dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the real information exchanged between codes is done via these approximations. By this innovative approach, the amount of information to be treated and sent by the MPI code is considerably reduced. In Figure  6 , the different solver discretizations are shown. The representation of the mesh for a single pin is illustrated by (a), where the fuel gap and clad are modeled by SYRTHES and the fluid domain is modeled by NEPTUNE-CFD. For each axial level of this mesh, the averaged values of the TH parameters are taken (b); these are moderator temperature and density and Doppler temperature. These TH values are approximated to polynomial functions (c). The DYN3D-SP3 code reads the TH values at the required location by interpolation of the polynomial functions (d). The NK computes the information generating a power map distribution (e).
Validation of the NHESDYN Coupled System Since the involved codes are already in the process of validation, the coupled NHESDYN system will be verified and validated by a code-to-code comparison. For this purpose, the DYNSUB system, developed at KIT by coupling DYN3D-SP3 with the subchannel code SUBCHANFLOW  [7, 14] , was selected. A set of test problems were defined to check the prediction capability of the new NHESDYN system compared to DYNSUB where the thermal hydraulic conditions, for example, coolant mass flow, temperature, and system pressure, were changed in time. The geometrical and material data as well as the thermal hydraulic conditions for the test cases were derived from the OECD/NEA and US NRC PWR MOX/UO2 core transient benchmark  [15]. The cross-section data were also taken from the mentioned benchmark.
Modeling Issues for the Code-to-Code Comparison. For a consistent code-to-code comparison between NHESDYN and DYNSUB we made sure that both coupled codes use the same thermophysical material data for the fuel, gap, and cladding, for example, thermal conductivity, density, and the heat capacity as defined in  [15] , the same water/steam tables, for example, the IAPWS  [16] , the same gap width (0. 05 mm) filled with helium, and the same heat transfer coefficient. In DYNSUB, a constant heat transfer coefficient (HTC) of 10 4 W/m 2 K is used. In NHESDYN, the temperature gradient over the gap is calculated by solving locally the conduction within a solid using the thermophysical properties of helium. This is a simplification of the complex heat transfer phenomena that can take place in the gap, normally a combination of conduction, convection, and radiation.
Discretization Issues in SYRTHES and NEPTUNE-CFD. In the coupled code NHESDYN, the solid domain is solved  by SYRTHES while the fluid domain is solved by NEPTUNE-CFD. In Figure  8 , the meshing for SYRTHES (fuel pin) and for NEPTUNE-CFD (fluid domain) is given. Two meshes were considered for both domains: a coarse mesh (shown in (a) and (b)) and a refined mesh (see (c) and (d)). The coarse mesh has 44 axial levels while the refined mesh consists of 60 axial levels. The pin model in DYNSUB consists of 24 axial levels. In radial direction, reflective boundary conditions are considered for this single fuel rod problem. (a) (c) (b) Z Z (d) X X X Y X (a) (f) (e) Z Z (.
But the differences between the calculations are smaller than one degree. In Figure  13 , the evolution of the multiplication factor ( eff ) during the iteration steps during the coupled NHESDYN steady state simulation is given. In a steady state case the coupled system reaches convergence after four NK iterations. The TH needs more time to develop a temperature and density profile after a change in the power; to reach a steady scenario for the TH hundreds of iterations are needed by executing the convergence loop. Case R1 provides the best result regarding  eff compared to the reference value. In addition the coarse mesh lowers CPU times, mainly due to the reduced number of cells for the SYRTHES code in charge of solving the thermal conduction. With the coarse mesh a 60 seconds transient takes 5 minutes of CPU time to be solved; this time is multiplied by a factor of 7 for the refined mesh.
