dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Miguel A. Blázquez ,  David Alabadí ,  Paco Madueño  and  Cristina Ferrándiz  for discussion, the  GIPSI team (CEA Saclay ) especially  A. Martel  for technical support and for hosting web site and  A. Mathelier  for its critical reading of the manuscript. This work was supported by the  Charmful program  ( ANR11-BSV2-005-01 ) from the  Agence Nationale de la Recherche program  to  FP and an ATIP+ program  from the  Centre National de la Recherche Scientifique  to FP and EGM.
Usage To use PSE on a given model, the following information must be specified: • which model parameters constitute the genotypic space (some model parameters may be excluded from the search space by fixing their values or initialising them randomly), • a set of measures describing the simulation output, constituting the phenotypic space • bounds on the parameters, • discretisation step size on the phenotypic dimensions, • μ, the size of the initial population, • the probability of reevaluating a selected individual without performing any crossover and mutation, • if the parallelised steady state model is used, λ, the size of the evaluation pool, • if the island steady state model is used, the number of islands run in parallel and the population size for each island. Software and hardware infrastructure PSE was developed in Scala within the MGO library, an open source library for evolutionary algorithms.
The source code is available as a git repository (https://github. com/openmole/mgo). The models presented below were also developed in Scala within the Simpuzzle project, and also available as a git repository (https://github. com/ISCPIF/simpuzzle). The simulation experiments described below were deployed on the European Grid Infrastructure through the biomed virtual organisation. Parallel deployment of the simulations on the grid was performed with OpenMOLE (http:// www. openmole. org/) [31] , an open source software developed at ISC-PIF dedicated to the execution of simulation experiments on distributed computing environment (multicore machines, clusters, grids). MGO is tightly integrated to OpenMOLE, which allowed to easily use PSE within OpenMOLE to drive the experiments. The script files, models and instructions necessary to reproduce the experiments presented below are available in the github repository https://github.
MARIUS was developed to reproduce the particular structure of the Soviet system of cities with a parsimonious set of generic rules. A study showed its ability to reproduce the cities historical hierarchical structure evolution from 1959 to 1989  [27] , i. e. hierarchisation of the cities in a context of population growth. The corresponding model parameters calibrated values are shown in Table  4. In this paper, we use PSE to find out which possible degrees of hierarchisation and growth the model can produce. Four main types of outcomes can be expected, the first three of them were already confirmed by historical data as mentioned above: • hierarchisation in a growth context (e. g. United-States in the XXth century, Russia between 1959 and 1989), • hierarchisation in a shrinkage context (e.
MARIUS The full ODD description of the model is available in the S1 Appendix and on github (https:// github. com/ISCPIF/marius-method/blob/master/ODD-MARIUS. pdf). The following is a brief overview. The MARIUS model includes basic economic rules of interurban interactions. The system is made of collective agents: the 1145 agglomerations of more than 10 000 inhabitants in 1959, localised via actual latitudes and longitudes in former Soviet Union. It is initialised with empirical populations and a fictive wealth quantity estimated as a superlinear function of population (i. e. with the parameter populationToWealthExponent being greater or equal to 1). At each step: 1. Each city updates its economic variables according to its population: supply and demand, modelled as superlinear scaling quantities.
In the second step, we apply a model selection approach to the obtained hierarchy in order to define LD blocks. Finally, we perform Group Lasso regression on the inferred LD blocks. We investigate the efficiency of this approach compared to state-of-the art regression methods: haplotype association tests, SMA, and Lasso and Elastic-Net regressions. Results: Our results on simulated data show that the proposed method performs better than state-of-the-art approaches as soon as the number of causal SNPs within an LD block exceeds 2. Our results on semi-simulated data and a previously published HIV data set illustrate the relevance of the proposed method and its robustness to a real LD structure. The method is implemented in the R package BALD (Blockwise Approach using Linkage Disequilibrium), available from http://www.
We decided to use the definition in Equation 1 as we noticed that it led to better estimation of the number of groups in our simulation studies, which were performed under a variety of parameters and on several data sets. For the reference distribution, we followed the initial strategy proposed in the original Gap statistic paper  [13]  and simulated each reference feature according to a uniform distribution over the discrete set {0, 1, 2}. We chose to simulate B = 100 reference samples since we empirically observed that it was sufficient to provide a stable estimation of the number of groups.
