dataesr/scientific-paragraphs-categorization huggingface dataset:
Cluster 2 incorporated the remaining strains, that showed characteristics shared by the remaining wine strains. Our next goal was to construct a computation model robust enough to predict the T. delbrueckii biotechnological potential, based on its phenotypic and metabolic profiles. Eleven predictive models were used, and their performance was evaluated in terms of AUC, classification accuracy, F1 score, precision and recall (Supplementary Materials, Table  S2 ). Neural networks were revealed as the classifier with the best performance metrics, and were selected for further analysis. Table  2  shows the confusion matrix obtained using the neural networks' classification of the dataset, after 10-fold cross validation. The AUC score of the obtained predictions was 0. 718, which is considered as moderately high  [41] , and well above that of an arbitrary classification (AUC = 0.
Funding: This work was supported by the  UID/BIA/04050/2013  ( POCI-01-0145-FEDER-007569 ) and  UID/BIA/04050/2019  strategic programs and by the project  PTDC/BIA-MIC/32059/2017 , which is funded by national funds through the  FCT-Fundação para a Ciência e Tecnologia  and by the  ERDF-European Regional Development Fund  through the  COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI)  and  Sistema de Apoio à Investigação Científica e Tecnológica (SAICT). FS-S and TF were supported by  Fellowships  ( UI/BD/150873/2021  and  2021. 04595. BD, respectively) from  Fundação para a Ciência e Tecnologia, Portugal. Informed Consent Statement: Not applicable.
The readings were analyzed with Bioscreen software (Growth Curves USA, Piscataway, NJ, USA) and GraphPad Prism 8. 0. 1 (GraphPad Soft-ware, San Diego, CA, USA). Statistical comparisons were made using an analysis of variance (two-way ANOVA) followed by a Tukey-Kramer post hoc test. P values less than 0. 005 were considered significant. To determine the fungistatic and fungicidal effects, a growth inhibition of >70% for 48 h was considered fungistatic, and a yeast kill of ≥99% for 72 h was considered fungicidal. Moreover, previously determined FLC MICs (data not shown) were used as a control.
Hence, it drives the amount of energy that can be mobilized for biomass growth. A constant redox cofactor potential E acc is considered as electron acceptor. This offers a favorable potential to run biomass growth while limiting the catabolic energy harvested. Calculations of conversion yield (Y X , Y e ) and maximum specific biomass rate are detailed in Supplementary Information. All these rates integrate the part of energy for maintenance, and therefore the threshold concentration necessary to run bacteria metabolism. Then, negative growth rate of biomass could be encountered for low concentration. Solving method and simulation parameters. The model has been developed using finite element method software Comsol® Multiphysics. The Comsol® file is available in Supplementary Information. Simulations have been running on Intel Core Processor i5-9400H 4 Core, 8 M Cache, 2.
Results and discussion Fitting procedure for determination of reference conditions parameters. The comparison between the simulation results and the experimental data made it possible to define the references parameters describing the growth of the biofilm. The electroactive biofilm reference model was chosen with values initially corresponding to a thin, acetate-fed, fast-growing biofilm with Geobacter sp. as the dominant species. The growth parameters of this type of biofilm come from very reproducible results obtained at the University of Gent  50. In order to be able to generalize the model to other types of electroactive biofilms (i. e. older, richer in EPS, etc. ), a calibration was carried out by varying the conductivity; the density; EPS production and initial biomass distribution (cf.
Instrumentation and GC-MS Conditions The analysis was performed on an Agilent 7890 B gas chromatograph coupled with an Agilent 5977 A mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) with an MPS autosampler, a thermal desorption unit (TDU), and a cooled injection system (CIS) (Gerstel, Mülheim, Germany). MSD Chemstation version F. 01. 00 data acquisition software (Agilent Technologies, Santa Clara, CA, USA) was used to program the GC-MS. After extraction, desorption was performed thermally in the TDU at the recommended temperature of 300 • C for CAR/PDMS. After desorption, the injection was performed in split mode; the analytes were focused on the CIS at -10 • C for 2 min, then brought to 250 • C at a heating rate of 12 • C per second and maintained for 2. 5 min. A DB-5MS capillary column (5% diphenyl cross-linked 95% dimethylpolysiloxane, 30 m × 0.
