dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Our comprehensive molecular and cellular characterization of the adult zebrafish telencephalon expands knowledge on telencephalic evolution and will facilitate targeted studies of its diverse cell populations. In particular, we identify underappreciated heterogeneity among brain macrophages and conserved ontogenies of interneuron families across vertebrates. In addition, using a novel consensus-clustering approach we identified distinct robust subpopulations of qRG. This is independent of the specific genome assembly, feature choice or clustering parameters and is reproducible across datasets and supported by subsequent crossspecies comparisons and in situ validation. Heterogeneity among RG is explained to some extent by spatial origin, with homologous territories between mouse and zebrafish maintaining their positional identity from development to adulthood.
We expected to detect cells that retained expression of a similar gene set across bilaterians but were unable to do so in non-deuterostomes besides insects. Although we attempted to maximize sensitivity by surveying many datasets and including only those with high coverage, this approach can produce false negatives for rare cell populations or due to imperfect genomic annotations. Alternatively, it is possible that the functions associated with the astrocytic synapomere were initially performed by other cells including neurons themselves and became the property of glial cells in vertebrates. In that case, even if a common set of ancestral glia existed before they might not have expressed genes enriched in current vertebrate macroglia and would have been missed with our approach. Additional targeted studies will be helpful to shed light on the origins and evolution of glia, their coevolution with neurons and will benefit from focusing on genes identified in this study.
Acknowledgments: We thank the  ZEN team  for input, the  Institut Pasteur CB UTechS  service platform for expert assistance with FACS analysis and scRNAseq processing, and  Thibaut Brunet  for critical reading of the manuscript. We are greatly indebted to  Isabel Fariñas  for sharing data prior to publication, and for insightful discussions. Funding: Work in the L. B-C. laboratory was funded by the  ANR (Labex Revive) ,  La Ligue Nationale Contre le Cancer  ( LNCC EL2019 BALLY-CUIF ), the  Fondation pour la Recherche Médicale  ( EQU202203014636 ),  CNRS ,  Institut Pasteur  and the  European Research Council (ERC)  ( AdG 322936 ). D. M. is a member of the  Médecine Science MD-PhD program  and  Sorbonne Université  and was recipient of a PhD student fellowship from  Ecole Normale Supérieure.
Scale bar, 5 cm. Data are presented as mean ± SD (n > 3 biological replicates, and 35 plants were tested in each of the biological replicates). Significance of differences (***P < 0. 001) was determined by Student's t-test. Significance was calculated by comparing the mutant or overexpression with the wild type and marked on the mutant and overexpression column. c Leaf phenotypes of 8-week-old WT, alkbh1 and OEALKBH1 plants inoculated with the Xoo isolate PXO99A. Lesion lengths were measured and the values are means ± SEM (n > 30 biological replicates). Significance was calculated by comparing the mutant or overexpression with the wild type and marked on the mutant and overexpression column. d Phenotype and seed setting rate of the mutant and overexpressed plants under normal and high-temperature conditions.
6mA DIP-seq and analysis For 6mA IP-seq, rice genomic DNA was extracted from 2-week-old sterile-cultured seedlings and incubated with RNase A overnight. The 6mA IP-seq was performed using published protocols  [3]. Briefly, 5-μg genomic DNA was sonicated into 200-400 bp and end-repaired, followed by adapter ligation. DNA was denatured at 95 °C for 10min and chilled on ice for 10 min. An aliquot of the denatured DNA was saved as input. The remaining DNA was immunoprecipitated overnight at 4 °C with anti-6mA antibodies purchased from SYSY (202-003). DNA was eluted by 6mA salt competition. After size selection and PCR amplification, the DNA fragments were sequenced with the HiSeq 2500 (Illumina). Low-quality reads were removed from raw data by using the Trimmomatic package (http:// www. usade llab.
org/ cms/ uploa ds/ suppl ement ary/ Trimm omatic/ Trimm omatic-Src-0. 35. zip), and the clean data were mapped to the MSU7. 0 rice reference genome (ftp:// ftp. plant biolo gy. msu. edu/ pub/ data/ Eukar yotic_ Proje cts/o_ sativa/ annot ation_ dbs/ pseud omole cules/ versi on_7. 0/ all. dir/) using Bowtie2 with parameters permitting less than two mismatches. The reads were mapped to bacterial, fungal, and archaeal genomes (ftp:// ftp. ncbi. nlm. nih. gov/ genom es/ genba nk/). Then, MACS (model-based analysis of chromatin immunoprecipitation followed by sequencing (ChIP-seq)  [63] ) was introduced to locate enriched regions to call 6mA peaks by comparing reads from the IP sample with the input sample. The DiffBind (version 3. 13) Bioconductor package  [64]  was used to find differential peaks between alkbh1 and WT samples.
