dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , in the form of mitochondria cristae), we masked the insides of the organelles (Fig. 2b ) A set for validation of 256 x 256 x 256 pixels from the HeLa-1 dataset was annotated in the same manner. For the model training, cubes of 64 x 64 x 64 pixels were extracted at evenly spaced positions in the ground truth cubes (spacing of 32 pixels in all dimensions) thus forming the training data for each epoch. The cubes were shuffled, augmented, and fed into the network with a batch size of 2. Augmentation was performed using affine transformations, additive noise (Gaussian), anisotropic smoothing (anisotropic Gaussian filter with random orientation), random displacement of z-slices to mimic slight misalignments as well as brightness and contrast alterations. Note that the data scaling as part of the affine transformations was performed in a range of 0.
differing input resolutions. The network was trained for 200 epochs and model states with good validation scores were saved. After the training procedure candidate models with the highest validation score were examined qualitatively on their capability to predict membranes on different datasets and the best-performing model was selected. The model we publish alongside this paper was trained for 97 epochs. The model is additionally available within the Bioimage Model Zoo (44), at bioimage. io with doi ID: 10. 5281/zenodo. 7274275. The inference was performed with a batch size of 64 x 64 x 64 pixels and an overlap of 32 pixels. To avoid too much overhead, especially on compute clusters, we process the full dataset in blocks (256 3 or 384 3 voxels), from which the batches are then extracted and written to a respective result volume after computation.
S1 ). Multicut. We performed multicut computations using the elf package (https://github. com/constantinpape/elf) which implements the Multicut workflow introduced by Beier et al. (20). For the annotation workflow, we used a nonlearned version of the Multicut workflow  (45) , where edge weights for optimizing the graph partitioning are directly derived from the intensity values of the membrane prediction map at the respective supervoxel edges (yielding a cumulative boundary probability over each supervoxel edge). For automated segmentations, we used learned edge probabilities which were computed by Random Forest classification (see section Automated segmentation). In both cases, the probabilities are converted to edge weights by w e,β = log 1-p e p e + log 1-β β such that probabilities p e < 1 -β yield positive values, representing attractive edges (adjacent supervoxels should be merged), and probabilities p e > 1 -β yield negative values representing repulsive edges (adjacent supervoxels should be split).
By manipulating the value of the beta parameter and comparing the resulting model performance, one can determine the optimal value for the parameter. We set an initial value of the parameter at 0. 7 and test the result in the validation set. Then, through a process of incrementally increasing or decreasing this value by 0. 05, a determination can be made as to whether a higher or lower value is more advantageous for the given segmentation result. The decision can be done by following the accuracy of IoU in a new annotated cube (a validation cube) or by visual inspection of the result. This iterative process allows for the fine-tuning of the segmentation result, ultimately leading to improved quality when applied to the rest of the dataset. Annotation Workflow. The annotation workflow implemented in the CebraANN package is performed on and thus optimized for, 256 voxel cubes extracted from the full dataset.
The true output for each edge was determined from a dense segmentation such that an edge should be merged if the corresponding two supervoxels belong to the same object (attractive edge) or split if the corresponding two supervoxels belong to different objects (repulsive edge). The semantic classifier was trained using statistical features obtained from the supervoxels and predicts which class (target organelle or background) the respective supervoxel most likely belongs to. Both semantic and instance segmentation is combined by first generating an instance segmentation from the output of the instance classifier (see section Multicut) and then performing a supervoxel-level majority vote within the resulting instances to assign a class to each instance. The random forest classifiers are used from the scikitlearn  (46)  python package and set up of 200 trees with a maximum depth of 50 for both the semantic and the instance classifiers.
These parameters were determined empirically to yield both good results while keeping the computation cost as low as possible. Block-wise computation and stitching. For the automated processing which is generally performed on the full datasets, the data is processed in blocks, individually for the three distinct sub-steps of the pipeline: (i) Computation of membrane prediction, (ii) computation of supervoxels and (iii) computation of a segmentation. The block size can be varied depending on the available resources, however, we generally use a block spacing of 3843 voxels. Due to processing with an overlap to mitigate boundary effects, the processed blocks are actually larger: We use overlaps of 32 pixels for the membrane prediction and 128 pixels for the supervoxels and segmentations. The resulting computations are stitched back to respective full volumes.
