dataesr/scientific-paragraphs-categorization huggingface dataset:
Hybridization and data analyses RNA processing, Cy3-labeling and hybridization were made following the manufacturer's instruction for One-Color Microarray base gene Expression Analysis (Agilent Technologies). Hybridization was performed on Agilent microarrays Agilent-045382 UGSF flax 45K v1. 0 array based upon flax genome coding sequence (Lus-names)  (Wang et al. , 2012)  available at Phytozome 2. Some genes that have genolin-names  (Fenart et al. , 2010)  were added according to the EST database. The 4-plex array contains 45,220 60-mer in situ oligonucleotides per block. Hybridization and washing were performed following Agilent manufacturer's instruction, and slides were immediately scanned at 5 mm pixel -1 resolution using an Axon GenePix 4000B scanner driven by GenePix Pro 6. 0 software (Molecular Devices Corporation, Sunnyvale, CA, USA).
Scanned images (TIFF format) were then submitted to grid alignment and expression data analyses. For each slide, a lowest and print-tip median normalization was performed using R packages 3  as implemented in CLC bio software 4  followed by an inter-slide normalization. The 3 control samples were filtered for p-value < 0. 05, and the average was calculated for each gene. A fold change (FC) value was calculated between individual treated samples and the mean of corresponding controls. Differentially expressed genes (DEGs) were selected for a threshold >2. 0 or ≤0. 5. Functional annotation of DEGs was based on NCBI GenBank, and related genes' physiological processes were assigned with NCBI, AmiGO 2 Gene Ontology and UniProt. KEGG pathway analysis was also used to identify relevant biological pathways of selected genes.
Image analysis and protein identifications Scanning of the 2D-DIGE gels was carried out using a Typhoon Imager 9400 (GE Healthcare) at three different wavelengths corresponding to the different CyDyes as described in  Grimaud et al. (2013). The generated gel images were analyzed with DeCyder 7. 0 software, using the Cy2 channel as a standard. The multiple 2-D DIGE gels were matched, and the significance of changes in the abundance of specific proteins from different genotypes was established. By including the internal standard on each gel in the experiment, together with the individual biological samples, it is possible to measure the abundance of each protein spot on a gel relative to its corresponding spot in the internal standard present on the same gel (i. e. as a ratio). All matched spots were manually verified on all gels.
Enzymatic digestion and protein identification Differentially abundant spots (based on the statistical analyses described below) were excised from the gels and digested using an EVO2 Workstation (TECAN). All MS and MS/MS analyses were carried out by using a 5800 MALDI TOF/TOF (AB Sciex, Foster City CA, USA)  (Chabi et al. , 2017) , and Protein identification was performed using MASCOT server with a Linum database downloaded from phytozome (Lusitatissimum_200_V1. 0 with 43484 sequences; 17356610 residues) according to  (Wang et al. , 2012).
Bioinformatics and statistical analyses 2. 5. 1 Data deposition Transcriptomic data have been deposited to the NCBI online repository with accession number GSE222066. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 5  partner repository  (Perez-Riverol et al. , 2022)  with the dataset identifier PXD039389.
Transcriptomic analysis A script utilizing library functions in R with a Bonferronicorrected p-value of less than 0. 05 and a cut-off of +/-1. 5 log2 ratio was used for all experimental conditions to identify genes displaying a significant change in expression (differently expressed gene DEG) over the repetitions. Only genes with smooth expression profiles were retained. Differential analyses were performed for each variety vs the other 6 varieties giving 21 possible combinations. The differentially expressed genes and differently abundant proteins were annotated according to KEGG database using ShinyGO online tool 6   (Ge et al. , 2020). The MapMan framework for functional classification  (Schwacke et al. , 2019)  with Mercator4 v. 2 online tool 7  was also used to assign the protein sequence annotation.
Proteomic analysis Statistical analyses were performed with the Extended Data Analysis (EDA) module in DeCyder 2D Differential Analysis Software v. 7. 0  (Goulas et al. , 2006). For every set of matched spots, the average intensity was compared to the standard deviation on these intensities for a given spot between the two compared varieties. Only spots present on all gel images from each set of samples were considered. One-way ANOVA analysis was performed for every set of matched spots, comparing the average and standard deviation for a given spot between Diane and Oliver groups to find significant differences (ANOVA, p-value ≤ 0. 05; average ratio >1. 5) above the experimental variation.
