dataesr/scientific-paragraphs-categorization huggingface dataset:
All but one are confirmed bona fide clusters with measured redshifts, including 184 spectroscopic redshifts. Among these confirmed clusters 12 were confirmed with follow-up programmes conducted by the Planck collaboration (see Paper 1 for details). The remaining non-confirmed cluster candidate is a high-reliability CLASS1 candidate, meaning that its characterization as a cluster is supported by data in other wavebands (see Paper 1 for details). It is thus considered as a bona fide cluster. The distribution on the sky of this baseline cosmological sample is shown in Fig. 1. In addition to our reference sample, we consider two other samples drawn from the PSZ for consistency checks. One is based on the detections from the second implementation of the MMF algorithm, MMF1, described in Paper 1.
External datasets When probing the six parameters of the ΛCDM model, we combine the Planck clusters with the Big Bang nucleosynthesis (BBN) constraints from  Steigman (2008) , Ω b h 2 = 0. 022±0. 002. We also use either the H 0 determination from HST by  Riess et al. (2011) , H 0 = (73. 8±2. 4) kms -1 Mpc -1 , or baryon acoustic oscillation (BAO) data. In the latter case we adopt the combined likelihood of  Hinshaw et al. (2013)  and Planck Collaboration XVI (2014), which uses the radial BAO scales observed by 6dF-GRS  (Beutler et al. 2011) , SDSS-DR7-rec and SDSS-DR9-rec  (Padmanabhan et al. 2012; Anderson et al. 2012) , and WiggleZ  (Blake et al. 2012). Cluster counts in redshift for our Planck cosmological sample are not sensitive to all parameters of the ΛCDM model. We focus first on (Ω m , σ 8 ), assuming that n s follows a Gaussian prior centred on the best-fit Planck CMB value  4  (n s = 0.
For SPT we show the 'cluster-only' constraints from  Reichardt et al. (2013). For our own analysis we show our baseline result for SZ+BAO+BBN with a prior on (1b) distributed uniformly in [0. 7, 1]. The figure thus demonstrates good agreement amongst all cluster observations, whether in optical, Xrays, or SZ. Table  3  compares the different data and assumptions of the different cluster-related publications.
Comparison with Planck primary CMB constraints We now compare the PSZ cluster constraints to those from the analysis of the primary CMB temperature anisotropies given in Planck Collaboration XVI (2014) (see Footnote 6). In that analysis σ 8 is derived from the standard six ΛCDM parameters. The Planck primary CMB constraints, in the (Ω m , σ 8 ) plane, differ significantly from our own, in particular through favouring a higher value of σ 8 , (see Fig. 11 ). For (1b) = 0. 8, this leads to a factor of 2 larger number of predicted clusters than is actually observed (see Fig. 7 ). There is therefore some tension between the results from the Planck CMB analysis and the current cluster analysis. Figure  10  illustrates this with a comparison of three analyses of primary CMB data alone (Planck Collaboration XVI 2014;  Story et al.
2012; Hinshaw et al. 2013 ) and cluster constraints in terms of σ 8 (Ω m /0. 27) 0. 3. It is possible that the tension results from a combination of some residual systematics with a substantial statistical fluctuation. Enough tests and comparisons have been made on the Planck data sets that it is plausible that at least one discrepancy at the two or three sigma level will arise by chance. Nevertheless, it is worth considering the implications if the discrepancy is real. As we have noted, the modelling of the cluster gas physics is the most important uncertainty in our analysis, in particular through its influence on the mass bias (1b). While we have argued for a preferred value of (1b) 0. 8 based on comparison of our Y 500 -M 500 relation to those derived from a number of different numerical simulations, and we suggest a plausible range of (1-b) from 0.
A different mass function may also help reduce the tension. Mass functions are calibrated against numerical simulations that may still suffer from volume effects for the largest haloes, as shown in the difference between the  Tinker et al. (2008)  and  Watson et al. (2013)  mass functions. This does not seem sufficient, however, given the results presented in Fig. 8. One might instead ask whether the Planck data analysis could somehow have missed a non-negligible fraction of the total number of clusters currently predicted to have S/N > 7, resulting in a lower observed number count distribution. This is linked to a possible underestimate of the true dispersion about the Y 500 -Y X relation at a given M 500. It would be necessary for Planck to have missed ∼40 percent of the clusters with predicted SZ S/N > 7 in order for the SZ and CMB number count curves in Fig.
