dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis For acquired myelin-sensitive metrics, normality was tested with the Shapiro-Wilk test. Because not all data were normally distributed, we used the Steel-Dwass nonparametric test for multiple comparisons to compare ROI volumes and the values of SyMRI MVF , T1w/T2w, MTsat, and RD among plaque, periplaque, and NAWM tissue. The percentage changes in plaque or periplaque relative to NAWM were also calculated and compared among different metrics using the Steel-Dwass test. The signs of negative percentage changes were inverted before comparison.
Statistical Analysis For acquired myelin-sensitive metrics, normality was tested with the Shapiro-Wilk test. Because not all data were normally distributed, we used the Steel-Dwass nonparametric test for multiple comparisons to compare ROI volumes and the values of SyMRIMVF, T1w/T2w, MTsat, and RD among plaque, periplaque, and NAWM tissue. The percentage changes in plaque or periplaque relative to NAWM were also calculated and compared among different metrics using the Steel-Dwass test. The signs of negative percentage changes were inverted before comparison. Spearman's rank order correlation analysis was used to investigate the correlation among myelin-sensitive metrics for plaque, periplaque, NAWM, and all regions. The signs of negative coefficients were inverted for further analysis and classification.
Spearman's ρ correlation coefficients were classified as follows: 0-0. 30, very weak; 0. 30-0. 50, weak; 0. 50-0. 70, moderate; 0. 70-0. 90, strong; and 0. 90-1. 00, very strong. A 2-sided p-value <. 05 was considered significant. Shapiro-Wilk and Steel-Dwass tests were performed with the software package R, version 3. 3. 3. Calculation of correlation coefficients and comparisons of these were performed on Matlab (release R2015b, MathWorks, Natick, Massachusetts, United States).
Sequence Quality Control, Genome Alignment, and Expression Quantification Sequence reads were converted from BCL to fastq file format, quality checked, filtered, demultiplexed, and had PCR adapters removed using Bcl2fastq 2. 0 (Illumina) software. Reads with a quality score of less than Q30 and a barcode mismatch of 1 base or more were removed. When analysis of the data for this project commenced, no Chinook salmon reference genome was available. Because of this, reads were initially mapped according to reference genomes of three related species: Coho salmon (Okis_V1), rainbow trout (Omyk_1. 0), and Atlantic salmon (ICSASG_v2). Recently, the Chinook salmon genome was published (Otsh_V1. 0), and so reads were remapped to the Chinook genome. Downstream analysis (e. g. differential expression and functional annotation) is based on these Chinook mapping results.
Differential Expression Analysis and Metabolic Pathway Enrichment Downstream analysis of read counts per gene, including examination of genes that were differentially expressed (DE) between white and red salmon, was completed in R v3. 4. 4 (http://cran. rproject. org/). Data structure was examined and validated prior to DE analysis, which included outliers and batch effect assessment, using base R tools to produce PCA plots, read density plots, hierarchical clustering, and pairwise sample comparisons. The DESeq2 package  (Love et al. 2014)  was used for DE analysis based on 3 experimental group comparisons: white salmon livers vs red salmon livers, white salmon muscle vs red salmon muscle, and white salmon pylorus vs red salmon pylorus. DESeq2 fits counts per gene to a negative binomial generalized linear model (GLM), estimating log2 fold change and expression strength of each gene between experimental groups.
A Wald test is used to test the significance of gene expression and the p values are false discovery rate (FDR) tested using the Benjamini-Hochberg method  (Benjamini and Hochberg 1995). Genes with an FDR adjusted p value of less than 0. 05 were considered to be significant. Functional enrichment in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways and GO (Gene Ontology) terms was assessed based on the list of significantly DE genes using the ClusterProfiler package v3. 6. 0  (Yu et al. 2012). ClusterProfiler is an analysis and visualization module that is based on statistical analysis from the DOSE (Disease Ontology Semantic and Enrichment) package  (Yu et al. 2015). DOSE performs a hypergeometric test to estimate overrepresentation of DE genes per pathway. As with DE analysis, significance values were FDR tested using Benjamini-Hochberg.
p = 3. 2e-02). Fig. 2  Principal component analysis (PCA) performed on the transcription data of muscle, liver, and pylorus of both red and white Chinook salmon. Groups have been colored and shaded using the R package "vegan" and are based on 85% confidence levels for data range differentially expressed genes per pathway. Furthermore, several pathways are linked by few associated differentially expressed genes as displayed in the concept network (Fig. 8 ).
