dataesr/scientific-paragraphs-categorization huggingface dataset:
Diagnostic set-up The experiments are performed at TEXTOR, a medium-sized limiter tokamak (R a 1. 75 m, 0. 46 m 0 = = ). TEXTOR is equipped with a heterodyne O-mode correlation reflectometer operating in the frequency range 26-40 GHz  [8, 9]. The reflection layer is defined by the local electron density, guaranteeing that the reflection layer for the probing frequency is independent of the magnetic field. The system measures density fluctuations for k 3 cm 1 ⩽ ⊥ -. From the measured data turbulence properties and plasma rotation at three different locations (one top and two low field-side antennae arrays) are obtained. All antennae are pyramidal horns without focusing elements. The top array consists of four receiving antenna for measuring perpendicular plasma propagation (v ⊥ ), de-correlation time ( dc τ ) and turbulence wavelength (λ ⊥ ) [10].
The experiments are performed with two reflectometers. Whereas the launcher of the top array can be fed by both, to measure in addition radial correlations the LFS launcher is served only by one reflectometer. For several discharges the second reflectometer is used in a frequency hopping operation to allow the measurement of radial correlations from the top array and a combination of long-range and radial correlations between both antennae arrays. The reflected signal from top (four channels) and LFS array (two channels) are down-converted to 20 MHz and feed six IQ detectors. The output of the IQ detectors is digitized at a sample rate of 2 MHz. Data is taken during a time interval of 6 s starting with the discharge.
The analysis distinguishes between the positive and negative frequency branches as discussed in section 4. 2. For the coherence in the positive frequency branch f ( ) QC Γ increases first with increasing r Δ (see figure  13 ). For r 7 mm Δ ⩾ , f ( ) QC Γ decreases. To estimate the maximum position the dataset for combination BG is approximated by a spline (solid line in figure  13 ). A flat maximum at r 8. 2 mm Δ = is estimated. This behavior is found for all antennae combinations and the result is taken as additional evidence that the turbulence is not embedded within a iso-density surface. A further point of interest is the fixed radial position for which the maximum in f ( ) QC Γ is achieved when looking at different combinations. Since each combination has a different poloidal propagation time, any additional radial propagation in the outward direction should result in a shift of f ( ) QC Γ towards larger radial separation.
Discussion One of the important questions which stood at the beginning of the investigation was whether it is possible to deduce the toroidal velocity from the LRC data. As shown in section 4. 2, no information from the t Δ -values on the parallel propagation can be obtained. However, as the local safety factor can be determined from the LRC  data the effective poloidal and toroidal angles traveled by the turbulence can be deduced. Having determined the toroidal distance s R • c Δϕ = , where R c is the major radius and q Δϕ Δθ = , the toroidal velocity (v ϕ ) can be calculated by linear regression of the data (see figure  18 ). In the discussed case v 26 km s 1 = ϕ -is calculated. This value is in the same order as calculated from MHD analysis at the q = 1 of TEXTOR ohmic discharges  [17].
The ability to access the camera pixel stream depends somewhat on camera mode; however with the CANARY AO system  (Myers et al. 2008 ) and many others, camera customisation, interface development, and custom software has made this possible. CANARY uses the Durham AO real-time controller (DARC) for wavefront control  (Basden et al. 2010; Basden & Myers 2012)  which is optimised for low latency operation, and has the ability to process sub-apertures individually once pixels become available. We have therefore implemented the TVM algorithm within this system (which we discuss in §2. 2), applying TVM on a per-sub-aperture basis.
On-sky measurements Because on-sky time was limited, we did not attempt to explore a large parameter space of seeing conditions, spot size, signal level and readout noise. Rather, we have selected a target where noise is evident within the sub-apertures (Fig. 10 ), and operated the CANARY AO system in SCAO mode both with and without TVM. The data presented here were taken on the night of 12th July 2014 with CANARY on the William Herschel Telescope, for just over one hour from about 3am. We have taken four sets of observations, during which the CANARY SCAO loop was closed, and H-band science images obtained. Each set of observations commenced with a measurement without TVM, and then a number of measurements (8 or 20) with different TVM strength (estimated noise standard deviation) factors (increasing monotonically from 0 to 2).
