dataesr/scientific-paragraphs-categorization huggingface dataset:
However the long-term impact of EBM on the risk of POR (especially surgical) remained unknown. Our results indicated for the first time that an EBM influenced the risk of reoperation for CD, leading to a delayed time before surgical POR. In addition, we confirmed that the EBM group experienced less clinical POR over time than the non-EBM group, in a long period of follow-up. As our cohort overlapped a very long period with different available medications overtime, we did not show any impact of the postoperative treatment, especially biologics, in this population. The main limits of this study were the retrospective and monocentric design. In addition, the time of endoscopy (median = 9. 5 mo after surgery) and the step-up strategy were not standardized for all the patients. However, we observed for the first time the positive impact of an EBM on the risk of reoperation in CD, in a cohort monitored during almost 30 years (1986-2015) and based on a Pathology Department electronic database (consecutive patients).
We test the statistical isotropy and Gaussianity of the cosmic microwave background (CMB) anisotropies using observations made by the Planck satellite. Our results are based mainly on the full Planck mission for temperature, but also include some polarization measurements. In particular, we consider the CMB anisotropy maps derived from the multi-frequency Planck data by several component-separation methods. For the temperature anisotropies, we find excellent agreement between results based on these sky maps over both a very large fraction of the sky and a broad range of angular scales, establishing that potential foreground residuals do not affect our studies. Tests of skewness, kurtosis, multi-normality, N -point functions, and Minkowski functionals indicate consistency with Gaussianity, while a power deficit at large angular scales is manifested in several ways, for example low map variance.
The results of a peak statistics analysis are consistent with the expectations of a Gaussian random field. The "Cold Spot" is detected with several methods, including map kurtosis, peak statistics, and mean temperature profile. We thoroughly probe the large-scale dipolar power asymmetry, detecting it with several independent tests, and address the subject of a posteriori correction. Tests of directionality suggest the presence of angular clustering from large to small scales, but at a significance that is dependent on the details of the approach. We perform the first examination of polarization data, finding the morphology of stacked peaks to be consistent with the expectations of statistically isotropic simulations. Where they overlap, these results are consistent with the Planck 2013 analysis based on the nominal mission data and provide our most thorough view of the statistics of the CMB fluctuations to date.
In particular, the seeds that gave rise to the present large-scale matter distribution via gravitational instability originated as quantum fluctuations of the inflaton about its vacuum state. These fluctuations in the inflaton produce energy density perturbations that are distributed as a statistically homogeneous and isotropic Gaussian random field. Linear theory relates those perturbations to the temperature and polarization anisotropies of the CMB, implying a distribution for the anisotropies very close to that of a statistically isotropic Gaussian random field. The aim of this paper is to use the full mission Planck data to test the Gaussianity and isotropy of the CMB as measured in both intensity and, in a more limited capacity, polarization. Testing these fundamental properties is crucial for the validation of the standard cosmological scenario, and has profound implications for our understanding of the physical nature of the Universe and the initial conditions of structure formation.
Fig. 13. Fraction of the Gaussian random field realizations in which the coldest peak is as cold as or colder than that observed, as a function of SMHW filter scale for Commander (red), NILC (orange), SEVEM (green), and SMICA (blue). Inspired by the analysis of the WMAP first-year data in  Larson & Wandelt (2004)  which found fewer extreme peaks than expected, we additionally evaluate whether the distributions of maxima and minima are separately consistent with simulations. The mean of all maxima, and the negative of the mean of all minima, are calculated for the filtered map, and the observed values are compared to the simulated distributions in Fig. 12. The observed minima/maxima means are found to be in good agreement with the fiducial values. The probability that the coldest peak seen on the sky is consistent with the adopted fiducial cosmology is evaluated as a function of both filter shape and size by counting the number of simulations with x coldest < x (sky) coldest.
