dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Supplementary Tables  S3  and S5  show the full list of human (Table  S3 ) and mouse (Table  S5 ) differentially expressed genes with raw expression values. Supplementary Tables  S4  and S6  show the list of significantly enriched pathways in human (Table  S4 ) and mouse (Table  S6 ) datasets identified by IPA software. The raw RNA-sequencing data are uploaded in full to the Gene Expression Omnibus database: GSE221027. Supplementary Figures  S1  and S2  show the data from all the behavioral tests conducted that were not included in the main part of the manuscript. Supplementary Figure  S3  shows the behavioral data presented in Figure  2  as stratified by the parental iPSC line of the transplanted cells. Supplementary Figure  S4  shows representative images of Aβ-positive aggregates in transplanted mouse brains and the quantification data.
668, p = 0. 002; Figure  5C ; Table  S2 ), estrogen receptor signaling (Z = -2. 982, p = 0. 029), oxytocin signaling (Z = -2. 236, p = 0. 011), and G beta gamma signaling (Z = -2. 111, p = 0. 010). IPA analysis of upstream gene regulation suggested the activation of the transcriptional regulator TCF7L2 (TCF4, Z = 4. 243, p < 0. 001), an important effector of the Wnt/β-catenin signaling pathway. No significant difference in the level of TCF7L2 mRNA expression was observed between the groups (Figure  5D ). IPA upstream analysis also suggested inhibition of transcriptional regulator HTT (huntingtin; Z = -2. 381, p < 0. 001) primarily associated with the Huntington's disease. There was no significant difference in the levels of HTT mRNA either (data not shown). NOTCH1 expression was significantly downregulated by the PSEN1 ΔE9 mutation in the transcriptomics dataset (adj p = 0.
048; Figure  5D ). Interestingly, the PSEN1 ΔE9 human IPA upstream analysis also suggested inhibition of transcriptional regulator HTT (huntingtin; Z = -2. 381, p < 0. 001) primarily associated with the Huntington's disease. There was no significant difference in the levels of HTT mRNA either (data not shown). NOTCH1 expression was significantly downregulated by the PSEN1 ∆E9 mutation in the transcriptomics dataset (adj p = 0. 048; Figure  5D ). Interestingly, the PSEN1 ∆E9 human cells exhibited a higher expression of myelinating oligodendrocyte signature genes  [29] [30] [31] , although the effect was stronger in cells originating from the AD2 parental line in comparison to AD3 (Figure  5D ). The sex of the parental line (male AD2 vs. female AD3) may have contributed to the observed variation.
Supplementary Materials: The following supporting information can be downloaded at: https:// www. mdpi. com/article/10. 3390/cells11244116/s1, Figure  S1   S1 : Human DEGs PSEN1 ∆E9 vs. CTRL; Table  S2 : Ingenuity Pathway Analysis of human DEGs; Table  S3 : Mouse DEGs PSEN1 ∆E9 vs. CTRL; Table  S4 : Ingenuity Pathway Analysis of mouse DEGs; Table  S5 : The correlation of upregulated mouse DEGs with cell-specific markers derived from Cell Marker Augmented 2021 database using Enrichr online tool (https://maayanlab. cloud/Enrichr/ (accessed on 1 June 2022)). Author Contributions: Conceptualization, C. R. , T. M. , H. T. and J. K. ; data curation, L. M. , K. T. , H. T. and T. R. ; formal analysis, L. M. , K. T. , H. T. and T. R. ; funding acquisition, I. H. , Claire Rampon, T. M. , H. T. and J.
Funding: The project was funded through the following funding organizations under the aegis of JPND: Finland, Academy of Finland (No 301253 J. K. , 301234 T. M. ); France, Agence Nationale de la Recherche (C. R. ). This project has also received funding from the European Union's Horizon 2020 research and innovation program (grant agreement No 643417), from Sigrid Juselius Foundation, from Business Finland, and from Biocenter Finland through Biocenter Kuopio (H. T. ). Grant numbers are not applicable for the latter foundations. Open access funding was provided by the University of Helsinki.
