dataesr/scientific-paragraphs-categorization huggingface dataset:
Global analysis of metabolic pathways Using the CycADS  [108]  pipeline and Pathway Tools  [109] , we have generated BioCyc metabolism reconstruction databases for S. oryzae and its endosymbiont S. pierantonius. We compared S. oryzae metabolism to that of other arthropods available in the ArthropodaCyc  [110]  collection and we explored the metabolic exchanges between weevils and their endosymbionts. The metabolic reconstruction reveals that, despite its large genome for an endosymbiotic bacterium, S. pierantonius relies on its Table  1  Assembly statistics of S. oryzae's genome in comparison to Curculionidae genomes and T. castaneum  [6, 7, [51] [52] [53] [54] [55] [56] [57]  *All genes, no NCBI RefSeq annotation report available host for several central compounds, including alanine and proline, but also isocitrate, inosine monophosphate (IMP), and uridine monophosphate (UMP), to produce essential molecules to weevils, including the essential amino acids tryptophan, phenylalanine, lysine, and arginine, the vitamins pantothenate, riboflavin, and dihydropteroate as a folate precursor, and nicotinamide adenine dinucleotide (NAD) (Additional file 2: Supplemental Note 2).
Among all Class II/TIR superfamilies, TIR length varies between tens of base pairs to ≈1 kbp (Fig. 2E ). We identified short elements, composed mostly of their TIR sequences (Fig. 2E ), typical of MITEs. Interestingly, the unknown TIR families show an average size smaller than 1 kbp, while TIRs with an annotated superfamily, show larger sizes (Additional file 2: Figure  S3 ), suggesting that most unknown families could be indeed nonautonomous MITEs. MITE size ranges were previously described from around 100 bp to copies reaching more than 1 kbp  [249]. Finally, the distribution of the proportions of TIR relative to the consensus length appears superfamily-specific (Fig. 2E  and Additional file 2: Figure  S3 ), and unknown families recapitulate these patterns. In conclusion, while most unknown TIR families seem to be composed of MITEs, we cannot exclude that our homology database is limited, likely missing some unknown protein domains.
These two libraries were sequenced at KeyGene (Wageningen, The Netherlands). Finally, five male samples were used to build Nanopore libraries with the SQK-LSK109 kit and without DNA fragmentation step. The libraries were independently sequenced on five MinION R9. 4 flow cells. These libraries were built and sequenced at the sequencing platform of the IGFL (Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, France). Statistics and accession numbers from all the sequencing runs are listed in the Additional file 1.
No diploid peak nor significant haplotig contamination was observed. Quality of the assembly was assessed by computing several metrics using (i) QUAST v5. 0. 2  [304]  with a minimal contig size of 100 bp and the --large and -k options, (ii) BUSCO v4. 0. 5  [50]  using the Insecta ODB10 database and the -geno option, and (iii) KMC v3. 0. 0  [305]  to evaluate the percentage of shared 100-mers between the assembly and the merged Illumina reads. Genome size prediction was performed with GenomeScope v2. 0  [58] , findGSE v1. 94. R  [60] , and gce v1. 0. 2  [59]  based on 21-mer histograms generated by JellyFish v2. 2. 10 [306]  on the R1 reads from error-corrected Illumina overlapping PE library. Three contaminant scaffolds corresponding to the mitochondrial genome and an artifact were removed from the assembly prior to the annotation step.
TE library construction In order to annotate the S. oryzae repeatome, we collected and combined cutting-edge bioinformatic tools to (i) create and (ii) classify a non-redundant library of repeated elements (Additional file 2: Figure  S1 ). First, we separately ran RepeatModeler2 (v2. 0. 1)  [244]  and EDTA v1. 7. 8  [245]  on the assembled genome. Together, these programs include most of the recent and long-trusted tools used to detect generic repeats, but also include specific modules, such as for LTR and TIR elements. Preliminary analyses of the S. oryzae genome with RepeatModeler1  [307]  and dnaPipeTE (v1. 3)  [47]  suggested a rather large fraction of Class II DNA elements with terminal inverted repeats (TIRs). Thus, MITE-Tracker  [308]  was incorporated in our pipeline and ran independently on the genome assembly using 1-and 2kbp size cutoffs to detect Class II elements harboring TIRs with high sensitivity.