Implementation The proposed three-step method has been implemented in an R package called BALD for "Blockwise Approach using Linkage Disequilibrium". This package is available from http://www. math-evry. cnrs. fr/publications/logiciels. We have used the packages grplasso for Group Lasso regression and quadrupen for Lasso and Elastic-Net regression  [20] , both of which are available from CRAN at http://cran. r-project. org. A naive implementation of the constrained clustering described in Section 'Inference of LD blocks from genotypes' would consist in (i) calculating the p(p -1)/2 LD measures for each pair of SNP and (ii) performing constrained hierarchical clustering on the obtained similarity matrix. As p is typically of the order of 10 4 to 10 5 for a single chromosome in a standard GWAS, such an implementation with space complexity of O(p 2 ) is not appropriate.
Indeed, for a single chromosome of length p = 10 5 , this algorithm would require storing of the order of 10 10 numeric values of LD before applying the clustering algorithm. To overcome this difficulty, our implementation takes as input the n × p matrix of genotypes X, and calculates the LD measures incrementally as they are required by the clustering. The LD measures are calculated directly from genotypes using the Bioconductor R package snpStats  [21, 22] , which handles missing values. For illustration, it takes 4. 5 hours (on a standard 2. 2 Ghzsingle CPU) to analyze a whole genome of 500k simulated SNPs (for Affymetrix 500k arrays) genotyped on 100 individuals. Note that this step is designed to be applied chromosome by chromosome since it uses the LD measure as a similarity. The second step of the proposed algorithm consists in model selection via the Gap statistic.
Various approaches have been proposed to select causal SNPs from GWAS data. The method described in Section 'A three-step method for GWAS' is compared to two groups of methods: • three methods that do not explicitly take a block-structure information into account: SMA, and two penalized regression approaches: Lasso  [6]  and Elastic-Net  [7]. • two methods that do explicitly take the block-structure information into account: the haplotype association module of the PLINK genome association analysis tool  [23] , and the Group Lasso applied to the true SNP groups. The latter approach cannot be applied in practice, but is very useful to analyze the contribution of the different steps of the proposed method. We will refer to this approach as the "oracle Group Lasso".
Data set The HIV data set consists of p = 20, 811 SNPs genotyped for n = 605 Caucasian subjects and the plasma HIV-RNA level as phenotype. It corresponds to the phenotype and the genotype data related to the chromosome 6 of the GWA study conducted by  [11]. A small number of SNPs were discarded from the study because they generated undefined values of LD. The filtered data set thus contained 20,756 SNPs. Missing values were imputed using the Bioconductor R package snpStats  [21]. For the proposed approach, this imputation was performed after the constrained clustering described in Sections 'Inference of LD blocks from genotypes' and 'Estimation of the number of groups', as the proposed constrained clustering algorithm handles missing values. The same data set was used to perform the haplotype association approach.
Averaged profiles will be sufficiently precise over large bins (monthly, 5° latitude) since both instruments are dense samplers, but this may not be the case at finer spatio-temporal resolutions. Our assessment of stability furthermore demonstrates the potential of drift correction schemes, especially when HALOE, OSIRIS or SCIAMACHY data are involved (and likely GOMOS and SMR as well). Eckert et al. (2014)  have recently explored this approach, by correcting MIPAS trends for a drift relative to Aura MLS. In practice, however, the drift estimate between two satellite records is not sufficiently well constrained, especially for a short overlap period, which makes it very challenging to obtain robust corrections. Finally, the impact of the auxiliary data should not be forgotten, since profile representation conversions are typically required.
We observed considerable changes in bias (MIPAS, SCIAMACHY and, to a lesser extent, UARS/Aura MLS) and stability (UARS/Aura MLS) due to the auxiliary data provided along with the ozone data sets. The use of a common source of stable auxiliary profiles eliminates additional discrepancies between the contributing records. Our results suggest that ECMWF (operational and ERA-Interim) and MERRA fields impact ozone trends in a consistent way over the entire stratosphere.
