight 12 to 14-month old Angus steers (n = 64 animals) with an average starting body weight of 405 ± 31. 8 kg were used. All cattle were naïve to virtual fencing for the data collection period of the current study. Data were collected from the 8 groups in a staggered method across a period of 3 months as there were limited numbers of both neckband devices and available paddocks to test all groups simultaneously. Additionally, groups 1-4 were part of a larger trial assessing the behavior and welfare of cattle exposed to electric tape or virtual fences conducted at the CSIRO Chiswick site in Armidale, NSW from January through March 2019 and full details of that experimental protocol can be found in  Campbell et al.
Data The time-stamped GPS positional data which recorded approximately every second when the animal was near the fence line and/or walking/grazing were downloaded from the neckband device. All audio cue and electrical pulse data of individual animals across 3 days of fence activation were also downloaded from the neckband device. One day of GPS data from the last day of the habituation period for each group was also included for control comparisons of social facilitation of movement patterns when no VF was present. Data editing was carried out in the SQL server  (34). To prepare the dataset for analysis of social facilitation via GPS movement patterns and cues received, the original VF dataset was edited to eliminate records of: (1) before the first animal moved into the exclusion area, and (2) data during the night as it was assumed visual contact during learning would be limited.
Thus, across 3 study days, the data used were based on sunrise and sunset in Armidale at the time of investigation for each group as follows: from 6 a. m. to 8 p. m. (groups 1 and 2), from 6:45 a. m. to 7:30 p. m. (groups 3 and 4), and from 7 a. m. to 7 p. m. (groups 5-8). For the first day, the time of the first interaction with the VF in each group (varied from 8 a. m. to 3:30 p. m. across the groups) was considered the starting time point. The control dataset for each group was of the same time periods across 1 day prior to activation of the VF.
The animals were considered to have been socially-facilitated if they moved back into the inclusion zone without receiving a signal themselves within that particular event and when at least some of them had had previous experience with the VF. Data to study the social facilitation of the VF, therefore, was limited to the second event onwards when at least some animals had experienced the VF. The responses of other animals in the group were assessed in terms of their movement pattern (heading forward or turning back) within the group relative to the VF and position relative to other animals to indicate if they were staying within the inclusion zone based on the leader animals' interaction with the VF. In summary for each event the animals were considered as below: Leader (s): The animal (s) who touched the VF first and received the signal as an audio cue or audio cue/electric pulse combination for each event.
05. For the overall comparison of SFaS and SFoS in which the average of these two parameters for study groups was used, the comparison was performed using a paired t-test. The percentage values were arcsine-transformed to meet the assumption of normality, but the raw values are presented in the results. In addition to quantifying the social facilitation during avoidance of the VF, the number of audio cues each animal received prior to receiving their first electric pulse were calculated to determine how social facilitation affected the associative learning between the audio cue and electrical pulse. These were calculated across the full dataset (including nighttime hours). Finally, a Spearman correlation between the number of received audio cues before the first pulse and the proportion of "audio-only" cues (i.
The optical densities (gray-scale levels of the corresponding pixels of the pre-processed image) along with the surface area were determined by means of ImageJ software. The background optical density, calculated from a spinal cord sections processed without the addition of a primary antibody, was measured and subtracted. Then, all fluorescence intensity values obtained were normalized to the lowest intensity density in the vehicle-treated group (recorded intensity density of the ROI divided by the lowest intensity density of vehicle-treated ROI). We statistically evaluated four rats (5 slices each) that were randomly selected from each group. Images were taken by means of a Zeiss Axio Observer microscope with a 20× objective and Axio Vision4 software (Carl Zeiss Vision GmbH, Germany). The mean of the group was obtained from 16 ROIs (4 ROI per animal).