Informed Consent Statement: Not applicable. Data Availability Statement: Supplementary Tables  S3  and S5  show the full list of human (Table  S3 ) and mouse (Table  S5 ) differentially expressed genes with raw expression values. Supplementary Tables  S4  and S6  show the list of significantly enriched pathways in human (Table  S4 ) and mouse (Table  S6 ) datasets identified by IPA software. The raw RNA-sequencing data isuploaded in full to the GEO database: GSE221027 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE221027 (accessed on 20 November 2022)). Supplementary Figures  S1  and S2  show the data from all the behavioral tests conducted that were not included in the main part of the manuscript. Supplementary Figure  S3  shows the behavioral data presented in Figure  2  as stratified by the parental iPSC line of the transplanted cells.
In this study, the authors mapped siRNAs to full-length Sirevirus copies, reasoning that loci important for recognition and silencing should be associated with a larger number of siRNAs than other regions of the elements. Indeed, an excess of siRNAs mapped to showed that pre-miRNA windows of ~110 bp typically have MFEs <-40 kcal/mol and that a minimum MFE threshold of -40 kcal/mol can determine the ability of a ~110 nt pre-miRNA locus to form a hairpin structure. By focusing here on regions of similar size to these pre-miRNA transcripts, and by employing their threshold cutoff of of -40 kcal/mol, we in effect use miRNA loci as a 'positive control' for single-stranded RNAs that are known to self-prime into hairpin substrates, allowing us to test the quality of our computational inferences. After performing computational annotation of the extent and folding strength of secondary structures in genes and TEs, we mapped siRNAs datasets from multiple libraries, allowing us to integrate secondary structure information with small RNA complement.
Altogether, we examined 373,485 features representing 15 distinct categories (Table  1 ). For each feature, we predicted MFEs in sliding windows of 110 bp using RNAfold, mimicking previous work  (Wang et al. , 2009; Bousios et al. , 2016). The advantages of this window-based approach were that we could use pre-miRNA transcript loci as positive controls, that we could explicitly compare results to the -40kcal/mol MFE threshold, that the output could be compared across features, and that it measured local folding properties across the length of specific features. Because each nucleotide of a feature corresponded to one sliding window (for all but the final 109 nucleotides of a sequence), predicting the MFE of windows was a massive bioinformatic undertaking. In total, we calculated the MFE for 3.
For these analyses, we assumed that the secondary structure designations from B73 (structured or unstructured) applied to the 28,866 shared genes across the 26 lines  (Hufford et al. , 2021). With these designations, we first confirmed that structured genes were more highly expressed across all lines using pangene expression data  (Hufford et al. , 2021) (Fig S11; unpaired t- tests, all P < 2. 2e-16), indicating that this subset of genes broadly shared the patterns of expression of B73 across lines. We then measured gene expression among lines. For each shared gene, we calculated the coefficient of variation (CV) of expression across the 26 lines. Structured genes had a significantly higher CV than non-structured genes (P < 0. 01, permutation test)(Fig  5a ). This was true both for comparisons between all genes in each group and between a downsampled subset of structured genes that was equal in size to the set of unstructured genes.
Secondary structure and siRNA production For detecting secondary structure, we have included two positive controls: miRNA precursor loci  (Wang et al, 2009)  and Copia/RLC elements  (Bousios et al. , 2016). As expected, these two feature sets have the most extreme MFE statistics among our sample. Copia/RLC elements have the lowest minMFE values, reflecting previously recognized regions of strong secondary structure (Fig  1a ), but it is worth noting that other retrotransposons have similarly low minMFE distributions (Fig. 1a ). miRNAs do not have the lowest minMFE among our dataset, perhaps due to their short length, but they have the lowest meanMFE and the highest proportion of their sequences included in low (<-40 kcal/mol) MFE windows (Fig  1 ). These two positive controls provide a basis for comparison to other feature types.
In this explanation, the structured regions are first highly targeted by siRNAs and then structure evolves as a component of the evolutionary arms race between TEs and their hosts. Whatever the cause, we find the structure:siRNA relationship to be consistently significant across genomic features. If self-priming is the primary mechanism, our results offer additional insights into the process. First, we note that correlations between siRNAs and secondary structure explain a relatively low proportion of the variation in siRNA mapping across the genome. For example, across the entire dataset of 373,485 features, minMFE explains at most 10% of the siRNA mapping results (Table  2 ). This value can be higher within other MFE metrics and specific feature categories -e. g. , propMFE explains 24% of siRNA variation in CACTA/DTC elements -but the explanatory power of MFE statistics is typically between 3.
