dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and software availability The model was deposited in the BioModels  (Chelliah et al. , 2015)  database as MODEL1901210001 in SBML format and a COPASI  (Hoops et al. , 2006)  file to reproduce simulations with the parameter set shown in Supplementary file 2. BudJ  (Ferrezuelo et al. , 2012) , CoinRICSJ (Moreno and Aldea,  2019)  and the microcolony_size macro can be obtained as ImageJ (Wayne Rasband, NIH) plugins from ibmb. csic. es/groups/spatial-control-of-cell-cycle-entry. Jay Unruh's plugins can be obtained from research. stowers. org/imagejplugins. Prion propensity plots were obtained by the PAPA software  (Toombs et al. , 2012 ) located at combi. cs. colostate. edu/supplements.
Acknowledgments The authors apologize to the many researchers whose work is not specifically referenced due to space limitations. The authors are supported by  Inserm ,  CNRS ,  Unistra ,  ANR-16-CE92-0031  ( EPIFUS ),  ANR-16-CE16-0015  ( ToFU ),  Alsace Alzheimer 67 ,  France Alzheimer  (AAP SM 2017 # 1664 ),  Fondation pour la recherche médicale and Axa Banque Patrimoniale research fund.
Figure2. Alignment of conserved regions of the amino acid sequences of Mad10 proteins found in MTB. Twelve sequences were retrieved from databases: 1. Desulfamplus magnetovallimortis BW-1, 22  2. Candidatus Magnetomorum sp. HK-1, 32  3. Desulfonatronum sp. ML-1, 33  4. Desulfonatronum sp. ZZ-1, 33  5. Desulfovibrio magneticus RS-1, 34  6. Candidatus Magnetobacterium casensis, 35  7. Candidatus Magnetobacterium bavaricum, 36  8. uncultured Nitrospirae bacterium 1, 9. uncultured Nitrospirae bacterium 2, 10. uncultured Nitrospirae bacterium 3, 11. Nitrospirae bacterium HCH-1 and 12. Candidatus Magnetoovum chiemensis. 37 The numbering of amino acids corresponds to the sequence of Desulfamplus magnetovallimortis BW-1. The black boxes represent regions predicted to be helical, using Jpred4 on the sequence of Desulfamplus magnetovallimortis BW-1.
Data are presented as mean +/-SEM. �� p<0. 01, unpaired Students t-test. B) H&E staining of C57BL/6 mouse brain hippocampus following intra-hippocampal injection of the indicated vectors compared to PBS control. H&E staining revealed neuronal, from peripheral tissues via an IV injection of vector did not have an imbalance in total IgG/ functional IgG (Fig 3A right graph). We next investigated whether there was evidence for neurotoxicity as a result of IgG expression. For our initial characterizations of the AAV-αAβ IgG vector, we had used the huIgG version of this antibody which has more direct translational potential for humans and allowed for clear detection in mice. However, to test for any toxicity or neuroinflammation that could be related to brain IgG expression without the confounding variable of xenogenic huIgG exposure, we used an AAV vector -termed AAV-αAβ msIgGexpressing the original mouse version of the αAβ IgG  [21, 26].
LCMSMS The LC/MS/MS experiments were carried out on the Q Exactive Mass Spectrometer (Thermo Scientific) coupled with NanoAcQuity LC system (Waters). The IgG from tissue homogenates were specifically enriched and isolated with CaptureSelect HuIgG affinity resins (Thermo Fisher). The enriched IgGs were digested by incubation with trypsin/Lys-C (1:100 w/w) overnight at 37˚C after DTT reduction and alkylation. The digestion was terminated by the addition of 1% formic acid (FA). The resulted tryptic peptide mixtures were loaded and separated onto a microcapillary column (75-μm id, 15 cm HSST3, 1. 8μm, Waters). Data were acquired in the PRM mode with the resolution of 70,000 (at m/z 200), AGC target 5 × 106, and a 500 ms maximum injection time. The scheduled inclusion list was generated based on the profiling data of the control IgGs.
Surface plasmon resonance Aβ 1-42 peptide (Bachem H-1368) was incubated in 10mM HCl at 1 mg/mL overnight at 37˚C, shaking at 600rpm. The resulting fibril solution was directly immobilized on a CM5 sensor chip (GE Healthcare) using amine coupling. Antibody or scFv-IgG solutions generated at 50, 30, 20, 10 and 5nM in PBS-+P buffer (GE Healthcare) were injected at relatively high flow rate (50 μL/min) to limit avidity effects. The data were processed using Biacore T200 evaluation software and double referenced by subtraction of the blank surface and buffer-only injection before global fitting of the data to a 1:1 binding model.
