dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Although different (sub)compartments and developmental stages (8 DAP vs 13 DAP) were used, we retreated both RNA-seq raw data-set using the same bioinformatic pipeline and the same genome version (see Material and Methods) in order to increase comparability. We then performed a PCA on joint datasets. The first principal component (PC1) carries 43. 7% of the variance and clearly separates the two datasets (Supplemental Figure  3A ). It may reflect a "batch effect", combination of biological effect of the age of sampling (8 DAP vs 13 DAP), and technical differences between the two transcriptomes (growing environment, library preparation, etc). The next components group together samples from the two datasets, and still carries a relatively high fraction of the variance (26. 9% and 9.
7% for PC2 and PC3, respectively). When PC2 was plotted against PC3, 13 DAP Emb is most similar to 8 DAP Emb samples among the 8 DAP samples (Supplemental Figure  3B ), indicating that although important differences exist between these two datasets, these two embryo samples share some similarities in their transcriptomic profiles. Likewise, the 13 DAP AS is most similar to the 8 DAP Emb samples among 8 DAP samples (Supplemental Figure  3B ). The 13 DAP SAL groups most closely to the two 8 DAP samples BETL and ESR. Interestingly, the 13 DAP EAS samples form an independent group that is closer to the two 8 DAP starchy endosperm samples (which are the central starchy endosperm (CSE) and the conducting zone (CZ)), among the 8 DAP samples (Supplemental Figure  3B ). In summary, we have generated RNA-seq profiles from 13 DAP maize kernel compartments and embryo/endosperm interfaces.
EAS cells originate from the starchy endosperm (SE) and undergo cell death. Despite the preferential or specific expression of EAS marker genes, and consistent with their SE-like morphology, EAS cells also showed some transcriptomic characteristics of the SE such as a strong expression of genes encoding ZEIN storage proteins (Supplemental Figure  6 ). The presence of ZEIN transcripts in the EAS region is supported by in situ hybridization data  (Woo et al. , 2001). In order to perform a more global comparison, we asked to which samples from the Zhan et al (  2015 ) dataset (at 8 DAP) our EAS transcriptome was most similar, using PCA (Supplemental Figure  3 ). Interestingly, on the third principal component (PC3), EAS at 13 DAP was most similar to two specific starchy endosperm sub-386 regions at 8 DAP: the central starchy endosperm (CSE) and the conducting zone (CZ) (Supplemental Figure  3B ).
RNA extraction and RNA-seq Total RNAs were extracted with TRIzol reagent, treated with DNase using the Qiagen "RNase-Free DNase Set", and purified using Qiagen RNeasy columns according to the supplier's instructions. RNA-seq libraries were constructed according to the "TruSeq_RNA_SamplePrep_v2_Guide_15026495_C" protocol (Illumina®, California, USA). Sequencing was carried out with an Illumina Hiseq2000 at the IG-CNS (Institut de Génomique-Centre National de Séquençage). The RNA-seq samples were sequenced in paired-end (PE) mode with a sizing of 260 bp and a read length of 2×100 bases. Six samples were pooled on each lane of a HiSeq2000 (Illumina), tagged with individual bar-coded adapters, giving approximately 62 millions of pairs per sample. All steps of the experiment, from growth conditions to bioinformatics analyses, were managed in the CATdb database  (Gagnot et al.
RNA-seq read processing and gene expression analysis RNA-seq reads from all samples were processed using the same pipeline from trimming to counts of transcripts abundance as follows. Read quality control was performed using the FastQC (S. Andrew, http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The raw data (fastq files) were trimmed using FASTX Toolkit version 0. 0. 13 (http://hannonlab. cshl. edu/fastx_toolkit/) for Phred Quality Score > 20, read length > 30 database by comparing sequences from Silva  (Quast et al. , 2013)  and  RFAM (Kalvari et al. , 2018)  to the Zea mays B73 genome sequence ; we then used this custom database to filter the RNA-seq reads with sortMeRNA version 2. 1b  (Kopylova et al. , 2012). Reads shorter than 25 bases at the end of this processing, or with no mate, were discarded.
The genomic mapper HISAT2 v2. 2. 0  (Kim et al. , 2015)  was used to align read pairs against the Zea mays B73 genome sequence  (AGP v4; Jiao et al. , 2017) , using the gene annotation version 4. 40 provided as a GFF file  (Wang et al. , 2016) ; a first mapping pass was performed with the complete set of read pairs to discover unannotated splicing sites, before the per-sample mapping, with options "-k 10 --no-discordant -no-softclip" and allowing introns of length 40 to 150,000 bp. Mapped reads were counted by gene (not distinguishing isoforms) using FeatureCounts  (Liao et al. , 2014). The genome sequence and annotation file used was retrieved from the Gramene database (http://www. gramene. org/, release 51, in September 2016;  Gupta et al. , 2016). Normalization, differential analysis and PCAs were performed with DESeq2  (Love et al.