Global statistical analyses Sparse Partial Least-Squares Discriminant Analysis (sPLS-DA) analyses were conducted by using RStudio software (2022. 02. 03 build 492) and the R-package mixOmics (v. 6. 1. 2). The PLS method searches for the highest covariance between variates, which are a linear combination of initial variables, taking into account all variables in the input matrices. sPLS combines this approach with a Lasso penalization to enable the selection of variables the most explanatory of varieties discrepancy. The arithmetic means of the gene expression value and their log2 transformation were used for statistical analysis. Morphometric and mechanical data were tested for normality by the Shapiro-Wilk test. We used the ANOVA test, Pearson correlation coefficient, Student's t-test for normally distributed data, Spearman correlation, and the Kruskal-Wallis test if the normality test was not met.
Transcriptomic analysis of 7 flax varieties and correlation with morphometric parameters We evaluated the abundance of 42 963 transcripts (99. 3%) among the 43 384 annotated genes in the flax genome 8. Comparative whole transcriptomics of the fiber-bearing outerstem tissues from the 7 varieties allowed us to identify 1041 differently expressed genes (DEGs) that showed significant differences in transcript accumulation (Figure  2 ; Supplementary Table  1 ). According to MapMan annotation, only 40 of them were classified into cell wall organization (Supplementary Table  1 , bold characters). However, further expert curation allowed us to highlight 57 more genes related to cell wall metabolism (Supplementary Table  2 ). These 1041 DEGs were also annotated according to KEGG categories (Figure  2 ).
Our results show that glucosinolate-, diterpenoid-and zeatin-biosynthesis are pathways in which the genes are the most overrepresented (up to 8-fold). Circadian rhythm, tryptophan, starch, sucrose metabolism, and phenyl biosynthesis also showed significant enrichment (approx. 4-fold). Finally, biosynthesis of secondary metabolites and metabolic pathways showed less fold-enrichment (approx. 2-fold), but with a much higher number of significant DEGs. All 1041 significant DEGs were processed with mixOmics Rpackage to Principal Component Analysis (PCA, Figure  3A-i ), and Sparse Partial Least-Squares Discriminant Analysis (sPLS-DA, Figure  3A -ii). As shown in Figure  3A -i, the PCA scree plot showed that 75% of the total variance was explained with the first three principal components (43, 18, and 14% of the total variation, respectively).
One peptide spot (Master number 1046) that was more abundant in the Diane proteome represents a mixed sample, which included two peptides identified like ascorbate peroxidase 1 and ferretin 1 (Table  1 ; Supplementary Figure  1 ). Both of them were included in further analysis. The transcript annotations of genes coding the 32 identified proteins were classified according to MapMan annotation, and only 1 of them could be classified directly into cell wall organization, Lus10037377, a xyloglucan endotransglucosylase/hydrolase 32 [homologous to ELF4-like in Arabidopsis thaliana genome (AT2G36870. 1)] (Supplementary Tables  1, 2 ). Interestingly, both in Diane and in Oliver, proteins with highest variations in abundancy ratio (Table  1 ) were two different protein spots (Master number 1269 and 1284, respectively) identified as the same polyketide cyclase/dehydrase and lipid transport superfamily protein (Lus10008932), roughly twice less in Diane (4.
Discussion The fineness and mechanical properties of fibers are key quality parameters for textiles and NFCs that are determined by two main processes in addition to scutching: i) fiber formation during the growth of the flax plant and ii) fiber separation and extraction during the post-harvest retting process  (Djemiel et al. , 2020). Both of these processes are, in turn, determined by the flax genotype and environmental conditions (including the composition of the retting microbiome). In this paper, we combined Omics performed on bast fiber bearing tissues and morphometric/mechanical analyses in seven flax varieties grown under field conditions to provide  Pearson correlation coefficient (PC) between the expression level of genes selected on transcriptomic data analysis and morphometric (the surface of a fiber cell, mm 2 ; the number of fibers in the bundle).
Funding This work was carried out within the framework of the  FIBRAGEN  project financed by the national funding agencies, namely the  German Federal Ministry of Education and Research [BMBF ,  Project Fibragen ;  FKZ: 0315911 ]; the  French National Research Agency  [ ANR-10-KBBE-0003 , Fibragen]; the  Spanish Ministry of Economy and Competitiveness -General Management of Scientific and Technical Research  [ PIM2010PKB-00672 ]; and  Genome Canada  and the  Natural Sciences and Engineering Research Council of Canada.