Any extension would need to modify the power spectrum on the scales probed by clusters, while leaving the scales probed by primary CMB observations unaffected. The inclusion of neutrino masses, quantified by their sum over all families, m ν , can achieve this (see  Marulli et al. 2011 and Burenin 2013  for reviews of how cosmological observations can be affected by the inclusion of neutrino masses). The SPT collaboration  (Hou et al. 2014 ) recently considered such a possibility to mitigate their tension with WMAP-7 primary CMB data. There is an upper limit of m ν < 0. 93 eV from the Planck primary CMB data alone. 7 If we combine the Planck CMB (Planck+WP) likelihood and the cluster count data using a fixed value (1b) = 0. 8, then we find a 2. 8 σ preference for the inclusion of neutrino masses with m ν = (0.
Thus, ideally, we would have full followup of a complete Planck cluster sample. For large samples, however, full follow-up is costly and time consuming. This has led to the widespread use of mass estimates obtained from massproxy relations. These relations are generally calibrated from individual deep observations of a subset of the sample in question (e. g. ,  Vikhlinin et al. 2009a) , or from deep observations of objects from an external dataset (e. g. , the use of the REXCESS relations in Planck Collaboration XI 2011). For the present paper, we will rely on mass estimates from a mass-proxy relation. In this context, the M 500 -Y X relation is clearly the best to use. Y X , proposed by  Kravtsov et al. (2006) , is defined as the product of M g,500 , the gas mass within R 500 , and T X , the spectroscopic temperature measured in the [0.
A. 3. 2. Effect of use of an external dataset The above results assume a mass estimated from the baseline Y X -M 500 relation, derived by  Arnaud et al. (2010)  from an external dataset of 20 relaxed clusters (Eq A. 1). How does this relation compare to the individual hydrostatic X-ray masses of the Planck cosmological cluster sample? Of the 71 clusters in the baseline sample: -58 objects have temperature profile information extending to various fractions of R 500 , of which -10 cool-core objects have temperature profiles measurements at least out to R 500. Thus, while spatially-resolved temperature profiles are available for 58 of the 71 clusters with XMM-Newton observations, we must be careful in interpretation of these data. The Arnaud et al. relation was derived from a carefully chosen data set consisting of relaxed, cool-core objects having wellconstrained temperature profiles out to around R 500 , i.
A. 1), taken from  Arnaud et al. (2010) , is not measurable; neither is it measurable for the best-fit Chandra Y X -M HE 500 relation published in  Vikhlinin et al. (2009a). Using a smaller sample of 10 systems,  Arnaud et al. (2007)  measured an intrinsic scatter of σ log M HE 500 |Y X = 0. 039 (9 percent), in excellent agreement with the results of the simulations of  Nagai et al. (2007)  for the scatter of the M HE 500 -Y X relation for relaxed clusters (8. 7 percent, their Table  4 ). It is somewhat larger than the intrinsic scatter of the relation between the true mass and Y X derived by  Kravtsov et al. (σ log M 500 |Y X = 5 -7 percent) but close to the results of  Fabjan et al. (2011)  the results of  Kay et al. (2012). Partly this is due to the presence of outliers in our dataset (as discussed in Paper 1), and it may also be due to projection effects in observed data sets  (Kay et al.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project= planck&page=Planck_Collaboration.
HMIP-2 is a human quantitative trait locus affecting peripheral numbers, size and hemoglobin composition of red blood cells, with a marked effect on the persistence of the fetal form of hemoglobin, HbF, in adults. The locus consists of multiple common variants in an enhancer region for MYB (chr 6q23. 3), which encodes the hematopoietic transcription factor cMYB. Studying a European population cohort and four African-descended groups of patients with sickle cell anemia, we found that all share a set of two spatially separate HbF-promoting alleles at HMIP-2, termed "A" and "B. " These typically occurred together ("A-B") on European chromosomes, but existed on separate homologous chromosomes in Africans. Using haplotype signatures for "A" and "B," we interrogated public population datasets. Haplotypes carrying only "A" or "B" were typical for populations in Sub-Saharan Africa.
