dataesr/scientific-paragraphs-categorization huggingface dataset:
It is unlikely that severe structural disruption and mobilization of specific ions will result in reorganization of elements into structures of characteristic shape/volume; therefore the direct identification of element reservoirs in a cell may still be valid  (Miranda et al. , 2004). As an example of the use of classic methods, we have relied on classic chemical methods for the preservation for the 3D mapping of iron by EFTEM-tomography as a basis for development of a software to calculate 3D chemical maps (EFTEM-J). This technique was applied to observe ferritin associated with the fungal cell wall, a rigid structure composed mainly of polysaccharides localized externally at the plasma membrane of fungi  (Messaoudi et al. , 2013). Regardless of the method of sample preparation used, cellular stress must be minimized during fixation to prevent modifications in molecular distributions of elements.
Some techniques resolve at the tissue level while others resolve at the cell or even sub cellular level; using the correct interpolation scheme can address this issue. Furthermore, differences of contrast in images can cause the same structures to be highlighted differently. These differences are due to the method of acquisition or to the different location of chemical elements within the sample. To combine the information obtained from different methods, investigators my use a feature based approach or an intensity based approach for registration and alignment of the images. The feature-based registration requires the operator to manipulate the software to identify which features are common between the data sets. Automatic feature detection using scale-invariant feature transform (SIFT) technique  (Lowe, 2004)  has undergone great advances, but only a few studies have been applied to multimodal datasets, as SIFT descriptors were not designed to be modality invariant.
Presently, to combine images from different modalities, several software packages are available such as Amira (http://www. fei. com/software/ amira-3d-for-life-sciences/), ATNs (http://stnava. github. io/ANTs/ ), elastix  (Klein et al. , 2010; Shamonin et al. , 2013) , (http:// elastix. isi. uu. nl/), niftyReg (http://cmictig. cs. ucl. ac. uk/wiki/index. php/NiftyReg), procedures based on matlab  (Avants et al. , 2014)  or ImageJ  (Schneider et al. , 2012)  with the plug-in Align3 TP (http:// www. med. harvard. edu/JPNM/ij/plugins/Align3TP. html). However, these packages do not address some problems specific to chemical imaging such as missing-wedge artifacts in EFTEM-tomography. New methods allowing multimodal approaches with non-rigid registration are currently under development to facilitate the integration between different chemical imaging and structural imaging methods.
Absolute quantification by correlation of data for ion beam and synchrotron radiation analyses An important achievement in chemical element mapping in biological samples is the production of quantitative data, termed here as chemical element imaging. PIXE and SXRF produce readily quantifiable results expressed as mass of the element per scanned surface (i. e. , ng/cm 2 ). A number of software programs have been developed to provide quantitative data from PIXE analysis  (Campbell et al. , 2010; Ryan, 2001)  or from SXRF analysis  (Ryan et al. , 2005; Sole et al. , 2007). If the total chemical content is measured (all elements have Z > 10), then the quantification is total (i. e. , g/g). In the case of biological samples, however, the total element content is not measured due to the presence of low Z elements such as H, C, N and O.
Concluding remarks The technological improvements, developments of accurate software for analysis, and popularization of the techniques of STEM, EFTEM, NanoSIMS, PIXE, and other analytical methods have encouraged a great expansion in the field. These techniques have allowed research groups that had previously focused purely on structural analysis to obtain more information about their samples with analytical microscopy. Chemical mapping methods that were never mutually exclusive are now applied in complementary and correlative approaches. This union of methodologies is at the frontier of structural and analytical microscopy and it is encouraging that in several biological fields, most notably in cell biology, analytical microscopy is becoming a suitable technique to unravel the chemical and elemental mechanisms of the cell.
Acknowledgments This work was supported by  Agence Nationale pour la Recherche (ANR)  program  PIRIBIO  ( ANR-09-PIRI-0029-01 ) for the biological study. Technical developments required for SIMS-EFTEM correlative imaging have been supported by  ANR-11-BSV8-016. MML da Cunha is supported by  Fundac ão de Amparo a Pesquisa Carlos Chagas Filho -FAPERJ  and  Conselho Nacional de Pesquisa -CNPq, Brazil. We also acknowledge the  PICT-IBiSA  for providing access to chemical imaging equipment.
