dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants The reported results included data from two cohorts. The first group of healthy full-term infants (N = 39, Mean age: 14. 15 ± 4. 79 weeks, age range: 5. 6 to 23. 6 weeks, 11 girls) was studied elsewhere to investigate the functional maturation of visual Event Related Potentials (ERP) to lateralized faces  (Adibpour et al. , 2018). A subset of these infants (N = 22, Mean age: 14 ± 4. 96 weeks, age range: 5. 6-22 weeks, 7 girls) was also tested to study ERP responses to central faces. To compare the results obtained for infants with adults, we additionally included a second group of young adults (N = 13, Mean age: 23. 39 ± 2. 32 years, age range: 21 to 27. 1 years, 6 females) who were presented with the lateralized faces following the same paradigm as infants. To further investigate the maturation effect in some of the analyses, we split the data, and contrasted the youngest (<12weeks, N = 14) with the oldest infants (>16 weeks, N = 13), because the first trimester (<12 weeks) is a period of rapid maturation of the visual pathways, from the retina to V1 in term of cell maturation, tracts myelination and synaptogenesis leading to drastic changes in visual perception followed by slower changes  (Dehaene-Lambertz and Spelke, 2015).
EEG protocol and pre-processing EEG recordings were acquired with EGI net comprising 128 electrodes for infants and 256 electrodes for adults and digitized in realtime at a sampling rate of 250 Hz. EEG data were further pre-processed in EEGLAB software. Recordings were band-pass filtered between 0. 5 and 20 Hz. The choice of low-pass cutoff was guided by the previous observation that upto 54 weeks of age, only ∼2-4% of total band-limited power was concentrated in frequencies >10Hz  (Marshall et al. , 2002). The signal was further segmented into epochs of 1. 9 s (-0. 4 to 1. 5s relative to the onset of face presentation). Channels and trials contaminated by motion or eye-blink artifacts were automatically rejected (after epoching) by an algorithm developed to detect abrupt changes as large drifts within each trial (>150 μv).
Whereby, for each subject, the ERP template was defined independently from other subjects (as the average topography at the time of peak of P1 or P400 ERP Where, 𝜏 𝜏 is Pearson correlation coefficient; (Φ𝜏 (𝜏, 𝜏)) is the d (𝜏 ) + 1 dimensional activity vector at time 𝜏 and trial 𝜏, where each dimension represents the activity of a different neighbor of sensor 𝜏 (including itself). Neighbors of each channel were extracted from the channelconnectivity matriX estimated using the "find_ch_connectivity" function of MNE-python  (Gramfort et al. , 2013). For each subject, instead of single global between-trial variability time-series, now we obtained one time-series each for each sensor. This absolute sensor-level between-trial variability was further z-scored across time to obtain relative variability for each sensor.
Significant reductions in variability time-courses were tested using one-sample t-test and correction for multiple comparisons and temporal non-independence was applied using cluster-based permutation test as implemented in MNE-python  (Gramfort et al. , 2013). Grouplevel differences between paired groups of variables (lateral vs central faces) were tested using nonparametric two-tailed WilcoXon signed rank test. Group differences between age groups were tested using nonparametric Kruskal-Wallis test, followed by pair-wise comparisons using Mann-Whitney U test with Bonferroni correction.
Ongoing variability dominates ERP While characteristic ERPs existed, single-trial responses were noisy and hardly resembled grand averaged responses (Fig. 2A ; Fig. S1B ), typically remaining one-or two-order of magnitude larger than ERP amplitudes in both adults and infants, with extremely variable topographies and no clear peaks or valleys corresponding to the ERP. To illustrate the relationship between the ongoing and evoked activity, we show a 12 s-long-segment of continuous EEG data for a representative infant (age = 15. 4 weeks) in Fig. 2B. At every time point, brain topography is represented as a point in a three-dimensional projection after dimensionality reduction (performed by applying Principal Component Analysis (PCA) to activity amplitudes, see methods). If stimulus presentation always evoked a similar single-trial trajectory, the EEG trajectory time points recorded immediately following the presentation of a face stimulus should cluster together within this low-dimensional projection.
