dataesr/scientific-paragraphs-categorization huggingface dataset:
These measurements have been used to correct for changing sensitivity of the diagnostic, greatly improving agreement of the ECE profiles with those produced by LIDAR and HRTS. Concerning the measurements in this database, Figure  8a  shows that the ratio between LIDAR and ECE measurements (limited to T LID > 4keV) is not correlated with the vacuum magnetic field (B vac ). Figure  8b , instead, shows the ratio between second and third harmonics against B vac. Here, it can be observed that, while most of the points for baseline pulses that reach T X3 ∼ T X2 are concentrated at B vac ∼3. 3 T and higher, similar examples can be found also for cases around ∼2. 8 T.
Conclusions and prospects A very large database of T e measurements (more than 14000 time points from 246 discharges) was collected from high performance DD and DT pulses performed at JET. These include a variety of plasma conditions and different scenarios, some of which were tested using DT mixture for the first time. These measurements, taken with LIDAR Thomson scattering and an X-mode interferometer, showed discrepancies when high T e were reached. Differently from what had been observed in the previous studies of this kind  [3] [4] [5] , here the discrepancies were less pronounced and, most importantly, they were not only in the direction of T ECE > T LID for high T e. In fact, a richer phenomenology was observed, including also T ECE < T LID , with different scenarios showing different behaviours.
Comparison of the peaks of the second and third harmonics of the ECE spectra suggests the presence of non-Maxwellian features of the EEDF for most of the pulses in this data set. Even for plasma conditions where τ X3 was large enough to have T X2 ∼ T X3 , in fact, T X2 > T X3 was observed instead in many pulses. The possibility that EDF distortions could result in different measurements from ECE and Thomson diagnostics can be investigated using simple models that applies an arbitrary perturbation in velocity space to a Maxwellian EEDF and then calculating the resulting ECE spectrum. Such a study, employing a bipolar distortion of a Maxwellian EEDF, comparing the results of the model with the experimental measurements presented in this database, obtained promising results  [24]. However, at the moment, a clear physical cause for EDF distortions which could give rise to these discrepancies has not yet been identified.
Several hypothesis have been suggested and are being investigated, including, for example, effects related to the fast ion population (high NBI/ICRH power and DT fusion reactions). The ions could interact with the electron population directly through collisions or through the excitation of MHD modes (such as Alfvén modes) that can then transfer energy to the electrons. The latter possibility is being investigated via gyrokinetic simulations for parameters approaching those of some cases included in the database. The database presented in this work could be further expanded by the inclusion of other diagnostics. For example, equilibrium reconstructions that included con-.
To reduce the correlations among the 8 collisional parameters that define the βHTP profile  24  , we fixed a subset of them to ab initio values inferred from quantum-scattering calculations of H 2 -H 2 collisions (described in Supplementary Note 3). For every fit we left as free parameters the unperturbed (zero-pressure) transition frequency ν 0 , which is the quantity of main interest here, and the linear pressure shift of the line center δ 0 , which is hard to be accurately predicted by theory and can be better constrained by measurements. Figure  2c, d  show the residuals of two types of fitting conducted in a global form over the entire multi-pressure dataset, using just 5 (Fig. 2c ) or 2 (ν 0 , δ 0 , Fig. 2d ) free parameters, respectively (details given in Supplementary Note 4). In both cases we observe structured residuals at a level of 1% of the line maximum, with similar reduced chisquare values (1.
As a second source of systematic uncertainty, we evaluated the impact on ν 0 of the uncertainty on the ab initio parameters kept fixed in the fitting. For every such parameter varied in its confidence interval (see Supplementary Notes 4 and 5 for details) we calculated trajectories similar to those reported in Fig. 3  and considered the half peak-to-peak distance of the outer extrapolations as the corresponding uncertainty contribution to ν 0 , to be summed up in quadrature to the other contributions. This procedure leads to an additional term of systematic uncertainty of 220 kHz. To quantify the statistical uncertainty, we applied a bootstrap procedure taking advantage of the many spectral datasets acquired, running many fits on randomly assembled spectral datasets obtained by combinations of spectra acquired in different days over several months.