, 2014 ) under R version 3. 6. 2 (R Development Core Team, 2005). The PCAs were done using the 1000 genes with the highest variance, after applying the variance stabilisation transformation (VST) described by Anders and Huber, 2010 and implemented in DESeq2 v1. 24. 0. In parallel, FPKM values (Fragments Per Kilobase of transcript per Million mapped reads) and confidence intervals were estimated using Cufflinks version 2. 2. 1  (Roberts et al. , 2011)  with options "--frag-bias-correct --multi-read-correct --max-multiread-fraction 1".
Functional annotation of Zea mays transcriptome, GO term enrichment analysis The Zea mays B73 genome sequence v4  (Jiao et al. , 2017)  and the gene annotation v4. 40 were used to predict transcript sequences using the gffread script from the Cufflinks package v2. 2. 1  (Trapnell et al. , 2013). In each isoform sequence, the putative ORFs were   et al. , 2013). They were also compared to UniprotKB protein database version 2017_12 (2019). The complete Swiss-Prot database of curated proteins was used, (containing 41 689 plant sequences and 514 699 non-plant sequences) but only the plant subset of the non-curated database TrEMBL, containing 5 979 810 sequences). The comparison was carried out using WU-BlastP v2. 0MP  (Altschul et al. , 1990)  with parameters "W=3 Q=7 R=2 matrix=BLOSUM80 B=200 V=200 E=1e-6 hitdist=60 hspsepqmax=30 hspsepsmax=30 sump postsw".
Blast output was filtered using custom Perl scripts to keep only matches with log10 (e-value) no lower than 75% of the best log10(e-value). GeneOntology terms  (Ashburner et al. , 2000)  associated to matched proteins were retrieved from  AmiGO (Carbon et al. , 2009)  with all their ancestors in the GO graph, using the SQL interface. For each Zea mays protein, we kept the GO terms associated to all its matched proteins or at least to 5 matched proteins. For each Zea mays gene, we merged the GO terms of all its isoforms. For subsets of genes selected based on their expression pattern, we used our GO annotation to perform an enrichment analysis. The enrichment of a gene subset in a specific GO term is defined as:   2007; Pavlidis et al. , 2004). Custom Perl scripts using GraphViz  (Ellson et al.
Analysis of gene categories and orthology Analysis of orthology to rice and Arabidopsis (Table  3 ) was based on Maize GDB annotations (https://www. maizegdb. org/, Andorf et al. , 2016). ZEIN genes were selected based on a previous gene list  (Chen et al. , 2014 (Chen et al. , , 2017) )  and on Gramene database annotations (http://www. gramene. org/, Gupta et al. , 2016). The list of cell death associated genes was based on previously published lists  (Arora et al. , 2017; Fagundes et al. , 2015). Heat maps were drawn with the online Heatmapper tool (http://www2. heatmapper. ca/, Babicki et al. , 2016).
github. com/TransDecoder/TransDecoder/wiki), and the amino acid sequence was predicted. From 46 272 genes, 138 270 transcripts were predicted, leading to 149 699 amino acid sequences. The predicted protein sequences were annotated for functional domains with InterProScan v5. 27-66. 0 (Jones et al. , 2014) using databases Pfam v31. 0 (Punta et al. , 2012) and Panther 12. 0 (Mi.
Omnibus,Edgar et al. , 2002, http://www. ncbi. nlm. nih. gov/geo) under project ID GSE110060 (secure token for reviewers access: wxifegswbhabhcr). RNA-seq data as FPKM values is available via the eFP Browser engine (http://bar. utoronto. ca/efp_maize/cgibin/efpWeb. cgi?dataSource=Maize_Kernel), which 'paints' the expression data onto images representing the samples used to generate the RNA-seq data.
Acknowledgements We acknowledge  Justin Berger ,  Patrice Bolland  and  Alexis Lacroix  for maize culture,  Isabelle Desbouchages  and  Hervé Leyral  for buffer and media preparation, as well as  Jérôme Laplaige ,  Marie-France Gérentes  and  Ghislaine Gendrot  for technical assistance during samples dissections. We also thank  Sophy Chamot  and  Frédérique Rozier  for sharing protocols for in-situ hybridization. The sequencing platform ( POPS-IPS2 ) benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). We acknowledge support from the  Pôle Scientifique de Modélisation Numérique (PSMN)  of the  ENS de Lyon  for the computing resources. We acknowledge support by the  INRAE Plant Science and Breeding Division  for the project SeedCom to TW. NMD was funded by a  PhD fellowship  from the  Ministère de l'Enseignement Supérieur et de la Recherche.