Following this initial step, 15,510 consensus sequences obtained from RM2, EDTA, and the two runs of MITE-tracker were successively clustered using MAFFT (v6. 864b)  [65] , Mothur (v. 1. 45. 2)  [309] , and Refiner  [307]  to reduce redundancy in the repeat library to a total of 2754 consensus sequences (Additional file 2: Figure  S1A , https://github. com/ clemgoub/So2). Then, we inspected the quality of the raw library by calculating the genomic coverage of each consensus. We ran the library against the genome using RepeatMasker (v4. 1. 1) (52) and implemented a simple algorithm "TE-trimmer. sh" to trim or split a consensus sequence wherever the genomic support drops below 5% of the average consensus coverage (Additional file 2: Figure  S1A , https://github. com/clemgoub/So2). To mitigate any redundancy generated by the splitting, the newly trimmed library was clustered before being re-quantified using RepeatMasker (v4.
1. 1) [307]. At this step, we removed any consensus under 200 bp and represented by less than the equivalent of two full-length copies (in total bp). In addition, TAREAN (RepeatExplorer2 v0. 3. 6) [310]  was used to detect and quantify candidate satellite repeats. We obtained an ab initio repeat library of 3950 consensus sequences automatically generated (Additional file 2: Figure  S1A ). To refine and improve the quality of the TE consensus sequences, we then turned it over to DFAM  [311]  who processed the ab initio library following their recent guidelines  [312]. First, any sequences mostly composed of tandem repeats were removed using a custom script to remove any sequences that were greater than 80% masked and/or had a sequence less than 100 bp. To generate seed alignments for each consensus, the consensus sequences were used as a search library for RepeatMasker (v4.
Significant hits (P ≤ 0. 01) against known TEs and viruses were removed and all other left consensus were removed from the TE library. In conclusion, there are 21% unknown repeats, and the number of total TE consensus sequences in S. oryzae is 3361. The data can be obtained from https:// doi. org/10. 5281/zenodo. 5128603. In order to assess the relevance of our custom TE analysis pipeline, we ran and compared the unfiltered outputs (out. tbl file) of RepeatMasker v4. 1. 1 using either the TE library produced by RepeatModeler 2. 0. 1, EDTA v1. 7. 8, or our final library. An optimized TE library should minimize the total number of consensus while being able to capture as much TE in the genome as possible. Thus, we compared the total number of consensus built in each library as well as the total percent of the genome masked by each respective library.
Estimation of the repeat content The total repeat content of the S. oryzae genome was analyzed using RepeatMasker (v. 4. 1. 1) and our classified library of 3361 consensus sequences and the following parameters: -s -gccalc -no_is -cutoff 200. The subsequent alignments were parsed with the script "par-seRM. pl" [315]  https://github. com/4ureliek/Parsing-RepeatMasker-Outputs) to remove hits overlap and statistically analyzed with R version 4. 0. 2.
Genomic distribution of TE copies The distribution of TE copies across the S. oryzae genome was assessed using two different approaches over six different genomic regions namely TSS ± 3 kbp, 5′ UTRs, exons, introns, 3′ UTRs, and intergenic regions. Briefly, the coverage of all TE copies was computed over a sliding window of 100 bp across the whole genome sequence using the makewindows and coverage tools from the bedtools package  [280]  and the bedGraphToBigWig UCSC gtfToGenePred tool. Then the different genomic regions were retrieved from the S. oryzae annotation file (GFF format) using the gencode_regions script (https:// github. com/saketkc/gencode_regions) and the UCSC gtfToGenePred tool (https://github. com/ENCODE-DCC/kentUtils). A matrix containing the TE coverage per genomic region was generated using the compute-Matrix tool from deepTools  [279]  and used to generate metaplots using the plotProfile tool.
dnaPipeTE comparative analysis in Sitophilus species To compare the TE content of S. oryzae to four related species of Sitophilus (S. granarius, S. zeamais, S. linearis), we used dnaPipeTE v. 1. 3 [47]. dnaPipeTE allows unbiased estimation and comparison of the total repeat content across different species by assembling and quantifying TE from unassembled reads instead of a linear genome assembly. Reads for Sitophilus species were produced as described above. Using our new classified library (3 390 consensus) as TE database in dnaPipeTE, we were further able to identify the phylogenetic depth of the repeat identified in S. oryzae.
RNA sequencing and TE expression analysis Individuals of both sexes of S. oryzae were reared on wheat grains at 27. 5 °C with 70% relative humidity. Ten midguts and ovaries from 10-day-old adults were dissected in diethylpyrocarbonate-treated Buffer A (25 mM KCl, 10 mM MgCl 2 , 250 mM Sucrose, 35 mM Tris/HCl, pH 7. 5). RNA was extracted in triplicates with RNAqueous-Micro (Qiagen), following the manufacturer recommendations. Single-indexed libraries were built using the SENSE mRNA-Seq Library Prep Kit V2 (Lexogen), following the manufacturer recommendations. Libraries were then pooled in an equimolar range and sequenced using high-throughput reagents on an Illumina NextSeq 500 (86 bp in single end). Raw sequencing reads were deposited at SRA archive BioProject PRJNA746240. Adapter sequences and low-quality reads were filtered out with Trimmomatic (v0.
