dataesr/scientific-paragraphs-categorization huggingface dataset:
3. Cross sections for the 6 Li(n,t), 6 Li(n,n), 6 Li(n,tot), 10 B(n,n), 10 B(n,α 1 γ), 10 B(n,α) and 10 B(n,tot) reactions and their covariance matrices (including crossreaction covariances) obtained in the R-matrix evaluation were used in the combined least-squares fit with all other data from the GMAP database. These data include reactions with heavy nuclides and ratios between light and heavy nuclide cross sections. Finally, the outlying experimental data were analyzed and additional components of uncertainty were added to these data points to restore consistency and to bring the general chi-square per degree of freedom close to unity. 4. Refitting the derived GMAP fit for the lithium and boron standards using the R-matrix EDA code. The goal was to calculate standard cross sections for those reactions in any energy grid and produce a  table of those standard cross sections in a much denser grid than the one used in the GMA fit.
D. Prompt Fission Neutron Spectra The 235 U thermal prompt neutron fission spectrum (PFNS) is very important for reactor applications. It is also used as a reference for validating evaluated cross sections for neutron dosimeters used in many applications. Improvements in the evaluation of this spectrum were made by including measurements of the spectrum made relative to the 252 Cf spontaneous fission neutron spectrum. The evaluation was done with GMAP for which both those spectra together with 233 U and 239 Pu PFNS were evaluated simultaneously, and by considering all ratio measurements as shape data. Only spectra data were used in this evaluation. Due to the much smaller uncertainties of the 252 Cf spectrum, the impact was largely on the 235 U spectrum. The 252 Cf spontaneous fission neutron spectrum evaluation of Mannhart was used in this evaluation.
A similar problem was pointed out by Duran  [92]. This forced us to include in the new fit, besides 238 U(n,f) of Ref. [50] , also all 238 U(p,f) measurements above 200 MeV available in the EXFOR database  [93]  except for the obvious outliers. The updated evaluation (GMA fit 2017) for 238 U(n,f) is shown in Fig. 24. For comparison, the high energy data from the FISCAL parameterization  [94]  are also shown in Fig. 24 ; however, after downscaling by factor 0. 87. It is worth mentioning that inclusion of the new (n,f) and additional (p,f) data in the GMA fit has no impact on other (n,f) reactions, as is shown for nat Pb(n,f) in Fig. 25. It should be noted that the region close to the minimum of the fission cross section around 150 MeV and above depends also on pion physics. For 238 U fission produced by protons, there is an increasing cross section for the production of pions competing with neutrons  [95].
In two measurements where γ-ray detectors were located at other angles the correction for angular dependence was applied by the authors. The 2017 evaluation of this reaction is an update of the evaluation performed in 2012  [97] , which additionally used the results of nuclear reaction modelling, since the experimental database was insufficient at that time. Since 1965 seven independent measurements of the 984 keV γ-ray yield and seven measurements of inelastically scattered neutrons have been carried out. Only two experiments used the enriched 48 Ti samples, the others used natural titanium. In most cases the absolute scale was derived from experimental comparison with the 1 H(n,n), 235 U(n,f) or 238 U(n,f) cross sections. The most recent experiments with direct detection of 984 keV γ-rays were carried out by LANSCE  [54]  with enriched 48 Ti sample and by JRC-Geel  [55, 98]  with nat Ti.
At energies above 9. 314 MeV we reduced the JRC-Geel data for the contribution from 49 Ti(n,2nγ 948keV ) 48 Ti reaction using the corresponding cross section from TENDL-2015  [99]. This correction amounts to ≈ 10 % at 14 MeV. After correction, the LANSCE and JRC-Geel measured data agree within declared uncertainties. For the present non-model evaluation in the energy range of interest, we selected data from 8 experiments. These experimental data, the GMA fit and resulting uncertainty, ≈ (3-6) %, are shown in Fig. 27  (more details are available elsewhere  [100] ). The pronounced resonances with significant cross section changes become visible below 3 MeV, that restricts the usage of this reaction at lower energies. The comparison with the ENDF/B-VIII. 0 evaluation  [4]  shows a difference of ≈ 4 % at the cross section maximum.