Acknowledgements. This work was supported by grants to  Murat Saparbaev  from  la Ligue National Contre le Cancer «Equipe Labellisee», Electricité de France  ( RB 2017 ) and  French National Center for Scientific Research  ( PRC CNRS/RFBR n1074  REDOBER); and to  Bakhyt T. Matkarimov  from a  Nazarbayev University ORAU  grant and  MES RK  grants  АР05133910 ,  AP05134683  program  BR05236508. The English language was corrected and certified by shevchuk-editing. com.
5B ). The plot was skewed towards higher WFA staining around hM4Dicells, consistent with hM4Di effect on the PNN. Linear regression analysis yielded a slope of 0. 2, which did not significantly differ from the zero slope value expected from independence between WFA staining intensities of hM4Di+/PV+ and hM4Di-/PV+ cells (p=0. 12). These results suggest that PNN density is regulated by each PV interneuron independently of its PV cell neighbors. PV interneurons express multiple genes involved in synthesis and degradation of the PNN  (Okaty et al. , 2009; Rossier et al. , 2015). We thus searched through published transcriptomic database of single cells collected from the primary visual cortex of P56 mice  (Tasic et al. , 2018)  for the expression of genes that may enable PV interneurons to regulate their PNN cell-autonomously.
In each field, all PV+ (or ipsilateral PV+/mCherry+ or PV+/GFP+) cells were selected for PNN density analysis (Fig. S1 ). Images were acquired with constant brightness and contrast, and variable exposure time. Perisomatic PNN was delineated manually based on WFA staining intensity (Fig. S1 ), forming ring-shaped regions of interest (ROIs) defined using the XOR function of the FIJI software (ImageJ). For each ROI, we measured area and mean WFA fluorescence intensity, which was normalized for exposure time. For each animal, the average fluorescence intensity of contralateral ROIs was used to normalize fluorescence intensity of each ipsi-and contralateral ROI. For each animal, 3-4 bilateral sections were used for this analysis. Data obtained in different mice for a given condition were compared using a Kruskall-Wallis nonparametric test.
Since no significant difference was observed between mice for a given condition, data were pooled. Between-group comparisons were performed using Mann-Whitney nonparametric test. In order to investigate cell-autonomous regulation of the PNN, WFA staining intensity was compared within pairs of PV+ cells: one being hM4Di+ (mCherry+) and the other being its closest hM4Di-(mCherry-) neighbor. Confocal images were acquired with a 40X objective at the lateral edge of the hM4Di expressing zone within the V1 area, in order to maximize the number of hM4Di+/PV+ and hM4Di-/PV+ cell pairs. Pairs were selected based on a distance criterion: the mean of the minimal distance between hM4Di+/PV+ cells determined for each section [range: 70-106 µm, n=7 sections, N=3 mice] was used as the maximal radius to select pairs of hM4Di+ and hM4Di-PV+ neighboring cells.
, 2015). A half hour recording was extracted in each condition for analysis using a custom-made software based on Labview (National Instruments). The EEG signal was filtered with 1-4 Hz, 6-10 Hz, 30-50 Hz, 55-95 Hz and 100-150 Hz pass-bands and mean power values were extracted for each band and normalized to control condition. Data obtained in different conditions were compared using a Student t-test. Throughout this study, treatments (ligand vs. PBS) or experimental conditions (transduction of PV-Cre or Emx1-Cre with DREADDs-or PSAM-encoding AAVs) were allocated randomly across mice, and results are given as mean ± standard error of the mean (SEM), and a p-value below 0. 05 was considered statistically significant. show responses of a hM4Di-expressing interneuron to depolarizing current step in control conditions and upon bath application of CNO (0.
ACKNOWLEDGEMENTS We thank  Matthias Meurer  and  Michael Knop  for discussions, reagent exchanges, the C-SWAT library and their help with the construction of the NUbiCA yeast strain collection. We also thank  Thimo Kurz  for TUBE expression plasmids,  Rémy Le Guevel  for the ImPACcell platform and the  IGDR and Biosit  for support and infrastructure.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique  [G. L. D. , S. L. and grant  PICS07394  to G. R. ], the  Institut National de la Santé Et de la Recherche Médicale  [G. R. ], the  University of Rennes  1 [M. L. B], the  Agence Nationale de la Recherche  [grant  ANR-16-CE11-0021-01  to G. R. and  ANR-16-CE13-0002-02  to S. L. ],  La Ligue  contre le cancer [G. R. ] and Biosit [G. R. ].
