dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq analyses Total RNA was extracted with TRIzol from hand dissected ovaries or 50 μl of 3-to 5-hours embryos in 1× PBS and 1 μg was treated with DNase (Fermentas). After an initial quality control, libraries were prepared using the RNA RiboZero Stranded protocol by Fasteris. Indexed adapters were ligated and multiplexed sequencing was performed using Illumina HiSeq 2000 and 2500 (table  S2 ). A DESeq2 analyses on all D. mel. genes (release 6. 36) was performed using the three biological replicates for each genotype, control, or Kdm3 GLKD in ovaries and the two biological replicates in embryos. The original files have been deposited at the GEO under accession number GSE203279.
RNA and protein was digested by RNase A and proteinase K treatments, respectively, before purification using phenol/chloroform protocol. Barcoded libraries were prepared using Illumina technology, which were sequenced on a NextSeq High (Illumina) by Fasteris for the ChIP H3K9me3 and H3K9me2 and by the Jean Perrin facility for the ChIP Rhino (table  S7 ). Sequences and projects have been deposited at the GEO under accession number GSE203279.
Data analyses All analysis scripts were deposited on Dryad (https://doi. org/10. 5061/dryad. n2z34tn1p). For alignments, we used a cleaned version of r6. 36 version of D. melanogaster genome (dm6, Flybase) in which sequences corresponding to unmapped (chrU), Y chromosome (chrY), and mitochondrial chromosome (chrM) have been removed (dm6_clean). For sRNA-seq analyses, we first discarded the reads matching tRNA (dmel-all-tRNA-r6. 30, Flybase), miRNA (dmel-all-miRNA-r6. 30, Flybase), miscRNA (dmel-all-miscRNA-r6. 30, Flybase), and rRNA (dm3-rRNA-sequences, Galaxy Tutorial) using sR_Bowtie -m3 (Galaxy version 2. 1. 1). Then, unique mapper reads were obtained using sR_Bowtie -m0 (Galaxy version 2. 1. 1) and the dm6_clean genome as reference. Reads (23 to 29 nt) were aligned on 124 additional piRNA production loci with sR_Bowtie -m0 (Galaxy version 2.
Acknowledgments: We thank  D. Dorer ,  S. Henikoff ,  J. Brennecke ,  M. Buszczak , and the  Bloomington Stock Center  for providing stocks. We thank the  TRiP at Harvard Medical School  ( NIH/NIGMS   R01-GM084947 ) for providing transgenic RNAi fly stocks used in this study. We thank  J. Brennecke  for providing antibodies. We thank flybase. org for providing databases. We thank  R. Zamy  for technical assistance. We thank  L. Pile  for critical reading of the manuscript. We thank  C. Antoniewski  for helpful advice and development of the ARTbio platform (http://artbio. fr/). We thank anonymous reviewers and  P. Zamore  for comments that greatly improved the original manuscript. Funding: This work was supported by:  PhD fellowships  from the  Ministère.
Egg shRNA showing epistasis interactions with hfp and Kdm3 shRNA is TRiP line #36797. Mock shRNA are y shRNA TRiP line #64527 on chromosome 2 or w shRNA TRiP line #33644 on chromosome 3. Flies were grown on a corn-based medium made of agar-agar (11 g/liter), yeast powder (75 g/liter), corn flour (90 g/liter), and 35 mM methyl 4-hydroxybenzoate as antifungi. All crosses were performed at 25°C in incubators. Humidity was controlled through the use of desiccators. No special precautions were taken with regard to the light/dark cycles. Kdm3 CDS was amplified from wild-type genomic DNA (CantonS) and cloned into p(UASp-EGFP) at the Eco RI site in frame with the EGFP sequence (for primers, see tableS11). Sequence integrity was verified by sequencing. The sequence is available at Genbank under accession number OP765907.
Primary antibodies were incubated overnight at RT. After rinsing, the sections were incubated with biotinylated goat anti-rabbit secondary antibody (1:800, GE Healthcare) for 2 h at RT. All the antibodies were diluted in PBS, 0. 5% Triton X-100, and 2. 5% donkey serum. The peroxidase-conjugated extra-avidin method and diaminobenzidine as the chromogen were used to visualize the reaction product. The numbers of labelled Aβ plaques were manually counted in the hippocampus (CA1, CA3, and dentate gyrus (DG)). For the quantification of GFAP and Iba1 staining, we employed densitometry analysis using ImageJ software (Analyse > Set Measurements: Area and Integrated Density; Analyse > Measure). Images were acquired using a DP70 digital camera (Olympus Iberia, S. A. U. ) connected to an Olympus BX41 microscope.