Acknowledgements This research was supported by grants from the  Fondation NrJ , from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 695710 ), and from the  Fondation de France  (call Neurodevelopment and Autism 2012). This work, carried out in part within the  Institut de   Convergence ILCB  ( ANR-16-CONV-0002 ), has benefited from support from the  French government , managed by the  French National Agency for Research (ANR)  and the  EXcellence Initiative of AiX-Marseille University  (A *  MIDEX ). DB has been supported by the  University of Strasbourg Institute for Advanced Studies  ( USIAS-2020-044 ) where part of this research has been conducted.
32 Å À1 by rotating the sample to change the incidence angle of the monochromatic neutron beam, λ ¼ 5. 21Å. The intensity of the reflected beam was detected using a 30 Â 30 cm 2 2D 3 He position-sensitive detector. The measurement time was around 3 h. First, the Si substrate was characterized in a "beam-from-air" configuration to determine the layer parameters associated with the substrate. Subsequently, measurements with the substrates in the neutron reflectometry cell filled with electrolyte were carried out both at OCV and 15 min after starting a potentiostatic hold at 50 mV versus the Li counter electrode. Data were reduced using the software pySAred. In the data reduction process, the beam was normalized using the total reflection plateau. At low incidence angles, the incident beam was larger than the substrate, and to account for this, an over illumination correction, or "footprint" correction, was applied.
[44]  To aid the data interpretation, a slab model for the depth profile of the sample was constructed and fitted to the data. Each layer in the model was assigned a thickness, a neutron SLD, and a roughness (expressed as a Gaussian profile). These model parameters were fitted using the DiffeRential Evolution Adaptive Metropolis (DREAM) MCMC algorithm in the software Refl1D. [45]  From these fits, the uncertainties of the fitted parameters in the model could be estimated. The SLD profiles for all measurements on the same substrate were refined simultaneously to find a suitable model to describe the layers on the surface of the Si block (Figure  S4  and S5, Supporting Information). The model included six different layers: Si, Ti, Cu, Cu 2 O, a layer representing the SEI/EDL, and bulk electrolyte with an interfacial roughness between each layer.
Acknowledgements The authors acknowledge  Grzegorz Greczynski  for kindly providing us access to use his magnetron sputtering system and the Swedish research council for funding the national infrastructure Super ADAM. FORMAS  and  VINNOVA  competence center BASE are acknowledged for funding the research. J. B. is grateful for support from the  Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials (AFM) at Linköping University  ( Faculty Grant   SFO Mat LiU  No. 2009 00971 ), F. E. , A. Z. , and J. B. thank the  Swedish Science Council  for support through the project " Isotope-enriched boron carbide containing amorphous multilayer neutron optics " ( VR 2019-04837 ).
S1 ; www. wolffiapond. net). Genome Sequence, Assembly, and Analysis. To aid in exploring the developmental innovations of the Wolffia genus, we performed whole-genome sequencing with a combination of strategies, including Nanopore PromethION (ultra-long), Illumina NovaSeq 6000 and Hi-C reads, and Bionano to construct the reference genome for accession wa7733 (referred here as Waus). We generated 18. 46   genomes. Based on these paired-end reads, we estimated the genome size to be 353,335,418 bp with a heterozygosity of less than 0. 3% (SI Appendix, Fig. S2 ; Dataset, Table  S2 ), which indicates that the Waus genome is very homozygous. We used Nanopore reads to assemble the W. australiana genome into contigs. The G1 (genome version 1) contig length was 358. 51 Mb, with an N50 size of 17. 96 Mb.
The G2 was 362. 72 Mb (Dataset, Table  S3 ). We also identified 4,345,907 bp of sequences corresponding to the mitochondrial and chloroplast genomes or to other contaminants. We distinguished true genomic sequences from other contaminants based on their GC contents (SI Appendix, Fig. S3  A and B). The Bionano data were used to correct the G3 genome, and five gaps were produced, resulting in 26 sequences for G4 (size of 358. 77 Mb) that we assembled into 20 pseudochromosomes (Fig. 2 A-C ; Dataset, Table  S4 ). The mapping rates of RNA-seq against the genome assembly were 95. 1% (Dataset, Table  S5 ). Fluorescence in situ hybridization (FISH) analysis on prometaphase chromosomes confirmed 20 pairs of homologous chromosomes, representing 40 somatic chromosomes in the W. australiana genome (SI Appendix, Fig.