For each block of the membrane prediction, the overlap region is removed and the remaining inner block is written to the resulting volume. For the supervoxels, the current maximum supervoxel ID is monitored and added to each newly written block such that unique supervoxel IDs throughout the final dataset are ensured. For the segmentation, we perform more complex stitching to ensure subsections of objects that span one or more block boundaries are assigned the same object ID. Object correspondence identification is performed by IoU of objects at block boundary planes (after cropping the overlap). If objects at these block boundaries overlap more than 50 % they are considered the same object and thus assigned a common ID.
Data visualization and 3D rendering. To visualize and browse full EM datasets including membrane prediction, supervoxel maps, and segmentations, we used the MoBIE viewer  (39). A CebraEM project is an extension of a Mo-BIE project such that it can be loaded directly into the viewer where each desired volume can be selected for visualization and segmentations or supervoxels can be viewed as an overlay over the respective EM data volume or membrane prediction map. For the 3D rendering of segmentation results, we used Drishti  (47). In order to enable the rendering of full datasets, we downsampled segmentation maps by a factor of 6 (with respect to the annotation resolution). Additionally, the segmentation maps were binarized (value of 255 for all instances of the target organelles and 0 for background) and smoothed with a Gaussian kernel of sigma = 0.
An exception is the Platynereis-5 dataset, where we assigned random RGB colors to each mitochondria instance before loading the respective volumes into Drishti. Golgi, and d) mitochondria. For the ER, we performed three iterations (column It-ID) with six initial ground truth cubes for training, eight in the second, and 11 in the final iteration (column n_GT). The IoU score was computed in 8 independent annotated cubes, not used for training, for the ER, and 3 cubes for NE, Golgi, and mitochondria, each. The IoU score increased from 0. 64 to 0. 93 from the initial to the second iteration (It-ID 0 to it-ID 1). We observed no increase in IoU when adding more ground truth after the third iteration (It-ID 2). The highlighted cells indicate the value of the beta used for the final inference on the full datasets.
3 µm 3. The full volume of the target cell covered by the dataset was approximately 3600 µm 3. In the table, the average value of IoU accuracy, for a total of three validation cubes, and for a range of beta values from 0. 5 to 0. 8. The highlighted columns correspond to the beta values used in the final segmentation shown in Figure  S3. The maximum value of IoU might not always correspond to the chosen beta value, since the choice was also based on visual inspection of the validation result and additionally takes the segmentation quality on the instance level into account which is not reflected in the IoU. A value of β = 0. 5 performed best in terms of instance segmentation while yielding an IoU close to the maximum achieved by slightly higher values of β.
50 0. 45 0. 54 0. 44 0. 61 0. 35 0. 73 0. 17 0. 74 0. 18 c) GOL 0 4 0. 20 0. 08 0. 23 0. 08 0. 29 0. 08 0. 42 0. 12 0. 53 0. 20 0. 59 0. 12 0. 74 0. 19 1 6 0. 32 0. 09 0. 46 0. 18 0. 53 0. 11 0. 55 0. 16 0. 65 0. 04 0. 70 0. 02 0. 77 0. 15 d) MIT 0 3 0. 76 0. 24 0. 86 0. 08 0. 87 0. 07 0. 89 0. 09 0. 96 0. 04 0. 97 0. 03 0. 94 0. 07 1 4 0. 51 0. 35 0. 56 0. 32 0. 68 0. 33 0. 73 0. 31 0. 94 0. 05 0. 96 0. 04 0. 97 0. 04 Supplementary Table S1. 4. Evaluation of segmentation scores (IoU) for HeLa-1. For analysis, segmentations were computed with different values for the parameter β (data columns). The analysis was performed for four organelles: a) ER, b) nuclear envelope, c) Supplementary Table S1. 3. Annotated volumes and annotation time for each cube in Hela-1. Subset annotation of the Hela-1 dataset for ER, NE, Golgi, and mitochondria (Mito).
Acknowledgements This work was supported by  Thailand Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B) ,  NXPO , grant number  B17F640002  to NJ, and the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence   Integrative Biology of Emerging Infectious Diseases  (grant number  ANR-10-LABX-62-IBEID ) to LL. The personnel exchange for the collaboration was supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  734486  ( SAFE -Aqua ). The 4G2 antibody was a gift from  Dr. Bunpote Siridechadilok  ( BIOTEC ). The African ZIKV  DAK AR 41524 ,  NR-50338  was obtained through  BEI Resources ,  NIAID ,  NIH , as part of the  WRCEVA program.