Definition of Transient Cases for the Testing of NHESDYN. To test and validate the new NHESDYN coupled system by a code-to-code comparison with the DYNSUB code, the following three transient cases are defined. (i) Case 1: variation of the coolant temperature at fuel rod inlet. (ii) Case 2: variation of the coolant mass flow rate. (iii) Case 3: variation of the system pressure. These transient cases are used to check the robustness and numerical stability as well as the prediction accuracy of NHESDYN. The initial conditions for all transient cases are given in Table  1  and they were taken from the previous converged steady state based on the OECD/NEA and US NRC PWR MOX/UO2 core transient benchmark  [15]. The simulations performed have been tested with the two mesh refinement cases R1 and R2.
Summary and Conclusions In this paper, the main features of coupled system NHESDYN are described in detail. The peculiarities of the communication between the involved solvers in the frame of the MPI implementation are also presented. The testing and validation of the prediction capability of NHESDYN are carried out by a code-to-code comparison with DYNSUB, which has the same neutronic solver but a subchannel code instead of NEPTUNE-CFD/SYRTHES. For this purpose, a single pin problem was defined. Three postulated transient scenarios were defined to check the prediction accuracy of this new coupled system, namely, variation of the coolant inlet temperature, variation of the mass flow rate, and variation of the system pressure. These transient scenarios were then predicted by NHESDYN and DYNSUB using the same material properties for the fuel rod as defined in OECD/NEA and US NRC PWR MOX/UO2 core transient benchmark  [15].
Based on the code-to-code comparison, it can be stated that NHESDYN is able to predict the behaviour of the fuel rod under all postulated conditions satisfactorily. By this it is demonstrated that the coupling approach and the exchange among the solvers are consistent and are working properly. The real advantages of the new system NHESDYN compared to DYNSUB can only be shown, when a pin cluster or a fuel assembly is simulated with a very detailed resolution as usual for CFD codes. Hence the work presented here is the first step in the development of NHES-DYN as a high fidelity simulation tool. The promising results obtained till now and presented here are very encouraging for the further development and testing of NHESDYN in solving large problems as fuel rod clusters of fuel assemblies of a PWR core.
During the transients, a small delay in the evolution of the moderator temperature was calculated by NHESDYN compared to DYNSUB. The reason for it may be the different spatial discretization used in the thermal hydraulic solvers of NHESDYN (NEPTUNE-CFD) and DYNSUB (SUBCHAN-FLOW). The code-to-code comparison has demonstrated the prediction capability of NHESDYN for both steady state and transient test problems. Finally, the modularity of the developed coupling scheme makes it possible, for example, to replace the serial SP3transport solver by a parallel Monte Carlo one, so that the coupled system can be run fully in a parallel mode in the frame of high fidelity simulations for reactor design and safety.
Morphology Analyses and Cell Counting All analyses, cell counting and determination of markers coexpression were carried out on single images from stacks with cell to cell identification. The analyses were performed on non consecutive sections. Images were analyzed and prepared using ZEN 2009 light Edition (Zeiss software), ImageJ 1. 45 (NIH), Adobe Illustrator and Photoshop. No correction was applied to the images and for a better visualization, especially for thin structures like fibers, only contrast and brightness were adjusted in the images used for the figures. When images of a same set of experiments are illustrated, the same adjustments were applied to allow comparison. Cell density, distribution and morphology. At each medullospinal level of interest (see above and fig. 1D ), the analysis was typically repeated in 3 animals and carried out for each animal and each of the 4 levels in 3 different sections (i.
In order to assess the cellular localization of PKD2L1 proteins, we first determined the cell polarity of PKD2L1 + S-CSF-cNs and identified the somatodendritic versus axonal compartments using subcompartment-specific antibodies (figs. 3  and 4 ). Using anti-MAP2 antibodies, we showed that the thick neurite projecting towards the cc was MAP2 + and thus of dendritic nature (fig. 3A2  and A3 ). This dendrite strongly co-expressed PKD2L1 with the highest level of immunoreactivity on the terminal bud (fig. 3A1  and A3 ). Interestingly, these data also indicated a complete absence of MAP2 + cells in the ependymal layer and suggested that all MAP2 + cells projecting to the cc were PKD2L1 + S-CSF-cNs (see figs. [3] [4] [5]. The dendritic nature of this thick neurite was further confirmed by the absence of immunolabeling against the subtype of neurofilaments with a molecular weight of 160,000 (NF-160), a selective marker for axons (fig.