Identification MassHunter Qualitative Analysis version B. 06. 00 (Agilent Technologies, Santa Clara, CA, USA) was used to integrate the peaks of the chromatograms being above the analytical noise. First, the volatile compounds were identified by comparing their mass spectra to those of compounds in the commercial databases, National Institute of Standard and Technologies (NIST) and Wiley11 (R > 800). Then, the retention indices of the compounds were calculated relative to the n-alkanes (C7-C30) and compared to those of compounds in the NIST online database (https://webbook. nisT. gov/chemistry/cas-ser. html)(accessed on 28 August 2021). Finally, the identifications were confirmed by injecting the available standards (mentioned above) into the GC-MS based on the comparison of mass spectra and retention times.
Statistical Analysis For statistical analysis, peak area values of the total ion chromatograms were measured with MassHunter and transferred to Excel (Microsoft Excel 2013, version 15. 0. 5363. 1000). For this purpose, for each plant providing two flowers, an average of the areas of the peaks of the compounds was calculated in Excel. Principal component analysis (PCA) using FactoMiner pakage (version 2. 4) and heatmap using the pheatmap package (version 1. 0. 12) were performed with the R software (version 3. 6. 3, company Foundation for Statistical Computing, Vienna, Austria) based on a univariate scaling method. Heatmap data was clustered using Ward's method. Odor characteristics were obtained from the "The Good Scents" company network database (www. thegoodscentscompany. com, accessed on 27 August 2021).
The genomic coverage of the oligopaint library used in these experiments was sufficient to visualize TADs in this locus, but not to detect specific regulatory interactions within the doc-TAD (Fig. 3d ). Thus, we performed a similar analysis using a published dataset with a genomic coverage that enabled the detection of cis-regulatory interactions (Fig. S3c )  28. Nc11/12 and nc14 cells occupied different regions of the UMAP embedding (Fig. 3e ), consistent with the results obtained with the lower coverage oligopaint library (Fig. 1i ). In addition, doc1 active and inactive nuclei also intermingled within the nc14 pattern and did not segregate from each other. Consistent with these findings, TAD volumes were similar for active and inactive nuclei for the higher resolution library (Fig. S3d ).
Notably, despite this heterogeneity, the chromatin conformations of single nuclei before and after the emergence of ensemble TADs were distinct enough to occupy different regions in the 2D UMAP embedding of the conformational hyperspace. We observed similar results in datasets from untreated and cohesin-depleted human cells. Thus, tracing the path of chromatin in a single nucleus at the TAD-scale and with relatively sparse sampling (20-50 barcodes) is sufficient to predict, with reasonable confidence, the presence of mechanisms leading to TAD formation. We insist, however, on the probabilistic nature of this prediction, given that (1) single nuclei conformations may fall, with low probability, into regions of the UMAP space displaying overlapping conformations; and (2) the regions of the UMAP space attributed to each population are not unequivocal.
Acquisition of Hi-M datasets Experiments were performed on a homemade, wide-field epifluorescence microscope with previously described procedure  28. Briefly, embryos were attached to a 10% poly-L-lysine-coated 40 mm coverslip and mounted into a FCS2 flow chamber (Bioptechs, US). The sample was flowed with 1800 µL of the fiducial readout probe (25 nM Rhodamine-labeled probe, 2 × SSC, 40% v/v formamide) for 15 min then washed with 2000 µL of washing buffer (2 × SSC, 40% v/v formamide), 1000 µL of 2× SSC then incubated 10 min with 0. 5 mg/ml DAPI in PBS to stain nuclei. The imaging buffer (1× PBS, 5% w/v Glucose, 0. 5 mg/ml glucose oxidase and 0. 05 mg/ml catalase) was injected to prevent the bleaching of the fiducial readout probe. In a typical experiment, 10-15 embryos were immobilized and selected for imaging, using a field of view of 200 × 200 µm and 60 z-stacks (250 nm thick), and at the following excitation wavelengths: 405 (DAPI), 488 (RNA) and 561 nm (fiducial).
Image processing The acquired dataset consisted of image stacks with 2048 × 2048 pixels and 60 slices (voxel size 0. 106 × 0. 106 × 0. 250 µm 3 ). Raw images supplied by the camera were in DCIMG format and were converted to TIFF using proprietary software from Hamamatsu. The TIFF images were then deconvolved using Huygens Professional v. 20. 04 (Scientific Volume Imaging, https://svi. nl). Further analysis was done using a homemade software pipeline written in python  28. First, images were z-projected using sum (DAPI channel) or maximum intensity (barcodes, fiducials) projection. Then, for each hybridization round, the image of the fiducial channel was aligned to the reference fiducial image in a two-step process: (1) Global alignment by cross-correlation of the two images split in 8 × 8 non-overlapping blocks and averaging over the translation offset of all 64 blocks.