Because of the natural variability of seeing, we also show, in Fig. 11(b) , the H-band Strehl ratio as a function of Fried's parameter, r0. In this case, r0 is computed from reconstructed pseudo-open-loop slope measurements, which are, in turn computed from a time-series of the on-axis closed-loop wavefront slope measurements and the recorded DM actuator commands. The line fitted through these data points is the best-fit to the cases with TVM implemented (with a regression correlation 0. 38). There is evidence here that the TVM performance is above the level of performance without TVM (represented by circles), indicating that the use of TVM has improved performance. However, again, there is some uncertainty, due to the lack of on-sky measurements, and so this should not be taken as conclusive: the statistical significance is low.
Survival distributions were estimated by Kaplan-Meier method and compared between CT-RT groups using the log-rank test. Prognostic effect of CT-RT group was explored using proportional hazard Cox model. As the purpose of this study was to estimate the potential effect of the CT-RT modality (q1w or q3w) on survival, the strategy of modeling implemented consisted in adjusting this variable on the unbalanced covariates revealed by the descriptive analysis, as they may be the confounding factors. All interactions between the type of CT-RT and these variables were also tested and incorporated into the multivariate model (if significant at 5% level). All analyses were performed using the SAS software version 9. 3 (SAS institute Inc, Cary, NC, USA).
Data sets and data processing The data acquisition and processing have been described in detail in previous papers  (Farges and Vila, 2003; Adohi et al. , 2008). However, we will summarize the main points. The device set up at Korhogo is a vertical-sounding IPS 42-type transmitter-receiver with an antenna 80 m wide and 25 m high. The transmitter pulse has a 2 µs rise time and is 10 µs wide. The peak power transmitted is 5 kW. The ionosonde operates in the high frequency (HF) range [1-22 MHz] and provides the plot of the virtual height as a function of frequency every 15 min. Two height parameters are commonly determined from such a plot. The bottom level altitude h F is extrapolated from the low-frequency asymptotic value of the virtual height. The upper level altitude hpF2 is read on the ordinary trace and is taken as the virtual height for the plasma frequency 0.
Results The fusion-evaporation events are selected setting conditions on the total detected charge and on the coincidence between a residue at forward angles (when Z res <12) (RCo) and light charged particles (LCP) detected in GARFIELD. Only complete events where the total charge of the entrance channel (Z det = 12) is detected are retained for this analysis. Experimental data are compared to the predictions of a Monte Carlo Hauser-Feshbach code (HF )  [6, 7]  for the evaporation of the compound nucleus 24 Mg, at ε * = 2. 6 A. MeV, corresponding to a complete fusion source, and filtered through a software replica of the experimental set-up. For both reactions the best reproduction of the systematics of the fusion cross sections is obtained assuming two different maximum value (J 0max ) for the angular momentum distribution of the hot fused source  [8].
Conclusions In this work we discussed preliminary results for the 14 N+ 10 B reaction at 80 MeV beam energy, measured at LNL-INFN with the GARFIELD+RCo experimental set-up. Using the same selection described in Ref. [7, 9]  we have compared experimental data to the 12 C+ 12 C reaction and to HF statistical model calculations for the decay of the 24 Mg * source. The selected sample is compatible with the expected behavior of a complete fusion-evaporation reaction, with some exception of specific channels corresponding to the emission of two or three α particles in coincidence with a Oxygen or Carbon residue. The experimental branching ratio excess for α particle emission has been quantified for both reactions, putting in evidence an effect due to the cluster nature of projectile and target in 12 C+ 12 C reaction but, at the same time, an indication of the persistence of cluster correlations also in the hot fused 24 Mg, as suggested by data from 14 N+ 10 B collisions.
Microsatellites analyses We used 15 microsatellites amplification in both species  [34]. All experimental procedures for microsatellite genotyping were strictly identical to those described in Valette et al. [34]. The average number of alleles per locus (A), the observed and expected heterozygosity (respectively H O and H E ) were computed with GENETIX 4. 05  [35]  and estimated for each species and the hybrids. Deviations from Hardy-Weinberg equilibrium were tested using the exact probability test of Guo & Thompson  [36]  available in GENEPOP 3. 2a  [37]. Significance levels were calculated at each locus and over all loci for each group. Genotypic linkage disequilibrium between each pair of loci was estimated by Fisher's exact tests with GENEPOP 3. 2a software. Both tests for deviations from Hardy-Weinberg equilibrium and for linkage disequilibrium used a Markov chain (1 000 dememorization steps, 100 batches, 1 000 iterations).