Bio-Plex multiplex assay Plasma levels of insulin, gastric inhibitory polypeptide (GIP), glucagon-like peptide-1 (GLP-1), leptin, ghrelin, glucagon, resistin, and plasminogen activator inhibitor-1 (PAI-1) were measured using a multiplex immunoassay system with a commercial kit (Bio-Plex Pro™ mouse diabetes 8-plex immunoassay, Bio-Rad, Hercules, CA, USA, cat. number: #171F7001M). Plates were run on a Bio-Plex MAGPIX™ Multiplex Reader with Bio-Plex Manager™ MP Software (Luminex, Austin, TX, USA). Hormone concentrations were expressed in pg/mL, and detection limits were 68. 29 (insulin), 4. 31 (GIP), 0. 59 (GLP-1), 5. 07 (leptin), 0. 64 (ghrelin), 0. 50 (glucagon), 184. 89 (resistin) and 2. 98 (PAI-1) pg/mL.
, 2019)  version 2023. 5 in a conda environment which is based on the Ubuntu Linux operating system. The taxonomy database used was Silva 138. 1 version  (Quast et al. , 2013) , and the RESCRIPt plugin was employed for its curation and preparation. Phylum analysis focused on both the Bacteroidetes and Firmicutes phyla. Taxonomic compositions were compared at family level in terms of relative abundance using QIIME 2.
, 2017). Statistical significance was set at P ≤ 0. 05. For microbiota analysis, sequencing data was processed and analysed using specific software and packages. The paired-end sequences of each sample were exported in FASTQ format. The quality of the sequences was first assessed using the FastQC software. For the quantitative analysis of the relative abundance of the microbiota in the samples, R 4. 1. 2 was used in the Rstudio working environment, version 2023. 03. 1. The analysis utilized the "stats", "xlsx", "car," "dplyr," and "readr" packages, along with the Bioconductor "bio format" package. A paired-end demultiplexed sequencing protocol was used to import the sequences, and the dada2 denoise-paired command  (Callahan et al. , 2016)  was employed for used for quality filtering, denoising, and merging of paired-end reads.
Core metrics were obtained to assess alpha and beta diversities. Alpha diversity measures the species richness within each community, while beta diversity examines the differences in composition, specifically the abundance of different taxa, among different samples. Alpha rarefaction analysis was performed at a depth of 25,000 per sample. The sequences were grouped into operational taxonomic units (OTUs) using a 97% similarity threshold. Taxonomic assignment was carried out using an own trained classifier using the q2feature-classifier plugin  (Bokulich et al. , 2018). Statistical inference was performed using the Kruskal-Wallis test and Mann-Whitney U for each OTUs, allowing for comparisons and identification of significant differences between groups. The entire generated information, along with the used scripts, has been hosted on the following link: github.
Effect sizes were estimated based on previous literature to ensure adequate statistical power for the multitude of outcome measures. Additionally, adjustments for multiple comparisons were considered in our statistical analyses to enhance the reliability of the findings. The data were presented as mean ± standard error of the mean (SEM). Statistical analyses were conducted using GraphPad Prism version 9 (GraphPad Software, Inc. , La Jolla, CA, USA) for general analyses, while R software was used specifically for microbiota analysis.
We examined the behavioural phenotype of HTZ and HZ 5xFAD transgenic mice at two stages of AD: 5 months (early onset) and 12 months of age (middle onset). Anhedonia, characterized by a loss of interest or pleasure, is a core symptom in mood disorders like depression  (Anisman and Zacharko, 1990)  and has been associated with increased cortical Aβ load in AD patients  (Babulal et al. , 2016; Naudin et al. , 2015; Reichman and Coyne, 1995). In rodents, sucrose/saccharin intake is commonly used to measure hedonic response  (Scheggi et al. , 2018; Strekalova et al. , 2004). Our data showed that at 5 months of age, both HTZ and HZ mice showed reduced sucrose intake below the anhedonia criterion of 65%  (Scheggi et al. , 2018). These findings align with other AD mouse models, such as the 3xTg-AD model, which also exhibited depression-related behaviour and lower sucrose preference  (Romano et al.
