dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical methods -implementation of the Hall effect We implemented the Hall effect in the Eulerian code RAMSES  (Teyssier 2002). Since the Hall effect is dispersive, the introduction of new waves in the system modifies the Riemann problem that needs to be solved on each cell interface. For this reason we have adapted the method of  Lesur et al. (2014)  implemented in the PLUTO code  (Mignone et al. 2007 ). This method is similar to  Tóth et al. (2008)  and is based on a direct modification of the Riemann solver. Therefore, we do not consider the Hall effect as a source term, as it is the case for the ambipolar and Ohmic diffusions in RAMSES  (Masson et al. 2012).
The constrained transport in RAMSES In RAMSES, the magnetic field is naturally defined at the center of each orthogonal face, meaning that for the cell centered on indexes (i, j, k) (for the x, y and z directions), B x , B y , and B z are defined at (i -1 2 , j, k), (i, j -1 2 , k), and (i, j, k -1 2 ) respectively. The code uses the constrained transport (CT)  (Evans & Hawley 1988)  to solve both the induction equation of ideal MHD  (Teyssier et al. 2006; Fromang et al. 2006 ) and the ambipolar and Ohmic diffusions  (Masson et al. 2012). This method exploits the Stokes theorem by using the value of the electromotive forces (EMF) on cell edges to compute the magnetic flux through cell interfaces. For instance, Figure  1  shows which EMFs are used to evolve the x-component of the magnetic field at the cell interface (i -1/2, j, k).
Comparison to other implementation Our implementation of the Hall effect in RAMSES followed the work of  Lesur et al. (2014) , who included it in the Eulerian code PLUTO  (Mignone et al. 2007 ). While both codes use the CT algorithm,  Lesur et al. (2014)  use a 1D-Riemann solver to compute the EMF while we have included the Hall effect into 2D-Riemann solver, which, to the best of our knowledge, has never been done before. Both codes use a second-order scheme (Runge-Kutta for PLUTO), and share the same convergence properties for the Hall effect. The approach is also similar to the implementation of  Tóth et al. (2008)  for the BASTRUS code  (Tóth et al. 2006) , though they implemented the Hall effect using an explicit time integration within an implicit code. Since implicit schemes are unconditionnally stables, they are not strictly bound to the whistler speed to choose their time-step.
This emancipation results in a faster code because larger time-steps are allowed. Furthermore, without the constraint of the whistler speed, the scheme order is increased by one. The drawback of the BAS-TRUS code is a less precise scheme at small scales because the shortest whistler waves cannot be correctly propagated. The Hall effect has been implemented in the Eulerian code ZeusTW  (Stone & Norman 1992; Krasnopolsky et al. 2010 ) according to  Sano & Stone (2002). The term (∇ × B) × B is evolved at t n+1/2 using a finite difference scheme to evaluate the curls in an explicit way. The resulting Hall-EMF is then added to the ideal EMF u × B into a modified constrained transport method. The tests in  Sano & Stone (2002)  show that the dispersion relation of the Hall effect is verified, but we have not found any information about the accuracy of the scheme.
Due to their nature, Lagrangean codes cannot use the constrained transport method. In the SPH code PHANTOM  (Wurster et al. 2014; Price et al. 2017)  as well as in  Tsukamoto et al. (2015)  and  Tsukamoto et al. (2017) , the three non-ideal MHD terms were implemented as source terms. Testing them with the ambipolar diffusion shows second order convergence, but no information is given for the Hall effect, nor the propagation of whistler waves at various resolution. They do, however, recover the theoretical dispersion relation, and perform the shock-test described in section 4. 2 with results fitting the expected profile within 3%  (Wurster et al. 2016).
400 kyr, corresponds to the time at which the core expands due to the centrifugal force, as seen in the evolution of maximum density in dotted line. This "bouncing" also happens at a smaller scale for the λ J /∆x > 16 simulation at t = t c + 0. 300 kyr, but is tempered by the lower excess of angular momentum and the additional gravitational force (due to the higher density). We observed the same behavior when we limit the maximum resolution. In the following set of simulations, we use the 16 points per Jeans length criterion, but we did not allow the code to refine above a given level. Figure  18    ) Maximum density (g cm -3 ) (log) t after t C (kyr) momentum evolution for maximum levels from 10 (∆x = 16 au) to 15 (∆x = 0. 5 au). The increase of the angular momentum starts at the same time after the formation of the first Larson core for every simulation.