Hybrids genetic assignment NewHybrids. We used NewHybrids version 1. 1Beta3  [42]  to assign individuals to a species or to one of the different hybrid classes: F1, F2, and backcrosses. The analysis was performed with the genetic data collected from 15 microsatellites genotyped in 133 individuals. We used Jeffrey's prior for both the allele frequencies and the mixing proportions. We ran 10,000 iterations as a burn-in period, followed by 20,000 MCMC repetitions. To assess our power to assign individuals, we used two different assignment strategies with different stringency criteria. In the first one (relaxed criteria), assignments were made to the most likely species or hybrid class. In the second one (strict criteria), assignment was made only for the individual with a probability higher than 0.
90 to belong to one of the species or hybrid classes. HIest. In addition to estimating admixture coefficient to assign individual to species (Structure and NewHybrids methods), we used the R package HIest  [43] , which enables us to conjointly estimate the ancestry coefficient (S; equivalent to admixture coefficient Q in Structure) and interclass heterozygosity (H I ). Joint estimation of these parameters allows assessment of whether the studied hybrid system fits a simple two-generation hybridization model, as assumed by a classification method that relies on a restricted set of classes produced by two generations of hybridization (e. g. NewHybrids). This method assumed that allele frequencies of the hybridizing species were known, so we used cluster allele frequencies that had been previously estimated by Structure for K = 2 as an input for HIest.
Islands population genetic structure We used a Discriminant Analysis of Principal Component (DAPC)  [45] , implemented in the adegenet package  [46]  available for R version 2. 15. 2  [44] , to investigate the population genetic structure between species and between populations within species. It has been proven that this multivariate approach is well suited to identify complex genetic structures, such as the hierarchical population structure  [47] , which can be expected in the studied system with relationship both at the species (species and hybrids) and population levels (islands). In this situation, application of the hierarchical Evanno method  [48]  to determine the best number of clusters would be difficult as the presence of admixed individuals (hybrids) at the uppermost hierarchical population genetic structure level (species level at K = 2) would be removed from the analysis, in order to identify clusters within species.
Mitochondrial Introgression in hybrids All 20 I. from St. Barthélemy (Anguilla Bank), Basse-Terre and Petite Terre Guadeloupe Bank) and Chancel (Martinique) showed the same haplotype as already referenced in Genbank (AF 217786)  [49]. For the 18 I. iguana analyzed, 2 individuals from Martinique and Guadeloupe (Grande-Terre) showed the haplotype specific to I. delicatissima (AF 217786). Among the 16 others, we found 5 haplotypes in our study that corresponded to haplotypes, which had already been identified by Stephen et al. [18]  and  Malone et al. (2000)   [41]  (JQ 340914 (Grande-Terre), HM 352505 (Saba), AF 217782 (St. Lucia) GB U66231 (CAM) and GB 217783 (Martinique). Out of the 18 hybrids analyzed, 10 and 8 individuals showed haplotypes respectively described in I. delicatissima and I.
Acknowledgments This project was financed by the  Directions régionales de l'Environnement et du Logement de Martinique et de Guadeloupe , as well as the  Natural Reserve of St. Barthélemy. The sponsors did not play any role in designing this study, collecting and analyzing data, deciding to publish, or preparing the manuscript. The biological samples of I. delicatissima and I. iguana were mainly provided by the last-mentioned author, as well as the  Groupe d'Étude et de Conservation de l'Iguane des Petites Antilles de Guadeloupe (GECIPAG) , the  Iguana delicatissima de St. Barthélemy (IDSB) , both  Natural Reserves of St. Barthélemy  and  St. Martin , and by the  Office National de la Chasse et de la Faune Sauvage de Martinique (ONCFS). All Saba samples were collected by the  Natural Reserve of St.