Différents chemins sont suivis selon les pays considérés ; en Europe, le chemin se présente long et difficile, suscitant, à l'heure actuelle de nombreux débats mais très peu d'exemples concrets, en regard du très faible nombre de financements spécifiques. REMERCIEMENTS Eric Pailhoux remercie chaleureusement Jean-Louis Guenet et Pascale Chavatte-Palmer pour leur invitation et pour lui avoir donné l'occasion de s'exprimer en séance de l'Académie Vétérinaire de France. Les auteurs remercient l'Agence Nationale pour la Recherche qui au travers de trois projets financés (ANR-06-GenAnimal TEGOD ; ANR-09-GENM-009-03 GENIDOV ; ANR-09-BLAN-0015-01 PRIFAGENE) a permis de développer ces technologies de modifications ciblées du génome chez les mammifères de rente, à l'INRA de Jouy en Josas.
Différents chemins sont suivis selon les pays considérés ; en Europe, le chemin se présente long et difficile, suscitant, à l'heure actuelle de nombreux débats mais très peu d'exemples concrets, en regard du très faible nombre de financements spécifiques. REMERCIEMENTS Eric Pailhoux remercie chaleureusement Jean-Louis Guenet et Pascale Chavatte-Palmer pour leur invitation et pour lui avoir donné l'occasion de s'exprimer en séance de l'Académie Vétérinaire de France. Les auteurs remercient l'Agence Nationale pour la Recherche qui au travers de trois projets financés (ANR-06-GenAnimal TEGOD ; ANR-09-GENM-009-03 GENIDOV ; ANR-09-BLAN-0015-01 PRIFAGENE) a permis de développer ces technologies de modifications ciblées du génome chez les mammifères de rente, à l'INRA de Jouy en Josas. BIBLIOGRAPHIE • Bassols A, Costa C, Eckersall PD, Osada J, • Hodges CA & Conlon RA.
g. Meyers et al. , 2008; Meyers, 2019). Testing geological datasets for astronomical forcing is not always straightforward, due to lack of independent time control and because formulation of the most appropriate null hypothesis can be challenging as a consequence of practical and conceptual differences between models and real geological data, including the presence of a range of noise sources and complex distortional relationships between time and stratigraphic depth  (Ripepe and Fischer, 1991; Meyers et al. , 2008; Meyers, 2012; Proistosescu et al. , 2012; Hilgen et al. , 2015; Zeeden et al. , 2015; Meyers, 2019). However, to address this problem, a number of statistical techniques have been proposed to evaluate a potential astronomical imprint within "un-tuned" depth scales and astronomically tuned time series (e.
To evaluate the effect of these frequency modulations on astrochronologic testing, we present a synthetic test series comprised of amplitude demodulated obliquity (Fig. 1d ), spanning the last 6 Ma (using the 'astrochron' R package  (Meyers, 2014; R Core Team, 2017) ; see supplementary R code for model generation). This synthetic series is analagous to a stratigraphic record that has been tuned to the theoretical obliquity solution  (Laskar et al. , 2004) , but lacks expected amplitude modulation (it plays the same role as axial precession does in the study by  Zeeden et al. , 2015). Relatively narrow-band filtering of the demodulated synthetic test series (Fig. 1e, 1k ), here exemplified as a ~37-43 kyr Taner bandpass filter  (Taner, 1992) , expresses amplitude properties similar to the original ~1.
Validating obliquity-dominated astronomically tuned datasets Similar to precession, one means to overcome the problem of artificially imposing obliquity amplitude modulation is via a minimal tuning approach  (Lourens and Hilgen, 1997; Zeeden et al. , 2015). However, minimal tuning approaches for obliquity may be challenging to impossible in practice, because tuning to the long-term obliquity amplitude variation (~1. 2 Myr) is not very helpful in records with sedimentation rates that vary on shorter time-scales. Additionally, the exact position of these long term minima and maxima may be difficult to pinpoint in geological datasets. However, tuning using one tie point per ~175 kyr (s3-s6) obliquity amplitude variation cycle may be feasible in cases where this modulation is clearly visible in data  (Boulila et al.