Acknowledgements We gratefully acknowledge IBIMA joint support structures for research (ECAI). Likewise, we are obliged to the staff of the animal housing and central research facilities at  Universidad de Málaga. This research was funded by the  European Regional Development Funds-European Union  ( ERDF-EU)   Fatzheimer  project  EULAC- HEALTH H2020 , grant number  EU-LACH16/T010131 ;  Instituto de Salud Carlos III (ISCIII)  through the project  PI22/00427  and co-funded by the  European Union ;  Ministerio de Economía, Industria y Competitividad, Gobierno de España , grant number  RTC-2016-4983-1 ;  EU-ERDF  and  Instituto de Salud Carlos III (ISCIII) , grant numbers  PI19/01577  and  PI19/00343 ;  Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía , grant number  P18-TP-5194.
RELATED WORK 3. 1 Scaling QD to Neuroevolution The challenge of evolving diverse solutions in a high-dimensional search space has been an active research subject over the recent years. ME-ES  [5]  scales MAP-Elites to high-dimensional solutions parameterized by large neural networks. This algorithm leverages Evolution Strategies to perform a directed search that is more efficient than random mutations used in Genetic Algorithms. Fitness gradients are estimated locally from many perturbed versions of the parent solution to generate a new one. The population tends towards regions of the parameter space with higher fitness but it requires sampling and evaluating a large number of solutions, making it particularly data inefficient. In order to use the time step level information and hence improve data efficiency, methods that combine MAP-Elites with Reinforcement Learning  [28, 30, 31, 38]  have emerged and proved to efficiently evolve populations of highperforming and diverse neural network for complex tasks.
Baselines We compare DCG-MAP-Elites with four algorithms: CVT-MAP-Elites  [39] , MAP-Elites-ES  [5] , PGA-MAP-Elites  [28]  and QD-PG  [31]. We could not compare to PBT-MAP-Elites  [30]  as its implementation is not yet available. We use QDax implementations and each experiment is replicated 20 times with random seeds, over one million evaluations. The source code of DCG-MAP-Elites is available at github. com/adaptive-intelligent-robotics/DCG-MAP-Elites including a container in which all the experiments can be replicated.
'Negative control' cells were labeled with 100 nM. Media (with doxycycline) was finally changed and pure AF568 dye in solution (5 nM) was added in the free well of the chamber before imaging. Microscopy: FCS. FCS measurement and 3D imaging were performed on a Zeiss LSM880 microscope using a C-Apochromat Zeiss UV-visible-IR ×40/1. 2-NA objective and operated with ZEN Blue software, equipped with an incubator chamber controlled at 37 °C and 8% CO 2. FCS measurements were automated using the macro FCSRunner. The power of the 561 laser was set to 0. 01 for all FCS measurements. For pure AF568 dye, two consecutive measurements of 10 s were performed for five points per field of view, for least four fields of view per experiment. For all measurements in cells, a single measurement of 30 s was performed for a single point per cell, followed by one acquisition for the whole field of view, single stack at the same z position as the FCS measurement, with the same laser power and the following parameters: ×8 zoom and 128 × 128 pixels per field of view (resulting in a pixel size of 0.
FCS data processing. Background average signal in negative control cells was calculated using FCSFitM. FCS measurements were processed using FluctuationAnalyzer. All parameters were kept at default value except the following: • Step Modify and correlate: 'Base freq': 1,000,000 (dye measurement) or 100,000 (cell) • Step Intensity correction: 'Base freq': 1,000,000 (dye measurement) or 100,000 (cell)'Offset': 0 (Dye) or the average intensity from negative cells (cell) • Step Fit correlations: all fitting were performed using the model 'two-component anomalous diffusion with triplet-like blinking' with weighted fit, two runs of optimization and initial guess. For free dye and freely diffusing control Halo-NLS, the fraction of the first component was fixed to one resulting in practice in a one-component model.
The average confocal volume was calculated based on all the dye measurements for each individual experiment separately. Diffusion coefficient estimation. diffusion coefficients were calculated based on equation (2): D protein = (w 0 /2) 2 /τ protein (3) where D protein is the diffusion coefficient of the protein, w 0 the lateral radius of the confocal volume estimated in the previous step based on dye measurements and τ protein the diffusion time of the protein estimated from the autocorrelation fitting. Diffusion coefficients were calculated for each population from the fitting, the first one being the one with the highest diffusion coefficient (corresponding to the free fraction, Extended Data Fig. 5d ).