Voight et al (2006)  later introduced a test statistic (iHS) based on the standardized log-ratio of the integrals of the observed decay of EHH computed for the ancestral and the derived alleles at the focal SNP. Finally, cross-population statistics were proposed, to contrast EHH profiles between populations: XP-EHH  (Sabeti et al , 2007)  and Rsb  (Tang et al , 2007). These haplotype-based methods of detecting selection have largely been applied on human data  (Vitti et al , 2013) , a wide range of livestock (see, e. g. Flori et al , 2014; Bosse et al , 2015; Barson et al , 2015)  and plant species (see, e. g. Wang et al , 2014; Jin et al , 2016) , and also non-model species (see, e. g. Roesti et al , 2015; Mueller et al , 2016). A few years ago, we developed rehh  (Gautier & Vitalis, 2012) , a package for the statistical software package R (R Development Core  Team, 2008) , to detect recent positive selection from the analysis of longrange haplotypes.
Since then, two alternative programs were released: selscan  (Szpiech & Hernandez, 2014) , which introduces multithreading to improve computational efficiency and hapbin  (Maclean et al , 2015) , which in addition to multithreading offers considerable gain in computation time thanks to a new computational approach based on a bitwise algorithm. Here we propose a major upgrade of the rehh package  (Gautier & Vitalis, 2012) , which includes an improved algorithm to enumerate haplotypes, as well as multi-threading. These improvements decrease the computation time by more than an order of magnitude, as compared to the previous rehh version (1. 13), which eases the analysis of big datasets. Below we provide a brief overview of the statistics and tests available in rehh 2. 0, and give a detailed worked example of the analysis of chromosome 2 in humans (HSA2), from HapMap samples CEU (Utah residents with Northern and Western European ancestry from the CEPH collection) and JPT+CHB (Japanese in Tokyo, Japan and Chinese from Beijing, China).
Computation For comparison purposes, the different haplotype data sets were analyzed using the software packages rehh (both the previous version 1. 13 and the new version 2. 0), selscan (version 1. 1. 0b) (Szpiech & Hernandez, 2014)  and hapbin (version 1. 0. 0) (Maclean et al , 2015). Default options were generally used except for the minimal threshold on the minor allele frequency (MAF) that was set to 0. 01 for all programs. In addition, for the selscan program, both the window size around the core SNPs (--ehh-win option) and the maximum allowed gap in bp between two consecutive SNPs (--max-gap option) were set to 10 9 (this was made to disallow these options that are not considered in other programs). Similarly, the --max-extent option was inactivated by setting --max-extent=-1. For the hapbin programs (i.
e. , Finally, for each analysis and parameter set, the estimation of computation times was averaged over ten independent runs. All analyses were run on a standard computer running under Linux Debian 8. 5 and equipped with an Intel R Xeon R 6-core processor W3690 (3. 46 GHz, 12M cache). Note that the Unix command taskset was used to control the number of working threads for the analyses with the hapbin programs (since neither the ihsbin nor the xpehhbin programs allow to chose the number of threads to be used).
Significant overlapping windows were then merged, leading to a total of 11 regions harboring strong signals of selection, which characteristics are detailed in Table  1  (see also Figure  1 ). As expected, most of the regions identified in previously published genome-scans for samples with the same origin  (Sabeti et al , 2007; Tang et al , 2007; Voight et al , 2006)  overlap with the regions identified here (Table  1 ). For instance, regions #6 and #7 that are in the vicinity of the EDAR gene (under selection in Asian populations) and the LCT gene (under selection in European populations), respectively, have been extensively characterized in the literature (e. g. ,  Peter et al , 2012). Interestingly, we detected more regions than previously reported in the aforementioned studies, most probably because our analyses are based on a larger dataset and different cut-off values.