METHODS B73 annotation and secondary structure prediction Version 4 of the B73 maize genome and version 4. 39 of the genome annotation were downloaded from Gramene (www. gramene. org). B73 TE annotations (B73v4. TE. filtered. gff3. gz) were retrieved from https://mcstitzer. github. io/maize_TEs/ (Jiao et al. , 2017). TE and gene annotations were cleaned for redundancy (e. g. , the same feature annotated by different annotation authorities) using custom scripts, and separated into annotation files for different feature categories. Bed files were then generated for each annotation feature, with a standardized naming convention for each feature: Feature Type::Chromosome:Start Position-End Position (e. g. , exon::Chr1:47261-47045). Fasta files for each feature were generated using Bedtools getFasta.
These fasta files were divided into 110 nucleotide sliding windows (1-nt step size) for use in the secondary structure prediction program RNAfold v2. 4. 9 from ViennaRNA  (Hofacker et al. , 2011). MFE calculations per window were extracted from RNAfold predictions using a Python script, and the MFE summary metrics  (minMFE, meanMFE, varMFE, and propMFE)  were calculated for each feature, based on all windows in that feature. As described in the main text, minMFE was calculated as the lowest MFE window in the feature; propMFE was calculated as the number of low MFE windows (<-40 kcal/mol) divided by the total number of windows in the feature; meanMFE was the mean of all 110 bp window MFE values; and varMFE was the variance across all windows. Bed files representing regions of significant, miRNA-like secondary structure were created by combining all overlapping windows of <-40 kcal/mol MFE.
Overlapping MFE windows were converted to bed format using an inhouse Python script. The location, category and summary statistics for each feature is available as Table  S5. To determine whether a feature contained significant structure, the feature sequence was randomized by shuffling the position of nucleotides across the length of the feature. This approach maintained the GC content of the feature but not the primary sequence. Randomized sequences were then subjected to identical MFE calculations -i. e. , they were split into 110 bp windows for RNAfold prediction. This process was repeated five times for each feature, and the minimum MFE of each randomization was recorded. The significance of observed structure vs the five randomizations was assigned using a Wilcoxon one-sided test with Benjamini-Hochberg correction in R.
For plotting the location of low MFE regions across featureS (Figs 2 & S4), we split each feature into 100 equally-sized bins across the length of the feature from 5' to 3' end and counted the number of <-40 kcal/mol regions overlapping each bin. To find motifs in low MFE regions of different feature types, bed files from concatenated low MFE regions were extracted using Bedtools getfasta. These fasta files were fed into the MEME motif finder (v5. 4. 0)  (Bailey & Elkan 1994)  with the DNA alphabet in Classic mode (i. e. , enrichment of sequences in a single reference sequence and no control sequence) for each feature category. We selected the top 10 overrepresented sequences. The scripts used for MFE calculations and analyses are available on Github (https://github. com/GautLab/maize_te_structure).
Small RNA Library Analysis Small RNAseq libraries were downloaded using NCBI SRA tools and SRAExplorer (https://github. com/ewels/sra-explorer), from the sources indicated in Table  S1. Adapters, regions with low quality, and low quality reads were trimmed from small RNA RNAseq libraries using fastqc and cutadapt v0. 39  (Bolger et al. , 2014). Adapter sequences varied among libraries, and so were identified and validated in each library using a custom bash script that searched for sets of known maize siRNAs of each length (21-24 nt) in each unprocessed library and confirmed the identity of the adapter sequence connected to each known siRNA sequence. The list of adapters derived for each library is included in Table  S4. Trimmed reads were then filtered and split based on size matching 21, 22 and 24 nucleotides in length, creating three fastq files for each library.
For correlations between siRNA species density vs. MFE measurements of features (Table  2 ), linear models of siRNA species per nucleotide as a function of secondary structure metrics (minMFE, meanMFE, etc) were fitted using the base R (v4. 1. 0) lm() function. To fit these models, siRNA species were summed across all 24 libraries for each feature so that observed siRNA species had an equal weight across libraries. These linear models can be expressed as: ( + 1) ~ To test the significance of differences in siRNA species density between high and low MFE regions within features, mixed effects models were fit for each siRNA size class using the R package lme4  (Bates et al. , 2015). In these models, siRNA mapping counts from each library were not combined, meaning that each smRNA library:feature pair was counted individually.