Conclusions We have implemented the Hall effect in the AMR code RAMSES to study its impact on star formation and circumstellar disks. The method relies on the modification of the 2D HLL Riemann solver, at the heart of the constrained transport scheme. Our scheme shows a second order convergence in space and the whistler waves are propagated at the correct frequencies. However, the truncation errors leads to a strong numerical dissipation of short whistler waves, as the fourth power of the wave number, and the AMR method seems inefficient to reduce this dissipation. During the collapse of a dense core, the Hall effect generates the rotation of the fluid, and thus plays a significant role in the regulation of the angular momentum. While the gas in the midplane is set into rotation (in a direction that depends on the sign of η H and the direction of the magnetic field), counter-rotating envelopes develop on each side of the mid-plane by conservation of the angular momentum.
Objective: The genetic effects of an individual on the phenotypes of its social partners, such as its pen mates, are known as social genetic effects. This study aims to identify the candidate genes for social (pen-mates') average daily gain (ADG) in pigs by using the genome-wide association approach. Methods: Social ADG (sADG) was the average ADG of unrelated pen-mates (strangers). We used the phenotype data (16,802 records) after correcting for batch (week), sex, pen, number of strangers (1 to 7 pigs) in the pen, full-sib rate (0% to 80%) within pen, and age at the end of the test. A total of 1,041 pigs from Landrace breeds were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which comprised 61,565 single nucleotide polymorphism (SNP) markers. After quality control, 909 individuals and 39,837 markers remained for sADG in genome-wide association study.
Animals and phenotypes The phenotypic dataset on ADG of animals was obtained through performance tests of Landrace pigs (n = 20,130) between 2005 and 2015 in South Korea (Landrace Sunjin, Republic of Korea, http://dad. fao. org/). These pigs were fed ad libitum, and water was constantly accessible through nipple drinkers. Performance evaluation for individual ADG (iADG) was initiated when the pigs were 29. 9±3. 5 kg in live weight and ended when the animals reached 89. 9±8. 2 kg. Social ADG (sADG) was the average ADG of unrelated pen-mates (strangers). The sADG dataset consisted of 16,802 records, excluding the pens with only full-sib mates. We used the phenotype data after correcting for batch (week), sex, pen, number of strangers (1 to 7 pigs) in the pen, full-sib rate (0% to 80%) within pen, and age at the end of the test.
Genotyping and quality control Genomic DNA of Landrace pigs was extracted from blood samples using DNA isolation kit (Promega, wizard genomic DNA purification kit, Madison, WI, USA) according to manufacturer's instruction. A total of 1,041 pigs from Landrace breeds were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which comprised 61,565 single nucleotide polymorphism (SNP) markers. Quality control was carried out using PLINK software. SNP markers were removed if they had genotype missing rate >0. 1 (GENO), minor allele frequencies (MAF) <0. 05, and Hardy-Weinberg Equilibrium <0. 0001. We selected individuals that had call rate >0. 90 (MIND). Finally, 39,837 markers and 1,038 subjects (909 subjects for sADG) passed the quality control and were used in the following analysis.
Genome-wide association study (GCTA implementation of MLMi and MLMe) The genome-wide association study (GWAS) and heritability estimate were conducted using a mixed linear model (MLM) based association analysis by estimating the genetic relationship matrix (GRM) implemented in GCTA software. We implemented the MLM based association analysis by including (MLMi) and excluding (MLMe) the candidate SNPs  [10]. The phenotype y for MLMi using MLMA option is modeled as: y = Kc+g+e where c is a vector of fixed covariates (including the affine term) with corresponding coefficient matrix K; g is a vector of genetic effects with The genome-wide association study (GWAS) and heritability estimate were conducte 99 model (MLM) based association analysis by estimating the genetic relationship matrix 100 GCTA software.
102 The phenotype y for MLMi using MLMA option is modeled as: ). We can therefore estimate σ g 2 by the restricted maximum likelih 110 software. This is the same GRM used in previous work on principal components anal 111 MLMe using the MLMA-LOCO option is identical to that of MLMi, except that marke 112 are evaluated using a GRM constructed from the remaining autosomes, by pre-computin.