99 2. 8E-06 941 AT5G02790. 1 GSTL3 PF00043 Glutathione S-transferase family protein 7 Lus10041602 1. 75 1. 1E-02 1125 AT3G52150. 1 PF00076 RNA-binding (RRM/RBD/RNP motifs) family protein 8 Lus10023180 1. 67 3. 6E-06 1307 AT1G65980. 1 TPX1 PF08534 thioredoxin-dependent peroxidase 1 9 Lus10026241 1. 63 7. 2E-03 797 AT5G36700. 1 ATPGLP1, PGLP1 2-phosphoglycolate phosphatase 1 10 Lus10010340 1. 58 4. 8E-06 639 AT1G04420. 1 PF00248 NAD(P)-linked oxidoreductase superfamily protein 11 Lus10031708 1. 56 4. 3E-05 183 AT3G60750. 1 PF02779, PF00456 PF02780, Transketolase 12 Lus10013537/ Lus10012499, Lus10022673 1. 55 4. 7E-03 1046 AT1G07890. 1/ AT5G01600. 1 APX1, ATAPX01, ATAPX1, CS1, EE6/ ATFER1,FER1 PF00141/ PF00210 ascorbate peroxidase 1/ ferretin 1 13 Lus10017158 1. 53 1. 3E-02 1354 AT5G57040. 1 PF00903 Lactoylglutathione lyase/ glyoxalase I family protein (Continued).
1 PF04199 Cyclase family protein 6 gi|171462119 1. 80 3. 4E-04 1420 Expansin-related protein 3 precursor 7 Lus10043025 1. 77 6. 6E-04 249 AT5G25880. 1 ATNADP-ME3, NADP-ME3 PF00390, PF03949 NADP-malic enzyme 3 8 Lus10042468 1. 66 4. 7E-06 1078 AT1G78380. 1 ATGSTU19, GST8, GSTU19 PF02798, PF00043 glutathione S-transferase TAU 19 9 Lus10025889 1. 60 5. 3E-03 1391 AT5G48580. 1 FKBP15-2 PF00254 FK506-and rapamycin-binding protein 15 kD-2 10 Lus10013078 1. 58 4. 9E-03 1526 AT3G27830. 1 RPL12, RPL12-A PF00542 ribosomal protein L12-A 11 Lus10026370 1. 54 2. 9E-02 223 AT3G08590. 1 PF01676, PF06415 Phosphoglycerate mutase, 2,3-bisphosphoglycerate-independent PF00006, 12 Lus10035263 1. 52 3. 0E-02 402 AT5G08680. 1 PF11421, PF02874, ATP synthase alpha/beta family protein PF00306 13 Lus10011125 1. 51 5.
4E-04 232 AT5G25880. 1 ATNADP-ME3, NADP-ME3 PF00390, PF03949 NADP-malic enzyme 3 14 Lus10020717 1. 51 1. 6E-02 1165 AT1G06680. 1 OE23, OEE2, PSBP-1, PSII-P PF01789 photosystem II subunit P-1 15 Lus10002620 1. 50 7. 1E-03 69 AT5G17920. 1 ATCIMS, ATMETS, ATMS1 PF01717, PF08267 Cobalamin-independent synthase family protein 16 Lus10018156 1. 50 1. 0E-04 291 AT1G21750. 1 ATPDI5, ATPDIL1-1, PDI5, PDIL1-1 PF00085 PDI-like 1-1 17 Lus10026643 1. 50 3. 5E-04 1094 AT2G47730. 1 ATGSTF5, ATGSTF8, GST6, GSTF8 PF02798, PF00043 glutathione S-transferase phi 8.
Socioeconomic inequalities in childhood Body Mass Index (BMI) are becoming increasingly more pronounced across the world. Although countries differ in the direction and strength of these inequalities, cross-national comparative research on this topic is rare. This paper draws on harmonized longitudinal cohort data from four wealthy countries-Germany, the Netherlands, the United Kingdom (UK), and the United States (US)-to 1) map cross-country differences in the magnitude of socioeconomic inequalities in childhood BMI, and 2) to examine cross-country differences in the role of three energy-balance-related behaviors-physical activity, screen time, and breakfast consumption-in explaining these inequalities. Children were aged 5-7 at our first timepoint and were followed up at age 8-11. We used data from the German National Educational Panel Study, the Dutch Generation R study, the UK Millennium Cohort Study and the US Early Childhood Longitudinal-Kindergarten Study.
Data and sample This paper draws on longitudinal cohort data harmonized and analyzed in the project Development of Inequalities in Child Educational Achievement (DICE). Japan and France are also included in the DICE project. Unfortunately, however, as longitudinal data on BMI was not available for these two countries for the specific developmental period that is the focus of the current study, we could not include these countries. Table  1  provides a brief outline of the data. The German, UK, and US samples are representative of the respective country populations. For the Netherlands, the sample is representative of Rotterdam only, which is the second largest city in this country (for further details see (DICE technical appendix, 2022)). Response rates for the surveys are reported in Appendix A.