These mixed effects models can be expressed as:. For these calculations, feature-library pairs with zero siRNA species in either non-structured or structured regions were removed from each dataset. ( + 1) ~ + ( We further tested skew differences from zero using Wilcoxon one-sided tests in R. tissues (E-GEOD-50191)  (Walley et al. , 2016). The statistical significance of differences between expression of genes in different structure classifications was determined using unpaired t-tests between structured and unstructured genes. Linear models of expression versus each measurement of secondary structure were separately fit for expression in each tissue type and graphed using ggplot2 (). These linear models can be expressed as: The coefficient of variation (CV) of expression was calculated for each gene between the 26 lines using the normalized RPKM expression data from  Hufford et al.
, 2013). We kept track of the number of NAM lines that overlapped to produce a merged ACR and its position in B73. In contrast to epigenetic features, which we assumed had the potential for similar functions even when they overlapped even if the exact positions did not match, structural variants (SVs) were only counted as shared between lines when exact coordinates matched. The expected overlap was calculated as the proportional of genic space taken up by low MFE regions * the total length of features. Custom scripts for these analyses can be found at https://github. com/GautLab/maize_te_structure, and additional supplementary files can be found at https://figshare. com/projects/siRNAs_and_secondary_structure_in_maize_genes_and_TEs/1507 14. shows the statistic for each of the 15 feature groups defined in Table  1.
96 Dynamic Array™ IFCs (BMK-M-96. 96, Fluidigm) as previously published  [34]. Preamplification of the samples, chip loading, and RT-qPCR were performed in accordance with the protocol of the manufacturer. The results were analyzed using the Fluidigm PCR analysis software v. 4. 1. 3. Firstly, 6. 5 ng of each cDNA were initially pre-amplified (10 min 95 • C activation and 14 PCR cycles at 15 s 95 • C for 4 min, 60 • C) with PreAmp Master Mix (100-5581, Fluidigm) with a pool containing all the primers (200 nM), excluding the 16S rRNA primer sets. Pre-amplified samples were diluted to 1/5 after an exonuclease treatment (M02935, NEB, Biolabs, Ipswich, MA, USA). In order to prepare samples for loading into the IFC, a mix was prepared consisting of 440 µL 2× TaqMan Master Mix (Applied Biosystem 4369016, Thermofischer Scientific, Maryland, USA), 44 µL 20× DNA Binding Dye Sample Loading Reagent (100-7609, Fluidigm), and 44 µL 20× EvaGreen dye (31000, Biotium, San Francisco, CA, USA), plus 132 µL TE buffer.
Relative expression of the target genes was determined by the ∆∆CT method  [35] [36] [37]. Mean ± standard error of mean values for each group are expressed in fold changes relative to the BE treatment for all genes. Primer sequences used to am-plify all paralogs genes and accession numbers of the primers are presented in Table  1. All gene sequences of trout used were identified by in silico analysis by Genomicus software v. 100. 01 (www. genomicus. biologie. ens. fr; accessed on 1 November 2020) and Ensembl (http://www. ensembl. org, Ensembl Release 102; accessed on November 2020, trout genome available) and queried against mammals or fish genome using the BLAST tool in Ensembl and in NCBI (https://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 1 November 2020) to confirm the gene identification (Table  3 ).
Primer Design The primer sequences used to amplify all genes and accession numbers of the primers are presented in Table  3. For gene targets that had not previously been validated, primers were tested on a pool of cDNA, and amplified products were systematically sequenced. PCR primers (Table  3 ) for the target regions were designed for each gene using Primer3 software  [38]. To optimize annealing temperature for the respective gene, PCR was performed with specific primer pairs, and analysis of PCR products was done by electrophoresis on agarose gel (1. 5%)  [39]. 2. 6. RNA Extraction, Pre-Processing, and Illumina Small RNA Deep Sequencing for Plasma Samples Plasma samples were homogenized in TRIzol reagent (Trizol BD) at a ratio of 400 µL of fluid per milliliter of reagent (N = 5 for EE, N = 6 for BE).