The differential sensitivity of the color pixels of the camera at pump and Stokes wavelengths allow disentangling the two colors and control their relative displacements in both the NF and FF. This takes place in real time thanks to a LabView software that, upon calculation of the pump and Stokes centroids from the fitting of their intensity profiles, optimizes the overlap of the Stokes beam onto the pump beam through a 4-channels proportional-integral-derivative (PID) controller followed by 4 piezoelectric tip-tilt mirror adjusters (see Supplementary Note 6 for a photograph of the apparatus). The system provides an angular root mean square (RMS) stability of 6 μrad (corresponding to RMS fluctuations of the measured frequency detuning by 65 kHz) over few hours. Active stabilization of thermodynamic gas parameters.
Uniform and constant temperature of the gas inside the multipass cell is obtained by encasing the cell inside a box made with thick Styrofoam. A pair of rubber heater stripes (Minco) is placed onto the cell central cylinder, and two fans circulate the air inside the Styrofoam box to ensure temperature homogeneity better than 100 mK on the outer surface of the cell. The temperature is measured through a calibrated temperature sensor (a Pt100 probe paired to a 6 ½ digit multimeter, Keysight 34461A) with an accuracy of 50 mK, whose output is used by a software PID controller (implemented in LabView) to regulate the current passing through the heater stripes and stabilize the temperature to 303. 1 K. To compensate for small leaks of the cell, a constant flow of about 10 -2 L min -1 is established in the cell using two flow controllers (Bronkhorst F-201CV-1K0-RAD-33-V), the first one installed between the H 2 cylinder and the cell, the second one between the cell and the vacuum pump.
Acknowledgements This work was supported by the  Italian FARE-MIUR  project  CH2ROME  (Grant no. R164WYYR8N ). P. W. is supported by the  National Science Centre in Poland , Project No. 2019/35/B/ST2/01118. H. J. is supported by the budgetary funds on science projected for 2019-2023 as a research project under the  "Diamentowy Grant" program.
2D-F ). Overall, data were consistent with SHH-MB, TP53 wild-type. Methylation profiling was performed on DNA extracted from FFPE tissue section in enriched tumor areas (tumor purity > 90%) and processed using Infinium Methylation EPIC BeadChip (850k) array (Illumina). Methylationbased tumor classification using the methylation classifier v11b4 (available at https:// www. molec ularn europ athol ogy. org) assigned the methylation class MB, subclass SHH-A (children and adult) with a calibrated score of 0. 92  [5]. The reanalysis of the samples with the most recent version of the methylation classifier (v12. 5) assigned them to the same methylation class (MB SHH-activated, subtype 4) with a calibrated score of 0. 88. Methylation data have been deposited in NCBI's Gene Expression Omnibus (GEO; Series accession number GSE225302) and are accessible through https:// www.
ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE22 5302. High-density DNA methylation arrays allowed for determining copy number alterations that were consistent with gain of chromosome 3 and focal loss in chromosome 7 with no other relevant chromosomal aberration such as MYCN or MYC amplification and/or deletions of chromosome 9q (PTCH1) (Fig. 2G ). Of note, gain of chromosome 3 may be relevant as chromosome 3q is a frequent cytogenetic alteration of MB SHH-A methylation class occurring in adults. Unexpectedly, immunostainings highlighted tumor components displaying a robust and diffuse GFAP immunoreactivity, usually not present in MB, along with tumor areas displaying a classical immunoreactivity for Synaptophysin (Fig. 3A, B ). Interestingly, double immunostains combining GFAP and Synaptophysin indicated that, in the GFAP-enriched areas, expression of GFAP and Synaptophysin was mostly mutually exclusive (Fig.