To the best of our knowledge, no preclinical study in NHP has yet reported the evaluation of the repeatability and accuracy of the different [ 18 F]FDG PET quantification approaches in a standardized preclinical setup. In the present study, we performed repeated brain [ 18 F]FDG PET acquisition in six healthy NHP using a standardized procedure to control for experimental factors. First, the test-retest variability of PET data metabolism outcome parameters was assessed, and compared for accurate interpretation of brain [ 18 F]FDG images. Finally, we evaluated the robustness of the outcome parameters by considering their respective confidence interval on an independent group of naive NHP.
PET imaging was performed on a Concorde Focus220 microPET scanner providing high sensitivity and high spatial resolution (FHWM = 2 mm) (Siemens, TN, USA). A 17-min-transmission scan (using a 57 Co point source) was performed prior to PET acquisition to allow for attenuation correction, followed by a 60 min dynamic emission scan acquired in 2D mode. Data acquisition started simultaneously with the i. v. bolus injection of [ 18 ]FDG (into the same vein as for propofol injection) using an automated injection pump at a rate of 4 mL/min during 30 seconds. PET images were corrected for radioactive decay, scatter, attenuation and detector inhomogeneity, were rebinned in 27 frames (4×15, 4×30, 2×60, 5×120, 3×300, 9×600 sec. ) and reconstructed as previously described (OSEM-2D)  [17]. To estimate the test-retest variability, a second scan was performed under exact same conditions after an interval of 27 ± 6 days.
Arterial glycemia (Gly) and venous blood samples were withdrawn simultaneously just before [ 18 F]FDG injection. Both measurements were compared at test and retest sessions. All glycemia samples were measured in duplicate with a GM9 glucose analyser (Analox, Stourbridge, UK), which was calibrated at 5 mmol/L using standard solutions. The bias and repeatability of the GM9 analyzer was determined from 10 measures of a glucose standard. The four additional and independent animals were scanned once under the same conditions so as to address the robustness of the quantification methods. PET image analysis. Time frames were summed to create an integrated image for automatic co-registration with the T2-weighted MRI using a dedicated PMOD software (PMOD Technologies, version 3. 6, Zurich, Switzerland).
Anatomical regions of interest (ROI) were automatically delineated on individual MRI images using the CIVM atlas from the Primatologist segmentation pipeline for the macaque brain  [18]  and were as follows: thalamus, globus pallidus, caudate, putamen, global cortex, midbrain, pons, cerebellum, frontal and occipital cortices. Kinetics of the radioactivity of each ROI was then extracted from the dynamic PET data as a time-activity curves (TACs). For each animal, the same MRI segmentation was applied to extract TACs from test and retest PET images. The Patlak graphical analysis was applied to the regional TACs in PMOD to quantitatively estimate the cerebral metabolic rate of glucose (CMRglu) under the hypothesis that the tissue includes a compartment in which the tracer is irreversibly entrapped  [10, 19].
Statistical analyses Data are presented as mean ± SD unless otherwise indicated. Statistical analysis was performed using the R project free software version 3. 3. 1 (https://cran. r-project. org/). The homogeneity of experimental conditions (body weight, temperature and injected dose at test and retest) were analyzed using Wilcoxon signed-rank test. Plasma exposure (AUCplasma ) across animals were compared using a one-way analysis of variance (the distribution of residues was normal and homogeneous). Comparison of plasma exposure between test and retest was performed using a paired Wilcoxon rank sum test. The stability of the brain concentration of [ 18 F]FDG was assessed using a two-way ANOVA where macaques and times were defined as explanatory variables (normal and homogeneous distribution of residues), followed by Tukey post hoc testing in TACs of all anatomical regions of interest.