All results were obtained using the integrated StepOne TM Software (version 2. 3, TermoFisher Scientific,) and further assessed through the 2 -∆∆Cq method normalized to two appropriate endogenous reference genes selected by NormFinder: gapdh/Rn01775763_g1/ and beta-Actin/Rn00667869_m1. Gene expression was normalized against healthy WT littermates of the same age and compared between the groups. Finally, all data were recalculated to relative quantities and transformed to a log2 scale according to the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) recommendations  [63].
Statistical Analysis All numeric data are presented as mean values and analyzed with one-way ANOVA except for the behavioral results where two-way ANOVA with repeated measures was used (Sigma-Plot 9. 0, Systat Software, San Jose, CA, USA). Group differences are presented as mean ± standard error of the mean (s. e. m. ), while RT-qPCR results were calculated as described earlier for the 2 -∆∆Ct method and the Kaplan-Meier method was used to determine the difference in survival possibility rate between the groups of NP-iPS-treated (asymptomatic and early symptomatic) and vehicle-treated animals. Statistical significance was established at p values under 0. 05 (*), p < 0. 01 was considered as of a high statistical significance (**), p < 0. 001 was considered a very high statistical significance (***) and p < 0.
The advection is performed using the Advect software 3. The LS function is then re-distanced to remain a signed distance function, using the mshdist software 4  (based on  Dapogny and Frey, 2012). Next, the normal vectors to the new interface are computed. Near the interface the normal vector n is computed using the H function, and far from it is computed using the re-distanced LS function. This choice was made because using the H function to compute the normal vectors far from the interface yields spurious oscillations, a problem that does not arise with the LS function  (Zhao et al. , 2014a,b; Zhao L. -H. et al. , 2014). A point of the domain is considered to be in the vicinity of the interface if its distance to the interface is smaller than twice the parameter ǫ in Equation (10). Then, the shape and the width of the H function are maintained thanks to a re-initialization step  (Olsson et al.
Initialization of the Model The model needs to be initialized with a LS function, representing either a real firn porous structure or an idealized geometry. The initial porous structure can be derived from 3D voxel models obtained with Xray tomography of firn samples  (Barnola et al. , 2004; Gregory et al. , 2014; Burr et al. , 2018). To simplify the boundary conditions, the tomography models are padded with ice material on all sides, meaning that the ice/pore interface lies away from the boundaries of the domain D. Starting from a 3D voxel model, the signed distance is computed on a Cartesian grid using the Fast Marching Method (FMM;  Sethian, 1999). It is performed using the scikit-fmm 5  python package. The signed distance is then smoothed using a gaussian filter to remove the jags of the interface.
ACKNOWLEDGMENTS We thanks  Christophe Martin  and  Didier Bouvard  for pointing to us the usage of Level-Set models in the sintering community. We thank  Maurine Montagnat  for the useful discussion on ice mechanics. We thank  Olivier Gagliardini  for his help with the Elmer software. We thank  Charles Dapogny  for the useful discussion on the Level-Set method, mesh adaptation, and advection of scalar field. We thank  Martin Schneebeli ,  Henning Löwe , and  Anouk Marsal  for their involvement with the tomography images. The Lock-In drilling operation was conducted by  Phillipe Possenti ,  Jérôme Chappellaz ,  David Colin ,  Philippe Dordhain , and PM. We are thankful to MT and MR for reviewing this article, and to JG for editing it.
FUNDING This work was supported by the  French INSU/CNRS LEFE projects NEVE-CLIMAT  and  HEPIGANE. The Lock-In drilling operation was supported by the  IPEV  project no. 1153  and the  European Community 's  Seventh Framework Programme  under grant agreement no. 291062  ( ERC ICE&LASERS ). The tomography images were obtained by the  Institute for Snow and Avalanche Research SLF (Davos, Switzerland ).