36)  [316]  and clean reads were aligned to the S. oryzae genome with STAR aligner (v2. 5. 4b,  [317] ) and featureCounts from subread package  [318]  to obtain gene counts. We also used the STAR aligner in single-end mode to map the clean reads against all TE copies extracted from the genome with the following options: --outFilterMulti-mapNmax 100 --winAnchorMultimapNmax 100 --out-MultimapperOrder Random --outSAMmultNmax 1. The mapped bam files were used as input to TEtools software  [319]  to determine TE family expression. Genes and TE family counts were used as input for DESEq2 package (v1. 30) [320]  to determine differential TE expression between ovary vs gut tissues as well as ovaries from symbiotic and aposymbiotic weevils. Differentially expressed TEs were defined whenever the adjusted p value was smaller than 0.
05 and log2 fold change was higher than 1 or smaller than -1. We used the aforementioned STAR alignment parameters to map transcriptomic sequencing reads from midgut and ovaries of S. oryzae (Accession: SRX1034971, SRX1034972, and reads from this study deposited under the BioProject ID PRJNA746240), D. melanogaster (Accession: SRX029389, and SRX045361), and Ae. albopictus (Accession: SRX1512976, SRX1898481, SRX1898483, SRX1898487, SRX3939061, and SRX3939054) against the TE consensus sequences for each species.
Acknowledgements The authors acknowledge supercomputing resources made available by the  Rhône-Alpes Bioinformatics center (PRABI-AMSB , http://www. prabi. fr) to perform the NGS data analyses. The authors would like to thank  S. Robin ,  F. Legeai , and  A. Bretaudeau  at the  INRAE BioInformatics Platform for Agroecosystems Arthropods (BIPAA)  (https://bipaa. genouest. org)for the S. oryzae genome integration. The authors would like to thank  F. Barbet ,  S. Croze , and  N. Nazaret  from profileXpert for Illumina sequencing of Sitophilus oryzae libraries. The authors thank the network  REacTION  and its coordinators ( N. Ponts ,  G. Le Trionnaire ,  I. Fudal ,  M. Jubault ), funded by  INRAE-SPE , for organizing a Bisulfite-seq workshop that allowed the production of the data presented here.
On day 31, late retention of extinction was tested in context A (late extinction recall) followed 1. 5 h later by a fear renewal test in context B, similarly using blocks of four CS À and CS þ presentations (Fig. 1 ). Freezing behavior was analyzed using an automated video monitoring system (Ethovision software, Noldus, The Netherlands). Freezing was defined as the difference of pixels (max. 0. 3%) between two consecutive frames during 1 s or more. Additionally, integrated data were manually corrected by a blinded observer for false positives. Time frames considered by the software erroneously as freezing, were subtracted manually from the total freezing time.
Acknowledgements The authors would like to thank  An Buckinx ,  Wissal Allaoui ,  Blazej Pedzich ,  Patrice Andrieu  and  Solene Pedron  for their technical support. Furthermore, we would like to thank the lab of  Prof. Van Waes  for their support in the optimization of the tDCS procedure in mice and to provide a custom-made direct current stimulator. This study was financially supported by the  Fund for Scientific Research Flanders  (FWO e  1158918N ), the  Willy Gepts Fund of the Vrije Universiteit Brussel , a grant  BOFSTA2017002501  for research at  Ghent University  ( BOFSTA2017002501 , awarded to MAV) and the  Boehringer Ingelheim Fund  (travel grant).
Results In the following, we investigate whether and how macaques associate visual and auditory stimuli based on their semantic content, and we characterize the neuronal bases underlying this audio-visual integration. We obtained neural and autonomic data from two macaques using functional magnetic resonance brain imaging and video-based heart rate tracking. Each task combined visual stimuli of identical social content with either semantically congruent or incongruent monkey vocalizations. On each block of trials, the monkeys could be exposed to either visual stimuli only, auditory congruent stimuli only, auditory incongruent stimuli only, audio-visual congruent stimuli or audio-visual incongruent stimuli, in a blocked design (Figure  1a ). Importantly, paired blocked conditions shared the same auditory stimuli, but opposite social visual content (Figure  1b ).