We therefore performed the pyrosequencing assay (PyroMark system using "IDH1/2 status" kit for Qiagen-Diatech) that revealed the rare IDH1(p. R132C) mutation, confirming the MRS findings. Interestingly, in addition to the IDH1(p. R132C) mutation, NGS analysis performed on Illumina MiSeq using Myriapod ® NGS-IL56G Onco-panel (NG032, Diatech-Pharmacogenetics) highlighted a concurrent high-frequency missense mutation (c. 677G > A; p. G226D) in the GNAS gene producing an amino acid change from nonpolar glycine (G226) to negatively charged aspartic acid that may affect Gsα protein conformation and function (Fig. 4A-B ). This mutation is not cataloged as a variant with clinical significance in any available database (NCBI, ClinVar, The Cancer Genome Atlas, cBioPortal, COSMIC). However, the possible molecular changes that could affect the GTP binding capacity suggest a pathogenic significance.
Interestingly, our data indicated that 2HG assessment may be also possible with routine MRS sequences, in case of very high 2HG concentration. Future dissemination of specific MRS expertise should favor incorporation of customized MR sequences in the clinical practice  [11]. This report emphasizes the importance of performing these investigations also in non-glial tumors that may help to unmask rare molecular alterations of potential value for tumor stratification and patient management. Further studies will be necessary to establish the effective frequency of IDH and GNAS mutations in MB and their prognostic relevance in the way of personalized medicine. IDH1-mutated SHH-MB may represent   [28]  (PDB: 1AZT). NGS analysis highlighted the presence of a missense mutation (c. 677G > A; p. G226D) in the GNAS gene causing an amino acid change from nonpolar glycine (G226) to negatively charged aspartic acid (D) that may affect Gsα protein conformation and function.
Funding This work was funded in part by  Italian Ministry of Health  (Grant No. RF-2016-02361014 ) and " Associazione dedicato A te" to PLP, Investissements d'avenir  (Grant No. ANR-10-IAIHU-06  and  ANR-11-INBS-0006 ) to FB and  NIH  grants  BTRC P41 EB027061  and  P30 NS076408  to MM.
When the 259 differentially regulated proteins between vtg2-mutant and Wt embryos were submitted separately (vtg2-mutant downregulated (Wt); N = 83, vtg2-mutant upregulated; N = 176) to a functional protein association networks analysis using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the zebrafish protein database, they resolved into networks with significantly and substantially greater numbers of known and predicted interactions between proteins than would be expected of the same size lists of proteins randomly chosen from the zebrafish database (Fig. 9 ). The network formed by proteins down-regulated in vtg2-mutant embryos (Wt; N = 83) is made up of a subnetwork of four Vtgs (Vtg1, Vtg2, Vtg3 and Vtg4) and another subnetwork made up proteins related to cellular homeostasis via cytoskeleton regulation, redox/detox activities, energy metabolism, and protein synthesis (protein folding and refolding activities).
Phenotypic observations For phenotypic observations, Wt and F2 vtg2-mutant couples (N = 5 each) were spawned 1 to 8 times, respectively, and embryonic development, survival rate, hatching rate, and larval development were subsequently observed until 20 dps. Fecundity and fertilization data was collected from a total of 5 and 44 batches from Wt and F2 vtg2-mutant couples, respectively. Survival and hatching ratio data was collected from offspring (F3) of the same couples. Fecundity (number of eggs per spawn) was recorded immediately after spawning and collected eggs were incubated in 100 mm Petri dishes filled with embryo medium (17. 1 mM NaCl, 0. 4 mM KCl, 0. 65 mM MgSO 4 , 0. 27 mM CaCl 2 , 0. 01 mg/L methylene blue) to assess embryonic development and phenotyping parameters. Hatching ratio was calculated based on the number of surviving embryos at 24 h and only spawns with > 5% survival rates were considered, therefore, this data was collected from 5 of Wt and 26 of vtg2-mutant batches, respectively.
The relative abundance of target cDNA within a sample set was calculated from a serially diluted cDNA pool (standard curve) using Applied Biosystem StepOne V. 2. 0 software. Similarly, the 2 -ΔΔCT mean relative quantification of gene expression method with the mean expression value of zebrafish elongation factor 1a (eif1a) and ribosomal protein 13a (rpl13a) were employed as housekeeping genes in this study. Primer sequences and properties for these genes are also given in S3 Table. Obtained data was subjected to independent samples Kruskal-Wallis nonparametric test (p < 0. 05) (IBM SPSS Statistics Version 19. 0. 0, Armonk, NY).