The Extreme-Horizon simulation The EH simulation is performed with the adaptive mesh refinement code RAMSES  (Teyssier 2002)  using the physical models from HAGN (D14). The spatial resolution in the CGM and IGM is largely increased compared to HAGN, while the resolution inside galaxies is identical, at the expense of a smaller box size of 50 Mpc h -1. The control simulation of the same box with a resolution similar to HAGN is called Standard-Horizon (SH). EH and SH share initial conditions realized with mpgrafic  (Prunet et al. 2008 ). These use a ΛCDM cosmology with matter density Ω m = 0. 272, dark energy density Ω Λ = 0. 728, matter power spectrum amplitude σ 8 = 0. 81, baryon density Ω b = 0. 0455, Hubble constant H 0 = 70. 4 km s -1 Mpc -1 , and scalar spectral index n s = 0. 967, based on the WMAP-7 cosmology  (Komatsu et al.
Acknowledgements. The  Extreme-Horizon simulation was performed as a "Grand Challenge " project granted by  GENCI  on the  AMD Rome extension of the Joliot Curie supercomputer at TGCC. We are indebted to  Marc Joos ,  Adrien Cotte ,  Christine Ménaché  and the whole  HPC Application Team at TGCC  for their efficient support. Collaborations and discussions with  Bruno Thooris ,  Eric Armengaud ,  Marta Volonteri ,  Avishai Dekel , are warmly acknowledged. We deeply appreciate comments from  Jérémy Blaizot  on the  Extreme Horizon  project. This research used the ramses code written mainly by  Romain Teyssier , the custom Hercule parallel I/O library, the kinemetry package written by  Davor Krajnović , and the disperse code from  Thierry Sousbie. This work was supported by the  ANR 3DGasFlows  ( ANR-17-CE31-0017 ) and made use of the Horizon Cluster hosted by the  Institut d'Astrophysique de Paris , run by  Stéphane Rouberol.
The SKA-LOW is expected to be an order of magnitude more sensitive than LOFAR, but the design baseline does not include the long baselines to match LOFAR's unique spatial resolution, and it will not extend to frequencies below 50 MHz. Although LOFAR cannot achieve the desired sensitivity, it is useful as a testbed to quantify the foreground isolation technique for high values of k ⊥ by testing this on real data. At the time of writing, no fully calibrated LBA dataset with the international baselines exists, but we expect this to change within the next year. Further work should include an assessment of improving the collecting area of a lowfrequency array with long baselines. For example, when designing a lunar array, the sensitivity of the individual dipoles must be improved over current existing hardware, then their station configuration must optimised to increase collecting area, and finally the array configuration must be taken into account to maximise the sensitivity to specific angular scales.
Conclusion Entire analyses were completed in triplicate, following data compilation using R program 1. 3. 959, MS Excel 2016, and GraphPad Prism version 7. 04. Pearson's correlation with a P value 0. 05 was applied to the data. The data has strongly supported the alternative hypothesis, which means there was certainly some effect of bacteriocin on the indicator pathogens, especially where it showed significant values for the activity of bacteriocins during temperature variations. In summary, bacteriocins extracted from Lactobacillus spp. had vigorous antimicrobial activity against Grampositive and Gram-negative pathogenic organisms, including S. aureus, MRSA, E. coli, Salmonella paratyphi, and Acinetobacter baumannii. Various levels of activity were retained or enhanced following exposure to a range of conditions of temperature, pH, bile salts, and UV light.
Statistical analysis Changes in the abundances of individual animal groups (gnats and midges, plant-and leafhoppers, spiders) with sampling date (2, 4, 6 weeks after transplantation) and surrounding landscape structure (non-rice dominated and rice-homogeneous) were analysed for sweep net samples only using generalised linear mixed models. Data for each taxon were examined separately to ensure that they met assumptions. The models were specified with "abundance" as response variable and independent variables "date" and "structure" representing sampling date and surrounding landscape structure, respectively. The response variable was log-transformed and specified with Gaussian distribution for gnats and midges. Negative binomial and Poisson distribution were specified for plant-and leafhoppers.