Then absolute measurements of those standards can have an impact on the gold cross section through those ratios. In effect, more than 400 cross section data sets had an impact to some degree on the gold standard capture cross section evaluation results. Accurate values were obtained below 200 keV, from the 2006 standards evaluation process for the gold capture cross section, however they are not recommended for use as a standard due to structure in the cross section there. The 5 % to 7 % difference referred to in Sec. II. G between the 2006 standards evaluation result in the Maxwellian energy region and the Ratynski and Käppeler evaluation was also an important concern. Experiments referred to in Sec. II. G were incorporated into the new GMAP evaluation along with the 2006 data in the standards database, resulting in the 2017 results.
In Fig. 29 , results of recent experiments  [33, 40]  are compared with the 2006 and 2017 standards evaluations. There is very good agreement. The Maxwellian averaged cross section at kT=30 keV for 197 Au(n,γ) is now a standard cross section. That cross section is 620 ± 4 mb before unrecognized uncertainty (see section III A) is taken into account. After taking that into account we obtain 620 ± 11 mb. The astrophysical database  [104]  (KADONIS 1. 0) has changed the MACS for the 197 Au(n,γ) cross section. Their new preliminary value, 613 ± 7 agrees with the 2017 standards evaluation within one standard deviation.
IV. COMPARISONS OF THE NEW EVALUATION WITH EXPERIMENTAL DATA AND PREVIOUS STANDARDS Figures 30-37 in this section show the results of the combined fits described above, which are the final standards evaluation results compared with the experimental data for the reaction cross sections and their ratios over various energy ranges. The standards evaluation final fits are shown by thick solid lines with evaluated uncertainties given at the nodes. The results of the 2006 standards evaluation are also shown. The experimental data shown in the figures are taken directly from the GMA database. "DS. " refers to datasets in the GMA database. In some cases the EXFOR  [93]  (Experimental Nuclear Reaction Data, sometimes abbreviated as X4) accession number is given that can be used to retrieve the data online.
Due to the large size of the database, only a subset of the data sets are plotted. For most of the figures, the dataset number, the name of the first author and the year of publication are given in the legend. The references for each dataset are given in Table  II  and those shown in Table II of Ref. [1]. Data are reduced to the original form in which they were obtained by the experimentalists: absolute cross sections, non-normalized (shape) cross sections, absolute ratio of cross sections and non-normalized (shape) ratio of cross sections. Absolute cross sections that had been normalized using the hydrogen-scattering standard were renormalized to the new standard. Data sets with shape cross sections and shape cross section ratios were renormalized with coefficients that give the best chi-square values relative to the final evaluation.
Uncertainties in the experimental data shown in the figures are the original uncertainties assigned by the authors in virtually all cases. Expanded uncertainties for the outlying experimental data used in the final combined fit are not shown. However, they can easily be envisaged as uncertainty bars which restore consistency with the final evaluation. The GMA database also includes covariance matrices of the uncertainties of the experimental data generated from partial components of the uncertainties and their correlative properties. Many data sets obtained at the same laboratory, or with the same sample or detector, are combined in data blocks that account for correlations between sets. Except for the light element standards, the curves are not fits to the data, they simply follow the data points.
It is recommended that the Mannhart evaluation be used for any applications. It is available at https://www-nds. iaea. org/standards/ ref-spectra/ together with the evaluated 235 U thermal prompt fission neutron spectrum. The reference fission cross sections for 209 Bi(n,f), nat Pb(n,f), 235 U(n,f), 238 U(n,f) and 239 Pu(n,f); and the prompt γ-ray production reference Cross Sections for 7 Li(n,n'γ) and 48 Ti(n,n'γ) will be listed and updated on the site https: //www-nds. iaea. org/standards/. As noted previously, the 3 He(n,p) cross section was not re-evaluated. The publication on the 2006 standards  [1]  contains the 3 He(n,p) evaluation. The GMAP evaluation estimates a point-wise cross section and its uncertainty at energy E using experimental data in the energy range from E1 to E2. However, for the 235 U(n,f) cross section an integral from 7.