S4 ). We also identified homozygous single nucleotide polymorphism (SNP) sites and insertion/deletion (InDels) by comparing the genome sequence reported here with those from other W. australiana species, yielding 6,764 SNPs and 3,166 InDels with minimal support of at least five Illumina reads (Dataset, Table  S6 ). The contig N50 of our Waus genome was 18,579,918 bp compared to 251,357 bp for wa7733, 102,226 bp for wa8730, and 742,788 bp for wa8730 (three other published W. australiana genomes)  (16, 17). Analysis of Benchmarking Universal Single-Copy Orthologs (BUSCO) demonstrated that our genome assembly is much more complete than previously sequenced and assembled W. australiana genomes, with BUSCO scores of 94. 55% (Waus), 77. 10% (wa7733), 80. 29% (wa8730), and 87% (wa8730) (Table  1 ; Dataset, Table  S7 ).
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint Genome Annotation and Phylogenetic Analysis. The W. australiana genome Waus contained 227. 8 Mb (or 63. 5% of the genome) of repetitive regions, consisting of both transposable elements (TEs) and tandem repeats (TRs). TEs with long terminal repeats (LTRs) represented the majority (41. 8%) of TEs, followed by DNA transposons (9. 2%), long interspersed nuclear elements (LINEs) (3. 9%), and miniature inverted-repeat transposable elements (MITEs) (1. 1%) (Dataset, Table  S8 ). We predicted 1,658 non-coding RNAs for a total length of 237.
2 kb, including 191 ribosomal RNAs (rRNAs), 867 non-coding RNAs (ncRNAs), eight regulatory RNAs, and 592 transfer RNAs (tRNAs) (Dataset, Table  S9 ). We also predicted 22,484 proteincoding genes with an average length per gene of 3,789 bp (Dataset, Table  S10  and S11 ). Finally, we compared the predicted Waus protein sequences to biological databases, resulting in functional annotation for 69. 5% of all genes. We also obtained support for 75. 04% of all predicted gene models in RNA-seq samples (Dataset, Table  S10 ). We determined the phylogenetic relationship between W. australiana and 14 other Viridiplantae species using a set of low-copy orthologous gene groups (Dataset, Table  S11  and S12 ). Specifically, the phylogenetic results and fossil calibration time revealed that W. australiana 7733 diverged from W.
australiana 8730 approximately 0. 24 million years ago (mya) (Fig. 2D ; Dataset, Table  S13 ). We also compared the Waus genome to the genomes from other plant species to elucidate key genomic changes associated with adaptation to a small plant stature. We thus identified the expansion of 196 gene families and the contraction of 3,029 gene families in the cluster of W. australiana relative to plant species with larger body plans (Fig. 2D ). For example, the AGAMOUS-LIKE (AGL) family involved in flowering typically clusters in 11 groups in flowering plants (Fig. 2E ; Dataset, Table  S14 ) but only defined nine groups in the Waus genome, suggestive of an incomplete AGL family in W. australiana. Similarly, many of the root-related small auxin up-regulated RNA (SAUR) genes were missing in W. australiana (Fig.
2F ; Dataset, Table  S14 ). (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint Millifluidic Setup for Tracking Plantlets. To track the developmental processes of individual plantlets as they physically separate from one another, we designed a millifluidic chip system for plantlet culture. The chip design is quite simple: We poured polydimethylsiloxane (PDSM) (using Momentive clear RTV615 potting and encapsulating compound in a 10:1 ratio) into molds to create 1-mm-wide channels (Fig. 3A ). Each chip can hold over 100 plantlets and is sufficient to carry out most experiments. To circulate the culture medium, we connected a peristaltic pump to the chip to help the liquid medium (half-strength Murashige and Skoog [MS]) flowing (Fig.