05 default parameters)  (44) , normalized by total read coverage per Mbp of the MAC+IES genome (CPM, counts per million) (Supplementary Table  S3 ). As a control, a sequencing dataset with 100% IES retention was simulated using ART_Illumina (v2. 5. 8 -l 80 -ss HS10 -p -qs 90 -qs2 90 -m 250 -s 50) on the MAC+IES genome, with the same sequencing depth as the XRCC4 KD dataset (Supplementary Figure  S3 ). IES boundary coverage was estimated from the simulated data using the method described above.
Differential expression (DE) analysis For each RNAseq dataset, raw fragment counts were determined for each gene using htseq-count (v0. 11. 2, --stranded=yes --mode=intersection-nonempty) on filtered BAM files (samtools v1. 3. 1) as described  (11). Version 4 of R (  45 ) was used for data analyses and to generate images (ggplot2 v3. 3. 6; VennDiagram v1. 7. 3)  (46, 47). For the correction of batch effects due to differences in RNA extraction conditions and sequencing protocols (HiSeq or NextSeq), we first validated the ComBat-seq procedure (48) (sva v3. 38. 0, R package, with no group condition) on technical replicates from the EZL1 and ICL7 time courses that were sequenced using both HiSeq  (43)  and NextSeq (25) (Supplementary Figure  S4 , Supplementary Table  S4 ). We then corrected all RNAseq datasets (except for the EZL1 time course) as the first step of our analysis workflow (Supplementary Figure  S5 ), before completing a first round of DESeq2 analysis (49) (DESeq2 v1.
Analysis of the motif in gene promoters To identify all the genes that carry the motif in their promoter, we used the FIMO 5. 4. 1 software  (51)  with default settings. We first extracted the data obtained for the set of 180 Co-PGM genes and defined a cut-off p-value that matches the significance threshold calculated from STREME enrichment factors (defined above). We then applied the resulting cut-off to the full list of motifs identified by FIMO. To compare the position of the motifs in their respective promoter, we considered the position of the first 5' nucleotide of the motif relative to the TSS and counted the number of motifs found in successive 5-bp windows within gene promoters.
Genome-wide identification of up-deregulated autogamy genes in PGM, KU80c and XRCC4 KDs PGM belongs to a cluster of 2037 differentially expressed genes that are transiently induced during autogamy, clustering in the intermediate expression peak, when PGR take place in the developing MACs  (11). To gain broader insight into how many genes are up-deregulated in the three KDs that we used in the present study to alter the normal course of PGR, we collected RNA sequencing data from independent autogamy time-course experiments performed on cells subjected to KU80c, PGM  (32)  or XRCC4 KDs (31) and compared them with four independent control time-courses. We applied principal component analysis and hierarchical clustering to the normalized datasets to group the samples in four developmental stages (VEGETATIVE, EARLY, INTERMEDIATE and LATE) (Supplementary Figure  S7 ).
ACKNOWLEDGEMENTS We would like to thank  Sandra Duharcourt  and  Eric Meyer  for fruitful discussions, sharing data ahead of publication and providing helpful advice and support as members of MBG's thesis advisory committee. We are grateful to  Anne Aubusson-Fleury  and  Anne-Marie Tassin  for the generous gift of TEU435 antibodies. We thank  Joël Acker ,  Julien Bischerour  and  Valerio Vitali  for critical reading of the manuscript, all members of the  Bétermier lab  for stimulating discussions,  Pascaline Tirand  and  Cindy Mathon  for technical assistance and  Tim Kamara  and  Camille Poitrenaud  for help in plasmid construction during their short-term internships.
FUNDING This work was supported by intramural funding from the  Centre National de la Recherche Scientifique (CNRS)  and grants from the  Agence Nationale de la Recherche  [" PIGGYPACK "  ANR-14-CE10-0005  to MB,  LS  &  OA , " LaMarque "  ANR-18-CE12-0005-02 , " CURE "  ANR-21-CE12-0019-01  to MB, " POLYCHROME "  ANR-19-CE12-0015  to OA] and the  Fondation pour la Recherche Médicale  [ FRM EQU202103012766  to MB]. MBG was supported by a  PhD fellowship  from  Paris-Saclay University  ( Ecole Doctorale   n°577 SDSV ) and a training grant from the  Department of Genome Biology of I2BC  to attend the " DU-Omique " course in bioinformatics ( Université Paris-Cité ). We acknowledge the sequencing and bioinformatics expertise of the I2BC High-throughput sequencing facility, supported by  France Génomique  (funded by the  French National Program "Investissement d'Avenir "  ANR-10-INBS-09 ).