The camera exposure time was set to 5 ms, the excitation with 633 nm laser (100% laser power) to 1 ms and the photoactivation with 405 nm laser synchronized with the off time of the camera (0. 477 ms between frames). The intensity of the 405 laser was adapted manually between 2 and 10% during the acquisition to optimize photoactivation to obtain enough tracking per experiment while remaining sparse enough to track single molecules accurately. Per cell, 30,000 frames were acquired. Snapshots of Xist-BglG-GFP were taken before and after the SPT with the 488 nm laser (200 ms exposure). Localization and tracking. Localization and tracking were performed using the pyspaz program (https://github. com/alecheckert/ pyspaz). Localization was performed using the function localize detect-and-localize-file with the following parameters: -s 1 -t 20, and all other parameters as default.
Tracking was performed using the function track track-locs with the following parameters: --algorithm_type conservative --pixel_size_um 0. 106 -e 3 -dm 10 -db 0. 1 -f 5. 477 -b 0 and all other parameters as default. Segmentation. For each acquisition, the two Xist-Bgl-GFP snapshots (before and after SPT) were combined as one multidimensional TIF file using a custom python script. These snapshots were used for segmentation of the Xist compartment and nucleoplasm using ilastik. The autocontext mode was first used to create probability maps, annotating pixels as Xist territory (high Xist-BglG-GFP signal), nucleoplasm (low Xist-BglG-GFP signal) and 'background' (between nuclei). The Tracking mode was then used with the probability maps as input to automatically segment and annotate nuclei and Xist territory (one model built to track Xist territory, one model to track nuclei).
To segment nucleoli, the density of single-particle localizations from the 10,000 first and last frames was calculated (a large number of frames is required to capture enough particles and avoid artificial 'empty regions'). The SPT densities and Xist-Bgl-GFP snapshots were combined into one multichannel image, and ilastik was used to segment nucleoli, Xi and nucleoplasm using the same strategy as for Xist compartment segmentation. All ilastik models and corresponding files are available on github at https:// git. embl. de/scollomb/collombet_et_al_rnapii_xist_compartment/-/ tree/master/SPT/ilastik. Trajectories assignment to subcompartments. Assignment of trajectories to nuclei, nucleoplasm or XC was performed using a custom python script interpolateMaskAndAssignTrajectories. py available on our github at https://git.
embl. de/scollomb/collom-bet_et_al_rnapii_xist_compartment/-/blob/master/SPT/interpolate-MaskAndAssignTrajectories. py. We used the following parameters: --olap_fracMin 0 --olap_fracMax 1 --pixel_subsampling_factor 1. For trajectories inside XC were defined as those for which at least one localization was found inside the XC mask (--olap_rule any) and trajectories outside XC as those for which no localization was found inside the mask (--olap_rule none). Trajectories entering XC and control regions. Control regions were created using a custom python script interpolateMaskAndAssignT-rajectories_moveMask_ROA. py available on our github at https://git. embl. de/scollomb/collombet_et_al_rnapii_xist_compartment/-/blob/ master/SPT/interpolateMaskAndAssignTrajectories_moveMask_ROA. py. In summary, this script finds control XC regions by randomly shifting and rotating the XC segmentation mask, while controlling that the shifted mask remains inside the nucleus, does not overlap with a previous mask and does not overlap with nucleoli.
Distribution of diffusion coefficient. The distribution of diffusion coefficient for RPB1/3 inside/outside Xist compartment was estimated using spagl  30  available at https://github. com/alecheckert/spagl, using the fss_plot function with default parameters (dz = 0. 7, pixel_size_ um = 0. 106, frame_interval_sec = 0. 005477). https://doi. org/10. 1038/s41594-023-01008-5 Bound/free fraction estimation. To estimate the bound and free fractions, a two-component model was fitted to the distribution of jumps using SpotOn  26. A custom version of the python implementation of Spo-tOn was adapted to run on python 3, which can be found on our github at https://git. embl. de/scollomb/collombet_et_al_rnapii_xist_compartment/-/tree/master/SPT/Spot-On-cli. The function fit-and-plot-2states was used with the following parameters: --time_between_frames 0.
FRAP acquisition was then performed only using the 561 channel to allow fast imaging with roughly 32 ms between frames. Photobleaching was performed after 80 frames on the Xist territory circle (or a second nucleoplasm region for bleaching control in the nucleoplasm), with a scanning speed of seven (corresponding to roughly 9 ms bleaching time) and a laser poxer of 60% (both parameters were manually optimized to allow more than 50% bleaching inside the defined circle while minimizing bleaching of the surrounding area). Data analysis. To correct for bleaching and background, an exponential decay function was fitted to the background and nucleoplasm regions measurements. The signal in the bleached ROI at all time points was then scaled to the prebleached signal, and the background was subtracted as follows: Bleaching was then corrected using the signal in the control region:  where Ic Mean square displacement (MSD) at increasing time interval dt for RPB3 SPT trajectories inside Xist compartment (red) or in shifted control regions (blue, see Extended Data Fig.