Genome-wide association study (GCTA implementation of MLMi and MLMe) The genome-wide association study (GWAS) and heritability estimate were conducted using a mixed linear model (MLM) based association analysis by estimating the genetic relationship matrix (GRM) implemented in GCTA software. We implemented the MLM based association analysis by including (MLMi) and excluding (MLMe) the candidate SNPs  [10]. The phenotype y for MLMi using MLMA option is modeled as: y = Kc+g+e where c is a vector of fixed covariates (including the affine term) with corresponding coefficient matrix K; g is a vector of genetic effects with g ~ N(0, Aσ g 2 ); A is the GRM defined by A jk = 1 𝑀𝑀 ∑ (x ij -2p i )(x ik -2p i) 2p i (1-p i ) 𝑀𝑀 i=1 , where xij = 0, 1, or 2 and M is the total number of autosomal markers; and e is a vector of non-genetic effects with e ~ N(0,Iσ e 2 ).
We can therefore estimate σ g 2 by the restricted maximum likelihood approach in GCTA software. This is the same GRM used in previous work on principal components analysis. Implementation of MLMe using the MLMA-LOCO option is identical to that of MLMi, except that markers on a given autosome are evaluated using a GRM constructed from the remaining autosomes, by pre-computing and storing the GRM constructed from all autosomes. The MLMe analysis is computationally less efficient but more powerful than the MLMi  [10]. The genomic inflation factor was calculated using the GenABEL package. A Manhattan plot, , where xij = 0, 1, or 2 and M is the total number of autosomal markers; and e is a vector of non-genetic effects with e ~ MLMi  [10]. The genomic inflation factor was calcu.
102 The phenotype y for MLMi using MLMA option is modeled as: where c is a vector of fixed covariates (including the affine term) with corresponding coefficient matrix K; g 107 is a vector of genetic effects with g ~ N(0, Aσ g 2 ); A is the GRM defined by A jk = 1 𝑀𝑀 ∑ (x ij -2p i )(x ik -2p i) 2p i (1-p i ) 𝑀𝑀 i=1 , 108 where xij = 0, 1, or 2 and M is the total number of autosomal markers; and e is a vector of non-genetic effects 109 with e ~ N(0,Iσ e 2 ). We can therefore estimate σ g 2 by the restricted maximum likelihood approach in GCTA 110 software. This is the same GRM used in previous work on principal components analysis. Implementation of 111 MLMe using the MLMA-LOCO option is identical to that of MLMi, except that markers on a given autosome 112 are evaluated using a GRM constructed from the remaining autosomes, by pre-computing and storing the GRM.
115 by the restricted maximum likelihood approach in GCTA software. This is the same GRM used in previous work on principal components analysis. Implementation of MLMe using the MLMA-LOCO option is identical to that of MLMi, except that markers on a given autosome are evaluated using a GRM constructed from the remaining autosomes, by pre-computing and storing the GRM constructed from all autosomes. The MLMe analysis is computationally less efficient but more powerful than the MLMi  [10]. The genomic inflation factor was calculated using the GenABEL package. A Manhattan plot, quantile-quantile plot (Q-Q plot), and a false discovery rate (FDR) implemented in the R package 'qqman' were computed. FDR ≤0. 20 was defined as significant associations. Linkage disequilibrium (LD) analyses were performed for significant SNPs using HAPLOVIEW 4.
RESULTS AND DISCUSSION The final dataset contained genotypes from a total of 1,038 pigs (909 pigs for sADG) for GWAS. The descriptive statistics of the observed and adjusted phenotype values are displayed in Table  1. The heritability was 0. 43 and 0. 14 in iADG and sADG, respectively (Table  2 ). This result indicates that, in addition to being influenced by the environment, iADG and sADG are affected by an additive genetic component, a finding justifying the execution of GWAS. A Q-Q plot comparing the distribution of observed chisquare statistics with the distribution of those expected under the null hypothesis is shown in Figure  1. For the MLMi method, the genomic inflation factors for iADG and sADG are 1. 02 and 1. 00, respectively. However, MLMe resulted in a high genomic inflation factor (1.