Image analysis Immunohistochemistry slides were scanned at 20x magnification using Scanscope XT brightfield image scanner (Aperio, Vista, CA) or AxioScanZ1 (Carl Zeiss Microscopy GmBH, Germany). Whole slide images (WSI) of GFAP IHC were viewed and analyzed using HALO image analysis software (Indica Labs, Corrales, NM, USA). For each WSI, the hippocampus region was manually annotated and analyzed for GFAP immunopositive area using HALO's automated area quantitation algorithm. For each sample, GFAP positive area was divided by the total tissue area for the selected region of interest (ROI) to obtain percent immunopositive area. For plaque analysis, 5μm coronal brain sections were collected from 6 month old APP mice at three different levels, 50μm apart. Cortical and hippocampal ROIs were manually annotated.
Am ore exhaustive comparison between the two growth temperatures revealed only one temperature-induced change of the tRNAlevels in the RNA-Seq data, which, however, did not overlap with another six found by MST. C orrelation between MST and RNA-Seq showed only moderate R 2 values (Figure  S7 ). Altogether,both datasets suggest that the tRNA levels in yeast are quite robust against temperature variation. To further expand the scope of MST-based quantification, we established similar assays for other RNAs directly involved in translation. Thus FCPs developed against four rRNAs present in yeast ribosomes detected them in equal stochiometric amounts in isolated ribosomes (Figure  S8 ). Furthermore,F CPs directed against polyadenylated RNA reflected differential poly-A length in synthetic mRNAs (Figure  S9 ), recapitulated an increase in poly-A content in yeast cells after cycloheximide treatment, and detected differential poly-A content in HeLa versus HEK293 cells (Figure  S10 ).
Overrepresentation analyses of gene ontological terms of the DEGs We submitted the 66 DEGs to Gene Ontology functional annotation analysis using the online program, DAVID  [15]  with the zebrafish genes in the microarray set as background (Fig. 1b ). Among the 66 DEGs submitted, functional terms associated with 55 annotated genes were identified and therefore classified by the DAVID program using terms derived from the following resources: UniProtKB keywords (pink circle); Gene Ontology Biological Process (red circle), Molecular second cluster included terms associated with 'mitochondrial transit protein' , which were enriched between 3-to 12-fold. The genes that most drastically changed expression (i. e. ribosome production factor 2 homolog (S. cerevisiae) [rpf2], ribosomal protein S27 (isoform 2) [rps27.
We additionally used PLS to model the link between transcriptomic data and survival rates, and a GA to select subsets of genes that best predicted survival rates (see Methods for details). We ran the PLS-GA procedure with 70 populations of 500 potential solutions, i. e. subsets of 1 to 20 randomly selected genes. We thus obtained 35,000 final potential solutions, which we evaluated by 10 runs of 2-fold cross validation (2-FCV); we defined the average cross-validated R 2 values from the 10 runs as the quality criterion for each individual. To confirm that the selected individuals were relevant, we compared the average 2-FCV R 2 values obtained on the actual dataset to the ones obtained on data generated by randomly permuting the survival rates of the different observations. Figure  6a  shows that the 2-FCV R 2 obtained for the final individuals using the actual survival rates were significantly higher than those obtained with the randomized data (p-value < 2.
With regard to the functional characteristics of the DEGs in the study using all samples, overrepresentation analyses of the GO terms by the DAVID gene ontology program found that genes that function in ribosome and translation were predominantly enriched. A recent study demonstrated that ribosome was one of the top overrepresented KEGG pathways in the expressed transcriptome of unfertilized zebrafish eggs, which suggests that the ribosome/translation process plays a major role in fertilization and subsequent embryonic development  [32]. Further, our results are consistent with previous findings that showed that translation-related transcripts were also differentially expressed in seabass eggs of different quality  [33]. Interestingly, the findings in this study correlate with our previous proteomic study which also demonstrated significant dysregulation of proteins with functions in protein synthesis in zebrafish eggs of varying quality  [34].
ENSDARG00000093926/ ENSDARG00000095522 Si:dkey-71b5. 2 / Si:dkey-71b5. 3 List of the 29 genes that were selected from the exhaustive analysis by Partial Least Square (PLS) regression and genetic algorithm (GA). The 10 genes that were differentially regulated in our microarray dataset are boldfaced and italicized never been applied to egg quality in fish. We succeeded in identifying statistically robust gene signatures with low number of genes (< 10) that predicted egg survival rates from transcriptomic data. In an effort to investigate the functional significance of some of the DEGs, we created CRISPR/Cas9 knockouts of otulina and sc29a1a due to their ovarianpredominant expression and dysregulation in bad quality eggs (Additional file 4, Fig. 2 ). We used the F0 mosaic females directly for experimentation since the otulina and slc29a1a transcript levels were dramatically reduced in their F1 eggs, which indicated that many of the oocytes in the mosaic females contained the mutant copy of the gene and thus had reduced gene expression.