Fluorescence-assisted nuclear sorting experiments benefited from the expertise of the  Imagerie-Gif core  facility, supported by the  Agence Nationale de la Recherche  [ ANR-11-EQPX-0029/ Morphoscope ,  ANR-10-INBS-04/ FranceBioImaging ,  ANR-11-IDEX-0003-02/ Saclay Plant Sciences ].
DATA AVAILABILITY Genes used in this study are accessible in ParameciumDB (https://paramecium. i2bc. paris-saclay. fr/)) as follows: CtIPa (PTET. 51. 1. G0650078), CtIPb (PTET. 51. 1. G0980137), EZL1 (PTET. 51. 1. G1740049), ICL7a (PTET. 51. 1. G0700039), KU80c (PTET. 51. 1. G1140146), ND7 (PTET. 51. 1. G0050374), PGM (PTET. 51. 1. G0490162) and XRCC4 (PTET. 51. 1. G1110086) (Arnaiz et al. 2020). The sequencing data generated for this study have been submitted to the ENA database (https://www. ebi. ac. uk/ena/browser/home) under accession number PRJEB60900. Plasmid maps, raw images, statistical data, scripts (Supplemental Codes) and numerical data used to produce all diagrams have been deposited at Zenodo (https://doi. org/10. 5281/zenodo. 7822015).
The speciation events may have been influenced here by climatic oscillations during the Pleistocene, with major aridification episodes dated roughly at 2. 8 Mya, 1. 8 Mya and 1 Mya  (deMenocal, 2004 (deMenocal, , 1995)). One major limitation of these molecular clock analyses is the reliance on only a few markers, as is the case with phylogeny reconstructions and species delimitations. While uncertainty is an inherent property of Bayesian divergence time estimation, it can be reduced by exapanding the size of the dataset (dos Reis and Yang, 2013). Therefore, it is expected that a reanalysis of divergence timing in Gerbillus, utilizing a large number of markers from the genome, will enhance the precision of the evolutionary history reconstruction. Here, we test recent hypotheses concerning the scenario of evolution of Gerbillus using restriction site-associated DNA sequencing (RADseq).
The sequences were assembled and edited using SeqMan II v. 4. 0 (DNASTAR, Madison, WI, USA). All newly obtained sequences were deposited in GenBank (Table  1 ). Sequences obtained in this study were combined with cyt-b sequences for species of Gerbillus retrieved from GenBank. The sequences were then aligned using MUSCLE v. 3. 8. 31  (Edgar, 2004)  through the graphical interface in Seaview v. 4. 6. 1  (Gouy et al. , 2010). To examine whether cyt-b conspecific sequences clustered together, we performed a phylogenetic analysis using maximum likelihood method in RAxML v. 8. 2. 12. The analysis included 100 rapid bootstrap repetitions under GTR + Γ substitution model, which is the only model available in the software  (Stamatakis, 2014).
RAD-seq library preparation and data processing A reduced complexity library was prepared based on the traditional RAD-seq protocol described by  Ali et al. (2016)  with the following modifications (see also  Grzywacz et al. , 2021; Piwczyński et al. , 2021) : i) For each individual, two samples of 75 ng of DNA each were separately digested using SbfI-HF restriction enzyme to avoid unexpected reaction failure. ii) P1 adapter-ligated fragments were sheared for 60 s to a peak target of 300 bp using an ultrasonicator Covaris M220 (Covaris, Inc. ). iii) Pippin Prep (Sage Science, Beverly, MA, U. S. A. ) was used to select fragments between 250 and 350 bp with prior library cleaning using AMPure XP (Beckman Coulter, Carlsbad, CA, U. S. A. ; 1 × ratio of bead to sample volume). iv) Eight independent PCRs (15 cycles) were carried out and subsequently pooled to reduce the effect of uneven fragment amplification.
v) Pooled PCR products were purified twice with AMPure XP (1 × ratio of bead to sample volume) to completely remove the remaining primers. The final library quantification was performed using a Qubit 3. 0 fluorometer and 2100 Bioanalyzer with the High Sensitivity DNA Analysis Kit (Agilent Technologies). Commercial paired-end sequencing of the multiplexed library was conducted on an IlluminaHiSeq 2500 instrument by the Macrogen company. Before running the assembly software, we filtered the low quality reads with Trimmomatic v. 0. 39  (Bolger et al. , 2014)  using the following options: TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:50. We deliberately did not set the LEADING option to preserve the barcode part of the reads before applying SLIDINGWINDOW option. For the de novo and reference assembly, we utilized only R1 data with the ipyrad v.