, 2014). A combined SAGE-OSIRIS record, on the other hand, produces more positive post-1998 trends in the uppermost stratosphere, by 3-4 % decade -1 at mid northern latitudes  (Bourassa et al. , 2014; Tummon et al. , 2015; Harris et al. , 2015). Two records that combine SAGE and GOMOS data lead to considerably more negative trends than other data sets in the lower stratosphere  (Tummon et al. , 2015; Harris et al. , 2015). Many of the ozone trend differences cannot be explained by statistical uncertainty. Our ground-based assessment of decadal stability suggests that these may be interpreted, at least for the better part, in terms of instrumental drift. Indeed, we noted a +8 % decade -1 drift above 40 km for OSIRIS and a -9 % decade -1 drift for SCIAMACHY 4  around 35 km. Additionally, we found indications of a -5 % decade -1 drift of GOMOS below 20 km.
5% decade -1 depending on altitude. Since GOZCARDS and SWOOSH include HALOE data, the drift is possibly somewhat larger in the middle stratosphere. Producing post-1998 estimates is a more intricate problem, due to the increasing number of contributing instruments, and due to the fact that none of these cover the entire period. We are inclined to a conservative approach, giving figures that should be considered upper limits to the actual drift. The SAGE-GOMOS record will be impacted by negative GOMOS drifts in the lower 1785 stratosphere. The SAGE-OSIRIS trends should be considered more uncertain in the upper stratosphere due to drifting OSIRIS data. Records that use Aura MLS as backbone (GOZCARDS, SWOOSH) should not be more unstable than about 2% decade -1 in the stratosphere. A merged Ozone_cci 1790 data set is likely also prone to larger uncertainty in the upper stratosphere (drifting OSIRIS, SCIAMACHY, SMR) and to some extent in the lower stratosphere as well (GOMOS).
Nevertheless, the altitude at which the quality of UTLS observations starts to degrade rapidly is clearly not only determined by the tropopause. It also depends on the measurement technique and instrument (e. g. UTLS observations of UV-visible star occultations being less sensitive than those of infrared emissions at the limb). There were furthermore no evident signs of seasonal patterns, except for the Arctic SCIAMACHY data which exhibit a 10% increase in bias and spread in boreal winter and a 10% decrease in bias and spread in boreal summer. We found no significant drifts at decadal time scales, most records are stable within about ±5% decade -1 in the middle and upper stratosphere, and, for some records, even within ±3% decade -1 (SAGE II, Aura MLS and MIPAS). However, the drift uncertainty should not be neglected, as our analysis is typically not sensitive (at 2σ) to drifts smaller than 2-3% decade -1 in the middle stratosphere and 3-4% decade -1 at lower and higher altitudes.
The pressure and/or temperature data that accompany the satellite ozone data sets are generally well suited for the conversion between ozone units or vertical coordinates. Bias, spread and drift in non-native ozone profile representations differ, respectively, not more than about 1%, 1% and 1% decade -1 , and somewhat more in the uppermost stratosphere. There are of course exceptions to these general observations. We noted more pronounced biases (~10 %) over much of the stratosphere for POAM II and SCIAMACHY, the latter also exhibits a clear hemispheric asymmetry. Two records show markedly poorer single-profile precision: SMR (entire atmosphere) and SCIAMACHY (upper stratosphere and Arctic). And three records drift significantly: HALOE in the middle stratosphere (-5% decade -1 ), and, in the upper stratosphere, SCIAMACHY (-9% decade -1 ) and OSIRIS (+8% decade -1 ).
There are also indications of a -5% decade -1 or more drift in the lower stratosphere for GOMOS, and in the upper stratosphere for SMR. Further confirmation is needed however for the latter two data sets. In the meantime, we advise caution when using GOMOS and SMR measurements at these altitudes. Finally, we observed for a few records a considerable impact of the accompanying auxiliary data (e. g. GPH retrievals) on ozone quality in nonnative profile representations. The ozone bias changes by 3-5% for MIPAS and SCIAMACHY; both MLS records (UARS and Aura) show a dependence of the drift (by 3% decade -1 or more) on vertical coordinate and/or ozone unit, and perhaps of the overall bias as well. We stress that these representation-dependent quality issues are unrelated to the satellite ozone retrievals themselves, and can be avoided by using another, external source of auxiliary information for any necessary conversions.