Origins of CO-B and CO-B2 The relationship of CO-B and CO-B2 prompted us to explore the biogeographic pattern of these alleles to better understand their evolutionary histories. To do this, we extracted and genotyped the 10kb region surrounding CO from whole-genome sequences (Monnahan et al. submitted) of a broader sample of tetraploid and diploid clades. This dataset includes 172 individuals from 11 tetraploid and 12 diploid populations (respectively 87 and 85 individuals) from across the A. arenosa range  (Fig 6A and 6B and S1 Table). This analysis indicated that among tetraploids, CO-B is found as a rare variant in some mountain populations, but reaches a relatively high frequency in BGS of around 30% (Fig 6B ). CO-B2 is at very high frequency in all lowland railway populations (STE, KOW, TBG), completely absent from mountain populations in the Alps and Carpathians, but found in BGS at frequencies around 30%.
lyrata genome  [24]  using TopHat2  [63]  and realigned unmapped reads using Stampy  [64]. We acquired read counts for each of the 32,670 A. lyrata gene models  [24]  using HTseq-count  [65]. We assessed quality of the count libraries by PCA and Euclidean distance analysis; biological replicates of each population are most similar to each other (Fig  1C ). We normalized for sequencing depth using DEseq2 in R  [66]  and further analyses were performed in MATLAB (MathWorks). PCA was performed using the 500 genes with the highest variability, as recommended in the DESeq2 package  [66]. The FLC locus in A. arenosa has three tandemly duplicated FLC-like genes  [39, 67]  of which two, AaFLC1 and AaFLC2 are clearly homologous to FLC from A. thaliana. RNAseq reads from the A. arenosa FLC region do not all differentially map to the two FLC duplicates of A.
Differentiation analysis To test for genetic differentiation, we used our previously published genomic short read sequences for A. arenosa  [42, 68]  complemented with similarly processed genomes to reach 6 TBG, 8 STE, 8 BGS, 10 GU, 7 HO and 8 KA individuals for a total of 47 individuals over 6 populations. We aligned reads to the A. lyrata genome  [24]  using BWA  [69]  and re-aligned unmapped reads using Stampy  [64]. We calculated G ST  [70] , Fay and Wu's H  [47]  and Tajima's D  [48]  over 25 SNPs windows using customized scripts (available at: github. com/baduelp/public) after genotyping the alignments with GATK  [71]  only considering bi-allelic sites with a sequencing depth per individual of 4 or more (2. 9 million SNPs). For population structure analyses, we performed PCA on MATLAB (MathWorks, and script available at github.
Funding was provided by the  E ´cole des Mines de Paris (PB) , a grant from the  U. S. National Science Foundation  ( NSF / IOS-1146465 ) to KB, and by the  UK Biological and Biotechnology Research Council (BBSRC)  via grant  BB/P013511/ 1  to the  John Innes Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
RNAseq read data and RADseq mapping data have been deposited in the NCBI SRA database under accession number SRP148726 (https://www. ncbi. nlm. nih. gov/Traces/ study/?acc=SRP148726) within the NCBI BioProject PRJNA472485 (https://www. ncbi. nlm. nih. gov/bioproject/472485). Custom scripts used are available at: github. com/baduelp/public.
, 2008)  (Figure  1h ). We then proceeded to analyse telomere length by Q-FISH in CMs from old mice using STED. telomere FISH intensities in telomeres co-localising with H2A. X (TAF) and those not co-localising with H2A. X (non-TAF) in aged mice and found no significant differences (Figure  S1f  & g ). Similar observations were made in CMs from aged human hearts (Figure  S1h ). Analysis of telomere FISH intensities by STED in individual cardiomyocytes revealed that telomeres with low FISH intensity co-localising with H2A. X are relatively rare (Figure  S1i ). Together, our data suggest that length-independent telomere-associated DNA damage is not due to the limitations of conventional confocal microscopy and that the shortest telomeres are not preferentially signalling a DDR. While CM proliferation is negligible in adult humans and mice, it is possible that TAF may be the outcome of a small fraction of dividing CMs.