Acknowledgments We would like to thank  Drs CS Zuker ,  P Durbec  and  M Cordero-Erausquin  for sending us transgenic mice models and  Drs JP Hugnot  and  T Doan  for the generous gift of NKx6. 1 and Rabbit anti-GFP antibodies, respectively. We are thankful to  Drs AE Fink ,  JP Hugnot  and the members.
MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc. QC. LSC. R); (ii) PCA and automatic outlier removal (Auto. PCA. R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto. MV. Regress. R); (iv) Biomarker-MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.
MS. MS. match. R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate. R). Availability and implementation: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands. github. io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC-MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository.
Introduction Untargeted metabolite profiling is a promising approach to discover novel risk factors for chronic diseases and biomarkers for disease diagnosis  (Wang et al. , 2011; Ritchie et al. , 2013). Liquid chromatography coupled to high-resolution mass spectrometry (LC-hrMS) instrumentation is being more routinely used for dataacquisition for metabolomic analyses in large-scale studies (e. g. >300 samples). Raw data need to be processed via computational tools to extract relevant information and meaningful biological conclusions. Processing of LC-hrMS raw data is currently facilitated by softwares such as XCMS, MzMine, MetaboAnalyst and Maven  (Smith et al. 2006; Pluskal et al. 2010; Clasquin et al. 2012; Xia et al. 2012). However, an automated software pipeline with minimal manual interaction for efficient, reproducible and objective large-scale metabolomic data analysis is also desirable.
Software tools can systematically perform all downstream aspects of metabolomic analysis following peak-picking. Development of such a workflow in the R language can offer several advantages such as availability of modular packages for functional programming and graphics and the ease of accessibility of a GUI for non-specialists. We have developed novel software, MetMSLine, coded in the R language which automates the process of untargeted metabolomic data analyses of large datasets from acquisition of data from LC-hrMS platforms through to unknown biomarker identification. V C The Author 2014. Published by Oxford University Press.
2. 1 Step 1: Pre-processing of raw data matrix from XCMS Large LC-hrMS metabolomics datasets contain unwanted variation introduced by MS signal drift/attenuation and multiplicative noise across the dynamic range. These effects can detrimentally impact biomarker discovery and MS features require rigorous quality assurance. PreProc. QC. LSC first zero-fills data, then if sample normalization is required the median fold change method can also be applied  (Veselkov et al. 2011). PreProc. QC. LSC then uses the QCbased locally weighted scatter-plot smoothing method to alleviate the effects of signal drift  (Dunn et al. 2011). The degree of smoothing is controlled by the smoother span value (e. g. f ¼ 1/5), this argument sets the proportion of points used to smooth at each point. Data are then Log-transformed and finally features analytically stable across the regularly injected (every 5-10 true sample) pooled QCs are retained (e.
Step 2: Removal of outliers The next function performs automated removal of outliers in the pre-processed data based on expansion of the Hotellings T2 distribution ellipse. The argument 'out. tol' (outlier tolerance) controls the proportional expansion of the ellipse (e. g. 1. 1 or a 10% proportional expansion). Any samples within the first and second component PCA score plot beyond this expanded ellipse are removed and the PCA model recalculated. Assuming outliers are detected Auto. PCA performs two rounds of outlier removal and saves details of outliers removed along with corresponding samples from the Y-variable data table supplied in the parent directory in. csv format.
Step 3: Multivariate regression Auto. MV. Regress utilizes continuous Y-variables to regress to the pre-processed MS dataset. Auto. MV. Regress creates a subfolder for each Y-variable supplied, then identifies potential biomarkers based on a user-defined correlation threshold (e. g. Corr. thresh ¼ 0. 3) and below a multiple testing corrected P-value (P ¼ 0. 01) and both scatterplots and box and whisker plots are generated. Potential biomarkers above the threshold are hierarchically clustered and 'X-Y' and 'X-X' heatmaps generated. Inter-feature clustering (X-X) is used to identify cluster ions from a list of 88 isotope, adduct, fragment and co-metabolite mass shifts.