The GALEX FUV flux were obtained by extracting the GALEX FUV magnitudes from the public COSMOS catalog, and converting these to fluxes. The Galactic foreground extinction is adopted from the NASA Extragalactic Database (NED) which is based on the  Schlafly & Finkbeiner (2011)  recalibration of the extinction maps by  Schlegel, Finkbeiner & Davis (1998). Additionally, the coordinates listed in Table  1  are based on the COSMOS HST ACS I band survey  (Sanders et al. 2007 ). The luminosity distance values, D, are obtained with the cosmological calculator (NED, Wright 2006) using the parameters from Planck Collaboration XIII (2015): H0 = 67. 8 km s - 1  Mpc -1 and Ωm = 0. 308 for a flat universe where Ωvac = 1 -Ωm. At a redshift of 0. 25 ≤ z ≤ 0. 32, the star-forming regions of these galaxies are enclosed by the 2.
HST/COS OBSERVATIONS AND DATA REDUCTION The selected galaxies were observed with COS onboard HST  (Green et al. 2012)   Before proceeding with the calibration and analysis we inspect the short-exposure images taken before the science observations and confirm that the galaxies are indeed on the detector. We present a brief discussion of these images in Appendix A. We retrieve the individual datasets from MAST and calibrate the exposures using a modified version of the official CalCOS pipeline introduced in  Leitherer et al. (2016). The standard CalCOS v3. 2. 1 software  (Fox et al. 2015)  performs a 1D spectral background subtraction, first computing the number of counts in predefined regions external to the science extraction box, scaling these counts by the ratio of pixel heights of the science/background regions, and finally subtracting these scaled counts from the science spectrum at each wavelength.
21d to improve the handling of negative values obtained after the 2D-superdark subtraction. More details on the exact changes made to Cal-COS v2. 21d are described in the Appendix B. In addition to the optimized background correction, we also perform a dedicated analysis of the COS pulse-height amplitude (PHA 1 ) similar to that described in  Leitherer et al. (2016). The modifications to the PHA filtering do not introduce additional noise. And finally, to further increase the S/N we decrease the size of the spectral extraction box from the standard 57pixel size to a 20-pixel size as suggested by the COS Team 2. The calibrated x1d files are taken and further analyzed using the IDL software developed by the COS Guaranteed Time Observer (GTO) Team  (Danforth et al. 2010). This software weight combines the different FP-POS exposures by interpolating onto a common wavelength vector accounting   for the non-Poissonian noise as described by  Keeney et al.
Optical Range The zCOSMOS survey  (Lilly et al. 2009 ) observed targets in the COSMOS field using the VIMOS spectrograph on the VLT at ESO's Cerro Paranal Observatory, Chile. In the zCOSMOS Data Release DR3 we find optical spectroscopic observations for 6 out of 7 targets studied in this work. We correct the observations for foreground reddening and redshift as described in Section 5. We derive the intrinsic color excess, E(B -V )OPT, from the Balmer decrements. Similar to the work of  Domínguez et al. (2013) , in our analysis we assume a value of (Hα/Hβ)int = 2. 86 for Case B recombination corresponding to a temperature T = 10 4 K and electron density ne = 10 2 cm -3 (Osterbrock 1989). The color excess is then obtained using the standard relation, E(B -V )OPT = 1. 97 log 10 (Hα/Hβ) obs 2.
ACKNOWLEDGEMENTS The work here was based on observations from Program ID 13313 made with the  NASA/ESA Hubble Space Telescope , obtained from the data archive at the  Space Telescope Science Institute (STScI). Based on zCOSMOS observations carried out using the  Very Large Telescope  at the  ESO Paranal Observatory  under Programme ID: LP175. A-0839. Support for this work has been provided by  NASA  through grant No. GO-13313  from the  Space Telescope Science Institute , which is operated by  AURA, Inc. , under  NASA  contract  NAS5-26555. MB was supported by the  FONDECYT  regular project  1170618  and the  MINEDUC-UA  projects codes  ANT 1655  and  ANT 1656.
No participant had to repeat a jump more than twice. The countermovement depth was identified by displacement-time data. The displacement-time data was calculated using the impulse method  [12]. Net impulse was obtained by integrating the net vertical force-with respect to time-from 2 s prior to the first movement of the participant  [13] , using the trapezoidal method  [14]. Subsequently, the center of mass vertical velocity was calculated by dividing the net impulse by the participant's body mass. The vertical center of mass displacement was derived by integrating the vertical center of mass velocity. To exclude the influence of weight and height on scores, all variables quantifying force were normalized to body weight (BW) and all variables quantifying displacement were normalized to leg height (i.