An improved processing of the input file The first major modification introduced in rehh version 2. 0 deals with the processing of input files (haplotype and SNP information files) using the function data2haplohh. Indeed our own experience with earlier versions of the package together with feedback from several users prompted us to optimize data import and to improve allele recoding, which was inefficient in version 1. x. Considering standard input haplotype file format (which is common to both versions), and with alleles encoded in the appropriate format ({0,1,2} for missing data, ancestral and derived alleles respectively), the new data2haplohh function is about 2. 5 times faster than the previous one (see Table  2 ). In addition, the allele recoding option results in slightly better processing performances, and is no more prone to errors as in version 1.
x. Finally, the new haplotype format (with haplotypes in columns), corresponding to the output file of the SHAPEIT phasing program (O'Connell et al , 2014), was found to be the most efficient to process (see Table  2 ). [Table  2  about here. ] With datasets of increasing complexity and size, such improvement in the processing of input files is critical to rehh users. Processing a data set as large as the JPT+CHB one (consisting of 342 haplotype with 110,200 SNPs) now takes less than 12 seconds. Note however that for this file a maximum of about 1 Gb RAM was used, for a net memory size change of 240 Mb. For larger data sets, RAM requirements may therefore be limiting for some computers. A faster and parallel algorithm to explore haplotype variability The second major modification introduced in rehh version 2.
0 concerns the core algorithm that computes the distribution of haplotype counts, which underlies the calculation of all the metrics of interest (iHS, Rsb and XP-EHH). As shown in Table  3 , this new algorithm allows to decrease the computation times by more than one order of magnitude, as compared to the algorithm implemented in rehh version 1. 13. Hence, for the computation of iHS in the CEU population (respectively, the JPT+CHB population) on a single thread, computation times were 13. 7 (respectively 21. 8) times smaller on average. Interestingly, the computation time for the JPT+CHB dataset (which is approximately 1. 34 times larger than the CEU one in terms of number of SNPs × number of haplotype) was only 1. 09 times slower than for the latter. Conversely, the computation time was 1.
73 times slower for JPT+CHB relatively to CEU with rehh version 1. 13. Although a more detailed profiling of the algorithm would be required, these results suggest that computational burden is approximately linearly related to the data set complexity. To further improve computational speed, haplotype structure is now performed using OpenMP parallelization across SNPs in genome-wide scans. Using four threads then lead to an additional decrease of about 3. 5 times in computation times (see Table  3 ). Overall, the whole analysis of the HSA2 haplotype files used in this study took about 1. 5 minutes (including the processing of input files) with rehh 2. 0, and more than 1. 3 hours with rehh 1. 3. This corresponds to the computation of iHS within the CEU and within the JPT+CHB populations, as well as the computation of Rsb and XP-EHH.
[Table  3  about here. ] Comparing rehh 2. 0 to the selscan and hapbin programs Finally, we compared rehh 2. 0 with selscan  (Szpiech & Hernandez, 2014)  and hapbin  (Maclean et al , 2015) , which were recently published. Both programs are written in C++ language and include parallelisation. Computation times for the different analyses, either on a single or four threads, are provided in Table  3. The new version of rehh outperforms selscan by about one order of magnitude. Moreover, running rehh on a single thread is still more than twice as fast as running selscan on four threads. It should also be noticed that running a full analysis consisting of the estimation of iHS within and XP-EHH between the CEU and JPT+CHB populations result in a significant additional burden with selscan (Table 3).
Conversely, hapbin was found to be more than five times faster than rehh 2. 0, most likely as a result of its more efficient algorithm to explore haplotype variability. Yet, given the small computation times achieved by both programs, rehh 2. 0 remains competitive relative to hapbin for most practical applications. Correlation between the estimated iHS and XP-EHH obtained with the different programs are given in  Table 4. Estimates of XP-EHH were in almost perfect agreement among the different software packages. Similarly, estimates for iHS were almost the same between rehh 2. 0 and selscan but slightly depart from those obtained with hapbin. Although we did not further investigate the origin of these discrepancies, this might probably be related to a different definition of haplotype homozygosity in hapbin, as compared to  Sabeti et al (2007).