Overall, the observing system of limb and occultation instruments produces ozone profiles that meet the ~10-15% accuracy requirements by climate users, most certainly over 20-40km, and perhaps also in the lower stratosphere. However, it remains unclear whether the current Level-2 records comply with the 1-3% decade -1 target on decadal stability. The combination of different data sets has received widespread interest in recent years, but also poses several challenges. Our results show that the merging schemes should be sufficiently refined to temper additional artefacts in the Level-3 data sets. Even then, the characteristics of merged records remain mostly defined by those of their contributors  (Tummon et al. , 2015). Multi-instrument comparison studies are therefore crucial to establish observational evidence.
Change (NDACC) and Southern Hemisphere ADditional OZonesondes programme (SHADOZ). The authors acknowledge the meticulous and sustained work of the PIs and staff at ozonesonde and lidar stations to acquire and 1940 maintain long-term ozone data records of high quality. The data records are publicly available via the NDACC Data Host Facility (http://www. ndacc. org), the SHADOZ archive (http://croc. gsfc. nasa. gov/shadoz) and the World Ozone and Ultraviolet Data Centre (http://www. woudc. org). NDACC and SHADOZ are supported by meteorological services and space agencies from many countries, with archives funded by NASA and NOAA. We acknowledge the work by the PIs of the ozonesonde (F. Posny) and stratospheric ozone lidar (T. Portafaix) observations at Réunion Island. The authors also thank the satellite science and processing teams and the contributing space agencies.
Measurements from the SAGE and HALOE missions are provided and maintained through support from NASA's Earth Science Division. The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is a joint contribution of Germany, The Netherlands and Belgium to ESA's environmental satellite Envisat and is funded by the German (DLR) and Dutch (NIVR) space agencies with Belgian contribution via BIRA-IASB. Sweden's Odin satellite carries the atmospheric and astronomical missions OSIRIS and SMR, developed and funded jointly by the space agencies of Sweden, Canada, Finland and France.
Acknowledgments Part of this work was funded by  ESA projects Multi-TASTE  and VALID, by the  EU  under  FP6  project  GEOmon  ( FP6-2005-Global-4-036677 ), and by  ESA's CCI Ozone project. D. Hubert ,  A. Keppens  and  T. Verhoelst  acknowledge national funding from the  Belgian Science Policy Office (BELSPO)  and  ProDEx projects SECPEA  and  A3C. K. Stebel  acknowledges funding from the  ESA/ProDEx projects Ex Val  ( C90190 ,  CN1-4 ,  2005-2011 ). J. A. E. van  Gijsel  and  D. Swart  acknowledge support from the  Dutch Ministry of Infrastructure  and Environment. Work performed at the  Jet Propulsion Laboratory  was done under contract with the  National Aeronautics and Space Administration. We are also grateful to  C. De Clercq ,  D. Pieroux  and  S. Vandenbussche  for their valuable input.
Quantification of in situ PLA signals Estimation of nuclei and cytoplasm size was performed using the Duolink ImageTool software (Olink Bioscience). In order to calculate the total number of spots per cell, an algorithmic procedure was developed and implemented in the Matlab environment (The MathWorks Inc. , Massachusetts, USA). "I" denotes an immunofluorescence image of m × n size. Such an image can be seen as a "stack" of three matrices of the same size; representing the red, green and blue values for each pixel indicated as IR, IG, IB for the three color bands, respectively. For every pixel at position (i, j) correspond three values; IR(i, j), IG(i, j), IB(i, j), ranging from 0 to 255. Image pre-processing was performed to identify pixels of red intensities less than a determined threshold value θ, in order to exclude background noise.