Morphological Traits. The Rootless Phenotype of W. australiana Cannot Be Explained by Gene Loss. A prominent morphological trait of W. australiana is its lack of roots. An attractive explanation would be that genes required for root development have been lost. However, we identified homologs for all known Arabidopsis genes involved in root development in the Waus genome (Dataset, Table  S14 ). The formation of an auxin gradient is crucial for root initiation and maintenance of all root types  (19, 20). However, in the region near the growth tip of W. australiana, cellular alignment required for an auxin gradient is hard to see (Fig. 1 ). This might result in the observed rootless phenotype. Loss of NACs that Specify Vascular Differentiation may Explain the Lack of a Vasculature. We observed no vascular tissue in W.
australiana, in contrast to its close relative, duckweed Spirodela polyrhiza (Fig. 4A ). However, the cell wall composition of W. australiana is similar to that of aquatic plants harboring vascular bundles (21) (Fig. 4B ). As xylem vessels usually possess thickened cell walls with specific patterns, referred to as the secondary cell wall (SCW), we looked for genes that are known to be involved in SCW formation in the Waus genome. We detected most SCW genes, indicating that cell wall biogenesis is likely intact in W. australiana (Dataset, Table  S15 ). However, in contrast to the 13 Arabidopsis and nine rice SCW-related NAM-ATAF1,2-CUC2 (NAC) gene family members (Dataset, Table  S15  and S16 ), which encode the upstream master regulators for SCW formation in vascular plants  (22) , we only identified one homolog in W.
Using a candidate gene list of about 500 Arabidopsis and 100 rice flowering genes as queries, we identified about 200 homologous genes in W. australiana (Dataset, Table  S14  and S16). Some of these genes exhibited differential expression between the C, I, and F groups, either upregulated (Fig. 5B ; Dataset, Table  S17 ) or downregulated (Fig. 5C ; Dataset, Table  S17 ). Among differentially expressed genes, we noticed that WausLG03. 251 (homolog of As an aquatic plant, the living conditions for W. australiana should be more stable than those of land plants because of the buffering capacity provided by water. Among the contracted gene families, we determined that miR156 is missing in W. australiana (Dataset, Table  S14 ).
Although its target transcripts from SQUAMOSA PROMOTER-BINDING-LIKE PROTEIN3 (SPL3) or the other family members SPL4, SPL5, SPL9, and SPL15 were present in the W. australiana genome, the loss of miR156, as well as miR172 (Dataset, Table  S14 ), may explain the seemingly abrupt shift from vegetative growth to floral organ differentiation brought upon by the absence of the phase change observed in Arabidopsis and other plants normally mediated by the miR156-SPL module  (23, 24). Despite the loss of sepals and petals in the W. australiana flower, all MADS-box genes were present in the W. australiana genome and appeared to be expressed (Dataset, Table  S14 ). This observation thus raises a question about the genomic maintenance of MADS genes participating in sepal and petal identity determination in an organism lacking these organs over the course of evolution.
We also carried out a transcription regulatory network (TRN) analysis  (25)  with singleplantlet RNA-seq data to decipher the regulatory mechanisms behind floral organ initiation and differentiation. While the overall topological structures of TRNs between C-I and C-F pairs (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint were quite similar, several network nodes distinguished the two TRNs (circled in Fig. 5 D  and E ). While none of these distinct node genes were curated explicitly as known "flowering genes" (Dataset, Table  S19 ), the node genes exhibiting the most prominent differences (P = 1.
Single-nucleus RNA-seq, Protein Structure Predictions, and Gene Transformation. A valuable model system should have a high-quality sequenced genome and should be amenable to genetic manipulation for the dissection of gene function. Here, we applied single-nucleus RNA-seq (snRNA-seq) and other techniques with W. australiana. In a pilot experiment, we collected 2 g of plantlets cultured in flasks under regular growth conditions for nuclear isolation  (27) , followed by snRNA-seq  (28). After data processing and quality control, we retained 15,983 nuclei and 14,812 genes for clustering, cell type annotation, and other analyses. Fig. 6  illustrates the clustering pattern obtained by Uniform Manifold of each cluster. We were surprised to identify almost all cell types (Dataset, Table  S20 ; for full information see Dataset, Table  S21 ), although the collected samples only consisted of boatshaped leaves (with guard cells) and growth tips (Fig.
australiana genome encoded AGL homologs belonging to nine groups (Fig. 2E ). Similarly, the 13 Arabidopsis SCW-related NAC genes had a single homologous gene in W. australiana, which might be responsible for the non-vascular phenotype of this tiny plant (Fig. 4 , Dataset, Table  S15 ). Based on recent findings of the dynamic features of chromatin structures, it is reasonable to hypothesize that the relative stable aquatic conditions experienced by W. australiana may have contributed to genome rewiring, including gene and/or gene family contraction. Other regulatory mechanisms may have also participated in the morphogenetic simplification of W. australiana. For example, the simple absence of a gene or gene family cannot explain all cases of morphological innovations, as we identified almost all known genes required for Arabidopsis root development in the W.