ChIP-seq data analysis DNA from chromatin immunoprecipitation was used to construct sequencing libraries following the protocol provided by the Illumina TruSeq ChIP Sample Prep Set A and sequenced on Illumina HiSeq 2500. Trimmomatic (version 0. 32) was used to filter out low-quality reads. Clean reads were mapped to the rice genome (MSU7. 0; http:// rice. plant biolo gy. msu. edu/) by Bowtie2 (version 2. 4. 2) using default parameters. SAMtools (version 1. 9) was used to remove potential PCR duplicates. The bam files were first converted to Wiggle files, and bigwig files were generated using bam-Coverage with the parameters "-bs 10 -normalizeUsing RPKM" in DeepTools  [68] , and the data were imported into the Integrated Genome Browser for visualization. The two replicates were merged using MACS2 in peak calling (version 2.
1. 1)  [63] , for H3K27me3 and ALKBH1 with the parameters "--broad --broad-cutoff 0. 1". The reads from input were used as controls for H3K27me3 and ALKBH1 ChIP-seq, respectively. The DiffBind (version 3. 13) Bioconductor package  [64]  was used to find differential peaks between alkbh1 and WT samples. The DESeq2 method implemented in Diff-Bind was used to test the differential peaks. fold change > 1. 5 and P-value < 0. 05 were used to define differential binding peaks.
Reverse transcription and RT-PCR One microgram of total RNA was reverse-transcribed in a reaction volume of 20 μL using DNase and reverse transcriptase (Vazyme; Nanjing, China; R233-01) according to the manufacturer's instructions to obtain cDNA. RT-PCR was performed as follows: 95 °C for 10 s, 45 cycles of 95 °C for 5 s, and 60 °C for 40 s. Disassociation curve analysis was performed at 95 °C for 15 s, 60 °C for 20 s, and 95 °C for 15 s. Three biological replicates were performed (separate experiments), each with three technical repeats (three identical samples within an experiment). Data were collected using the ABI PRISM 7500 sequence detection system. The rice ACTIN1 gene was used as the internal control. The primers used for RT-PCR are listed in (Additional file 7: Table  S6 ).
Accession numbers Sequence data from this article can be found in the GenBank/EMBL libraries under the following accession numbers: OsALKBH1 (LOC_Os03g60190), EMF2b (LOC_ Os09g13630), OsWRKY53 (LOC_Os05g27730), OsWRKY71 (LOC_Os02g08440), OsMYB2 (LOC_Os03g20090), OsNAC3 (LOC_Os07g12340), CYP94C2b (LOC_ Os12g05440), ZOS3-21 (LOC_Os03g60560), OsCPK4 (LOC_Os02g03410), OsALKBH1A (LOC_Os11g29690), OsALKBH2(LOC_Os06g17830), OsALKBH5 (LOC_ Os06g04660), OsALKBH8A (LOC_Os04g51360), OsALKBH8B (LOC_Os11g43610), OsALKBH10 (LOC_Os05g33310). The accession numbers of the genes are as follows: HsALKBH1 (Q13686), HsALKBH4 (Q9NXW9), MmALKBH1 (P0CB42), and CeN-MAD-1 (Q8MNT9).
week postpartum between the intervention and control groups. Secondary outcomes will be CB-PTSD symptoms, birth satisfaction, mother-infant bonding, perceived pain and stress during childbirth, maternal anxiety and depression symptoms, anaesthesiological data and acceptability of the procedure. Ethics and dissemination Ethics approval was granted by the Human Research Ethics Committee of the Canton de Vaud (study number 2022-00215). Dissemination of results will occur via national and international conferences, peer-reviewed journals, public conferences and social media. Trial registration number NCT05319665.
If the partner/ co-parent does not give his/her oral consent, there will be no intervention. The written consent of the midwife to be filmed is needed as well. Without it, there will be no intervention. The fourth stage will be the written consent obtained from the participant as soon as possible by a study collaborator in the recovery or hospital room. For ethical reasons discussed above, the group allocation will take place according to the study phase in which the childbearing woman arrives. During the first study phase, 35 consecutive participants will be included in the control group, and only then, in a second phase, 35 consecutive participants will be allocated to the intervention group. All participant data will be coded to ensure confidentiality.