Conclusion Although the R package rehh  (Gautier & Vitalis, 2012)  has been widely used since its first release, the increasing dimension of haplotype datasets typically available in most species led to serious limitations. This stimulated the development of alternative R-free solutions  (Szpiech & Hernandez, 2014; Maclean et al , 2015). In this study, we introduced substantial changes in the rehh package to improve its computational efficiency by one to several orders of magnitude. This was achieved by modifying the processing of the input files and, most importantly, by improving and parallelizing the core algorithm that computes the distribution of haplotype counts. In A) and B), the horizontal dotted lines indicate the |iHS| significance threshold of 4 that was used to identify significant regions (see Table  1 ) and the arrows at the top of the graph indicate the mid-position of the significant regions described in Table  1 ).
Comparison of the computation times (in seconds) required to process input data files with the data2haplohh function for the versions 1. 13 and 2. 0 of the rehh package. Two data sets consisting, respectively, of 236 and 342 haplotypes of 110,200 SNPs for the CEU and JPT+CHB populations were considered (see the main text). For each of these datasets, the table gives the average computation times (± standard deviation) across ten independent runs, either with or without (in parentheses) allele recoding (using the option allele. recode). In rehh the function scanhh computes iHH and iES simultaneously. It therefore needs to be run only once per haplotype data set. As a result, computing XP-EHH (and/or Rsb) requires almost no extra time, once iHS for the two populations has been computed.
Features The main features of the publicly available spectrum generators for the NMSSM are summarized in table 1. Here, two different kinds of spectrum generators are distinguished: dedicated tools which 4 Note that, in eq. (  10 ), only one element of the matrix m diag h at a time can correspond to a pole Higgs mass, and only for an appropriate choice of p 2. 5 For further reference, we define also m    1 : Public spectrum generators for the NMSSM and their features. The first two rows specify if the code is stand alone or gets necessary information from the Mathematica package SARAH, and in what language the code is written. The next three rows list the models that are supported: "GUT model" means that the code accepts as input a set of SUSYbreaking parameters at the GUT scale, and "CPV" shows if a Higgs sector with CP-even and -odd mixing is supported.
) in a hardcoded way, and multi-purpose generators which use the Mathematica package SARAH to derive this information for a given model. The advantage of the first approach, which is adopted by NMSSMTools, NMSSMCALC and SOFTSUSY, is that the tools can be used directly after compilation without further effort. In contrast, to use FlexibleSUSY and SPheno, one needs to first install SARAH 6  , which is then used to generate the necessary source code to get an NMSSM spectrum generator. These additional steps allow these tools to be used for a much broader range of models, i. e. not only other singlet extensions beyond the NMSSM are supported, but also many other extensions of the MSSM. Also, the features of the NMSSM version (like boundary conditions at all relevant scales) can be adjusted in the input files for SARAH or FlexibleSUSY without the need to modify the final code  7.
NMSSMTools The first public codes to be made available for the NMSSM were NMHDECAY  [42, 43] , NMSPEC  [44]  and NMGMSB, which are today collected in the package NMSSMTools. NMHDECAY was based on a suitably modified version of HDECAY  [63, 64, 65]. This package is available at http://www. th. u-psud. fr/NMHDECAY/nmssmtools. html So far NMSSMTools is restricted to the real NMSSM with and without Z 3 symmetry, but a version to support also CP violation is under construction  [66]. NMSSMTools allows to calculate the Higgs masses for a parameter point with three different options: 1. The most precise calculation makes use of the NMSSM corrections of ref. [36]. This provides a fully diagrammatic calculation of the Higgs masses at the one-loop level, including all contributions and the momentum dependence, plus the two-loop corrections of O(α s (α b + α t )) at zero external momentum.
In addition, the two-loop corrections known from the MSSM at O((α t + α b ) 2 + α τ (α τ + α b ))  [67, 68, 69, 57]  are included. 2. The second option implements the same corrections as the first one, but neglects the momentum dependence of the one-loop self-energies. (This makes the code faster, which is useful for large scans. ) 3. The third option is the original implementation of the Higgs mass calculation in NMSSMTools, which -as described in the appendix C of ref. [9]  -implements only a partial computation of the one-loop contributions, and restricts the two-loop contributions to the leading-log approximation. NMSSMTools also calculates the SUSY spectrum at the one-loop level, and the Higgs and sparticle decay branching fractions (the latter in NMSDECAY [70] using a suitable modification of SDECAY  [71] ).