Three days subsequently to X-ray irradiation, we monitored DDR in H9C2 cells using a 53BP1-GFP fusion protein  (Passos, Nelson et al. , 2010)  and time-lapse microscopy (Figure  2e ). Over a 10 hour time-course, the majority of individual DDR foci could be seen to be rapidly resolved with only approximately 20% of the original DNA damage foci persisting throughout the time course (Figure  2f ), similar to the % of TAF positive cells for this time point (Figure  2d ). To determine if persistent TAF can also be induced in adult CMs in vivo, we exposed young mice to a whole-body dose of 2Gy X-ray irradiation and measured the level of TAF in CMs after 11 months recovery. Both the mean number of TAF and % of CMs positive for TAF were significantly higher (Figure  2g ). Altogether, these data suggest that the majority of genomic DNA damage in CMs is reparable and only telomeric DNA damage is irreparable and persistent.
However, genes involved in the regulation of myofilaments, contraction, and CM hypertrophy were enriched in old mice  (Myh7, Capn3, Ankrd1, Myom3, Myot, Mybpc2). Confirmatory qRT-PCR showed age-dependent increase in hypertrophy genes Myh7 and Acta1, but not Anf (Figure  S7e  and f ). Interestingly, pro-inflammatory genes associated with the SASP  (Coppé, Patil et al. , 2008) , such as Il1a, Il1b, Il6, Cxcl1, and Cxcl2, were not differentially expressed between CMs from young and aged animals (Figure  4f ). To determine if SASP factors were secreted by CMs, we collected conditioned medium from isolated CMs from young and old mice. In accordance with the RNA-seq results, we did not find any significant differences in the levels of secreted proteins using a cytokine array (Figure  4f ). Our data are in contrast with a previous report that shows increased expression of SASP components in murine heart with age using whole heart homogenates  (Ock, Lee et al.
% change=(EST-EDT)/EDTx100. All analysis was performed in a blinded manner. RNA-Sequencing. Paired-end reads were aligned to the mouse genome (mm9) using a splicing-aware aligner (tophat2). Only unique reads were retained. Reference splice junctions were provided by a reference transcriptome (Ensembl build 67), and novel splicing junctions determined by detecting reads that spanned exons that were not in the reference annotation. True read abundance at each transcript isoform was assessed using HTSeq (Python) before determining differential expression with the tool DESeq2, which models mean-variance dependence within the sample set. Significance was determined using an FDR corrected p-value ≤ 0. 05. Alignment statistics for each replicate are included in the supplementary data. Computational Modelling.
Two different models were constructed to examine different hypotheses on how TAF accumulate in cells. The models were encoded in the Systems Biology Markup Language (SBML) modelling standard  (Hucka, Finney et al. , 2003)  with the Python SBML shorthand tool  (Wilkinson, 2011). Model simulations were carried out in COPASI  (Hoops, Sahle et al. , 2006)  and the results were analysed and plotted in R using ggplot2  (Wickham, 2009). We used stochastic simulation (Gillespie Direct Method) in order to be able to account for the variability in the experimental data. The models were deposited in BioModels  (Chelliah, Juty et al. , 2015)  and assigned the identifiers MODEL1608250000 and MODEL1608250001. Cufflinks FPKM Determination. To calculate library normalised read counts for each transcript in every replicate, we used the tool Cufflinks, which returns the proportion of reads per million, which mapped to each gene isoform.
Gene Set Enrichment Analysis (GSEA). We perform a GSEA by creating a ranked list of the DESeq2 results file, where genes are ranked by their log transformed, non-FDRcorrected p-values. This list is then used as an input to the GSEA software, which assesses positive or negative shifts of GO ontology classes in the global distribution of gene expression. This is achieved by the calculation of an enrichment score, which reflects the movement of a particular ontological class to the positive and negative extremes of the distribution of the expression of all genes. Permutation testing then allows for the estimation of significance prior to FDR correction. Heatmaps. Heatmaps were created in R using the ggplots package. These heatmaps displaying normalised (row scaled -Z-score) FPKM gene expression across a series of replicates were clustered using a Pearson correlative clustering approach in the "hclust" R package.