Conclusion MetMSLine presents a complete data processing method; it is easy to use as a GUI and should be very beneficial to researchers to rapidly process large-scale LC-hrMS dataset. It potentially requires minimal manual interaction with the software, when compared with the high-manual interaction required by commonly used softwares for LC-hrMS datasets such as MetaboAnalyst, MAVEN, apLCMS, MzMine and IDEOM  (Yu et al. 2009; Pluskal et al. 2010; Clasquin et al. 2012; Creek et al. 2012; Xia et al. 2012). The rapidity of the process allows great scope for parameter optimization and the subsequent ability to dedicate more time to result interpretation.
Motivation: Alignment-based methods for sequence analysis have various limitations if large datasets are to be analysed. Therefore, alignment-free approaches have become popular in recent years. One of the best known alignment-free methods is the average common substring approach that defines a distance measure on sequences based on the average length of longest common words between them. Herein, we generalize this approach by considering longest common substrings with k mismatches. We present a greedy heuristic to approximate the length of such k-mismatch substrings, and we describe kmacs, an efficient implementation of this idea based on generalized enhanced suffix arrays. Results: To evaluate the performance of our approach, we applied it to phylogeny reconstruction using a large number of DNA and protein sequence sets.
INTRODUCTION Comparative sequence analysis traditionally relies on pairwise or multiple sequence alignment. With the huge datasets that are produced by next-generation sequencing technologies, however, today's alignment algorithms reach their limits. Thus, with the growing number of completely or partially sequenced genomes, there is an urgent demand for faster sequence-comparison methods. Over the past two decades, a wide variety of alignment-free approaches were proposed  (Vinga and Almeida, 2003). Although aligning two sequences takes time proportional to the product of their lengths, most alignment-free methods run in linear time. They are, therefore, increasingly used for genome-based phylogeny reconstruction and for large-scale protein sequence comparison. It is known, however, that alignment-free methods are generally less accurate than alignment-based approaches.
As a further development of this idea, the shortest unique substring (shustring) approach has been proposed by  Haubold et al. (2005). These authors also derived an estimator for the number of substitutions per site between two unaligned sequences based on the average shustring length; they implemented this approach in the program K r  (Haubold et al. , 2009). ACS and shustrings can be calculated efficiently using suffix trees  (Weiner, 1973). As the aforementioned methods, most approaches for alignment-free phylogeny reconstruction are based on exact word matches. Recently, we suggested to use spaced-k-mers defined by pre-defined patterns of match and don't care symbols, instead of contiguous k-mers  (Boden et al. , 2013; Leimeister et al. , 2014). The aim of this study is to apply the idea of inexact matches to word matches of varying lengths.
(2010). They constructed a phylogenetic tree for these sequences based on alignments of 70 universal single-copy genes that we used as reference tree in our study. The total size of this sequence set is 135. 9 mb. As benchmark proteins, we used 218 sequence sets contained in the BAliBASE (v3. 0) database  (Thompson et al. , 2005). To obtain reference trees, we applied Maximum Likelihood  (Felsenstein, 1981) , implemented in the program proml from PHYLIP to the reference multiple alignments in BAliBASE. As these reference alignments are considered to be reliable, the resulting trees should also be reliable. In addition to these real-world sequences, we used the program Rose  (Stoye et al. , 1998)  to generate simulated DNA and protein families. Rose generates sets of related sequences based on a probabilistic model of substitutions and insertions/deletions for which the parameters can be adjusted by the user.