Limitations Harmonizing data across countries is challenging. First, varying sample sizes across countries bring differences in precision of the estimates. Sample sizes for all German subgroups and for low educated families in the US were quite small, leading to wider confidence intervals. This affected the power of the analyses to identify differences between parental education groups and likely explains the lack of statistical significance of the H/L gap in Germany at T1. Second, although the datasets used were representative of the national populations of Germany, the UK, and the US, the sample for the Netherlands represents the country's second largest city, whose population is more ethnically diverse, and harbors graver poverty than rural Netherlands  (CBS, 2019). This possibly led to overestimating inequalities for the Netherlands.
Although there is accelerometer data available in the UK dataset-which is arguably more reliable than parent-reports of their children's activity levels-those data were not available in the other countries. As some studies show that social desirability bias differs by social strata  (Kim and Tamborini, 2014) , underreporting of unhealthy behaviours and overreporting of healthy behaviours might have been more severe in the lower educated groups  (Nyberg et al. , 2016). This possibly biased our results towards underestimating the contribution of PA and ST to BMI inequalities. Additionally, as discussed above, our specific measurement of physical activity is limited in the sense that we could not include intensity of activity. Future data collections should address this limitation to allow for more nuanced understanding of how the links between SES, PA and BMI differ across countries.
Acknowledgements This research was undertaken as part of the  Development of Inequalities in Child Educational Achievement: A Six Country Study (DICE)  project, funded under the  Open Research Area (ORA) Round 5 Funding Scheme. We gratefully acknowledge funding support from the  Economic and Social Research Council  ( ESRC Grant   ES/S015191/1 ,  United Kingdom ); the  Agence Nationale de la Recherche  ( ANR  grant  ANR-18-ORAR-0001 , France), the  Deutsche Forschungsgemeinschaft (DFG, Germany ,  SCHN 1116/1-1 ;  WE 1478/12-1-) , the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO, Netherlands , grant number  464. 18. 102 ). We also acknowledge the following data sources for the current paper. Germany: This paper uses data from the  National Educational Panel Study (NEPS) : Starting Cohort 2, https://doi.
org/10. 5157/NEPS: SC2:9. 0. 0. From 2008  to 2013, NEPS data was collected as part of the  Framework Programme for the Promotion of Empirical Educational Research  funded by the  German Federal Ministry of Education and Research (BMBF). As of 2014, NEPS is carried out by the  Leibniz Institute for Educational Trajectories (LIfBi)  at the  University of Bamberg  in cooperation with a nationwide network. Syntax files on data preparation and analysis used to produce the German results are available under https://osf. io/439pa/Netherlands: The  Generation R Study  is conducted by the  Erasmus MC, University Medical Center Rotterdam  in close collaboration with the  Erasmus University Rotterdam  and the city of  Rotterdam. We gratefully acknowledge the contribution of children and parents. The  general design of Generation R Study  is made possible by long-term financial support from  Erasmus MC, University Medical Center Rotterdam, Netherlands Organization for Health Research and Development (ZonMw)  and the  Ministry of Health , Welfare and Sport.
Urine was presented embedded in a cotton stick in order to preserve non-volatile pheromones and mimic the natural detection method of physical contact between conspecifics. To avoid variability due to the intensity of the volatile components of the urine, only direct nose contact with the tip of the cotton stick was considered as explorative behavior (sniffing/exploration time). Experiments were monitored by a video recording camera fixed 15 cm above the box. Videos were collected and analyzed offline using SMART video-tracking software (PanLab S. L. ). (a) Odor exploration test: After a period of habituation (10 min), mice were exposed to two control trials (mineral oil for IA assays or water for urine tests) during 1 min separated by intervals of 1 min to avoid odor habituation  [36, 37].