Acknowledgements This work was supported by the  European Union (European Social Fund -ESF, Heracleitus II  (to M. Koutsioumpa -E. Papadimitriou),  Thales  (to V. Megalooikonomou) and  ΙΚΥ Fellowship of Excellence for Postgraduate studies in Greece -Siemens Program  (to E. Pantazaka). This work was also supported in part by a  Grant-in-Aid for Scientific Research for Challenging Exploratory Research 25670018  (to  K. Sugahara ) from the  Japan Society for the Promotion of Science (JSPS). The authors thank the  Advanced Light Microscopy facility of the Medical School, University of Patras  for using the Leica SP5 confocal microscope and the PhD candidate  Margarita Lamprou  for technical support.
PCR products were separated by 1% agarose gel electrophoresis in 1 × TBE buffer with 5. 5 V cm -1 during 2 h. Gels were stained with ethidium bromide, and banding pattern was visualized under the UV light using ST4 3026-WL/26M transilluminator (Vilber Lourmat, Torcy, France). RAPD-PCR amplification products, treated as described by  Lynch and Milligan (1994) , were scored automatically as 1 for presence and 0 for absence of bands using the software TotalLab TL120 1Dv2009 (Nonlinear Dynamics Ltd. , Newcastle, Great Britain), and their sizes were determined using 1 kb DNA ruler (Thermo Scientific, Fermentas, Vilnius, Lithuania). Only clear, distinct bands were scored (Fig. 3 ), the variation in intensity of bands was not taken into consideration, and a revision of band scoring and matching was performed manually.
Genetic diversity and genetic differentiation The parameters of genetic diversity, Nei's gene diversity, h,  (Nei 1973 ) and Shannon's information index, I,  (Lewontin 1972) , were calculated at the population and the species levels using the software POPGENE ver. 1. 31  (Yeh et al. 1997). Parameters of genetic differentiation of pairs of populations and at the species levels (F ST ), and gene flow (Nm) estimated according to the equation: Nm = 0. 5 (1 -F ST )/F ST were assessed with the software Arlequin ver. 3. 5. 1. 2 (Excoffier 2010 ) with 1000 permutations. The same software was used to perform an Analysis of Molecular Variance (AMOVA,  Excoffier et al. 1992 ) using a matrix of squared Euclidean distances among all genotypes in all three species. This analyses were performed at the ramet level (i.
Cluster analyses To further study genetic differentiation of populations in all three species, we applied three approaches. Firstly, we calculated the Jaccard's similarity coefficients (Jsc) to generate pairwise similarity matrices for each species used for constructing dendrograms with unweighted pairgroup method (UPGMA) in the software  PAST (PAleontologicalSTatistics, ver. 1. 89, Hammer et al. 2009). Statistical support was obtained with 100 bootstrap (BS) replicates. Secondly, we used a model-based Bayesian clustering method in STRUCTURE 2. 2  (Pritchard et al. 2000). RAPD multilocus phenotypes were treated as diploid multilocus genotypes with the unknown alleles as missing values, and run under allele frequencies correlated and admixture models. Iteration parameters were set to a burn-in period of 1000000 iterations followed by 5000000 iterations.
We run the analysis for each species independently setting the number of genetic groups, K, from 1 to 6 for V. myrtillus and V. uliginosum, and from 1 to 5 for V. vitis-idaea, and performed ten independent runs for each of the assumed K. To determine the optimal K value for each species, we used ΔK method of  Evanno et al. (2005)  which can detect higher levels of hierarchical structure. Thirdly, we conducted a Principal Component Analysis (PCA) based on the matrix of pairwise population Jaccard's similarity coefficients using the software PAST.
Spatial distribution of genetic diversity and genealogical relations of individuals To explore the spatial distribution of genetic variation and possible isolation-by-distance (IBD) pattern, we used Mantel test  (Mantel 1967 ) in Arlequin ver. 3. 5. 1. 2 to test for correlations between the genetic distances and geographical distances. We used a matrix of pairwise population Jaccard's similarity coefficients, and obtained statistical support with 5000 permutations. We constructed Minimum Spanning Trees (MST) in PAST software and inferred genealogical relations among individuals belonging to discrete populations at the genus and the species levels. MST trees were presented within PCA graphs generated previously, and the congruence between inferred genealogical lineages and their spatial distribution was inferred.