The asymmetry directions found here are consistent with those determined by other analyses in this paper. In the upper panel of Fig. 28 , we show the localvariance dipole amplitudes for the 8 • discs as a function of the central multipole of the high-pass filter, 0. In the lower panel of the same figure we show, as an example, the mean-subtracted and inverse-variance-weighted localvariance map using 8 • discs for the Commander componentseparation method. The pixels of the map are given in terms Article number, page 31 of 66 A&A proofs: manuscript no. planck_2015_iands_final of the lower-and upper-tail probabilities of the values from the data compared to the values from the simulations. The maps for NILC, SEVEM, and SMICA are very similar. The numerical values of the local-variance dipole amplitudes and directions for the Commander method are given in Table  21 ; the values for NILC, SEVEM, and SMICA methods are similar.
Dipole modulation: pixel-based likelihood In PCIS13 we presented an analysis of the apparent anisotropic distribution of large-scale power in the Planck 2013 temperature data within the parametric framework defined by  Gordon (2007)  and  Hoftuft et al. (2009) , who introduced an explicit dipole modulation field to model po- red), NILC (orange), SEVEM (green), and SMICA (blue). The grey regions, from dark to light, correspond, respectively, to 1 σ, 2 σ, and 3 σ percentiles from the 1000 FFP8 simulations processed by the Commander method. Lower panel: mean-subtracted and inverse-variance-weighted local-variance map for the 8 • discs and for the Commander component-separation method; each pixel is given in terms of the lower-and upper-tail probability of the measured value on that pixel compared to the values from the simulations.
The pixels in grey correspond to the centres of the 8 • discs on which the number of unmasked pixels in the full resolution map is lower than our threshold. The black curve superposed on the map indicates the boundary of the opposing hemispheres along the asymmetry axis. It is clear that the largest fraction of >95 % outliers (red pixels) lie on the positive amplitude hemisphere of the local variance dipole, while the <5 % outliers (blue pixels) are on the opposite hemisphere. The corresponding maps for NILC, SEVEM, and SMICA are very similar to the one shown here. tential hemispherical power asymmetry. The following is a direct update of that analysis using the Planck 2015 CMB data at N side = 32, retaining the the 2013 common mask to explicitly test for consistency with the earlier study.
2. With the distribution of ηs given by the simulations we then compare this to the data. That is, we calculate the probability that one would find oneself in a Hubble patch with a modulation amplitude up to ∈ [10, max ] that is as significant as (or more significant than) the modulation in the real data. The vertical line corresponds to max = 132 which was used as the search limit in  Bennett et al. (2011). The probability grows approximately logarithmically with max. This means that the adopted probability to exceed is fortunately not very sensitive to max, and for any reasonable choice is above 10 %. If max = 132 (as chosen by  Bennett et al. 2011) , the probability of achieving a modulation as large as the Planck data in this range is higher than 10 % (see Fig. 31 ). This is in agreement with the findings of the WMAP team (which found 10 % and 13 % in the same -range, using two different masks).
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ;  ERC  and  PRACE (EU). A description of the Planck Collaboration and a list of its members, indicating which technical or scientific activities they have been involved in, can be found at http://www. cosmos. esa. int/web/planck/planck-collaboration. Some of the results in this paper have been derived using the HEALPix package. Noordwijk, The  Netherlands Facoltà di Ingegneria, Università degli Studi e-Campus ,  Via Isimbardi 10 ,  Novedrate (CO) ,  22060 ,  Italy Gran Sasso Science Institute ,  INFN , viale F.
The removal of the specular signal was done using an algorithm of segmentation and filtration implemented in Matlab 8a (MathWorks, Natick, MA, USA)  [39]. To validate this procedure, synthetic profiles with known parameters were used. After that, a ±15% noise was added to these virtual measurement points by randomly varying their initial value. The fit demonstrated, then, its ability to find these settings only in absence of black spots. By applying this procedure of specular elimination (see Figure  2b ), images were ready to be used if they don't have black spots, caused by black points found on the skin of apples. The original color images of the backscattered light captured by the CCD camera were expressed in grayscale light intensity which ranges from black to white corresponding to I = [0,255], respectively.