WH8103 as well as two reference datasets used in previous studies, Rhodobacter sphaeroides and Homo sapiens chromosome 14 (http://gage. cbcb. umd. edu/). The quality of resulting scaffolds was compared to that of three other stand-alone scaffolders: SSPACE, SOPRA and SCARPA. For all three model organisms, WiseScaffolder produced better results than other scaffolders in terms of contiguity statistics (number of genome fragments, N50, LG50, etc. ) and, in the case of WH8103, the reliability of the scaffolds was confirmed by whole genome alignment against a closely related reference genome. We also propose an efficient computer-assisted strategy for manual improvement of the scaffolding, using outputs generated by WiseScaffolder, as well as for genome finishing that in our hands led to the circularization of the WH8103 genome.
Besides semi-automatically generating high quality scaffolds, it also provides valuable data to manually improve the scaffolding and also useful for genome finishing. Here, the efficiency of WiseScaffolder was tested on one of the newly sequenced genomes of marine cyanobacteria, Synechococcus sp. WH8103 (hereafter WH8103), as well as on two additional datasets from the GAGE study (Genome Assembly Gold-Standard Evaluation;  [14] ), Rhodobacter sphaeroides and Homo sapiens Chr. 14, representatives of the prokaryotic and eukaryotic domains, respectively. Quality of the results were compared to those obtained with three other stand-alone scaffolders (SCARPA, SOPRA and SSPACE) using QUAST  [15]  and other comparative genomic tools.
Comparison of WiseScaffolder with other scaffolders The recent advent of NGS technologies has triggered the development of a number of stand-alone scaffolders, the relative efficiency of which may be tricky to assess for bioanalysts and is highly dataset dependent. For instance, the E. coli strain K-12 substrain MG1655 dataset that was used to compare the recently published SCARPA to several other scaffolders  [12] , might be misleading because the assembly of such low quality data (insert size ~200 bp, reads ~36 bp) generally results into short contigs that are challenging to scaffold, whatever the efficiency of the scaffolder. Here, we rather chose to compare recent and/or widely used scaffolders using three genome datasets that were assembled from reads sequenced by Illumina with up-to-date performance (i.
e. , 101 bp paired reads, Table  1 ). These include the newly sequenced Synechococcus sp. WH8103 genome (42 contigs assembled using the CLC assembler; MP library insert size ~4 kb) as well as two reference datasets retrieved from the GAGE study  [14] , namely R. sphaeroides (177 Bambus2-assembled contigs  [16] ; MP library insert size ~3. 5 kb) and Homo sapiens Chr. 14 (3,541 CABOG-assembled contigs  [17] ; MP library insert size: 2. 3-2. 8 kb). To assess the efficiency of WiseScaffolder, the abovementioned datasets were scaffolded using three other stand-alone scaffolders: SSPACE  [9] , SOPRA  [10]  and SCARPA  [12]. Results of this benchmark are shown in Table  2. Additionally, for WH8103, we also tested the simultaneous assembly and scaffolding functions of the CLC assembler by assembling MP reads with distance constraint.
In the case of the cyanobacterial genome, CLC assembly of MPs proved much less efficient in terms of scaffolds number and size than the stand-alone scaffolders and was thus not further considered in the benchmarking. For all datasets, WiseScaffolder produced better results than the three other scaffolders in terms of contiguity statistics, as attested by a generally smaller number of genome fragments (i. e. , scaffolds + unscaffolded contigs), the larger size of these fragments, as well as better contiguity indexes (N50, LG50 and LG75, Table  2 ). For instance, for both prokaryotes, WiseScaffolder was able to build a single scaffold covering more than 50 % of the whole genome (LG50 = 1), while for the H. sapiens Chr. 14, this percentage was reached for 38 contigs with WiseScaffolder vs. 52, 74 and 460 for SCARPA, SSPACE and SOPRA, respectively.
The additional breakpoint observed in the SSPACE assembly compared to WiseScaffolder corresponds to two highly conserved hypothetical genes (orthologs of SYNW1563 and SYNW1565 in WH8102), both present in multiple copies in the two genomes. Many additional gaps were present in the assembly made with SCARPA and the absence of synteny of two of the three scaffolds with the WH8102 reference genome indicates the occurrence of chimerae (see Fig. 2d ). Altogether, WiseScaffolder gave the best results on this dataset (Table  2  and Fig. 2 ) and allowed us to obtain a genome of much better quality than most currently published draft genomes. Furthermore, a key asset of WiseScaffolder compared to other scaffolders is that the 'preprocess' subcommand also provides very useful outputs for resolution of chimerae (Fig.