Final size selection was performed on 3% gel cassette on a Pippin HT between 126 pb and 169 pb. Sequencing (single read 50 nucleotide) was performed using a NovaSeq 6000 (Illumina, Evry, France) with SBS (Sequence By Synthesis) technique. A total of over 449 million reads (sequences after Illumina Purity Filter) were obtained, with a number of reads per library ranging between 24 and 52 million. All reads were deposited into NCBI Sequence Read Archive under accession number SUB9869985. Reads were trimmed using cut adapt to remove the adapter sequence GCCTTGGCACCC-GAGAATTCCA and the random primers. Reads were subsequently aligned onto the trout genome (NCBI RefSeq assembly accession GCF_013265735. 2).
Statistical Analysis All statistical analyses were performed using R software (v3. 5. 2)/R Commander package. All data are expressed as mean (EE vs. BE) with standard deviations across biological replicates. Effects were considered statistically significant at p < 0. 05. Analyses were carried out on untransformed data, since criteria for normality and homogeneity of variances were fulfilled (Shapiro-Wilk and Levene's test, respectively). Values of mRNA level of all transcripts revealed by RT-qPCR were analysed by Welch's t-tests. If the criteria (normality and homogeneity) were still not met, a non-parametric test was used for the analysis.
Statistical Analysis All statistical analyses were performed using R software (v3. 5. 2)/R Command package. All data are expressed as mean (EE vs. BE) with standard deviations across b logical replicates. Effects were considered statistically significant at p < 0. 05. Analy were carried out on untransformed data, since criteria for normality and homogeneity variances were fulfilled (Shapiro-Wilk and Levene's test, respectively). Values of mRN level of all transcripts revealed by RT-qPCR were analysed by Welch's t-tests. If the crite (normality and homogeneity) were still not met, a non-parametric test was used for analysis.
Acknowledgments: This work was performed in collaboration with Sandra Fourre at the  GeT core facility, Toulouse, France  (http://get. genotoul. fr,accessed on 1 July 2020), and was supported by the  France Génomique National  infrastructure, funded as part of the " Investissement d'avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
A beta-binomial distribution accounts for uncertainty in the count and crop-fraction estimates  [53, 96]. The study includes 12,063,723 tree-years from North America, South and Central America, Europe, Africa, Asia, and Oceania (Supplementary Figure  1 ) from 751 species. Mature trees for 714 species (95% of 751 species) were used in this analysis (see Fecundity modeling and inference). Data coverage by sites is included in table  S2 , with sample sizes (tree-years) summarized by species provided in the file supplementSampleSize. csv.
Individual, habitat, and climate covariates Covariates were selected based on their ability to explain important variation in the data. In order to test hypotheses related to soil fertility we need covariates that can account for the other sources of variation in data that might obscur hypothesized relationships. Covariates included individual attributes for each tree (diameter and shade classes), climate (temperature and moisture deficit), and habitat conditions (soils and terrain attributes) in Supplementary Table  3. The quadratic term in diameter allows for changes in fecundity response with tree size  [21]. Shade classes follow the FIA/NEON classes from 1 (fully-exposed in open space) to 5 (fully-shaded in the understory). Shading quantifies the effects of competition on fecundity (  [14] ).
Cation exchange capacity, a widely used indicator for soil fertility  [97]  at 250-m spatial resolution, was evaluated as a weighted mean based on uncertainty layers from three soil depths (0-5 cm, 5-15 cm, and 15-30 cm) in a global dataset  [49]  (Supplementary Figure  1 ). Slope and aspect quantify effects on solar radiation and drainage on moisture condition. Slope and aspect were obtained from the NASA Shuttle Radar Topography Mission's (SRTM)  [98]  digital elevation model (DEM) with a spatial resolution of 30 m. The DEM grid was supplemented with the USGS National Elevation Dataset for MASTIF sites that were outside SRTM coverage. Slope and aspect were derived following  [99] u =   u 1 u 2 u 3   =   sin(slope) sin(slope) sin(aspect) sin(slope) cos(aspect)   (3) Temperature T and moisture deficit M are included in the model as site means and anomalies to account for the effects of both site variation and inter-annual variability  [100].