Methods We obtained approval for the survey from the Ethics Committee at the Wroclaw Medical University. Parents and children received detailed information regarding the background and the implementation of the study, and were given the opportunity to withdraw their children from participation at any time. Each participant had to sign consent and did so on behalf of their child. Two hundred and twenty-seven children and their parents were invited to take part in the study. However, due to incomplete data obtained in the questionnaires or errors in their completion, 19 of them were excluded. Finally, two hundred and eight children with AR participated in the study (89 girls and 119 boys, aged 6-17, mean age 11. 7 ± 3). They were recruited from randomly selected outpatient clinics in Wroclaw, the capital of Lower Silesian voivodeship in southwest Poland, and one rural region, in the distance of around 40 km away from Wroclaw.
| RNA-seq and de novo transcriptome assembly Raw sequence data from the HiSeq 2500 sequencer were deposited into the NCBI SRA under the accession no. PRJNA589878. The lowest number of contigs obtained following tissue de novo transcriptome assemblies was recorded for eggs  (19, 575) , whereas the highest was for larvae (50,757) and muscles (51,040). Meta-analysis revealed that a total of 43,501 contigs were obtained using the implemented approach (Table  1 ), from which 35,343 were recognized as unique, which were used for microarray design. Overall, the metatranscriptome was characterized by 93. 8% completeness as indicated by the BUSCO score. This score has been calculated on 4,584 Actinopterygii proteins and corresponds to 79. 1% complete single copy, 14. 7% complete duplicated, 2. 7% fragmented, and 3.
| Preparatory phase: RNA-seq The sequencing libraries were prepared with TruSeq Stranded mRNA Sample Preparation kit (Illumina, San Diego, CA) according to the manufacturer's instructions. Briefly, polyadenylated RNA was selected using oligo(dT) magnetic beads and was chemically fragmented. First-strand complementary DNA (cDNA) synthesis was performed using random primers and SuperScript II reverse transcriptase in the presence of actinomycin D. Following the cDNA synthesis and adapter ligation, PCR amplification of the generated cDNA was performed. The libraries were sequenced using an Illumina HiSeq 2500 system with 250-base pairs paired-end reads. The sequences were acquired using the Illumina data processing pipeline. The quality control of the samples and screen of contamination was performed using FastQC and FastQ Screen, respectively (both are open-source software available at http://www.
| Preparatory phase: Transcriptome assembly and annotation The tissue Fastq file pairs were processed in the same way as was done for the Phylofish database  (Pasquier et al. , 2016). Briefly, the read file for each tissue was assembled with the de novo RNA-Seq Assembly Pipeline (DRAP;  Cabau et al. , 2017)  runAssembly module (version v1. 92) using standard parameters following a multi-kmer Oases approach. The contig FASTA files were then merged using the DRAP runMeta module with standard parameters. The resulting contig set was processed with TransDecoder (https://github. com/ TransDecoder/TransDecoder version 5. 0. 2) to find contigs harboring an open reading frame. The contigs were also quality checked with BUSCO  (Simão, Waterhouse, Ioannidis, Kriventseva, & Zdobnov, 2015 ; version 3.
| Preparatory phase: Designing of pikeperch-specific microarray The pikeperch-specific microarray was designed with the eArray platform of Agilent (available at https://earray. chem. agilent. com/ earray) using the obtained transcriptome. The 8 × 60 k arrays contained 35,343 unique probes. The microarray design is accessible at Gene Expression Omnibus platform under the accession number, GPL27937. 4. 5 | Experimental phase: Broodstock management and spawning of domesticated broodstock Domesticated pikeperch broodstock (60 males and 60 females held at Asialor fish farm, Pierrevillers, France) were reared for 6 years in RAS under fully controlled environmental conditions. The system was equipped with biological and mechanical filtration, allowing the maintenance of high water quality parameters (oxygen levels always >80% of the saturation level; ammonia in the 0.
From the Cy3-cRNA preparation, 1. 65 μg was fragmented and hybridized on a subarray. Hybridization was carried out for 17 hr at 65°C in a rotating hybridization oven, and subsequently the array was washed and scanned with an Agilent Scanner (Agilent DNA Microarray Scanner, Agilent Technologies, Massy, France) using the standard parameters for a gene expression 8 × 60 k oligoarray (3 μm and 20 bits). Data were obtained with the Agilent Feature Extraction software (10. 7. 1. 1) according to the appropriate GE protocol and further analyzed with the GeneSpring GX software (Agilent ŻARSKI ET AL. | 947 Technologies, Santa Clara, CA). Data were published in the NCBI Gene Expression Omnibus  (Edgar, Domrachev, & Lash, 2002)  and are accessible through the GEO series accession number, GSE142412. The samples were randomly distributed on the microarray for hybridization.