Conclusions WiseScaffolder is a novel scaffolder designed not only to produce high quality scaffolds, but also maps of oriented contigs, allowing the user to manually refine and eventually close the genome sequence. Using three datasets representative of both prokaryotic and eukaryotic genomes and of the current standard of the Illumina technology, the automatic part of this pipeline led to a very limited number of scaffolds and was found to perform better than the well-known scaffolders SSPACE and SOPRA and the most recent one SCARPA. The reliability of the scaffolds obtained for Synechococcus sp. WH8103 was confirmed by comparison with a closely related reference genome sequenced by Sanger technology  [29]. Here, we also propose guidelines for genome finishing that use the outputs generated by WiseScaffolder to refine the scaffolding.
Genomic material Synechococcus sp. WH8103 (Roscoff Culture Collection strain: RCC2366), a clonal but non-axenic strain, was isolated by J. Waterbury in 1981 in the Northwestern Atlantic Ocean (28°30' 0" N, 67°23' 30" W). The genome sequencing dataset consists of two libraries sequenced by Illumina technology: a short insert PE library for genome assembly and a long insert MP library designed for genome scaffolding. Raw datasets are available via the Sequence Read Archive (SRA) under the EBI accession number ERP006796. The PE library was composed of 2 × 2. 10 7 reads of 101 bp with an average insert size of 75 bp and the MP library of 2 × 4. 1. 10 7 reads of 101 bp with an average insert size of 4. 2 kb (Table  1 ). In both cases, the coverage was around 800fold. The closely related complete genome of Synechococcus sp.
WH8102, previously sequenced using Sanger technology  [29] , was used as a reference to assess the quality of the scaffolding. Two other datasets from the GAGE study  [14] , available at http://gage. cbcb. umd. edu/data/ and for which complete reference sequences are available, were used in this study to test the efficiency of WiseScaffolder with regard to other scaffolders. These include a second prokaryote, R. sphaeroides, as well as a representative eukaryotic genome, Homo sapiens Chr. 14 (Table  1 ). In the case of R. sphaeroides, the dataset consists of an MP library of 2 × 10 6 reads of 101 bp with a 3,500 bp insert size and contigs generated using the Bambus2 assembler  [16]. For H. sapiens Chr. 14, we used contigs assembled using CABOG (Celera Assembler with the Best Overlap Graph) and an MP library of 2.
Polymorphism correction The final step consisted in a global polymorphism correction based on mapping of MP reads onto the assembled genome. First, a weight-position matrix was built, giving at each position the number of A, C, T or G nucleotides found in the reads. The matrix was then analyzed either to correct a nucleotide (when more than 50 % of the reads displayed a different nucleotide than the assembled genome at this position) or to flag the positions when the nucleotide identity was ambiguous (i. e. , when two nucleotides were equally abundant, no nucleotides had a frequency higher than 50 % or when the coverage at this position was too low). The script used to perform this step is available on the WiseScaffolder website.
Performance and comparison with other scaffolders The results obtained with WiseScaffolder were compared to that obtained with SSPACE (v2), SOPRA (v1. 4. 6) and SCARPA (v0. 241). While the mapping of the MP library against the contigs is included in the scaffolding process of SSPACE, this step was performed using Bowtie2  [32]  with default parameters for SOPRA, SCARPA and WiseScaffolder. For all three datasets, insert sizes used as setting for scaffolding are indicated in Table  1. Additional file 1: Table  S2  details the wall-clock running times and memory usage for the four tested scaffolders. Although WiseScaffolder tends to be slightly more memory and time consuming than its counterparts, the memory requirement remains perfectly compatible with current standard cluster capacity and the running time is only a matter of a few hours.
Implementation and availability Developed in Python with a few BioPython dependencies (essentially for the 'buildfasta' subcommand), WiseScaffolder is multi-platform and single-threaded. The code structure potentially allows the addition of scaffolding modules to the chain of actions. WiseScaffolder is open source and was installed on the local Galaxy instance of the ABiMS platform at http://webtools. sb-roscoff. fr. The application, the Galaxy wrapper, a handbook, test files and various peripheral scripts are available at http:// abims. sb-roscoff. fr/wisescaffolder.