The following criteria were established to ensure each jump was executed successfully. A countermovement jump from a higher countermovement depth was successful, when the countermovement was at least 5 cm larger than the self-selected jump. A countermovement jump from a lower countermovement depth was successful, when the countermovement was at least 5 cm shorter than the self-selected jump. When a countermovement jump did not comply with the established criteria, participants repeated the jump. No participant had to repeat a jump more than twice. The countermovement depth was identified by displacement-time data. The displacementtime data was calculated using the impulse method  [12]. Net impulse was obtained by integrating the net vertical force-with respect to time-from 2 s prior to the first movement of the participant  [13] , using the trapezoidal method  [14].
Statistical Analysis Statistical analyses were conducted, using SPSS 18. 0 software (IBM, Armonk, NY, USA). Means and standard deviations of each participant were computed for all the extracted variables (net vertical impulse, force applied, velocity, and center of mass displacement variables). Normality of the datasets was verified, using the Shapiro-Wilk test. If the data were normally distributed, a general linear model ANOVA with a repeated measures test was used. When a significant F-value was found, post hoc pair wise comparisons of means were examined, using the least significant difference post hoc test. Significance level was set at p < 0. 05. Since each participant performed three jumps from each.
Statistical Analysis Statistical analyses were conducted, using SPSS 18. 0 software (IBM, Armonk, NY, USA). Means and standard deviations of each participant were computed for all the extracted variables (net vertical impulse, force applied, velocity, and center of mass displacement variables). Normality of the data-sets was verified, using the Shapiro-Wilk test. If the data were normally distributed, a general linear model ANOVA with a repeated measures test was used. When a significant F-value was found, post hoc pair wise comparisons of means were examined, using the least significant difference post hoc test. Significance level was set at p < 0. 05. Since each participant performed three jumps from each countermovement depth (preferred, higher, and lower), the trial factor was included as a separate factor in the ANOVA.
Background: There is continued debate about the role of women and communities in influencing rising rates of caesarean section (CS). In settings where CS rates exceed recommended levels, mothers and babies are exposed to potential harms that may outweigh the potential benefits. There is therefore a need to understand how educational interventions targeted at women and communities to reduce unnecessary CS are perceived and used. This qualitative evidence synthesis aimed to explore what women and communities say about the barriers and facilitators to intervention effectiveness for these important groups. Method: Seven electronic databases were searched using predefined search terms. Studies reporting qualitative data pertaining to interventions, published between 1985 and March 2017, with no language restriction were sought.
Other sources As retrieval of qualitative research using databases alone is limited, the reference lists of all the included studies and existing quantitative reviews were back and citation chained  [21] [22] [23]. In addition, key articles cited by multiple authors (citation pearls) were checked on Google Scholar. The authors of published protocols were also contacted  [31, 32]. See Fig. 1  (PRISMA Flow Diagram) for an overview of the study identification, screening eligibility and inclusion process.
Types of intervention For the purposes of this synthesis an intervention was 'anything considered by study authors as an intervention additional to usual care undertaken with the aim of reducing unnecessary caesarean section'  [19]. This definition was purposively broad to encompass known and unknown interventions  [22]. Interventions targeted at women with a breech presentation (Robson Groups 6 and 7), multiple pregnancy (Robson Group 8), transverse or oblique lie (Robson Group 9) or preterm birth (Robson Group 10) were excluded. Identified electronic records were collated into one database and duplicates removed. Two synthesis authors (CK, SD) independently assessed each abstract to determine eligibility for inclusion against a priori inclusion and exclusion criteria. The full texts of all papers identified as potentially relevant were retrieved and also independently assessed by CK and SD.
Three of the twelve studies were qualitative  [37, 39, 41]  and sibling studies of trials  [49, 50]  included in the Cochrane Reviews of non-clinical interventions for reducing unnecessary CS  [9, 22]. Quality assessment of studies ranged from A to C. Table  1  lists the quality assessment grades assigned. Seven studies were assessed as having no, few, or only some flaws, with credibility, transferability, dependability, and confirmability unlikely to have been affected (A-B). For each of the 12 studies, the intervention is also described in Table  1. Table  2  is the CERQual  [35]  summary of qualitative findings table. The development from coding of initial concepts to emergent themes into final themes  [18]  (with supporting data quotes), and the CERQual  [35]  summary of evidence profile are provided as (Additional file 5: Table  S1 ; Additional file 6: Table  S2 ).