Acknowledgements We thank  L. Lavis  ( Janelia Research Campus, Howard Hughes Medical Institute ) for providing all the Halo-ligand dye used in this study; all members of the  Advanced Light Microscopy Facility  and the  Cytometry Facility at EMBL  for their help;  R. Tjian ,  F. Dossin ,  M. Lampe ,  A. Rybina  and  M. Hantsche-Grininger  for their help and their feedback on the manuscript. The work performed in E. H. 's laboratory was supported by a  European Research Council Advanced Investigator Award  grant no. ERC-ADG-2014 671027. The work performed in X. D. laboratory was financed by the grant no. NIH   1U54CA231641. S. C. is supported by a  European Molecular Biology Organization   long-term fellowship  (grant no. EMBO ALTF 275-2018 ) and supported by the  Joachim Herz Foundation.
Evolutionary studies on barley yellow dwarf virus (BYDV) showed that several virus populations might share the same consensus sequence while having different low-frequency SNPs patterns, highlighting the importance of characterizing virus genetic diversity beyond the consensus level  (35). Therefore, it is critical to profile and reports the presence of individual low-frequency SNP present to improve the resolution of viral population analyses and to characterize the contribution of variants to virus evolution and adaptation (changes in viral load, virulence, transmission, host range, etc. ). When analyzing the genetic diversity of a virus species in the HTS dataset at the SNP level, generating the proper reference consensus sequence(s) is essential. Indeed, if the sample was infected by several divergent isolates, e.
It has been recently applied to plant virus populations and has enabled an in-depth analysis of the virus population beyond consensus sequences reconstructed from HTS datasets  (35, 40). SNP frequencies can also be used as features for a principal component analysis (PCA) to reduce their complexity and increase their interpretability. SNP-based PCA has so far only been applied to study the evolution of a DNA virus infecting Drosophila  (41). In addition, the association between SNPs can be studied, e. g. , if they belong to the same viral molecule. For eukaryotes, a haplotype is a set of genomic polymorphisms that tend to be inherited together. By extension, we can call haplotype a series of mutations present on the same viral molecule compared to the virus's consensus (or reference) sequence.
Therefore, identifying haplotypes can improve the characterization of the viral population at the molecular level. SNPs can be associated with haplotypes if they are located close to each other in the genome so that they can be observed on the same sequencing (paired) reads. However, more distant SNPs are more difficult to associate with the short-read (i. e. 50-300 nt) sequencing technologies predominantly used in virus diagnostics and metagenomics studies. The emergence of new sequencing technologies generating long reads can solve this issue. Those technologies have already generated complete genomes of the viruses at high accuracy for small ssDNA circular genomes  (42) , but it can remain a challenge for longer viral genomes. To solve this challenge and exploit the massive amount of data generated by short-read technologies, we propose an innovative methodology to reconstruct haplotypes of distant SNPs from short sequencing reads based on their relative frequencies within and between datasets.
Bioinformatic analysis 2. 4. 1 Reads processing First, the obtained raw reads were paired and trimmed using Geneious Prime 2023 (version 10. 1. 5, Biomatters) software (https://www. geneious. com). The low-quality nucleotides showing quality scores below 20 and reads with lengths lower than 35bp were trimmed using BBDuk V38. 37  (47). Then, reads were merged, and duplicated reads were removed using the Dedupe V38. 37  (48)  plugin implemented in Geneious.
De novo assembly, Mapping, and Phylogenetic analysis De novo assembly was further performed using the RNA-Spades (49) V3. 13. 0 assembler implemented in Geneious. The obtained contigs were subjected to a BLAST search (50) (blastn and blastx) against the viral RefSeq database (retrieved in September 2020 -release 201) to check which contigs matched viral sequences in GenBank. All contigs matching a viral reference sequence in GenBank were extracted. All reads were further mapped (Geneious mapper V10. 1. 5)  on the viral contigs of interest with the parameter "Low sensitivity/ Fastest" used (10% mismatch). Furthermore, reads were mapped on the closest reference sequence in the database using the same parameters. The obtained mapped reads were used for SNP calling and in-depth analysis. SNP calling (Geneious V10.