Data analysis Data were pre-processed and analysed using AFNI  (Cox, 1996 ), FSL (Jenkinson et al. , 2012; Smith et al. , 2013) , SPM software (version SPM12, Wellcome Department of Cognitive Neurology, London, UK, https://www. fil. ion. ucl. ac. uk/spm/software/), JIP analysis toolkit (http://www. nitrc. org/projects/jip) and Workbench (https://www. humanconnectome. org/software/get-connectome-workbench). The T1-weighted and T2-weighted anatomical images were processed according to the HCP pipeline  (Autio et al. , 2020; Glasser et al. , 2013)  and were normalized into the MY19 Atlas  (Donahue et al. , 2016). Functional volumes were corrected for head motion and slice time and skull-stripped. They were then linearly realigned on the T2-weighted anatomical image with flirt from FSL, the image distortions were corrected using nonlinear warping with JIP.
nitrc. org/projects/gppi), using the CONN toolbox (www. nitrc. org/projects/conn, RRID:SCR_009550), an open-source Matlab/SPM-based cross-platform software. Specifically, we were interested in identifying the network activated throughout the runs and that could account for the dependence of auditory perception on the visual context of the task. The gPPI analysis was performed independently on each task, over the averaged LS and STS ROIs time series respectively, for each hemisphere. We report, in supplementary table 1 the cortical and subcortical regions the time series of which showed a significant temporal correlation with the seeds (p=0. 005 uncorrected). Are considered for discussion only the cortical regions the time series of which correlate, with either the LS or STS time series, in at least three of the six blocked conditions, for each of the left and right hemispheres (criterion 1), or in at least three of the six blocked conditions, in at least one hemisphere and in at least eight of the blocked conditions across both hemispheres (criterion 2, more stringent).
Behaviour and Heart rate During each run of acquisition, videos of the faces of monkeys S and T were recorded in order to track heart rate variations (HRV) as a function of blocked conditions and blocks  (Froesel et al. , 2020). We focus on heart rate variations between auditory congruent and incongruent stimuli. For each task, we extracted HRV during AC and AI blocs. As changes in cardiac rhythm are slow, analyses were performed over the second half (8s of each block). This has been done for each run of each task, grouping both monkeys. Because the data were not normally distributed (Kolmogorov-Smirnov Test of Normality), we carried out Friedman tests and non-parametric post hoc tests.
Acknowledgements S. B. H. were funded by the  French National Research Agency (ANR) ANR-16-CE37-0009-01  grant and the  LABEX CORTEX funding  ( ANR-11-LABX-0042 ) from the  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). We thank  Fidji Francioly  and  Laurence Boes  for animal care,  Julian Amengual  and  Justine Cléry  for their rich scientific exchanges during data collection and analyses,  Franck Lamberton  and  Danièle Ibarrola  for their MRI methodological support and  Holly Rayson  for help on visual stimuli collection.
3c ). Together, these results suggest that densely packed heterochromatin and phase-separated nucleoli in interphase cells have low permeability to mesoscale particles of 40 nm diameter. nucGEMs probe the mesoscale properties of the nucleoplasm The nucleus has been reported to maintain a lower mass density than the cytosol by volume scaling throughout the cell cycle, while the nucleolus has the highest mass density of any compartment  (Kim and Guck 2020). In addition, the nucleus has been reported to be less crowded than the cytosol at the nanometer length-scale of single GFP molecules  (Phair and Misteli 2000). However, there is very little information regarding the mesoscale properties of the nucleoplasm. We therefore collected large datasets to compare the rheological properties of the nucleoplasm and cytosol in S.
Confocal Imaging of GEMs: Micrographs were acquired on a Nikon Eclipse Ti Eclipse microscope mounted with Yokogawa CSU-X1 spinning disk unit, NIDAQ AOTF multilaser unit, and Prime 95B camera operating on Nikon NIS-Elements AR (v 5. 21. 03) software. We used CFI Apo 60x/N. A-1. 49/. 12 TIRF objective with a 470/40m excitation filter and ET525/36m emission filter (Chroma Technology Corp) in all mammalian acquisitions. Using a 488 nm laser the sapphire fluorophore was excited using 100% power and images were collected from a single focal plane at 100fps, binning 1, 512X512, and 8-bit pixel depth for 2 to 4 seconds.