Data were then obtained with the Agilent Feature Extraction software (10. 7. 3. 1) according to the appropriate GE protocol (GE1_107_Sep09) and imported into GeneSpring GX software (Agilent Technologies) for analysis. The data were first normalized by median centering, log-transformed, and and filtered considering expressed genes those which have a signal above background in at least 80% of the samples in at least one of the two conditions. Then differential expressed genes (DEGs) were determined by performing a T-Test with a Benjamini-Hochberg (BH) corrected pval < 0. 05. Genes and biological samples were finally classified according to their gene expression profiles using a hierarchical clustering method (Cluster software) and the results were visualized with Treeview  [49].
Gene ontology (GO) analysis The differentially expressed genes (DEGs) obtained from the microarray analysis were subjected to overrepresentation analyses using the DAVID version 6. 7 (https://david. ncifcrf. gov/) [15]  online program with Ensembl gene identifiers to elucidate enriched terms. The DAVID analyses were conducted using the Functional Annotation Tool based on terms derived from UniProtKB keywords, Gene Ontology BP, MF, and CC, and KEGG pathways with Benjamini multiple test correction (p < 0. 05).
Reverse transcription polymerase chain reaction and quantitative real-time PCR (qPCR) One μg of RNA was used as template for synthesis of cDNA using the Maxima First Strand cDNA Synthesis Kit (Thermo Fisher Scientific) as per the manufacturer's protocol. The cDNA samples were then diluted 20-fold and subjected to qPCR using the primers listed in Additional file 2. Primers were designed using the online program Primer3 (http://primer3. ut. ee) and extended across an intron when possible to eliminate the contribution from genomic DNA. qPCR was performed in triplicate using the GoTaq qPCR Mastermix kit (Promega, Madison, USA), which utilizes carboxy-Xrhodamine (CXR) as the reference fluorochrome, using the following cycling condition: 95 °C for 10 s and 60 °C for 30 s for 40 cycles. The data were collected with the StepOnePlus apparatus (Applied Biosystems, Foster City, USA) and quantitation of the samples was conducted using standard curves.
LSM couples member 14B (lsm14b), prefoldin subunit 2 (pfdn2), and ring finger protein 8 (rnf8) had the most stable expression in the microarray dataset and were thus used as internal controls for qPCR. Further, 18S rRNA, beta-actin (bact), and elongation factor 1 alpha (EF1α) were also used as internal controls for qPCR  [50]. The geometric means of all 6 genes were calculated and for normalization of the data quantity.
CRISPR-Cas9 genetic knockout CRISPR/Cas9 guide RNA (gRNA) were designed using the ZiFiT Targeter online software (version 4. 2)  [51, 52]  and were made against 3 targets within each gene to generate large genomic deletions, ranging from 130 to 1500 base pairs, that span exons to induce the formation of non-functional proteins. The gRNA sequences for otulina are: 5′ GGAGACGCATGAGGATGAAC 3′; 5′ GGAAACAAACAGCATATTCT 3′; and 5′ GGGT CAGGTATCAAATAACT 3′. The gRNA sequences for slc29a1a are: 5′ GGGAGCCGCGTTATCCTT3'; 5′ GGGGCTTGTCAGAAACTA 3′; and 5′ TAGGAAC-CATATACAAAAAA 3′. Nucleotide sequences containing the gRNA were ordered, annealed together, and cloned into the DR274 plasmid. In vitro transcription of the gRNA from the T7 initiation site was performed using the Maxiscript T7 kit (Applied Biosystems), and their purity and integrity were assessed using the Agilent RNA 6000 Nano Assay kit and 2100 Bioanalyzer (Agilent Technologies).
We used the average R 2 obtained for each individual as the final criterion to quantify the quality of each final subset of genes. To assess the relevance of the procedure, we applied the same GA to randomized datasets obtained by randomly permuting the survival rate of the different observations. Genes involved in egg survival are expected to appear often in the final subsets. The distribution of the selection frequency of each gene in the randomized datasets was computed. Genes with selection frequency in the actual data higher than the 95th or 99th percentile of the selection frequencies in the randomized data were considered as relevant. The distributions of the average 2-FCV R 2 of the final individuals obtained on the actual and randomized data were also compared. The relationship between gene expression and survival rate was considered significant when the actual R 2 values were significantly higher than the R 2 values of the randomized dataset.