A second, unfamiliar mouse (mouse 2, M2) of the same sex was placed in the chamber that had been empty during the first session. This second unfamiliar mouse was enclosed in an identical wire cage than M1 A. The test mouse had a choice between the first, already-investigated mouse (M1 B ) and the novel unfamiliar mouse (M2) which indicates their social preference or social novelty  [40]. Continuous video recordings were collected and analyzed offline using BORIS and SMART video-tracking software (PanLab S. L. ). Measures of time spent sniffing E, M1 A-B , and M2 were quantified for each session.
b Dispersion plots of the normalized sniffing time of young and aged mice in response to various IA dilutions. c Average of the sniffing time of middle-aged APP WT and APP/PS1 Het mice in response to IA samples normalized to the vehicle (mineral oil). d Dispersion plots of normalized sniffing time of APP/PS1 WT and APP/PS1 Het mice in response to various IA dilutions. Note that APP/PS1 Het mice showed higher exploration times for the 1:10 4 and 1:100 IA dilutions. e Fooddeprived animals performed a food finding test (schematics on the right) which revealed equivalent latencies to find hidden food pellets as shown in the dispersion data plot (left panel). Thick grey lines in the dispersion plot indicate mean ± SEM. Data were analyzed by a oneway ANOVA with Tukey's test to test multiple comparisons with more than one variable.
b Average sniffing time of the social habituation-dishabituation test performed by young and aged wild-type mice. c Dispersion plot of the sniffing time of young and aged animals during the social habituation-dishabituation test. d Average sniffing time of the social habituation-dishabituation test performed by middle-aged APP/PS1 WT controls and age-matched APP/ PS1 Het mice. e Dispersion plot of the sniffing time of middle-aged APP/PS1 WT controls and APP/PS1 Het mice during the social habituation-dishabituation test. f The habituation-dishabituation test was modified to assess potential deficits in long-term discrimination due to natural or pathological aging by presenting the same S1a sample 24 h after. g Paired data corresponding to the sniffing time during the first and second S1a presentation (24 h later) is plotted for each condition.
Functional imaging analysis The Calcium Imaging Analysis (CaImAn) package  (Pnevmatikakis et al. , 2016)  and Non-Rigid Motion Correction (NoRMCorre; Pnevmatikakis and Giovannucci, 2017) package were used to extract time courses from the calcium imaging data (using MATLAB R2018A, The MathWorks Inc. , MA, USA). First, motion correction was performed on the recordings with the NoRMCorre code. Next, using the CaImAn package's pipeline, regions of interest (ROI) were determined in the recordings using a constrained nonnegative matric factorization (CNMF) approach that finds the best spatial and temporal components explaining the observed fluorescence. This algorithm deals with heavily overlapping and neuropil contaminated movies, resulting in a background-corrected fluorescence signal per ROI.
To be able to track the same ROI over days, recordings were manually aligned to avoid artificially increasing the number of ROIs. To improve the signal to noise ratio, the extracted fluorescence signal was preprocessed by smoothing using a moving mean of 20 frames. After preprocessing the data, data was time-locked to the consummatory lick per trial, and each trial was subjected to visual inspection to exclude trials without a licking bout after the delivery. A 5-frame window before the delivery was used as a baseline to calculate ΔF/F, and the response was measured in the 20 frames subsequent to the delivery. Deliveries with a negative baseline value were excluded from analysis. ROIs with any ΔF/F value >2. 5 at any time point were considered outliers, and removed for subsequent analysis.
Statistical analysis For statistical analysis, a mixed model was used in RStudio (RStudio; https://rstudio. com/), running on R version 3. 5. 3 (the R Foundation, USA), using the lme4 and lmeTest packages. As a dependent variable, average fluorescence was taken, and stimulus (water/sucrose) and diet (chow/fcHFD) were taken as independent variables. To avoid artificially increasing the number of measurements, neuron number was entered as a random effect. Sex was included as a fixed factor to adjust for male/ female differences. For body weight and food intake, repeated measures ANOVA was performed in Graphpad Prism 8. 3. 0 (Graphpad software). For consistency and number of responses, chi-square tests were performed in Prism. All bar and line graphs were made in Prism, while heatmaps were created in MATLAB.
The binding of the miRNAs relies on 'seed pairing', i. e. , the perfect or 2 of 13 near-perfect complementary match of nucleotides 2-8 of the mature miRNA product  [5]. In silico database exploration has predicted binding sites on the promoter 5 -UTR coding domain of mRNA as well as on lnc-RNA. The equivalence of let-7a binding sites in 5 -UTR or 3 -UTR for repression has been demonstrated by transfecting HeLa cells  [6]. miRNAs can be detected in the cytoplasm, nucleus, nucleolus  [7] , and mitochondria  [8]. Noncoding ribonucleic acids (small or long ncRNA) are involved in epigenetic regulation. These molecules directly silence or activate chromatin at specific loci or through their integral role in the machinery that drives DNA methylation. Breast milk contains miRNAs in high amounts  [9] ; these have been proposed to act as epigenetic regulators  [10] [11] [12] [13].