Data collection During the CS, routine anaesthesiological data, such as haemodynamic parameters, perioperative shivers, nausea and use of medication will be obtained. Right after the separation of the mother and the infant, the participant's pain level will be assessed using the perceived pain item. At the end of the CS, the HMD will be removed and the investigator will assess the participant's pain and anxiety level using the perceived stress item and perceived pain item. For the intervention group, the tolerability of the intervention will be assessed as well. One week after birth, participants will receive a link to online questionnaires via email. The questionnaires will take about 10 min to complete and will assess the participant's experience and satisfaction of the birth, the perceived pain during childbirth, as well as symptoms of anxiety/depression and stress, and the mother-infant bonding (Childbirth Experience Questionnaire, Birth Satisfaction Scale, Mother-Infant Bonding Scale, Hospital Anxiety and Depression Scale).
DATA MANAGEMENT AND STATISTICAL ANALYSES The questionnaires will be filled out by the participants via REDCap (Research Electronic Data Capture, a secure web application for building and managing online surveys and databases-projectredcap. org). The rest of the data will also be collected using REDCap. The data will be stored on a secured server at Lausanne University Hospital that meets high security standards and performs automatic continuous backups. Access to the full final trial data set will be restricted to the Principal Investigator. The database will be exported from REDCap to the Stata V. 17. 0 software for the statistical analyses. For the primary analyses, group differences regarding the mean subscale and total scores of the CEQ-2 at 1-week post partum will be analysed using linear regression analysis.
Data were merged with two other genotyping datasets with mostly European flint (TUM dataset) and dent (DROPS dataset) germplasm. The final pan-European dataset consisted of 974 inbreds and 460,243 markers. Admixture analysis showed seven ancestral populations representing European flint, B73/B14, Lancaster, B37, Wf9/Oh07, A374, and Iodent pools. Subpanel of inbreds with SEE origin showed a lack of Iodent germplasm, marking its historical context. Several signatures of selection were identified at chromosomes 1, 3, 6, 7, 8, 9, and 10. The regions under selection were mined for protein-coding genes and were used for gene ontology (GO) analysis, showing a highly significant overrepresentation of genes involved in response to stress. Our results suggest the accumulation of favorable allelic diversity, especially in the context of changing climate in the genetic resources of SEE.
Additional information about the inbred lines from TUM and DROPS datasets is available in Supplementary Table  S1. The data from all three datasets were merged using a custom R script, and insertions/deletions were removed, leaving 500,167 markers. Markers were further filtered to remove excessive heterozygotes (2. 5%) and missing data (5%) in TASSEL software  [39]  version 5. 2. 64, leaving a final set of 460,243 filtered markers. The missing data were imputed using the LinkImpute method  [40]  with 50 sites in high linkage disequilibrium and 30 nearest neighbors. Overall mean heterozygosity proportion was low in unimputed data (0. 0175) as well as in the imputed data (0. 0181). Before imputation, proportion of missing data was 0. 00838, and imputation resulted in lowering this proportion to 0.
Population structure Population structure was determined by combining two methods in two different datasets. Datasets were: Southeastern Europe dataset with 455 inbreds with collection attributes in MRIZP database from Southeastern Europe, and a full dataset of 974 lines including the 572 inbreds in the MRIZP panel, the 155 inbreds of TUM panel and the 247 inbreds from DROPS panel. TUM and DROPS panels were subject to the same filtering and quality checking and imputation procedures as the MRIZP dataset. Principal coordinate analysis (multi-dimensional scaling, PcoA) was performed with 20 components assumed, with a thinned marker set using an identity-by-state distance matrix between 166,755 sites as input in TAS-SEL software version 5. 2. 64. The number of components for interpretation was chosen based on the presence of an "elbow" in the plot of eigenvalues, which appeared at three components in the analysis of a full dataset of 974 inbred lines and two components in the analysis of inbreds from the MRIZP-SEE subpanel.
To correctly infer the underlying genetic structure of the assessed germplasm, Admixture analysis  [41]  was performed with 166,755 thinned and imputed sites. Based on the findings of Puechmaille  [42]  that uneven sampling of subpopulations leads to underestimates of the true number of K, parameters MedMed K, Med-MeanK, MaxMed K, and MaxMean K were calculated using the StructureSelector software  [43]. All parameters converged at K = 7 for the full dataset of 974 inbreds, while non-zero values varied for MedMean K and MaxMean K between K = 2 and K = 3 in the MRIZP-SEE subpanel. All inbreds with Q > 0. 7 to any of the inferred groups were considered members of the associated group, while inbreds with Q < 0. 7 were considered admixed.