Genotyping and Sequencing Genotypes were generated from genomic DNA isolated from peripheral white blood cells. Genotype data from previous studies were included, which had been generated as described   (Menzel et al. , 2007a; Thein et al. , 2007; Makani et al. , 2010). Additional genotyping was performed in the London lab, by the Centre National de Génotypage (Evry, France), using the Sequenom procedure, and for the TwinsUK subjects, by the Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR. TaqMan assays (using Applied Biosystems reagents, procedures, and 3730 instrumentation) were performed in London to generate additional genotypes for the African British, Nigerian, and Tanzanian patients. Customized genotyping procedures were devised for rs66650371, rs11321816, and rs35786788, which are in close physical proximity to each other.
Association analysis In the four patient cohorts, genetic association of variants with %HbF and %F cell levels (both natural-log transformed) was analyzed by multiple linear regression (SPSS, Version 12, IBM), with age and sex included as covariates. The unstandardized regression coefficient ("β") was estimated as a measure of the effect each variant allele has on ln(HbF) levels, independent of sample sizes and allele frequencies, which differ across populations. Meta-analysis of the four groups was conducted, using a fixed-effects (inverse-variance weighted) model and included a test for heterogeneity. In the European twins cohort, regression analysis with ln(%F cells) was carried out using the regress procedure in Stata version 10. 1 after imputing missing genotypes with MACH 1. 0 (Y Li and G Abecasis).
Archaic hominins Denisova genotypes were accessed through the UCSC genome browser (track: Denisova High Coverage Sequence Reads). These originate from high-coverage genome sequence generated from a single individual  (Meyer et al. , 2012)  and therefore would likely not capture alleles that existed in low frequency in Denisovans. Neanderthal genotypes were also retrieved through the UCSC browser (track: Neanderthal Sequence Reads, by Ed Green, UCSC) and are based on low-coverage reads  (Green et al. , 2010)  from Neanderthal specimens from three individuals (Vi33. 16, Vi33. 25, and Vi33. 26). Sequence from a further three individuals did not cover the critical SNPs. Additional data were available from a high-coverage genome sequence of one ("Altai Neanderthal") individual  (Prüfer et al. , 2013) , and was publically available (http://cdna.
89% Trait values for individuals carrying each of these haplotypes in various genotype combinations are shown in Figure  6. The data mirror results from the patients: individuals carrying "A-b," "a-B," or the "A-B" combined clade show similar effects on HbF persistence, which suggests a "dominant in cis" model for the interaction of the two subloci, where "A" or "B" produce the full phenotype independent of which allele is present at the other sublocus. However, we were unable to formally reject the possibility that "A" and "B" show additive effects in cis (P = 0. 15); access to larger datasets or populations with higher frequencies of "A-b" and "a-B" are required to sufficiently power this test. Homozygotes for "A-B" showed significantly increased trait values (Fig. 6 ) compared to heterozygotes, suggesting an "additive in trans" model for the locus.
Global prevalence of HbF-promoting haplotypes The existence of common haplotype signatures for HbFpromoting alleles at HMIP-2-A/a and HMIP-2-B/b subloci across very different population groups (European/North Indian and Sub-Saharan African) suggests that the various instances of each, "A" and "B" allele, are derived from common ancestors and that they might be a general feature of human populations. To systematically investigate the presence of such alleles in populations across the globe, we looked for the presence of their characteristic haplotype signatures in public data from the 1000 Genomes Project  (Abecasis et al. , 2010) , a repository of full genome sequence for representative human populations. For this, we retrieved phase-aligned genotype data (statistically-derived most-likely haplotypes) for 325 variants detected in the HMIP-2 interval for 1197 individuals in fourteen populations (Phase 1) from the project's public online data repository.
