dataesr/scientific-paragraphs-categorization huggingface dataset:
The consumers were able to discriminate the different Bolognese sauces by sensory characteristics and 43 attributes significantly differed at p < 0. 05 across samples according to the Cochran's Q test (Table  S5 ). Citation of attributes across each attribute ranged between 9 and 58% (Table  3 ). The CATA data was separately reanalysed for each level of cooking ability. The citation frequencies of attributes were similar across all levels of cooking ability, as was the total number of significantly different attributes across samples at the p < 0. 05 level (Table  3 ; for individual significance levels, see Table  S5 ). In all analyses by cooking ability, the total number of significant attributes were lower than the analysis of the entire consumer data set, due to the lower number of cases included in the analyses.
4 ). The CATA data categorised by cooking ability were analysed with MFACT to determine the sample discrimination patterns between groups. Samples were consistently discriminated from each other when data was categorised by cooking ability level and the first two factors explained 65. 5% of the variance (Fig. 6 ). Bolognese made with beef was also consistently perceived as unique from the other Bolognese with meat substitutes. The consensus plots also indicated high similarity in scores projections between the various levels of cooking ability, suggesting that cooking ability did not play a strong role in the consumer's discrimination ability of Bolognese by sensory properties (Fig. 6a ). The similarity of scores projections were confirmed by the high RV coefficients between tables (cooking ability comparisons: low vs medium = 0.
Overview This study comprises five analytic parts to implicate novel genes and gene networks in WMH etiology (Figure  1 ). First, we performed an epigenome-wide association analysis to identify DNAm loci, both cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs), associated with WMH burden. The identified DNAm loci were then annotated for regulatory features, pathways, and association with other traits. Second, we investigated the contribution of genetic variation to variation in DNAm at the identified CpGs and used Mendelian randomization (MR) techniques to test for causal association with WMH burden and for the mediating role of expression of nearby genes. Third, we examined the role of DNAm at established WMH genome-wide association study (GWAS) loci. Fourth, we integrated gene expression and expression quantitative trait loci (eQTL) data to prioritize candidate genes associated with the identified CpGs.
Study subjects The sample included 9,732 middle-aged to older adults of European (EA) and African ancestry (AA) from 14 community-based studies. Our discovery sample includes 5,715 subjects of European ancestry (EA, n=4,610) and of African ancestry (AA, n=1,105) from Atherosclerosis Risk in Communities (ARIC),  12  Biobanking and BioMolecular resources Research Infrastructure (BBMRI),  13  Cardiovascular Health Study (CHS),  14  Coronary Artery Risk Development in Young Adults (CARDIA),  15  Framingham Heart Study (FHS) offspring study,  16, 17  Genetic Epidemiology Network of Arteriopathy (GENOA) study,  18  Lothian Birth Cohort (LBC) 1936,  19, 20  Rotterdam Study (RS),  21, 22  and Study of Health in Pomerania (SHIP)  23. To replicate our findings, we accessed data on 3,398 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI),  24, 25  FHS 3 rd generation study,  26  the Older Australian Twin Study (OATS),  27, 28  and the Rhineland study  29.
Annotation of regulatory features and traits We scored genomic positions of the identified CpGs according to RegulomeDB's  37  ranking criteria ranging from one (likely to affect binding and linked to expression of a gene target) to five (minimal binding evidence) and also computed a probability score within a range from zero to one (the most likely to be a regulatory variant). CpGs at the locations with significant regulatory features (rank category one or two, and probability score 0. 9) are discussed. We also identified enhancers or promoters mapped to CpGs using the database of genome-wide enhancer-to-gene or promoter-to-gene associations computed based on five elements: eQTLs, eRNA co-expression, transcription factor co-expression, capture Hi-C and gene target distance (GeneHancer DB).
Shared epigenetics with BP 8] [49]  To investigate the shared epigenetics between WMH burden and BP, we performed a pairwise multivariate association test using summary statistics from a previous EWAS of SBP and DBP  50. CpGs associated with both traits were tested against the null hypothesis H 0 : β WMH =β SBP or DBP =0. The test uses Z-scores for each trait and estimates multivariate test statistics accounting for the trait correlation calculated based on the null associations (trait-specific P>1x10 -5 ). This method is implemented in the 'metaUSAT' software. 51 To avoid false positive associations driven entirely by one trait, we included CpGs showing significance (P<0. 001) for both traits. Bonferroni threshold was set at 8. 33x10 -3 (=0. 05/6) based on the number of associations tested.