Signatures of selection and candidate genes The creation of genotyping arrays is based on genotyping the genetic materials with already discovered polymorphisms  [44]. This approach can generate considerable ascertainment bias, mostly seen through spurious minor allele frequency (MAF) distribution across loci and low SNP call rates for some accessions, which is due to the limited diversity used for array formation. In our research, the SNP call rate was > 0. 985 and MAF was > 0. 1 for all used markers. Despite the high quality of the generated genotype data, the efficient strategy proposed by Malomane et al. [45]  to mitigate the ascertainment bias was applied. Namely, stringent LD-based SNP filtering was carried out using the Plink 1. 9 software  [46]  with flag indep 50 5 2, representing a moving window variance inflation factor (VIF) based SNP pruning within 50 SNP windows and a moving step of 5, where VIF is calculated as VIF = 1/(1 -r 2 ).
Pruning left 58,264 markers in the dataset. The fixation index (F ST )  [47]  screening was estimated using VCFtools software version v0. 1. 16 by using the --weir-fst-pop flag and a window size of 50 SNP markers. The MRIZP-SEE subpanel was set as one population, while the remaining 519 inbreds from all three panels were treated as a second (contrast) population. Further, the markers that crossed an F ST threshold of 0. 153431 were analyzed for extended haplotype homozygosity per site (EHHS) in R package rehh version 3. 2. 2 [48]. The F ST threshold of 0. 153431 was determined at a α < 0. 001 cut- off level. Secondly, in the MRIZP-SEE subpanel, the scan for genetic hitchhiking was carried out using the Sweep Detector (SweeD) software  [49]. SweeD is an implementation of a likelihood-based sweep detection method proposed by Nielsen et al.
[34]  optimized for large SNP matrices and parallel computing. The method uses the likelihood of a neutral model calculated and based on all SNP markers as the denominator and the likelihood of selection for a certain genomic location as a numerator to compute the composite likelihood ratio (CLR) statistics. SweeD software version 3. 3. 2. was run in Linux OS with a grid size of 10,000 positions per chromosome for the calculation of CLR statistics. CLR threshold of 6. 530486 was determined from the top 100 hits, representing a cutoff threshold of α < 0. 001. Start and end markers of F ST and CLR significant hits were converted from AGPv2 to B73_RefGen_v4 assembly in the in Ensembl Plants  [50]  tool Assembly Converter (https:// plants. ensem bl. org/ Zea_ mays/ Tools/ Assem blyCo nvert er, accessed: 4 th January 2023).
Overlapping regions were also tested for extended haplotype homozygosity score (EHHS) in R/rehh library  [48]. Extended haplotype homozygosity score relies on reduction of genetic variation in the broader haplotypic regions over population of sequences. Since the EHHS robustness in selfing species is subject of independent research  [51] , the score was used in this research only as a confirmation of selection within genomic regions under selection detected by F ST and CLR. The converted markers were used to mine the EnsemblPlants Genes database, release 51 via the BioMart tool (https:// plants. ensem bl. org/ bioma rt/ martv iew, accessed: 4 th January 2023) for genes within the detected genomic regions. The mined genes from BioMart analysis were used as input for gene ontology (GO) analysis in Protein Analysis Through Evolutionary Relationships (PANTHER) Classification System (http:// panth erdb.
Panel composition and population structure The MRIZP panel consisting of 572 inbred lines segregated at 99. 99% of filtered and imputed markers with an average MAF of 0. 255 (Table  1 ). All parameters of Structure Selector  [43]  converged at seven populations (K = 7) in the full dataset of 974 inbreds, while differences were observed between parameters for K between 2 and 3 in the MRIZP panel (Supplementary Figs. 1  and 2 ). Group memberships analysis showed that most inbreds from the MRIZP panel had admixed origin, with only 135 inbreds showing membership coefficient (Q) > 0. 7, 68 inbreds showing Q > 0. 8 and 37 inbreds with Q > 0. 9 in any of the determined groups (Supplementary table  1 ). The relative lack of an   S1 ). Further, 44. 9% (257) inbreds showed Q > 0. 1 in the B37 group, 38.