This included data on 21 of the 22 variants associated with HbF variants in Europeans. When grouping the haplotypes into clades based on tag SNPs rs9399137 (representing "A/a") and rs9402686 (representing "B/b"), the remaining seven HbF-associated variants displayed a high-consensus pattern within these clades in all populations, analogous to what had been observed in the sickle patients. That is, in each of the populations sampled, haplotypes carrying the low-HbF variant for both tag SNPs display a high consensus for having low-HbF alleles at the remaining seven positions that define both subloci (ancestral clade; Fig. 7 ; Table  S4 ), haplotypes carrying the high-HbF tag allele at one of the subloci, but not at the other, again display a high consensus for the remaining high-HbF alleles at the same sublocus and for low-HbF alleles at the other one ("A-b" and "a-B" clades), and finally, haplotypes carrying high-HbF alleles for both tag SNPs carry high-HbF variant for all nine critical positions ("A-B" clade).
* P < 0. 005; * * P < 0. 0001; n. s. , not significant. A one-tailed t-test was used for comparing the genotype groups, i. e. , to confirm/reject the findings in the patients, i. e. , an HbF-increasing effect of the "A" and "B" containing haplotypes. and our own Gujarati dataset (22%), but also found at high frequency in Chinese (Han, 26%) and Japanese populations (24%), suggesting that this "Eurasian clade" was a distinguishing feature of the founder population of anatomically modern humans originally expanding into these regions. Mirroring the situation in British African and African American sickle patients, the presence of Eurasian clade in the African American population sample (at 6. 6% frequency) is likely due to European admixture. The three Latin American populations sampled in the 1000 Genomes project (Mexican American from Los Angeles, Colombian, Puerto Rican) exhibit a high frequency of HMIP-2 haplotypes.
Furthermore, the strong LD between the characteristic variants within each sublocus allows us to extend such studies into sparser datasets, where full haplotype signatures cannot be ascertained, but where individual component SNPs can be used to tag the presence of alleles "A" and "B". Such a dataset, providing us with an especially wide geographical and ethnic spread, was generated by the HGDP  (Pickrell et al. , 2009) , a study of 53 human populations genome-scanned with >600,000 SNP markers. We accessed and analyzed public HGDP genotype Figure  8  Frequency of HbF-promoting haplotype clades in 61 human populations. Phase-aligned genotypes for twelve SNP markers within the HMIP-2 interval were available for 53 populations from the Human Genome Diversity  (Pickrell et al. , 2009)  dataset (smaller pie charts).
6 ). Genetically, no obvious candidate for a biologically causative variant has yet emerged for the "B" high-HbF allele. None of its six signature SNPs appears to show consistently strong effects across all populations studied. Additional variants at "B/b" with strong trait association (e. g. , small deletions) might be absent from public sequence data due to uncertainties in allele calling, similar to the AAAC/AACCC length polymorphism rs11321816 (not HbF associated), which is missing from the 1000 Genomes dataset. Beyond "A" and "B", common alleles with comparable impact on erythroid traits are unlikely to exist at this locus, since association studies with haematological traits have consistently identified variants that belong to the "A-b," "a-B," or "A-B" clades, including African  (Makani et al.
Acknowledgements This work was supported by the  Medical Research Council, UK  (Grant  G0000111 ,  ID51640  to SLT). SM received funding from The  British Society for Haematology  (startup grant). SWM was a recipient of a split-site studentship from the  Commonwealth Scholarship Commission (UK Foreign and Commonwealth Office). The Twins UK study (TS) was funded by the  Wellcome Trust ; European Community's  Seventh Framework Programme  (FP7/2007-2013). The study also receives support from the  National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. Tim Spector is holder of an  ERC Advanced Principal Investigator award. SNP Genotyping was performed by The  Wellcome Trust Sanger Institute and National Eye Institute  via  NIH/CIDR.
Data analysis is still ongoing, while preliminary results are presented here. Fig. 3  shows the charge identification obtained with The masses are not correct on an absolute scale, and show a slight dependence on the energy-loss, which could be improved with an absolute energy and energy loss calibration. However, for the purpose of isotope selection, this is already sufficient. Isotope assignment was done after identifying 74 Se nuclides by means of a coincident γ spectrum. Together with the known primary beam, all other isotopes can be assigned by counting. EPJ Web of Conferences 02083-p. 2 of the 80 Kr beam induced by the gold target was studied to demonstrate the performance of AGATA at relativistic beam energies and to optimize the pulse shape analysis and tracking conditions. 80 Kr was chosen because of its large B(E2; 0 + 1 → 2 + 1 ) value of 37.