australiana genome (Dataset, Table  S14 ). More curiously, our pilot snRNA-seq experiment revealed the expression of genes that are (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint typically markers for tissues or organs missing in W. australiana plantlets. Notably, the genome of freshwater sponges was recently shown to harbor genes involved in nerve cell development, although this organism lacks this cell type  (29). It is possible that the expression of certain genes may occur prior to the emergence of a given morphogenetic event. Before the genes were coopted for specific morphogenetic events, they may have carried out other functions.
which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprintBiological materialsWolffia australiana wa7733 was from Prof. Hongwei Hou Lab at the National Aquatic Biological Resource Center, Institute of Hydrobiology, Chinese Academy of Sciences. W. australiana plants used for genome sequencing and transcriptome sequencing were cultured in half MS (Solarbio) solid medium (1% sucrose, pH 5. 5) under long-day (LD: 16-h light/8-h dark) conditions at 26 °C. The fresh plants cultured for 10-15 days were used for the extraction of genomic DNA for genome sequencing and total RNA for transcriptome sequencing from plants in a population under cultured conditions (Dataset, Table.
which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 17, 2022. ; https://doi. org/10. 1101/2022. 04. 16. 488569 doi: bioRxiv preprint 35 (B) UMAP visualization of 15,983 cells into 16 clusters (for detailed information, see Dataset, Table.
ACKNOWLEDGMENTS. We are grateful to  Hai-Qi Meng ,  Mei-Qin Chen  in the  Bai lab at Peking University (PKU)  for pilot experiments with Wolffia globosa;  Professor T. Oyama  at  Department of Botany Kyoto University  for providing W. australiana 7733;  Yi-Zhen Jia ,  Xiao-Fan Zheng ,  Yue-Yi Che  and others in  Dr. Feng Li 's  Lab at High School Affiliated to Renmin University (HSARU ) for their participation in establishing the PoC system and pilot morphological analysis;  Xing Zhang  of  HSARU  for designing the cover;  Kai-Le Wang  for manufacturing the incubator;  Shu-Qiang Chen  and  Hui Zhang  from  BGI-Shenzhen  for help with snRNA-seq data;  Prof. Wen-Qin Wang  of  Shanghai Jiaotong University  for providing information about duckweed genome studies;  Prof. Yue-Hui He  of the  School of Advanced Agriculture, PKU ,  Prof.
Yun-Yuan Xu  and  Jing-Yu Zhang  of the  Institute of Botany, Chinese Academy of Sciences ,  Prof. Tong-Da Xu  of  Fujian Agriculture and Forest University, Prof. Hong-Chang Cui of Florida University  for the Arabidopsis gene lists involved in flowering, auxin and root development. We thank the  Facilities Cores at National Center for Protein Sciences  and the TEM platforms at the  College of Life Sciences of Peking University  for cryo-SEM, TEM and micro-CT. We also thank  Jianlin Chen  at the  College of Engineering of Peking University  for cryo-SEM, TEM and micro-CT. This work was crowd-funded by all the authors. This work was supported by  Mississippi State University  (to L. L. ), the  International Partnership Program of Chinese Academy of Sciences  ( 152342KYSB20200021 ) (to H. -W.
Data availability. The genome sequence of Wolffia australiana wa7733 has been deposited at NCBI Genome under the accession CP092600-CP092619 for 20 chromosomes. Raw genome and transcriptome sequencing reads have been deposited into the NCBI sequence read archive (SRA) under the BioProject PRJNA808652 (for Nanopore), PRJNA808655 (for Illumina genome), PRJNA808685 (for Hi-C), PRJNA808734 (for BioNano), PRJNA808736 (for RNAseq for genome), PRJNA808739 (for single-plant RNA-seq), and PRJNA809022 (for singlenucleus RNA-seq). BioNano data have also been deposited into NCBI Supplementary Files under the accession SUPPF_0000004267. Genome sequence, single-plant and single-nucleus RNA-seq are also available at the Wolffia australiana wa7733 genome database: http://wolffiapond. net.