Data deposition: The RNA-seq data from this publication have been submitted to the GEO database (http://www. ncbi. nlm. nih. gov/geo/) Statistical Analysis. We conducted two-tailed t-tests, one-way ANOVA, Gehan-Breslow, and Mann-Whitney tests using GraphPad Prism. the MRC-Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing (CIMA) grant MR/K0063121/1. We thank Simon Tual-Chalot for the expert assistance in magnetic resonance imaging. We thank Prof. Roger Greenberg for kindly providing us with the TRF1-FOKI plasmid. We thank Gabriele Saretzki for expert assistance in TRAP assays, Thomas von Zglinicki for providing mice tissues and Glyn Nelson for assistance in live-cell microscopy. We thank Utz Herbig, Steve Kosak for insights into telomere biology. (a) Representative images of H9C2 cardiomyoblasts 4 days following transfection with a FLAG-tagged TRF1-FokI-D450A (top row) or TRF1-FokI (middle and bottom row) fusion protein (cell treatments the same for all subsequent panels in figure) (purple -FLAG; redtelo-FISH; green -H2A.
X/53BP1). Images are z-projections of 0. 1µM stacks taken with 100X objective. White arrows indicate co-localisation between telomeres and H2A. X/53BP1, with co-localising foci amplified in the right panels (taken from single z-planes where co-localisation was found). Scale bar represents 25m. b) Percentage of γH2A. X (left) or 53BP1 (right) foci co-localising with telomeres. Data are mean ± SEM of n=3 independent experiments. More than 50 cells were analysed per condition. Statistical analysis was performed by two-tailed t-test * P<0. 05. c) Histograms displaying telomere intensity for telomeres co-localising (bottom) or not colocalising (top) with γH2AX (left) or 53BP1 (right) DDR foci. Red dotted lines represent median. Statistical analysis performed using two tailed t test; * P<0. 05; More than 50 cells were analysed per condition.
Mann-Whitney tests show no significant difference in telomere intensity between TAF and non-TAF, with either H2A. X (left) or 53BP1 (right) DDR foci. d) Representative images of detection of senescent markers (purple -FLAG; red -Ki-67; light blue -DAPI; darker cytoplasmic blue -SA-β-Gal). Scale bar represents 100m. e) Mean percentage of FLAG-positive cells positive for Ki-67, p21, SA-β-Gal activity and cell size. Data are mean ± SEM of n=3 independent experiments. More than 100 cells were quantified per condition. For SA-β-Gal data are presented as mean of >100 cells representative of 1 experiment. Two additional independent experiments confirmed these findings (not shown). Statistical analysis performed using two tailed t test; * P<0. 05. f) Scheme depicting experimental setting: AC10 human cardiomyocyte cell line stably expressing an inducible TRF1-FokI was cultured for 2 weeks in mitogen-depleted media which led to terminal differentiation and loss of proliferation.
Acknowledgements: This work was funded by  BBSRC  grants  BB/H022384/1  and  BB/K017314/1  to JFP,  BHF  project grant  PG/15/85/31744  to GR and JFP,  NIH  grant  AG013925  and funding from the  Connor Group and Noaber Foundation  to JLK, and grant from  Région Midi-Pyrénées, France  to JMP, DM, and VDE. CJP and JFP were funded by.
We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria.
In this context, the number and spread of lesions in the patient's parenchyma (and their evolution)  2  has become a crucial information on the patient's disease status, which may then be used for validating the patient treatment. This task however requires the delineation of MS lesions: a tedious, manual operation performed by the radiologist. In addition, this delineation is prone to inter-expert variability, especially when the images being used for segmentation differ from a center to another (in terms of protocols, modalities and intrinsic MRI quality). Doing this task manually on large databases of patients is therefore almost impossible and automatic algorithms, thoroughly validated, have become a crucial need for the clinical community. To simplify the clinician's task, a large literature of automatic segmentation methods has been devised  [4] [5] [6]  with a large spectrum of algorithms from classical tissue intensity classification and lesion modeling to machine learning.
All published approaches are however evaluated on different datasets, usually not calibrated, and their results are therefore usually not directly comparable, making difficult the choice of the most relevant method adapted to a clinical context. To overcome this issue, competitions (so-called challenges) have been organized in MS lesion segmentation in the past years. The first one was organized at the MICCAI 2008 conference  7. It evaluated nine different methods on a database of 45 patient images (from two different centers: 20 for training and 25 for testing), with respect to a ground truth composed of two expert segmentations for each case. However, no protocol standardization was performed between the two sites, therefore two raters were not enough to handle the variability in the acquired images and get a sufficiently reliable consensus manual segmentation.