Quantification of mesoscale rheology: Time-averaged, ensemble-time-averaged mean-square displacement (MSD): GEMs were initially tracked with the ImageJ Particle Tracker 2D-3D tracking algorithm from MosaicSuite(Sbalzarini and Koumoutsakos 2005) and trajectories were then analyzed with the GEM-Spa (GEM single particle analysis) software package that we are developing in house: https://github. com/liamholtlab/GEMspa/releases/tag/v0. 11-beta For every 2D trajectory, we calculated the time-averaged mean-square displacement (MSD) at different time intervals: < 𝛥𝑟 ! (𝜏) > " =< [𝑥(𝑡 + 𝜏) -𝑥(𝑡)] ! + [𝑦(𝑡 + 𝜏) -𝑦(𝑡)] ! > " (1 ) where '<> " ' represents time averaging for each trajectory of all displacements under time interval. To reduce tracking error due to particles moving in and out of the focal plane, we selected particle trajectories with more than 10 time points.
Acknowledgements We thank  Diane Simeone  for the gift of human pancreatic nestin expressing (hPNE) cells,  Jef Boeke  for HeLa and HEK293 cells, and  Mario Pende  for  Neural Progenitor Cells (NPCs). We thank all members of the  Holt lab  for assistance and feedback. This work was funded by  NIH   R01 GM132447  and  R37 CA240765 , the  American Cancer Society -Cornelia T. Bailey Foundation   Research Scholar Grant ,  RSG-19-073-01-TBE , the  Pershing Square Sohn Cancer Award , the  Chan Zuckerberg Initiative , and the  Air Force Office of Scientific Research (AFoSR)  grant  FA9550-21-1-3503 0091.
Samples for flow cytometric-activated cell sorting (FACS) were sorted and samples for flow cytometry were fixed using eBioscience IC fixation (00-8222-49) or FOXP3 Fix/Perm kit (00-5523-00) according to the manufacturer's instructions. Intracellular staining was performed in eBioscience Perm buffer (00-8333-56). Cells were analysed using BD FACSFortessa or BD FACSSymphony and data were analysed using FlowJo (v10. 6. 2). For sorting, FACS Aria II and FACSAria FUSION were used in collaboration with the DKFZ FACS core facility. For UMAP and FlowSOM plots, BD FACSymphony data (mouse and human) were exported from FlowJo (v10). Analyses were performed as described elsewhere  39.
Single-cell RNA-seq and metacell analysis (mouse) Single-cell capturing for scRNA-seq and library preparation were done as described previously  40. Libraries (pooled at equimolar concentration) were sequenced on an Illumina NextSeq 500 at a median sequencing depth of ~40,000 reads per cell. Sequences were mapped to the mouse genome (mm10), using HISAT (version 0. 1. 6); reads with multiple mapping positions were excluded. Reads were associated with genes if they were mapped to an exon, using the Ensembl gene annotation database (Ensembl release 90). Exons of different genes that shared a genomic position on the same strand were considered to represent a single gene with a concatenated gene symbol. The level of spurious unique molecular identifiers (UMIs) in the data was estimated by using statistics on empty MARS-seq wells and excluded rare cases with estimated noise >5% (median estimated noise overall for experiments was 2%).
Peptides were eluted using a gradient of 5-30% buffer B (80% acetonitrile and 0. 1% formic acid) at a flow rate of 300 nl/min at a column temperature of 55 °C. Data were acquired by data-dependent Top15 acquisition using a high-resolution orbitrap tandem mass spectrometer (QExactive HFX, Thermo Scientific). All MS1 scans were acquired at 60,000 resolution with AGC target of 3 × 10 6 , and MS2 scans were acquired at 15,000 resolution with AGC target of 1 × 10 5 and maximum injection time of 28 ms. Analyses were performed using MaxQuant (1. 6. 7. 0), mouse UniProt Isoform fasta (Version: 2019-02-21, number of sequences 25,233) as a source for protein sequences. One per cent FDR was used for controlling at the peptide and protein levels, with a minimum of two peptides needed for consideration of analysis.
Isolation of RNA and library preparation for bulk RNA sequencing RNA isolation  17  and library preparation for bulk 3′-sequencing of poly(A)-RNA was as described previously  48. Gencode gene annotations version M18 and the mouse reference genome major release GRCm38 were derived from https://www. gencodegenes. org/. Dropseq tools v1. 12  49  were used for mapping the raw sequencing data to the reference genome. The resulting UMI-filtered count matrix was imported into R v3. 4. 4. Before differential expression analysis with Limma v3. 40. 6  50  sample-specific weights were estimated and used as coefficients alongside the experimental groups as a covariate during model fitting with Voom. t-test was used for determining differentially (P < 0. 05) regulated genes between all possible experimental groups.