The relationship between I C and I R is based on the Lambertian cosine law  [40]. Hence, the apparent reflectance I R was calculated by Equation (1): I R = I C cosϕ = I C R √ R 2 -d 2 (1) where R is the radius of the measured position on fruit surface, d is the maximum distance between edge and center of light scattering profile, ϕ is the angle of the measured position on fruit surface, taking into account the offset angle of 15 •. Lambertian cosine law was applied to recalculate the intensities captured by CCD camera for each position on the spherical fruit surface  [36]. The signal processing was performed using the software ImageJ, Labview 2009 and Matlab 8a (MathWorks, Natick, MA, USA), while the statistical tests were carried out using Statgraphics software, version 5. 1 (STSC, Inc. , Rockville, MD, USA).
The coefficient A has been introduced to take into account the internal reflection. Equation (  3 ) is the theoretical basis used to fit the normalized profiles by means of a trust-region method. A nonlinear least squares curve fitting algorithm was implemented on Matlab 8a (MathWorks, Natick, MA, USA) to extract the estimates of µ a and µ' s from the spatially resolved reflectance profiles. Figure  2f  depicts a fitting example based on an optimal sensing range of 2. 8-10 mm.
Backscattering Data Typical backscattering images related to Granny Smith, Golden delicious, and Royal Gala apples with and without skin, respectively, acquired at 633 nm, and 763, 784 and 852 nm, are displayed in Figure  3a-h , respectively. When the Granny Smith is illuminated at the wavelengths of 633 nm, and 852 nm (Figure  3a-d ), the size of backscattering images appears larger for the peeled sample than for the intact apple. The same effect can also be observed for the Golden Delicious illuminated at 763 nm (see Figure  3e, f )). In contrast, for Royal Gala at 784 nm (Figure  3g ,h), the image acquired with an unpeeled sample is wider. It is noticeable that, for all varieties, the widest images were obtained at 633 nm. To better analyze the typical backscattering images, different reflectance-scans were carried out over 18 crossing lines centered at the maximum intensity with angle step of 10°.
4  (Peakall and Smouse 2006). P-values were adjusted for multiple comparisons using sequential Bonferroni corrections  (Holm 1979). We used An asterisk marks samples obtained from an earlier study by  Robertson et al. (2007)  Micro-Checker 2. 2. 3 (van Oosterhout et al. 2004 ) to test loci for null alleles, large allele drop-out, and stutter-related miscalling in each population. Hmal10 and Hmal3 were removed from subsequent analyses (see Results) with nine loci remaining in the final dataset. The mean number of alleles per locus (A), the number of private alleles (N p ), as well as observed (H o ) and expected heterozygosity (H e ) were calculated in GenAlEx 6. 4 for different geographic levels, i. e. river catchment, region (geographic area encompassing several neighbouring rivers; see Fig.
3. 2  (Falush et al. 2003; Pritchard et al. 2000). The complete dataset was run for 500,000 chains following a 250,000 burn-in under the admixture and correlated allele frequency models for K = 1-10 and 10 iterations for each K. All other parameters were left at default settings. Change in alpha level was monitored to assess convergence of the runs. We then analysed the data for NI and SI separately using the same settings. Additionally, we analysed the NI and SI datasets including prior location information (the catchment each individual was sampled in) using LOCPRIOR  (Hubisz et al. 2009) , which aids the clustering process if the structure signal is weak. The best K value was evaluated by assessing the change in mean log likelihood of the data lnP(X|K) and Evanno's DK method  (Evanno et al.