8%. This is still below the expectations. Due to the complexity of the overall system, these values are likely to be improved because there is much room for optimization. In particular for data from the AGATA detectors, the results depend on complex software algorithms for PSA and γ-ray tracking that have to be further adapted and improved. After commissioning, several experiments were conducted in 2012 as part of the first PreSPEC-AGATA physics campaign, using relativistic secondary beams of unstable neutron-rich nuclei or isomers produced either by target-induced fission of a relativistic uranium beam or in primary fragmentation reactions. These experiments combine the development of dedicated FAIR-relevant tools and techniques for high-resolution in-beam spectroscopy of relativistic radioactive ion beams with timely questions of nuclear structure research, such as Coulomb-excitation of a band-terminating isomer of 52 Fe, low-lying E1 modes of neutron-rich 64 Fe, evolution of the nuclear shapes of heavy Zr isotopes, or the collectivity of neutron-rich isotopes near 208 Pb.
Statistical Tests Non-parametric tests, the Mann-Whitney test to analyze group differences and the Wilcoxon test for dependent samples, were performed. Spearman's rank correlation coefficients were calculated. Data are shown as median with interquartile range (IQR). Student's t-tests and one-way ANOVA analyses were used for PCR data and cell culture experiments. Statistical tests were calculated using SPSS (version 19. 0. 0. 1); box plots were made with the Sigma Plot software (version 12. 0). Statistical significance was considered at P,0. 05.
A Gene Expression Array Analysis Uncovered Increased SAA1 Expression in CIM Muscle The study design is outlined (Figure  1 ); data on patient characteristics and further clinical information are presented in Table  1. Patients with non-excitable muscle membrane indicating muscle pathology in CIM developed muscle weakness during ICU treatment, with a median MRC score of 3. 0 (interquartile range [IQR], 2. 9-3. 3), whereas patients with excitable muscle membrane showed a median MRC score of 4. 3 (IQR, 3. 5-4. 8; P = 0. 003). A non-excitable muscle membrane measured at median day 6 (4/13) was predictive for the development of muscle weakness with a sensitivity and a specificity of 80% each. To assess very early changes in gene expression, we first performed microarrays. Among 24,133 transcripts, 1,841 genes were differentially expressed in critically ill patients versus control patients (5% FDR).
The top 30 genes by fold-change increased or decreased in critical illness were revealing (Tables  S2  and S3 ). Additionally, exon expression interaction with the grouping control versus CIM patients versus non-CIM patients led to a set of 1,948 significant differential expressed transcript clusters (5% FDR). Viewing the top 30 genes by fold change differences between CIM versus non-CIM patients implicated several candidates differentially regulated in CIM (Tables  S4  and S5 ). Among these we found serum amyloid A 1 and 4 (SAA1, SAA4) and therefore reasoned that acute phase response occurred in muscle during critical illness, especially in CIM patients. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE53702 (http://www.
ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc = GSE53702). RT-PCR analyses on biopsy specimens confirmed the increased SAA1 (controls: 0. 09 (0. 09-0. 9), ICU-patients early time point: 8. 16 (2. 42-36. 6), P = 0. 005) and SAA4 (controls: 0. 55 (0. 42-1. 54), ICU-patients: 8. 3 (2. 56-29. 39), P = 0. 002) expression levels in critically ill patients. SAA1 and SAA4 expression remained unchanged between the early and late biopsy specimen (late time point: SAA1:14. 77 (4. 13-27. 2), P = 0. 003; SAA4:16. 7 (6. 18-40. 64), P = 0. 0478) (Figure  2A ). Subgroup analyses showed that SAA1 and SAA4 were exclusively increased in CIM patients (SAA1:30. 6 (7. 46-45. 8), P = 0. 001; SAA4:22. 8 (4. 42-51. 6), P = 0. 0001), but remained unchanged in non-CIM (SAA1:2. 42 (0. 53-4. 6), P = 0. 15; SAA4:1. 64 (1.