Obtained MS/MS spectra were searched against a target-decoy concatenated database created from the zebrafish Ensembl proteome database (Danio rerio_Zv9, March 2015) using Mascot (Matrix Science). Protein identification, quantification by spectral counts, and spectral count normalization were conducted as described by Yilmaz et al. 18. The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium 29 via the PRIDE  30  partner repository with the dataset identifier PXD042791 (available online at http:// www. ebi. ac. uk/ pride/ archi ve/ proje cts/ PXD04 2791). To detect significant differences in protein abundances based on group mean N-SC values (Wt vs. vtg2-mutant) an independent samples Kruskal-Wallis.
Protein identification, quantification, annotation and statistics A list with a total of 365 proteins identified in Wt and 1 hpf F3 vtg2-mutant embryos was filtered to exclude those detected in less than 4 biological samples and have less than 1. 0 fold difference in abundance and the remaining proteins (N = 259) were identified using online available functional annotation tools such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Database for Annotation, Visualization and Integrated Discovery (DAVID)  [31] [32] [33] [34]. These proteins were then classified into thirteen arbitrarily chosen functional categories that would account for > 90% of the proteins as originally suggested by Yilmaz et al. 18. Chi square analysis with a significance level of (p < 0. 05) was used to detect differences between groups in the distribution of differentially regulated proteins among functional categories.
Acknowledgements The authors thank  Dr. Amaury Herpin  for his advice on guide RNA selection,  Dr. Rolf Brudvik Edvardsen  for his advice on genotyping strategies,  Amélie Patinote  and the  INRAE ISC-LPGP  staff for fish breeding. Protim Core Facility was supported by structural grants from  Infrastructures en Biologie Santé et Agronomie (IBiSA) ,  Conseil Régional de Bretagne  (through  Biogenouest) ,  European Regional Development Fund ,  Rennes Métropole  and  University of Rennes  (through  Biosit ). This study was supported by  FP7 People 2013- Marie-Curie Actions-Intra-European Fellowships  ( FP7-PEOPLE-2013-MC-IEF ), Grant/Award Number:  626272 ;  Region Bretagne   in France- ( SAD-2013 ), Grant/Award Number:  13009218 ; and  Agence Nationale de Recherches , Grant/Award Number:  ANR-13-BSV7-0015.
To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red-list status is not a useful metric for informing species-specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data.
INTRODUCTION The International Union for Conservation of Nature (IUCN) Red List is a widely used criterion-based evaluation of species extinction risk and conservation status. The IUCN uses the best available data from across species ranges to evaluate demographic threats. Species are placed into 1 of several extinction risk categories based on species-wide risk assessments. These categories include the number of adult individuals (Criterion A); small range sizes, very high levels of population fragmentation, fluctuating populations, or few locations where species persist (Criterion B); small population size and declining populations (Criterion C); a very small number of individuals (Criterion D); or quantitative analysis of extinction risk, such as population viability analysis (Criterion E;  IUCN, 2012).
The relevance of red-list status as a proxy for intraspecific genetic diversity or vice versa hinges on the strength of this relationship and model predictive accuracy. However, predictive accuracy in this context has not been explored. If models of relationships between genetic diversity and redlist threat status can accurately predict threat status, then policy makers could use red-list status to assess or approximate genetic diversity levels in the absence of genetic data, which is still not available for most species. Additional metrics developed specifically for assessing genetic diversity status and trends would not be needed  (Hoban et al. , 2020 (Hoban et al. , , 2021;; Laikre et al. , 2020). Further, high predictive accuracy would mean that red-list status could also be assessed with genetic data for data-deficient species if available.
However, if the relationship between red-list status and genetic diversity is weak and has poor predictive accuracy, relying solely on red-list status would result in a lack of formal and sufficient protection for genetic diversity in natural populations, warranting the implementation of other indicators and proxies of genetic diversity (e. g. , the recently adopted effective population size >500 indicator [i. e. headline indicator A. 5 for Goal A and Target 4 of the Kunming-Montreal GBF] [CBD, 2022;  Hoban et al. , 2020] ). To evaluate the extent to which species genetic diversity is predictive of red-list status, we reanalyzed 3 genetic data sets (2 previously used for this purpose) containing estimates of genetic diversity obtained from different markers (mitochondrial gene sequences, microsatellites, and whole-genome sequences [WGS]).