The users may also easily create their own boundary conditions. For the NMSSM, SOFTSUSY extends the full three-family one-and two-loop RGEs of the MSSM  [77, 102]  using general two-loop expressions from the literature  [77, 78] , with the NMSSM specialization given in Appendix D of the NMSSM manual  [46]. These expressions were also cross-checked against two-loop NMSSM RGEs in the literature that use the 3rd-family approximation  [9] , and tested numerically against the RGEs from SARAH using an early development code from FlexibleSUSY. Finally, the RGEs for the vacuum expectation values are implemented using Refs. [83, 84]. The one-loop self-energies and tadpole corrections for the Higgs bosons were extended to the NMSSM using the expressions in Refs. [36, 37]. The NMSSM extension also includes the two-loop corrections at order O(α s (α t + α b )) from Ref.
[36]  and in addition uses the MSSM results from Refs. [67, 68, 69, 57]. Higgs and sparticle decays may be obtained by interfacing SoftSUSY with NMHDECAY  [42, 43]  and NMSDECAY  [70] , which is based upon Ref. [71]. These are both distributed as part of the NMSSMTools package, and SOFTSUSY provides a script to do the interface with this package automatically. The homepage of SOFTSUSY is http://softsusy. hepforge. org/ 3. 1. 5. FlexibleSUSY FlexibleSUSY is a Mathematica and C++ package, which creates spectrum generators for userdefined (SUSY or non-SUSY) models. It makes use of SARAH to obtain model-dependent details, i. e. couplings, RGEs, self-energies and tadpole corrections. The boundary conditions on the model parameters at different scales, as well as spectrum-generator specific configuration details are specified in a FlexibleSUSY model file.
FlexibleSUSY can therefore generate spectrum generators for the different MSSM singlet extensions provided by SARAH, for example the NMSSM with and without a Z 3 symmetry. The generated spectrum generators consist of C++ classes, which can be easily adapted allowing one to use the routines in a custom-made program for solving non-standard problems. For example one may build a tower of effective field theories, which could be useful for NMSSM scenarios with very heavy superparticles. Currently, FlexibleSUSY fully supports only real parameters. Complex parameters have been developed very recently in version 1. 1. 0, but they are still undocumented and undergoing testing. The automatic implementation of alternative boundary-value-problem solvers and automatic generation of towers of effective field theories are currently under development.
NMSSMCALC, on the other hand, is the only code that includes two-loop O(α t α s ) corrections to v, which become non-negligible for large values of λ. It has been demonstrated with one example, that the differences can be much more pronounced for GUT scenarios with values of λ close to the perturbativity limit. The differences in the threshold calculations among the codes can cause huge discrepancies in particular for light singlets and the predicted masses can differ by 10 GeV and more. In general, comparing the predictions of different codes may provide a ballpark indication for the theoretical uncertainty of the Higgs mass calculation. However, even if these codes agree very well, one should not necessarily assume that the uncertainty is small: it is known from the MSSM that spectrum generators performing a DR calculation (SOFTSUSY, SPheno, Suspect  [122] ) can agree quite well, while sizeable differences to the OS calculation of FeynHiggs exists.
Acknowledgements We thank  Peter Drechsel ,  Mark D. Goodsell ,  Werner Porod ,  Heidi Rzehak ,  Michael Spira ,  Kathrin Walz  and  Georg Weiglein  for many useful discussions. This work was supported in part by the  Research Executive Agency (REA)  of the  European Commission  under the  Initial Training Networks "INVISIBLES"  ( PITN-GA-2011-289442 ) and " Higgs-Tools " ( PITN-GA-2012-316704 ), and by the  European Research Council (ERC)  under the  Advanced Grant "Higgs@LHC"  ( ERC-2012-ADG_20120216-321133 ). The work of P. S. at LPTHE is supported in part by  French  state funds managed by the  ANR  ( ANR-11-IDEX-0004-02 ) in the context of the  ILP LABEX  ( ANR-10-LABX-63 ). The work of P. A. was supported by the  ARC Centre of Excellence for Particle Physics at the Terascale. Appendix A.