2005)  implemented in Structure Harvester 0. 6. 92 (Earl and vonHoldt 2012). Iterated runs were averaged with CLUMPP 1. 1. 2. (Jakobsson and Rosenberg 2007 ) and visualised with Distruct 1. 1  (Rosenberg 2004). Using microsatellite loci, we also performed a discriminant analysis of principal components (DAPC;  Jombart et al. 2010 ; a clustering approach which, unlike STRUC-TURE, does not rely on explicit model assumptions) with population prior information (catchment identity) using the ''adegenet 1. 3-1'' package  (Jombart 2008)  in R (R Core Team 2015). Analysis of molecular variance (AMOVA;  Excoffier et al. 1992 ) was performed for microsatellite loci in Arlequin with 1000 permutations to test for hierarchical differentiation between blue duck regional groupings (NI vs. SI, within island groupings; see Table  3 ).
For fast and slow mutation rates, a total of 174,986 and 169,130 genealogies were recorded respectively, and 150,000 genealogies were randomly sampled from the three replicates for each L-mode run. We compared 24 nested models (provided in the IMa2 software package) to the full model using likelihood ratio tests. We calculated the Akaike information criterion and model support to assess best model fit  (Burnham and Anderson 2002). We also estimated timing of divergence between the NI and SI mitochondrial lineages using the coalescent-based approach implemented in BEAST 1. 8. 2  (Drummond et al. 2012 ) using a strict molecular clock with rate prior. Mallard duck (GenBank accession: AY112938) was used as an outgroup. The HKY?I model of sequence evolution was chosen, as determined by jModeltest 2.
Genetic diversity For mitochondrial control region (mtDNA CR), we found 12 haplotypes, defined by 29 variable sites (Table  S2 , Supplementary material 1). Despite extra sampling at seven new sites (Fig. 1 ), as well as more extensive sampling in previously sampled areas, we only found the haplotypes previously identified by  Robertson et al. (2007). MtDNA diversity in the NI (h = 0. 576 and p = 0. 0020) was lower than in the SI (h = 0. 763 and p = 0. 0024). The same trend was observed for microsatellite data (Table  1 ). Despite differences in sample sizes, the overall genetic diversity within regions was generally low and similar across the species' range (Table  1 ).
Intra-island comparisons resulted in high and mostly significant pairwise differentiation. This gradual increase in genetic differentiation across increasingly larger geographical areas observed within islands is suggestive of isolation by distance. Mantel tests indeed revealed significant correlation between genetic (F ST /(1-F ST )) and log transformed geographic distances (Fig. S1 ) within the NI (r = 0. 67, p = 0. 006) and SI (r = 0. 41, p = 0. 035). STRUCTURE analysis found most support for two distinct clusters, within the complete dataset, corresponding to NI and SI populations (Fig. 3 ; Fig. S2A , Supplementary material 1), with some individuals from Bay of Plenty and southern SI showing high assignment probability to the other cluster. Within the NI, no further subdivision was supported (Fig.
S2B , Supplementary material 1; analysis with LOCPRIOR did not converge). Within the SI a northern SI and southern SI cluster could be distinguished with some suggestion of a clinal change in allele frequencies from North to South (Fig. S2B , Supplementary material 1). Using a LOCPRIOR, the northern SI and southern SI clusters could be more clearly distinguished, with Central SI forming a mixed assemblage (for K = 2; Fig. 3 ; Fig. S2C , Supplementary material 1). For K = 3, Central SI formed a separate cluster (Fig. S2C , Supplementary material 1). Change in mean log likelihood of the data lnP(X|K) and the Evanno's DK method supported K = 2 as the most likely number of SI clusters. AMOVA analysis based on microsatellites also revealed significant hierarchical population structure corresponding to the same clusters identified with STRUCTURE for the complete dataset and SI (Table  3 ).