1a, 1d ). We take advantage of this key attribute in our astrochronologic testing algorithm. We implement a test similar to 'testPrecession'  (Meyers, 2014; Zeeden et al. , 2015) , but modify the algorithm to include a contribution of noise to the dataset, which counters the expression of artificial modulations, proving a reliable test. There are several key issues at hand. First, the noise that is added should express the same general broadband structure as the data. Second, we want to add enough noise to counter the expresion of artificial modulations, but not too much to destroy true modulations; the relative amount of noise that must be added to a given record will be specific to the time interval under investigation (Fig. 2 ). To address the first concern, noise is generated using an AR1 model, with the same lag-1 autocorrelation coefficient as the real geologic data.
Regarding the second concern, the appropriate level of noise to add is determined by starting with a demodulated test series from the same time interval (generated from  Laskar et al. , 2004) , to which increasing levels of noise are added until the correlation between the synthetic amplitudes (demodulated obliquity plus noise) and true obliquity are ≤0. Once the appropriate level of noise is identified, it is added to the geologic data. This noise addition process will be unique to each dataset (adaptive), because the amount of noise required depends on the length, resolution and autocorrelation of a dataset and its age. For filtering obliquity, a relatively wide taner filter is used from 0. 015 [1/kyr] to 0. 038 [1/kyr] with a roll-off rate of 10 20 , see also Figures  1i  and 1l  for a visualisation of the filter properties.
The obliquity reference series  (Laskar et al. , 2004 ) is processed using an identical approach as the data, to generate the theoretical obliquity amplitude target used for comparison. Evaluation of the statistical significance of correlation between theoretical obliquity modulations and the lowpass filtered obliquity amplitude (including data + noise) is conducted via Monte Carlo simulation of phase-randomized surrogate datasets, as implemented in the R 'astrochron' package  (Ebisuzaki, 1997; Meyers, 2014; R Core Team, 2017). Each simulation uses surrogates that have identical power spectra to the the paleoclimate data and the theoretical obliquity, but with randomized phase. Comparison of the observed data correlation with those from Monte Carlo simulations of surrogates allows a rigorous evaluation of the null hypothesis (no correlation between obliquity target and observed modulations).
Testing time series To determine if obliquity amplitude modulations are truly present in datasets we use the filtering procedure as outlined above. This procedure tests if obliquity amplitude variations may derive from frequency modulation introduced by the astronomical tuning and data processing procedure, or whether they are an inherent property of the datasets. As test cases for the new validation approach using obliquity, we investigate the  (Imbrie and Imbrie, 1980)  ice model, as parameterized as by  (Lisiecki and Raymo, 2005) , followed by the LR04 benthic isotope stack  (Lisiecki and Raymo, 2005). We then investigate relatively long and continuous Quaternary datasets, specifically the ODP Site 659 dust record  (Tiedemann et al. , 1994)  and the magnetic susceptibility record from Arctic Lake El'Gygytgyn  (Melles et al.
, 2012; Nowaczyk et al. , 2013). Tests with these three datasets use a cutoff frequency of 1/500 [1/kyr] to focus on the low-frequency components of the obliquity amplitude; to compensate for secular drifts, the obliquity envelopes (not original data) of the datasets were linearly detrended before comparison with theoretical obliquity. The cut off frequency deviates from standard setting of the testTilt function, and this choice made deliberately here to obtain a useful and comparable outcome and basis for interpretation (various settings were tested). Following this, we use the new reconstructed obliquity amplitudes to evaluate mechanisms of climate change during the Pleistocene. Table  1  summarizes properties of the described case studies. Please note that procedures include simulation, and therefore results from different runs will not be identical.
Testing the LR04 δ 18 O benthic isotope stack A benthic foraminifer oxygen isotope stack for the last 5. 3 Ma was published (LR04;  Lisiecki and Raymo, 2005) , which serves as a standard reference dataset for numerous studies. The LR04 stack is often used as correlation target for time scale construction or consistency testing (e. g. Necula and Panaiotu, 2008 ; Buggle et al. , 2009; Marković et al. , 2012; Basarin et al. , 2014; Kaboth et al. , 2016). The LR04 stack was correlated to the Imbrie and Imbrie (1980) ice model, which uses northern hemisphere summer insolation as model input, and therefore exhibits patterns of precession and obliquity, as well as non-linear features. As a secondary constraint, obliquity was used to fine-tune portions of the LR04 stack  (Lisiecki and Raymo, 2005).
The LR04 stack has a trend towards higher variability and heavier δ 18 O values towards recent (Fig. 5 ). Testing this dataset results in a significant (p-value is 0. 015) correlation between the astronomical obliquity amplitude and the obliquity amplitude as reconstructed from this dataset, when focusing on the ~1. 2 Ma long-term component. We choose to linearly detrend the data amplitudes before comparison, because the trend toward higher δ 18 O variability and heavier values is unique to global climate evolution, and not a feature of the predicted astronomical variations.