DATA AND ANALYSES Our first data set was composed of estimates of mitochondrial DNA (mtDNA) cytochrome b diversity from 1036 bird species. This data set was compiled by  Canteri et al. (2021) , who used it to assess the relationship between red-list risk (threatened or not) and genetic diversity. Threatened species were those listed as vulnerable, endangered, and critically endangered. They concluded that red-list status captures species with low genetic diversity. Canteri et al. (2021)  obtained cytochrome b sequences from GenBank, a genetic sequence database, and measured genetic diversity at the species level with nucleotide diversity for species with 5 sequences minimum (median = 11 sequences per species across the data set). Our second data set consisted of genetic diversity estimates from WGS for 68 bird species from a data set compiled by Brüniche-Olsen et al.
(  2021 ) from 2 sequence databases, EMBL-EBI and NCBI. Genetic diversity was measured with genome-wide observed heterozygosity estimated from the site frequency spectrum for 1 sequence per species. With these data,  Brüniche-Olsen et al. (2021)  found that threatened species (endangered and critically endangered) had lower observed genomic heterozygosity than nonthreatened species. Finally, our third data set was the MacroPopGen database  (Lawrence et al. , 2018 (Lawrence et al. , , 2019)). This database contains site-level estimates of genetic diversity from microsatellite markers for vertebrate species (terrestrial vertebrates and freshwater fish) across North and South America harvested from the literature. For this data set, we obtained red-list statuses of species with the rredlist package  (Chamberlain, 2020) , an R interface for the Red List Application Programing Interface.
Gene diversity is the average probability that 2 randomly selected alleles in a population are different  (Nei, 1973). We averaged gene diversity across sample sites to obtain a species-level measure of genetic diversity comparable to the genetic diversity estimates in the mtDNA and WGS data sets. Although we tested these 3 marker types, biological differences among markers mean they are not all equally informative for conservation. Mitochondrial genomes are maternally inherited, behave as a single locus because they do not recombine, and have several protein-coding genes, meaning they do not evolve neutrally  (Galtier et al. , 2009). Mitochondrial DNA diversity is generally not correlated with genome-wide diversity or adaptive potential, 2 core targets of conservation genetics  (Kardos et al.
, 2015). Among bird species in our analyses, only 5 species were present across all 3 data sets (54 species shared between mtDNA and microsatellite data sets; 20 between mtDNA and WGS; 9 between WGS and microsatellite). Microsatellite genetic diversity was not correlated with mtDNA genetic diversity for species in both data sets (Figure  2 ). We modeled relationships between our measures of genetic diversity (nucleotide diversity for mtDNA data, observed genome-wide heterozygosity for WGS data, and gene diversity for microsatellite data) and red-list categories with the same 2 models fit to each data set. The first model type was an ordinal logistic regression implemented in R (R Core Team, 2021) with the polr function in the MASS package  (Venables & Ripley, 2002). The dependent variable was threat status ordered by risk (LC, least concern; NT, near threatened; VU, vulnerable; EN, endangered; CR, critically endangered).
Then, to more closely resemble previous work  (Brüniche-Olsen et al. , 2021; Canteri et al. , 2021) , we also tested how well genetic diversity classified broader binary red-list categories, threatened (comprised of CR, EN, and VU categories) versus nonthreatened (NT, LC), with binary logistic regressions. These models were standard logistic regressions: generalized linear models with a binomial error distribution fit with the glm function in R 4. 1. 1 (R Core Team, 2021). We assessed model predictive accuracy with confusion matrices generated with the caret package  (Kuhn, 2021). All analyses were repeated for mtDNA, microsatellite, and WGS FIGURE 2 Relationship of mitochondrial genetic diversity (nucleotide diversity) to microsatellite diversity (gene diversity) in 54 bird species (r = 0.