The gene expression data were scale normalized and log (2) transformed before statistical analysis. DEGs between the groups representing the eggs obtained from domesticated (samples D1-D8) and wild (samples W1-W7) females were identified with the use of the Gene Spring GX software. The differences between the groups were analyzed by unpaired t test after applying filters of a minimum twofold change in expression and a p <. 05. During the analysis the Benjamin-Hochberg correction was applied. Average linkage clustering analysis (Gene Cluster 3. 0) was performed for the differentially abundant genes (unsupervised linkage).
| Experimental phase: Functional analysis of DEGs For functional analysis of DEGs, gene symbols for human homologs of each DEG were obtained using basic local alignment search tool (BLAST; available at https://blast. ncbi. nlm. nih. gov/Blast. cgi) and UniProt online tool (available at https://www. uniprot. org/). Briefly, RefSeq number for each DEG was subjected to BLAST (using the "blastp" option) against UniProtKB/Swiss-Prot (Swissprot) database and the unique UniProt accession numbers of best hits were further used for retrieving the gene name using the UniProt Retrieve/ID mapping tool. The obtained gene symbols were used for enrichment analysis (with cutoff at FDR = 0. 05) using the ShinyGO v0. 61 online tool (available at http://bioinformatics. sdstate. edu/go/; Ge & Jung, 2018). A hierarchical tree, summarizing the correlation among the 30 most significantly enriched pathways, was also generated using this tool.
9), where (a) shows a hierarchical clustering tree summarizing the correlation among the significant pathways listed in Figure S3 (pathways with many shared genes are clustered together; bigger blue dots indicate the higher significance of p values) and (b) shows the visualization of the relationship between the enriched pathways (two pathways [nodes] are connected if they share 50% or more genes; darker nodes are more significantly enriched gene sets; bigger nodes represent larger gene sets; thicker edges represent more overlapped genes). Areas shaded with the same color in panels a and b represent the same set of enriched pathways F I G U R E 4 Results of STRING (v 11. 0) analysis where (a) presents the functional protein association network of gene set recognized for being involved in the developmental process (for details see Figure 3) where nodes and gene names shaded in green indicate the genes chosen for qPCR validation; (b) network statistics; (c) 10 most enriched biological processes with an indication of gene ontology accession number (#term ID), number of genes involved in the particular process (observed gene count), FDR value of the enrichment analysis as well as color scale allowing identification of particular process on the nodes visualized in the association network (part a); (d) five most enriched KEGG pathways.
I G U R E 5 Results of STRING (v 11. 0) analysis where (a) presents the functional protein association network of gene set recognized for being involved in the nervous system development (for details see Figure 3) where nodes and gene names shaded in blue indicate the genes chosen for qPCR validation; (b) network statistics; (c) 10 most enriched biological processes with an indication of gene ontology accession number (#term ID), number of genes involved in the particular process (observed gene count), FDR value of the enrichment analysis as well as color scale allowing identification of particular process on the nodes visualized in the association network (part a); (d) five most enriched KEGG pathways. FDR, false discovery rate; qPCR, quantitative polymerase chain reaction F I G U R E 6 Results of STRING (v 11.
0) analysis where (a) presents the functional protein association network of gene set recognized for being involved in the regulation of phosphorylation (for details see Figure 3) where nodes and gene names shaded in yellow indicate the genes chosen for qPCR validation; (b) network statistics; (c) 10 most enriched biological processes with an indication of gene ontology accession number (#term ID), number of genes involved in the particular process (observed gene count), FDR value of the enrichment analysis as well as color scale allowing identification of particular process on the nodes visualized in the association network (part a); (d) five most enriched KEGG pathways. FDR, false discovery rate; qPCR, quantitative polymerase chain reaction F I G U R E 7 Results of STRING (v 11. 0) analysis where (a) presents the functional protein association network of gene set recognized for being involved in the cell motility and localization (for details see Figure 3) where nodes and gene names shaded in red indicate the genes chosen for qPCR validation; (b) network statistics; (c) 10 most enriched biological processes with an indication of gene ontology accession number (#term ID), number of genes involved in the particular process (observed gene count), FDR value of the enrichment analysis as well as color scale allowing identification of particular process on the nodes visualized in the association network (part a); (d) five most enriched KEGG pathways.