Epigenetic regulation of known GWAS loci We next investigated the role of DNAm at established WMH GWAS loci, which may not have been detectable at the genome-wide significance threshold. Among 26 loci reported in the latest WMH GWAS,  8  we mapped 450K-array CpGs to 21 loci. EWAS associations at each of these 21 loci were pooled using the Brown's method (implemented in the package "poolr") adjusting for dependence among CpGs. 59 For dependency information, we calculated correlation among CpGs in the GWAS loci using ARIC methylation data (906 EA and 639 AA subjects). A GWAS locus with combined P was considered significant if P is smaller than Bonferroniadjusted threshold (0. 05/number of loci tested). Alternatively, we performed a moloc analysis at the 21 GWAS loci, again using the GWAS and brain QTL data.
Identification of biological pathways using multi-dimensional data integration Integrating multi-omics associations for WMH may boost power to identify novel genes influencing WMH burden. We integrated genetic 8 , transcriptomic  60  , and epigenetic genome-wide association studies of WMH using the R package 'mergeomics' (version 1. 2). 61 To reduce noise in the GWAS data, the top 50% of genetic associations  8  were included and pruned at r 2 <0. 5 based on HapMap3 LD information as recommended. 61 For transcriptomic associations, we used the recent WMH transcriptome-wide association study (TWAS) results. 60 For epigenetic associations, we used our discovery EWAS. Markers were primarily mapped to the nearest genes. For CpGs, cis-acting genes reported in the MesaEpiGenomics study  62  were additionally annotated.
For each GWAS, EWAS and TWAS, we tested marker-level enrichment with A C C E P T E D M A N U S C R I P T hierarchical permutation size of 20,000 based on biological pathways from pre-defined public databases: KEGG  45  , REACTOME  63  , Biocarta  64  , and the gene ontology knowledgebase  65, 66. Then, we meta-analyzed the enriched gene sets from association studies and identified the WMHassociated gene sets (FDR-adjusted P <0. 05). To describe the regulatory network of the identified gene sets and identify its local hub genes, we performed a weighted key driver analysis (wKDA) using the web-based software Mergeomics version 2. 0. 67 Gene regulatory network was constructed using in-house brainspecific Bayesian network (minimum hub overlap 0. 33 and directed edge type)  68  and visualized via Cytoscape version 3.
8. 2. 69 lso conducted an overlap-based drug repositioning analysis "PharmOmics" based on the identified key driver genes (FDR <0. 05) to predict potential drugs or small molecules targeting WMH. 70 PharmOmics comprises a curated drug signature database covering 941 drugs, constructed from transcriptomic data across >20 tissues from rat, human, and mouse. For our analysis, we selected drug signatures from relevant tissues (in vivo human transcriptome data in cardiovascular and nervous system, and in vitro transcriptome data from murine oligodendroglial precursor cells), and examined the overlap between these drug signature genes and key-driver genes from our identified WMH-associated gene sets.
Heritability of the WMH-associated CpGs Significant h 2 meth was estimated for cg17417856 (40. 4%, P=1. 37x10 -8 ), cg06809326 (26. 5%, P=1. 03x10 -4 ), cg23586595 (24. 2%, P=1. 47x10 -3 ), cg17577122 (14. 3%, P=2. 80x10 -2 ), and cg24202936 (15. 5%, P=1. 34x10 -2 ) (Table  2 ). Additional adjustment for BMI and smoking A C C E P T E D M A N U S C R I P T status did not significantly modify these estimates. In GWAS of the target CpGs in the ARIC EA sample, we observed significant cis genetic influence on cg06809326, cg13476133, and cg24202936 (Supplementary Figure  6 ). This result agrees with a previous publication that included the same dataset. 53.
SH3PXD2A encodes an adaptor protein (TKS5) involved in the formation of podosomes that act as sites of close contact to as well as degradation of ECM. 132 Gene set enrichment of identified DNAm loci and integrative crossomics analyses collectively point to a central role of the ECM in WMH burden. One of the two WMH burden networks identified through the Mergeomics approach centered around key driver genes involved in ECM organization and function and the top associated module was "regulation of actin cytoskeleton". Notably, actin polymerization and disassembly of junctional proteins within microvascular endothelial cells were shown to play a key role in early BBB disruption in a murine model. vessel stroke. 134 Statin therapy for cSVD has also been regarded as promising since individuals with high WMH burden typically carry higher vascular risk factors.