Inbreds from K2 (Wf9) were also mostly collected from Bulgaria (29), Romania  (6) , Hungary  (10)  and former Yugoslavia  (14) , while in K3, most of the lines were collected from Bulgaria (  14 ), former Yugoslavia (8), followed by Hungary (2) and Romania (2). The PcoA analysis of the full dataset (Fig. 2 ) showed groupings of inbreds assigned to different Ks in Admixture analysis. The most distinct groups in the 2D plane of the first two principal coordinates (Fig. 2A ) were those assigned to European flint material (K1), Stiff Stalk Synthetic (K2), Lancaster (K3), and Iodent (K7). Inbreds from K4 to K6 grouped more closely, with inbreds from the K4 pool forming two distinct clusters in PCoA analysis, the upper subcluster representing original B37 inbred in panels DROPS and TUM, and other representing inbreds around French inbred F564 (Argentinian flint) and P352 (Supplementary table  S1 ).
Regions on chromosome 1 between 141. 626 and 141. 627 Mbp fell within regions with significant CLR scores (Tables  2  and 3 ). Region on chromosome 2 at 200. 527 showed a corresponding CLR value of 5. 31 (not significant), although four Mbp apart. Regions with high F ST on chromosome 10 showed several counterparts with CLR > 5 (not significant) and with locations between 101. 655 and 106. 472 Mbp. Another approach used to detect selection in the 455 SEE inbred lines was the screening of changes in site frequency spectrum (SFS) of genomic regions compared to the neutral model, implemented in SweeD software  [49]. The change compared to the neutral model was used to calculate the composite likelihood ratio (CLR) test of the regions with a shift in SFS. The top 0. 1% hits from the composite likelihood ratio (CLR) test in SweeD software were considered significant, and 11 genomic regions were detected on chromosomes 1, 2, 3, 6, 7, 8, 9, and 10 (Fig.
Signatures of selection and pathway enrichment in Southeast Europe To further analyze the genomic implications of population expansion observed in the MRIZP panel, genomewide scans for signatures of selection were carried out. To facilitate the robust search for signatures of selection and avoid the detection of false positives in data generated by SNP-array suffering from ascertainment bias of polymorphism states, a stringent SNP pruning mitigation strategy was applied, as suggested by  [45]. The scan for sweeps was based on a SweepFinder method  [34]  implemented for large genomewide SNP data in SweeD software  [49]. The output statistics are based on a linkage disequilibrium of the selected variant disturbing its surrounding regions, creating shifts in the site frequency spectrum (SFS).
The potential remedy for such occurrences is to screen a broader region for sweep signals, rather than to aim to classify the sweep as "hard" or "soft". Our speculation is that the long regions with increased CLR detected in our study (Fig. 4 , Table  3 ) represent recent selection in the materials from the SEE panel (except signals on chromosomes 3 and 9). This is in accordance with the time that passed from the introduction of new US Corn Belt materials in Southeast Europe during the 1950s  [22]  and the first cycle of registered inbreds during the 1960s  [18]  This is also corroborated by the timing,, considering that most of the inbreds of the SEE panel were stored in MRIZP Genbank during the 1960s and 1970s, leaving time for only a single cycle of selection. In line with this speculation is also the finding of very low levels of F ST divergence between 519 inbreds from all three genotyping panels and 455 inbreds collected from Southeast Europe, except at four regions (Fig.
The CACTUS system allows to investigate the target thickness on the whole target surface, making possible a study of the uniformity between different areas of the target (''global'' uniformity). An ORTEC/AMETEK easy-MCA multichannel analyser with the MAESTRO software is used for data acquisition. Fig. 4  Close-up view of the measuring probe. The source and the detector are placed in the bottom and top part, respectively, of the moving arm. In the foreground the LED-video camera system is also visible. The measured target thickness is the average value over a spot surface, defined by collimators placed in front of the source and the detector. In measurements reported in the following, the geometry of the system was such that the alpha spot on the target was about 4 mm.
Materials and methods In this study, we re-analyzed high-density (128 channels) Electroencephalography (EEG) data previously reported by  Adibpour et al. (2018)  and compared our results with adults performing the same paradigm. Adult data were acquired independently from the infant data and have never been published. We derived three novel measures based on multivariate pattern analysis to track the single trial dynamics and variability induced by the visual stimuli: First, we sought to quantify how individual trial trajectories approach the well-known ERP components (referred hereafter as ERP flybys). This allowed us to evaluate how the single-trial distributions of latency and distances to the ERP templates develop during the first semester of life in comparison to adults. Secondly, we examined the between-trial variability to quantify how close (or far) individual trial trajectories remained from each other as they evolved through time.