(2) Local alignment in 3D of volumes each containing a single nucleus by cross-correlation. Barcodes were segmented using a neural network (stardist) 83 specifically trained for the detection of 3D diffraction-limited spots produced by our microscope. To extract the position of the barcode with sub-pixel accuracy, a subsequent 3D Gaussian fit of the regions segmented by stardist was performed with Big-FISH (https://github. com/fish-quant/big-fish  84  ). Barcode localizations with intensities lower than 1. 5 times that of the background were filtered out. Nuclei were segmented from projected DAPI images using stardist  83  with a neural network trained for detection of nuclei from Drosophila embryos under our imaging conditions. Barcodes were then attributed to single nuclei by using the XY coordinates of the barcodes and the DAPI masks of the nuclei.
Radius of gyration The radius of gyration was calculated from the pairwise distances using  2  , where d i,j is the pairwise distance between barcode i and j. The number of pairwise distances (N 2 ) is adjusted in case a pairwise distance was not detected for a given cell. Pairwise distances above a threshold (1. 0 µm) were set to NaN to remove outliers that would bias the radius of gyration towards large values. Cells with less than one third of all pairwise distances detected were excluded from analysis. Calculation of the radius of gyration is based on the standardresolution Hi-M library except for Fig. 3c , which uses the highresolution Hi-M data as a slightly higher fraction of nuclei could be used to calculate the radius of gyration for this data. R 2 g = 1 2N 2 ∑ i,j ðd i,j Þ.
Acknowledgements This project was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  (Grant  ID 724429 ) (M. N. ). We acknowledge the  Bettencourt-Schueller Foundation  for their prize 'Coup d'élan pour la recherche Française', the  France-BioImaging  infrastructure supported by the  French National Research Agency  (grant ID  ANR-10-INBS-04 , " Investments for the Future "), and the  Drosophila facility  ( BioCampus Montpellier ,  CNRS ,  INSERM ,  Univ Montpellier , Montpellier, France). M. G. was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -project  ID   431471305. O. M. is supported by an  FRM  PhD fellowship.
Data availability The data that support this study are available from the corresponding author upon reasonable request. Single nucleus pairwise distance matrices generated in this study have been deposited in the Zenodo database under https://doi. org/10. 5281/zenodo. 6861355 [https:// zenodo. org/record/6861355]. Previously published datasets used in this study include GSE86966, GSE65441, and GSE103625), and are listed in Supplementary Table  1. Source data are provided with this paper and are also accessible at https://doi. org/10. 5281/zenodo. 6861355 [https://zenodo. org/record/6861355].
Statistics Statistical analyses were performed using Statistica 9. 0 software (StatSoft, Maisons-Alfort, France). For all comparisons, values of p<0. 05 were considered as significant. Statistical significance in behavioral experiments was assessed using one to three-way analysis of variance (ablation, stimulus -mouse versus toy -, trial, session and treatment effects) followed by Newman-Keules post-hoc test or using a Kruskal Wallis ANOVA (marble burying, nesting). Significance of quantitative real-time PCR (qRT-PCR) results was assessed after transformation using a two-tailed t-test, as previously described  (62, 63) ; an adjusted p value was calculated using Benjamini-Hochberg correction for multiple testing. Outliers over twice the standard deviation were excluded from calculations. Unsupervised clustering analysis was performed on transformed qRT-PCR data using complete linkage with correlation distance (Pearson correlation) for drug, treatment and brain region (Cluster 3.
The mean duration of nose and paw contacts was calculated from these data  (1, 2, 6). When social interaction was tested several times, each experimental mouse met a different interactor mouse for each test. Three-chamber social preference test. The experimental protocol was adapted from  (7). As previously described (3-5), the test apparatus consisted of a transparent acrylic box (exterior walls blinded with black plastic film); partitions divided the box into three equal chambers (40 x 20 x 22. 5 cm). Two sliding doors (8 x 5 cm) allowed transitions between chambers. Cylindrical wire cages (18 x 9 cm, 0. 5 cm diameter-rods spaced 1 cm apart) were used to contain the mouse interactor and object (soft-toy mouse). The test was performed in low-light conditions (15 lx) to minor anxiety. Stimulus wild-type mice were habituated to confinement in wire cages for 2 days before the test (20 min/day).