To extrapolate the findings in blood to brain, we assessed the correlation with DNAm in brain, and utilized available brain QTL data. Due to the difficulties of getting both brain DNAm and MRI data from a large population-based sample, an EWAS of WMH burden using brain DNAm may not be easy to achieve. However, findings from this large blood-based study may provide a basis for an epigenetic candidate gene study in the brain.
Non-condensable gas in Acc tank was allowed to flow into the cold leg, i. e. no Acc cut-off after depletion of ECC. The pressure set-points for the safety injection systems were taken from pre-tests at time points that correspond to the LSTF reference transients multiplied by 2. 65. Secondary side. • Total failure of feed-water systems (low-load and emergency feedwater system) concurrent with SCRAM, i. e. no secondary-side cooldown. Test preparations started with the filling of the RCS, a de-aeration procedure and the set-up of the initial test conditions. The recording of data started as soon as stable conditions (pressures, temperatures) had been achieved. The opening of the break marked the start of test (SOT). The cut-off of secondary side systems (main-steam relief trains (MSRT), feed-water system) started before 10 s after SOT and took up to 35 s after SOT until the last MSRT was fully closed.
Conclusions and recommendations The blind benchmark activity within the OECD/NEA PKL-4 featured an IBLOCA experiment at the PKL test facility. The proposed test was based on the IBLOCA experiments previously performed at the LSFT facility during the OECD/NEA ROSA-2 programme with some scaling considerations. In total, 16 participants took part of the activity and submitted a pre-test calculation. A comparison between experimental data and blind calculations of the participants was carried out within the benchmark activity. Key phenomena were outlined based on the infor mation highlighted by the PKL operating agents and taking note from the CSNI validation matrix. The code simulations were firstly assessed by completing steady state and simulation adequacy tables. In general, most of the results of the majority of the participants showed a quite good agreement for simulating the overall behaviour of the transient: not only the primary system depressurization, the mass flow rate decrease and the end time of natural circulation was correctly reproduced in most of the simulations; but also core dry out was conservately predicted by 10 of 12 participants and 8 of 9 codes.
Three strain rates were investigated: 0. 05, 0. 2 and 0. 5 / s, with the superposed CSM oscillation of 2 nm at 45 Hz. Tests were also performed with CSM oscillation frequency of 110 Hz for the highest strain rate to investigate potential artifacts on the measured stiffness (elastic modulus) associated with interference between the lock-in amplifier and some component of the semistatic loading (see section 4. 2). The CLR tests were performed using a constant loading rate of 0. 2 mN/s, and 10 indents were conducted for each target load in the range of 2 mN-12 mN. The distance between neighboring indents was set as 40 lm for both the CSR and CLR tests to avoid interference from adjacent indents. Nanoindentation data obtained at depths below 100 nm were generally excluded from analysis due to relatively large statistical variability, most likely due to slight surface roughness variations  [7]  and reverse indentation size effects  [23].
The APT dataset acquisitions were performed with a CAMECA LEAP 4000X HR atom probe using a 50 K base temperature, 20 % pulse fraction and a detection rate of 0. 5 %, and the datasets were collected using voltage mode for better spatial resolution. A solute concentration-based cluster searching algorithm was developed by our group and applied to characterize the a' precipitates. The threshold Cr concentration was set as 30 at. % to reduce the effect of natural fluctuation. The size, number density and volume fraction of a' phase were obtained from this method. The cluster and matrix Cr concentrations reported here are the core and lowest value measured from the proximity histograms generated with Integrated Visualization and Analysis Software (IVAS)  [24]. The spherical equivalent radius was used to quantify the size of the a ' precipitates, which can be derived with the following formula r ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3N=4pqQ p , where N is the number of atoms in the cluster, q = 84.
Acknowledgement This work was supported by the  Office of Nuclear Energy , under contract as part of the SNAP consortium research activities. The fabrication of the FeCr binary alloys has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom research and training program  under Grant Agreement No. The APT work was conducted at the  ORNL's Center for Nanophase Materials Sciences , which is a  DOE office of Science User Facility. The authors would like to acknowledge funding from the  State of Tennessee and Tennessee Higher Education Commission (THEC)  through their support of the  Center for Materials Processing. The authors also thank  Dr. Caleb Massey  for help with the APT data collection and  Jennifer Hay  ( KLA company ) for technical support in nanoindentation testing.