0. 9. 61 pipeline  (Eaton, 2014). It is worth noting that, as confirmed by ipyrad developers (see discussion on Gitter, 25 September 2020, https://gitter. im/dereneaton/ipyrad), and based on our own experience  (Grzywacz et al. , 2021; Piwczyński et al. , 2021) , excluding R2 reads typically does not significantly impact the final result. In the first step, we demultiplexed the data twice allowing none or one mismatch in the barcode sequences. Taking into account the sensitivity of the de novo assembly to changes in clustering threshold (CT) parameter (i. e. , the minimum percentage of sequences similarity below which two reads are considered to have come from different loci;  Fernández-Mazuecos et al. , 2018; Shafer et al. , 2017; Takahashi et al. , 2014) , we tested, using two demultiplexed datasets, a wide range of CT from 0.
85 to 0. 99 incremented by 0. 01 resulting in a total of 15 analyses per dataset. The remaining parameters were set as follows: min_sam-ples_locus = 4 and max_Indels_locus = 8. Given the high proportion of repetitive elements in rodents' genome (up to 45%,  Lu et al. , 2020)  and the extensive karyotype reorganization among Gerbillus species  (Aniskin et al. , 2006) , we adopted a conservative approach by reducing max_SNPs_locus parameter to 0. 1 (compared to the default value 0. 2) to minimize potential alignment issues with repetitive regions. We employed two criteria to select the best CT: the average bootstrap support and the number of SNPs. The average bootstrap support was estimated based on a phylogenetic tree reconstructed using maximum likelihood (ML) approach implemented in RAxML v.
8. 2. 12 with rapid bootstrap repetitions  (Stamatakis, 2014). To assess the impact of missing data on the phylogenetic tree topology, we explored three additional values of min_samples_locus = 12, 20 and 32 corresponding to a maximum of 70%, 50% and 20% of missing data in the alignments, respectively. For each min_samples_locus value, we tested the entire range of CT values, i. e. , 0. 85-0. 99, and selected the best value based on the previously described criteria. The remaining parameters were left unchanged, as explained earlier. All analyses were conducted on two demultiplexed datasets. In the final set of analyses, we used two demultiplexed datasets for reference assembly, employing the genome of Psammomys obesus (GenBank: GCA_907164565). We generated assemblies using two values for max_SNPs_locus = 0.
Each chain was run for 1 million generations, with samples collected every 1 000 generations and the first 25% of samples discarded as burnin (a total of 6 [runs] × 1[chains] × 750[samplesburn-in] = 4 samples across all runs). All priors remained unmodified and in their default state. Effective sampling of the priors and MCMC convergence was established by ensuring effective sample size (ESS) and potential scale reduction factors (PRSF) were > 200 and ~ 1, respectively, using Tracer v. 1. 7. 1  (Rambaut et al. , 2018). ML analyses were conducted via CIPRES science gateway  (Miller et al. , 2010) , while the BI analyses were completed using the University of Technology, Sydney eResearch High Performance Computing Cluster. The main assumption of ML and BI methods applied here to concatenated datasets is that all sites across the genome share the same history.
However, this assumption carries a risk of reconstructing a highly supported, yet incorrect species tree  (Mendes and Hahn, 2018). To address this concern and consider potential gene tree heterogeneity due to incomplete lineage sorting, we utilized the SVDquartets method  (Chifman and Kubatko, 2014) , implemented in the most recent version of PAUP* v. 4. 0a  (Swofford, 2003)  for sequence data under the multispecies coalescent model. For the analyses, SNP files automatically generated by ipyrad were used, containing one randomly selected SNP per locus. We evaluated all possible quartets (exhaustive quartet sampling option) and treated ambiguities as missing data. The QFM algorithm was then applied to assemble quartets into a species tree  (Reaz et al. , 2014). Confidence in the groupings on trees was measured using standard non-parametric bootstrap procedure with 1 000 repetitions.
Values for QD fall in the range [0,1], with maximal value indicating an identical proportion of discordant trees and minimal value suggesting that only one of the two discordant topologies was sampled. QI quantifies the phylogenetic signal and ranges from [0,1]. A quartet is considered informative when its likelihood estimated using the ML method (in our case, RAxML) is higher than some cut-off value from the second-best quartet topology. In the extreme case, when topologies have indistinguishable likelihood values, QI equals 0. All three scores should be interpreted together to distinguish different sources of uncertainty, aided, for example, by interpretational guidelines provided by  Pease et al. (2018)  (see Table  1  therein). To estimate all three scores, we used python software quartetsampling v.