Transcripts with a significantly higher (above x-axis) or lower (below x-axis) abundance in the presence of rapamycin are highlighted in red (q ≤ 0. 05). The active var gene is highlighted in green (q = 2. 59 × 10 -6 ). Three replicates were used for untreated and rapamycin-treated parasites. P-values were calculated with a Wald test for significance of coefficients in a negative binomial generalized linear model as implemented in DESeq2  (Love et al, 2014). q = Bonferroni-corrected P-value. Mean_SEM of three independent experiments is shown. (Birnbaum et al, 2017). IFA localization studies and Western blot analysis confirmed the nuclear presence of Pf-DDX5 during the ring stage, and RIP RT-qPCR confirmed its association with RUF6 ncRNA (Fig  3 ). We used GFP-tagged Pf-DDX5 parasites to perform Co-IP assays to explore the interactome of Pf-DDX5.
MS full scans were performed in the ultrahigh-field Orbitrap mass analyzer in ranges of m/z 375-1,500 with a resolution of 120,000 at m/z 200. The top 20 intense ions were subjected to Orbitrap for further fragmentation via high-energy collision dissociation activation and a resolution of 15,000 with the intensity threshold kept at 1. 3 × 10 5. We selected ions with a charge state from 2+ to 6+ for screening. Normalized collision energy was set at 27, and the dynamic exclusion, at 40 s. For identification, the data were searched against the Plasmodium falciparum FASTA database (PlasmoDB-36 Pfaciparum3D7 Annota-tedProtein containing cas9 and the common contaminants) using Sequest HT through Proteome Discoverer (version 2. 2 or 2. 4). Enzyme specificity was set to trypsin, and a maximum of two-missed cleavage sites were allowed.
Oxidized methionine, carbamidomethyl-cysteine, and N-terminal acetylation were set as variable modifications. The maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 D for MS/MS peaks. The resulting files were further processed using myProMS  (Poullet et al, 2007) , v3. 9. 3 (https://github. com/bioinfo-pf-curie/myproms). FDR calculation used Percolator  (The et al, 2016)  and was set to 1% at the peptide level for the whole study. The label-free quantification was performed by peptide extracted ion chromatograms (XICs) computed with MassChroQ, version 2. 2. 1  (Valot et al, 2011). For protein quantification, XICs from proteotypic peptides shared between compared conditions (TopN matching) with two-missed cleavages were used. Median and scale normalization was applied to the total signal to correct the XICs for each biological replicate (n = 4).
Estimation of cell cycle progression To estimate the parasite age (i. e. , hours post-infection), we correlated transcript levels (average FPKM ≥ 10) from all three replicates of the +rapamycin and -rapamycin samples with the transcript levels of a reference microarray dataset  (Bozdech et al, 2003). Pearson's r correlation coefficients were calculated and visualized in R () with options cor (R Core Team, 2020) and heat-map2(), respectively.
e. , q) ≤ 0. 05. MA plots were generated using the "baseMean" (mean normalized read count over all replicates and conditions) and "log 2 FoldChange" values (rapamycin-treated over control) as determined by DESeq2. RPKM values were calculated in R using the command rpkm() from the package edgeR  (Robinson et al, 2010). Gene Ontology enrichments were calculated using the built-in tool at PlasmoDB. org (Aurrecoechea et al, 2017).
Acknowledgements We would like to thank  Jessica Bryant  for helpful comments and critically reading the article. This work was supported by  ERC  grants to  A Scherf  ( ERC AdG PlasmoSilencing ) and  S Baumgarten  ( ERC StG PlasmoEpiRNA ) and the  Agence Nationale de Recherche  (grant  ANR-11 LabEx-0024-01 ParaFrap  to  A Scherf ).
Proteome and Exoproteome Response to CtaA Deficiency To determine how CtaA deficiency affects cellular metabolism at distinct growth phases on MODG medium, we performed shotgun proteomics assays on three biological replicates for ∆ctaA and WT strains at three time-points. The time-points were: EE (early exponential growth phase, OD = 0. 1), LE (late exponential growth phase, OD = 1), and S (stationary growth phase, OD = 1. 5) (Figure  S1 ). The proteomics dataset acquired on the 36 samples (2 strains × 3 time-points × 3 replicates for cellular proteome and exoproteome) comprised 843,332 MS/MS spectra and a total of 27,804 validated peptide sequences. From these results, based on the confident detection of at least two distinct peptides per protein, 1922 proteins were identified in the cellular proteome (Table  S1 ), and 998 proteins in the exoproteome (Table  S2 ).