In this paper, we present the X-ray analysis of Sloan Digital Sky Survey DR8 redMaPPer (SDSSRM) clusters using data products from the XMM Cluster Surv e y (XCS). In total, 1189 SDSSRM clusters fall within the XMM -Newton footprint. This has yielded 456 confirmed detections accompanied by X-ray luminosity ( L X ) measurements. Of these clusters, 381 have an associated X-ray temperature measurement ( T X ). This represents one of the largest samples of coherently derived cluster T X values to date. Our analysis of the X-ray observable to richness scaling relations has demonstrated that scatter in the T X -λ relation is roughly a third of that in the L X -λ relation, and that the L X -λ scatter is intrinsic, i. e. will not be significantly reduced with larger sample sizes. Analysis of the scaling relation between L X and T X has shown that the fits are sensitive to the selection method of the sample, i.
Fig. 4  shows the distribution of these associations for all 43 random catalogues. Based upon a Gaussian fit to the distribution, we find we would, on average, randomly match to an extended XAPA source 22. 8 ± 5. 0 times. We thus estimate a contamination rate in the SDSSRM-XCS sample of 1. 5 per cent (23/1548). We note that since we made the simplifying assumption of a RM rd match when falling within a XAPA (circularised) region, and no eyeballing performed, this estimate is likely an upper limit. MNRAS  516, 3878-3899 (2022)  A detailed description of XCS3P can be found in LD11 , but a brief o v ervie w is as follo ws. Cluster spectra were extracted using the SAS tool EVSELECT and fit using XSPEC  (Arnaud 1996 ). The fits were performed in the 0. 3-7. 9 keV band with an absorbed APEC model  (Smith et al.
5 keV, close to our assumed value. The majority of the SDSSRM-XCS unm clusters have λ RM ≈ 20-30. We then measured a 3 σ upper limit on the countrate within those apertures, using the SAS tool EREGION AN ALYSE , which implements the method of  Kraft, Burrows & Nousek ( 1991 ). Point and extended sources are masked out from the analysis. The background region had radii with inner and outer values of 1. 05 r 500c and 1. 5 r 500c , respectively. In order to convert the count-rate upper limit into a luminosity upper limit, we used an energy conversion factor (ECF). First, an Auxiliary Response File (ARF) and Redistribution Matrix File (RMF) were produced at the position of the RM cluster, assuming the rele v ant o v erdensity radius. The ARF and RMF were then used to generate a f ak e spectrum in xspec using the FAKEIT tool.
Comparison to the literature 3. 4. 1. 1 Sample sizeWe have delivered one of the largest cluster samples with coherently measured T X values to date. The only equi v alent sample is the XCS First Data release (XCSDR1;  Mehrtens et al. 2012 ). XCSDR1 included 401 clusters with measured temperatures distributed across the entire extragalactic sky (i. e. extending beyond the SDSSDR8 footprint). In Fig. 8  (a), we show the number of SDSSRM-XCS T X clusters per T X bin (the subset of 150 in the SDSSRM-XCS T X , vol subsample is highlighted in blue). The T X value distributions from a non-comprehensive list of other recently published samples are o v erplotted as spline curves, described by the following: (i) The cyan curve shows the 57 XMM derived T X values for SDSS DR8 RM clusters (spanning a redshift range of 0.
S C A L I N G R E L AT I O N S D E R I V E D F RO M T H E S D S S R M -X C S S A M P L E S In this section, we present the scaling relations derived from some of the SDSSRM-XCS samples described in Section 3. 4 and Table  2. In Sections 4. 1 and 4. 2 , we focus on the sample with the most robustly measured X-ray properties and that is restricted to the RM volume limited redshift range i. e. the SDSSRM-XCS T X , vol cluster sample (see Section 3. 4 ). In Section 4. 3 , we present fits to samples with less conserv ati v e cuts, to e xplore the relativ e importance of sample size o v er measurement accurac y. Fits to the scaling relations were performed in log space using the R package LInear Regression in Astronomy ( LIRA) 14  , fully described in  Sereno ( 2016 ). Formally, scaling relations are fitted with a power law of the form Y = A + B • Z ± , ( 2 ) where var ( ) = σ 2 Y | Z and Z is the intrinsic cluster property.