Acknowledgements We thank  New Zealand Department of Conservation  staff and the blue duck recovery group for collecting the blood and feather samples used in this analysis, particularly  Andrew Glaser  and  Dr Murray Williams. We also thank anonymous reviewers for helpful comments on an earlier version of the manuscript. Samples were collected and held under permits ECHB-23214-RES and TT-23326- FAU. This work was funded by a subcontract to NJG through the  Sustaining and Restoring Biodiversity OBI FRST  contract  C09X0503  to Landcare Research together with funding from the  University of Otago. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creative commons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
This paper presents an algorithm that follows the sample-free approach to synthesise a population for agent based modelling purposes. While most existing algorithms rely on a sample dataset, the fact that this algorithm does not rely on one makes it a novel contribution. It has potentially widespread application for situations in which such survey data is not available. In contrast to existing sample-free algorithms, the population synthesis presented in this paper applies the heuristics to part of the allocation of synthetic individuals into synthetic households. As a result the iterative process which does this and which is normally the most computationally demanding and time consuming process, is required only for a subset of synthetic individuals. This means that the population synthesiser in this work is computationally e icient enough for practical application to build a large synthetic population (many millions) for many thousands target areas at the smallest possible geographical level.
This capability ensures that the geographical heterogeneity of the resulting synthetic population is preserved. The paper presents the application of the new method to synthesise the population for New South Wales in Australia in. The resulting total synthetic population has approximately million people living in over. million households residing in private dwellings across over , census collection districts (CCDs). Analyses show evidence that the synthetic population matches very well with the census data across seven demographic attributes that characterise the population at both household level and individual level. A Java-based open source implementation of the population synthesiser as well as sample input data is freely available at https://github. com/smart-facility/SPGen.
The basic principle behind the majority of population synthesisers found in the literature is to integrate an aggregated dataset with a disaggregated dataset. The aggregated dataset is a set of joint distributions (or crosstabulations) that describes the demographics of a relative small geographical area (the target area), the synthetic population of which must be generated. Such a dataset is normally available from the census data, such as the Summary Files in the US, the Small Area Statistics file in the UK, and the Community Profiles in Australia. The disaggregated data is normally survey data of sample households with demographic attributes of the household and those of its residents. Examples of such survey data is the Public-Use Microdata Samples in the US, the Sample of Anonymised Records in the UK, and the Confidentialised Unit Record File in Australia.
Once the fully joint distributions between all the control variables are constructed, records of individuals in a household are iteratively drawn from the survey data so that joint distributions of attributes of the resulting synthetic population match as closely as possible the distributions obtained from the IPF process. The household, the residents therein, and their attributes (both at household level and individual level) are stored as part of the resulting synthetic population. One critical assumption in the aforementioned population synthesisers is the availability of a disaggregated dataset from which household records are drawn to form the resulting population in the target area. This assumption is not always accurate either because such a survey does not exist or, more o en, it is inaccessible.
Description of the aggregated data. The aggregated data used in this study is from the Basic Community dataset in the Community Profiles data published by the Australian Bureau of Statistics (ABS) for the year. This dataset is freely available and contains information related to people, families and dwellings that characterise a given geographical area. The data is available at various geographical levels, ranging from CCD to State or Territory, e. g. New South Wales (NSW). CCD is the smallest geographical unit. The dataset was collected and processed in and was chosen in this study as a unit target area for population reconstruction so that the resulting synthetic population over the whole state of NSW best preserves the geographical heterogeneity of the real population characteristics. To give a perspective of scale, an average CCD in has around dwellings.
Tables from the Basic Community dataset used for the population synthesis in this work are briefly described below. • Census table "Relationship in Household by Age by Sex". This table provides information on the number of males and females in each relationship category in each age group. There are age groups which are " -years", " -years", " -years", " -years", " -years", " -years", " -years", " -years", and " years and over". A summary of relationship categories in census data and their corresponding relationship categories used in the population synthesis is in Table. Relationship category 'Visitor' is not considered in the population synthesis because of the inconsistent inclusion of this category across the tables. The counts of males and females in categories 'Husband (wife) in a registered marriage' and 'Partner in de facto marriage' include same sex couples.