Spinning disk microscopy Embryos were imaged using a Carl Zeiss Axio imager Z1 microscope equipped with a Yokogawa CSU X. 1 spinning-disk unit. Sample temperature was kept constant using a heated stage (PECON, TempController 2000-2). Imaging was performed using a 63x NA = 1. 0 water immersion objective, a Hamamatsu Orca flash 4. 0 camera and the Visitron Systems acquisition software (Visi-View).
Volume measurements PGCs of 12 hours post fertilization (hpf) old embryos from the Tg(kop-mcherry-f-3'nano-s3UTR) transgenic line were engineered to express cytoplasmic GFP by injection of gfp-nano-s3'UTR mRNA,  [22]  in addition to mCherry on their membrane. This allowed for a more reliable volume rendering by combining the cytoplasmic and membrane signals. Z-stack time lapses were obtained (13 slices x 2 μm, 25 time points in 5 sec interval). The 3D reconstruction and the volume measurement provided similar results when using connected components Plugin of the ICY software (http://icy. bioimageanalysis. org) or the Imaris 9. 1. 2 (Bitplane) alternative (S2 Fig). The comparison was conducted on two stacks from wild-type cells by applying a 2D median filter (half size = 3), thresholding and extracting the volume data using the "connected components function".
RNA expression and bleb frequency measurements mRNA was synthesized using the mMessage Machine kit (Ambion). RNAs were injected into the yolk of one-cell stage embryos. The experimental and control embryos belonged to the same clutch of eggs. For the data presented in S3B Fig, embryos from the Tg(kop-mcherry-f-3'nanos3UTR) transgenic line were injected with 100 pg of aqp3-gfp-nos3'UTR mRNA and imaged at 7hpf. For the bleb frequency in S3C Fig, embryos were injected with 400μM aqp1a + 400μM aqp3a morpholinos (see  [20]  for sequence) or 800μM of control morpholino  [23]. 50pg of gfp-nanos3'UTR mRNA were co-injected, to verify that the embryos were indeed injected. The imaging was performed at 12-16 hpf with 25 time points acquired at 5 second intervals. Blebs were counted manually using the Fiji software.
Analysis of the cytoplasmic flow To conduct an analysis of the flow within the PGCs, we expressed GFP in the cytoplasm by injecting Tg(kop-mcherry-f-3'nanos3UTR) transgenic embryos with 100pg of gfp-nanos3'UTR mRNA, and then imaged the formation of blebs at a rate of one frame every two seconds. The resulting cytoplasmic streaming towards the protrusion was then analyzed using BioFlow, as described in  [24]. This software extracts dense motion maps from consecutive pairs of images to highlight the redistribution of material within the cell during the evolution of a bleb. The underlying algorithm in  [24]  is based on a technique known as Optical Flow (OF) but introduces several modifications. In its original version, OF makes two central assumptions to extract motion information from a pair of images.
Flow of cytoplasm during bleb formation Our findings are thus consistent with the idea that the changes in cell shape during bleb formation involve primarily internal mobilization of cytoplasm, rather than influx of water from outside the cell into the forming bleb. To examine this possibility more directly, we expressed GFP in the cytoplasm of the germ cells and followed the dynamics of the signal flow during the course of bleb formation. This analysis was conducted using the BioFlow software that allows extracting the motion of intracellular material observed using fluorescence microscopy  [24]. As presented in detail in the methods section, the software extracts the optical flow (i. e. motion information) from video-microscopy data by following the images' intensity under the constraints of fluid dynamics.
Acknowledgments This work was supported by the  European Research Council  ( ERC, CellMig) , the  Deutsche Forschungsgemeinschaft (DFG) , and the IZKF Muenster to E. R. , and the Cells in Motion cluster of excellence (EXC 1003-CIM) to E. R. A. B. P. is part of the  Pasteur-Paris University (PPU)   International PhD Program. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skodowska-Curie  grant agreement No  665807 , and from the  Institut Carnot Pasteur Microbes & Sante ( ANR 16 CARN 0023-01 ). A. B. P is also affiliated with  Sorbonne Universite(UPMC, Paris 6 ) and EDITE. The BIA unit is partially funded by the  ANR  grants  ANR-10-INBS-04-FranceBioImaging  and  ANR-10-LABX-62-IBEID. We thank  Celeste Brennecka  for critical reading of the manuscript.