Proteome and Exoproteome Response to CtaA Deficiency To determine how CtaA deficiency affects cellular metabolism at distinct growth phases on MODG medium, we performed shotgun proteomics assays on three biological replicates for ΔctaA and WT strains at three time-points. The time-points were: EE (early exponential growth phase, OD = 0. 1), LE (late exponential growth phase, OD = 1), and S (stationary growth phase, OD = 1. 5) (Figure  S1 ). The proteomics dataset acquired on the 36 samples (2 strains × 3 time-points × 3 replicates for cellular proteome and exoproteome) comprised 843,332 MS/MS spectra and a total of 27,804 validated peptide sequences. From these results, based on the confident detection of at least two distinct peptides per protein, 1922 proteins were identified in the cellular proteome (Table  S1 ), and 998 proteins in the exoproteome (Table  S2 ).
A Top-20 method was used, with full MS scans acquired in the Orbitrap mass analyzer over an m/z range from 350 to 1500, at 60,000 resolution. After each scan, the 20 most abundant precursor ions were sequentially selected for fragmentation and MS/MS acquisition at 15,000 resolution. A 10-s dynamic exclusion window was applied to increase the detection of low-abundance peptides. Only double-and triple-charged ions were selected for MS/MS analysis. Sequences were assigned using the Mascot Daemon search engine (version 2. 5. 1, Matrix Science) against the B. cereus AH187 NCBI_20200622 database (7100 sequences). Peptide mass tolerance and MS/MS fragment mass tolerance were set to 5 ppm and 0. 02 Da, respectively. The search included carbamidomethylation of cysteine residues (C) as fixed modification; oxidation of methionine (M) and deamidation of asparagine and glutamine (NQ) were included as variable modifications.
All peptide matches with a peptide score associated with a Mascot p-value lower than 0. 05 were retained. Proteins were considered valid when at least two distinct peptides were detected in the same sample, resulting in a false discovery rate lower than 1%. NSAF values were calculated by dividing the number of spectra assigned to a protein in a given sample by its molecular weight, as recommended  [67]. Results were then statistically analyzed. The R tool Bioconductor DEP package (version 1. 12. 0) was used to perform PCA and determine changes in protein abundance between WT and ∆ctaA mutant at the different growth phases  [68]. Significant changes were selected where the adjusted p-value was lower than 0. 05 and the |fold-change| ≥ 1. 5 (|log 2 fold-change| ≥ 0. 56). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030118 and 10.
6019/PXD030118 for cellular proteome of B. cereus AH187, PXD030114 and 10. 6019/ PXD030114 for cellular proteome of ∆ctaA mutant, PXD030165 and 10. 6019/PXD030165 for exoproteome of B. cereus AH187 and PXD030163 and 10. 6019/PXD030163 for exoproteome of ∆ctaA mutant.
Data Availability Statement: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030118 and 10. 6019/PXD030118 for cellular proteome of B. cereus AH187, PXD030114 and 10. 6019/PXD030114 for cellular proteome of ∆ctaA mutant, PXD030165 and 10. 6019/PXD030165 for exoproteome of B. cereus AH187 and PXD030163 and 10. 6019/PXD030163 for exoproteome of ∆ctaA mutant.
Material and methods This study was conducted according to the local ethical regulations. We included all patients admitted for FGFR-related FCS (MS, CS, AS, PS) between 2000 and 2021 at the Craniofacial Unit of Necker -Enfants Malades University Hospital in Paris, France (Table  1 ). Patients and their families were contacted via email to take part into this study and for authorizing the retrospective data to be used. Written consent was obtained from the parents or from the subjects. We collected growth and weight curves for each patient from the 'Carnet de Santé', the standard follow-up pediatric booklet mandatory for every child in France. Data collection took place between March 2020 and March 2021. We furthermore collected the following clinical parameters for all patients: sex, date of birth, type of FCS (MS, CS, AS, PS), weeks of amenorrhea at birth, parent heights and history of somatotropin hormonal therapy.