ACKNOWLEDGMENTS The authors are sincerely grateful to ASIALOR for their support and access to their facilities as well as  Jean Babtiste Muliloto ,  Miroslav Blecha , and  Maud Alix  for their assistance during the reproduction of pikeperch. The study was funded by the project entitled: " Integrated analysis of transcriptome and proteome as a tool for assessment of quality and aging process in eggs of pikeperch " (Grant sponsor: National Science Center for Poland, Grant Number:  2016/22/M/NZ9/00590 ).
DATA AVAILABILITY STATEMENT RNA-seq data used for designing the microarray have been deposited in the NCBI SRA under the accession no. PRJNA589878 (available here: https://www. ncbi. nlm. nih. gov/sra/PRJNA589878). Microarray design and microarray expression data were deposited at Gene Expression Omnibus under the accession numbers: GPL27937 (microarray design available here: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GPL27937) and GSE142412 (microarray expression data available here: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE142412).
F I G U R E 5 Results of STRING (v 11. 0) analysis where (a) presents the functional protein association network of gene set recognized for being involved in the nervous system development (for details see Figure 3) where nodes and gene names shaded in blue indicate the genes chosen for qPCR validation; (b) network statistics; (c) 10 most enriched biological processes with an indication of gene ontology accession number (#term ID), number of genes involved in the particular process (observed gene count), FDR value of the enrichment analysis as well as color scale allowing identification of particular process on the nodes visualized in the association network (part a); (d) five most enriched KEGG pathways. FDR, false discovery rate; qPCR, quantitative polymerase chain reaction F I G U R E 6 Results of STRING (v 11.
9 fm where r 0 is defined as r = r 0 × (A 1/3 α + A 1/3 13 N ). Proton widths are deduced in all cases as Γ p = Γ tot -Γ α , except in the case of the three broad states at 7. 753, 8. 073 and 8. 224 MeV. The 17 F resonance parameters derived from this work are summarized in Tab. II, and spectroscopic information of 17 O states is given when pairing of analog states is established. The contribution of individual 17 F resonances to the astrophysical S-factor S(E) of the 13 N(α,p) 16 O reaction, calculated using the spectroscopic information given in Tab. II, is shown in Fig. 6a. Calculations were performed with the R-matrix code AZURE2  [54]  using channel radius r α = 7. 5 fm and r p = 6. 7 fm. Solid lines correspond to resonances for which the α-particle partial width is estimated from the analog states, and dashed lines correspond to resonances where θ 2 α = 0.
The broad resonance at 1664 keV may contribute across all the temperature range of interest because of its large natural width (Γ = 795 keV). In this work resonances up to an energy of 2. 4 MeV are considered. This corresponds to a cutoff tempera- ture of 1. 4 GK when the procedure relying on the cumulative distribution of fractional resonant rates given in Ref. [57]  is followed. Below this temperature the low, recommended and high 13 N(α,p) 16 O reaction rates come from the present Monte Carlo study. At higher temperatures the recommended reaction rate is calculated by normalizing the 13 N(α,p) 16 O Hauser-Feshbach reaction rate given in the STARLIB database  [27]. The reaction rates are given numerically in Tab. III.
Similarly, the effect of the uncertainty on the θ 2 α parameter has been investigated considering two additional cases, e. g. θ 2 α = 0. 03 ± 0. 02 and θ 2 α = 0. 05 ± 0. 02. As in Ref. [52]  we find that the un-TABLE III. Low, recommended and high thermonuclear rates of the 13 N(α,p) 16 O reaction are given in cm 3 s -1 mol -1 as a function of temperature (T9). Rates are derived from a Monte Carlo approach below the cutoff temperature (T9 = 1. 4) (see text), and come from the STARLIB database at higher temperatures. certainty on this value has a minor impact on the final recommended 13 N(α,p) 16 O reaction rate. Interference effects have been neglected in this work given the current level of uncertainty on the spin and parity, and the resonance strengths, of states within 2. 4 MeV above the 13 N+α threshold.