This work was supported by the  European Research Council  ( ERC, CellMig) , the  Deutsche Forschungsgemeinschaft (DFG) , and the IZKF Muenster to E. R. , and the Cells in Motion cluster of excellence (EXC 1003-CIM) to E. R. A. B. P. is part of the  Pasteur -Paris University (PPU)   International PhD Program. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skodowska-Curie  grant agreement No  665807 , and from the  Institut Carnot Pasteur Microbes & Sante ( ANR 16 CARN 0023-01 ). A. B. P is also affiliated with  Sorbonne Universite(UPMC, Paris 6 ) and EDITE. The BIA unit is partially funded by the  ANR  grants  ANR-10-INBS-04-FranceBioImaging  and  ANR-10-LABX-62-IBEID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Isolation and growth conditions In 2017, we isolated an unidentified bacterial strain from a fresh stool sample in the Hospital of Timone (Marseille). Screening was performed using MALDI-TOF MS on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously described  [7]. The spectra obtained (Fig. 1 ) were imported into MALDI BIOTYPER 3. 0 software (Bruker Daltonics) and analysed against the main spectra of the bacteria included in two databases (Bruker and the constantly updated MEPHI databases: https://www. mediterranee-infection. com/ urms-data-base/). The study was validated by the ethics committee of the Institut Fédératif de Recherche IFR48 under number 09-022. Initial growth was obtained after 72 h of culture in Colombia agar enriched with 5% sheep's blood (bioMérieux, Marcy l'Etoile, France) in strict anaerobic conditions at 37°C.
Strain identification The 16S rRNA gene was sequenced to classify this bacterium. Amplification was done by using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and sequencing used the Big Dye ® Terminator v1. 1 Cycle Sequencing Kit and ABI Prism 3130xl Genetic Analyzer capillary 3500xL sequencer (Thermofisher, Saint-Aubin, France), as previously described  [8]. 16S rRNA nucleotide sequences were assembled and corrected using CODONCODE ALIGNER software (http://www. codoncode. com). Strain Marseille-P4266 T exhibited a 96. 67% sequence identity with Acidaminococcus fermentans strain DSM-20731 T (GenBank Accession no. NR_074928), the phylogenetically closest species with standing in nomenclature (Fig. 4 ). We consequently classify Control - Esculine - Glycerol - Salicine - Erythrol - D-cellobiose - D-arabinose - D-maltose - L-arabinose - D-lactose - D-ribose - D-melibiose - D-xylose - D-saccharose - L-xylose - D-trehalose - D-adonitol - Inuline - Methyl-βD-xylopyranoside - D-melezitose - D-galactose - D-raffinose - D-glucose - Amidon - D-fructose - Glycogene - D-mannose - Xylitol - L-sorbose - Gentibiose - L-rhammose - D-turanose - Dulcitol - D-lyxose - Inositol - D-tagatose + D-mannitol - D-fucose - D-sorbitol - L-fucose - Methyl-αD- mannopyranoside - D-arabitol - Methyl-αD- glucopyranoside - L-arabitol - N-acetylglucosamine - Potassium gluconate - Amygdaline - Potassium 2- cetogluconate - Arbutine - Potassium.
Genome sequencing Genomic DNA was extracted with an EZ1 biorobot (Qiagen, Courtaboeuf, France) with the EZ1 DNA tissue kit and was sequenced using MiSeq technology (Illumina, San Diego, CA, USA) with the Nextera Mate Pair sample prep kit (Illumina), as previously described  [9]. The assembly was performed with a pipeline incorporating different softwares (VELVET  [10] , SPADES  [11]  and SOAP DENOVO  [12] ), on trimmed (MISEQ and TRIM-MOMATIC  [13]  software) or untrimmed (only MISEQ software) data. GAPCLOSER was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected by using different criteria (number of scaffolds, N50, number of N). The genome of strain Marseille-P4266 T is 2 605 487 bp long with a 53.
2 mol% G+C content and 23 contigs. The degree of genomic similarity of Marseille-P4266 T with closely related species was estimated using ORTHOANI software  [14]. Values among closely related species (Fig. 5 ) ranged from 63. 12% between Succiniclasticum ruminis and Succinispira mobilis to 98. 94% between Phascolarctobacterium faecium and Phascolarctobacterium succinatutens. When the isolate was compared with these closely related species, values ranged from 63. 61% with Succinispira mobilis to 77. 27% with Acidaminococcus fermentans.
Acknowledgements We sincerely thank  Sakazume Taku ,  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Akiko Hisada ,  Yusuke Ominami  and all the  Hitachi Team in Japan  for the collaborative study we are conducting together between  Hitachi High-Technologies  and the  Institut Hospitalo-Universitaire Méditerranée-Infection , and for the installation and services on the TM4000Plus microscope in our facility.