The luminosity-temperature relation deri v ed from the SDSSRM-XCS T X , vol sample The L r500 X, 52 -T r500 X relation is shown in Fig. 9. The SDSSRM-XCS T X , vol data points are shown as blue circles. A power-law relation between L r500 X, 52 and T r500 x is fit to the data, which we express as log L r500 X, 52 E( z) γ LT L 0 = log ( A LT ) + B LT log T r500 X T 0 ± σ L | T , (3) where A LT denotes the normalization, B LT the slope, γ LT the evolution with redshift, and σ L | T the intrinsic scatter. Note that the intrinsic scatter is given in natural log space and can be interpreted as the fractional scatter. We assumed T 0 = 4 keV, L 0 = 0. 8 × 10 44 erg s -1 (roughly the median values for the SDSSRM-XCS T X , vol sample) and a self-similar evolution of the relation where γ LT = 1. The fit to the SDSSRM-XCS T X , vol sample is highlighted by the blue solid line in Fig.
Scaling relations with all available X-ray data The SDSSRM-XCS T X , vol sample only contains a fraction of the Xray information available for SDSSRM-XCS clusters. In this section, we investigate whether there is a benefit to including additional clusters with less precise individual measurements. To explore the impact of T X measurement errors on the derived L X -T X relation (see Section 4. 1 ), we have added all 381 clusters with a measured T X value in the SDSS-XCS T X sample. The results are shown in Fig. 11 (a ) and best-fitting parameters given in Table  3. It is clear that there is no significant change in the fitted relation when less accurate T X values are included. There is some marginal benefit to including more clusters in the fit (e. g. the scatter drops a little, although not significantly).
The black solid line represents a fit to the data (including upper limits) with the light blue shaded region highlighting the 68 per cent uncertainty. In each plot, the grey bands represent the 1 σ , 2 σ , and 3 σ intrinsic scatter. Rykoff et al. 2016 ). As e xpected (giv en that the T X methodology was very similar to that used herein), there is an excellent agreement in the case of the RR14 XCS sample. The fit to the RR14 ACCEPT sample is also consistent with that to the SDSSRM-XCS T X , vol sample. Ho we ver, as can be seen in Fig. 12  (a), the slope is steeper (although at < 3 σ ). There is consistency in normalization at the pivot point ( λ 0 = 60). This contrary to that found in RR14 , who found an ≈40 per cent difference between their fits to RR14 ACCEPT and to R14 XCS because RR14 did not carry out any Chandra to XMM T X scaling.
Furthermore, the temperatures are determined within r 2500. The input data vector was obtained, and the T 2500 X -λ RM relation fit following Section 4. 2 i. e. with λ 0 = 60 and T 0 = 4 keV. The comparison of the data and fits are given in Fig. 12 (d). The F19 data are given by the dark-red triangles, with the LIRA fit given by the dark-red dashed line (and red shaded region the 68 per cent uncertainty). The SDSSRM-XCS T X , vol relation is mostly obscured by the F19 fit, because the results are so consistent. In summary, the results presented here (and in Section 3. 4. 1 ) are consistent with those in the literature and based on the largest compilation of T X and λ RM data to date. Furthermore, the extremely consistent comparison between this work and the results in F19  In each case, the black solid line represents a fit to the SDSSRM-XCS T X , vol data (as given in Table  3  ) and the light blue shaded region the corresponding 68 per cent uncertainty.
Comparison to the fit provided in Rozo & Rykoff ( 2014 , green dashed line and light green shaded region highlighting the 68 per cent uncertainty) using the R14 ACCEPT sample (note the relation has been scaled to XMM temperatures using  Rykoff et al. 2016  ) is shown in (a). Comparison to the data given in Hollowood et al. (  2019 ) is show in plot (b), scaled to XMM temperatures using  Rykoff et al. ( 2016 ) , with the LIRA fit to the data given by the brown dashed line and the 68 per cent uncertainty given by the brown shaded region. Comparison to the data given in  Oguri et al. ( 2018 )  is shown in plot (c), with the LIRA fit to the data given by the purple dashed line and 68 per cent uncertainty given by the light purple shaded region. Note, the richness is estimated from CAMIRA, denoted N mem(CAMIRA).