All resulting tree topologies were compared with the topologies of the respective reference trees using the RF metric  (Robinson and Foulds, 1981). For Neighbor joining and to calculate the RF distances, we used the programs neighbor and treedist contained in the PHYLIP package  (Felsenstein, 1989). Figures  2  and 4 -7 summarize our test results on the five groups of benchmark sequence sets that we used. The plots show the average RF distances between the produced trees and the corresponding reference trees. For kmacs, results are shown for various values of k. For FFP, CVTree, UA and the k-mer method, we also used a range of parameter values, but for each of these methods, the figures show only the best results on the respective group of benchmark sequences. Thus, for a fair comparison, these methods should be compared with the best results of kmacs in the corresponding figure.
On the other hand, K r , ACS and Clustal could be used with default parameters, which is clearly an advantage of these methods. Figure  2  contains the test results on the primate mitochondrial genomes. The best method on this dataset was our previously developed spaced-words approach; the tree topology produced by this method precisely coincides with the topology of the reference tree, i. e. the RF distance is zero. The second best methods were FFP and kmacs with k = 3, 4 and 64 k 117. ACS, CVTree, UA, kmacs with other values for k and K r performed worse on these data. As an example, Figure  3   B ) is based on kmacs with k = 70. Red branches represent differences to the reference tree topology. Except for these three species, the topologies of the two trees coincide, resulting in a RF distance of 2 between our tree and the reference tree Fig.
2. of alignment-free methods on a set of 27 primate mitochondrial genomes. RF distances between constructed trees and a reference tree are shown. The tree calculated by kmacs with k = 70 is shown in Figure  3 , together with the reference tree kmacs almost coincides with the topology of the reference tree; the RF distance between these trees is 2. On the Roseobacter genomes, the best methods were kmacs with k = 4 and 6, FFP and CVTree as shown in Figure  4. Spaced words and the generic k-mer approach performed slightly worse. None of the tested methods was able to exactly reconstruct the topology of the reference tree. UA is missing in this comparison, as this program is too slow to be run on full bacterial genomes in reasonable time. For our simulated DNA sequence sets, the results were similar as for the primate mitochondrial genomes; see Figure  5.
As with the previous dataset, the classical approach based on multiple sequence alignment performed best; this time the difference between alignment-based and alignment-free methods was larger. This may be because of the fact that multiple-alignment programs are often tuned to perform well on BAliBASE, the main database to evaluate multiple-alignment methods. Finally, the results on our simulated protein sequences are shown in Figure  7. As in most previous examples, spaced words and kmacs outperformed other alignment-free approaches and, as on BAliBASE, kmacs was slightly better than spaced words if k was sufficiently large. Surprisingly, on these benchmark sequences spaced words and kmacs even outperformed Clustal W and Maximum Likelihood, although not dramatically. So far, we evaluated alignment-free and alignment-based methods indirectly, by applying them to phylogeny reconstruction and comparing the resulting trees with trusted reference trees using the RF metric.
When used with moderate values of k, kmacs is faster than spaced words run with a set of 100 different  patterns. K r was more than one order of magnitude faster than kmacs and spaced words, respectively, although UA was the slowest method. The fastest method was our implementation of the generic word-frequency approach, followed by K r and CVTree. In general, spaced words used with the single-pattern option is only slightly slower than the k-mer approach. As shown in our companion paper, however, spaced words produces considerably better results when used with multiple patterns  (Leimeister et al. , 2014). We therefore applied only the multiple-pattern version in this study. The relatively long runtime of UA is partially because of the fact that this program is written in Java, while all other programs that we tested are written in C++.
Still, the program K r is roughly one order of magnitude faster than kmacs. One reason for this is that K r uses one single generalized suffix tree representing all input sequences, which can be calculated in time proportional to the number of sequences (Domazet-Lo so  and Haubold, 2009). In contrast, kmacs calculates one generalized enhanced suffix array for each pair of sequences, so its run time is quadratic in the number of sequences. On the other hand, calculating suffix arrays for two sequences at a time is less memory consuming, as one does not need to keep the suffix array for all input sequences simultaneously in main memory. Thus, our approach can be applied to larger datasets than K r. The two approaches that we developed, kmacs and spaced words, are slower than the corresponding approaches based on exact matches, ACS and the generic k-mer approach.