dataesr/scientific-paragraphs-categorization huggingface dataset:
Heritability. We first took a set of 3342 individuals corresponding to the intersection between baseline and follow-up data. The baseline and follow-up phenotypes were combined, normalized, and separated into baseline and follow-up series, so the normalized phenotypes at baseline and follow-up were directly comparable (i. e. , equal normalized phenotypes at baseline and follow-up correspond to equal raw phenotypes). We used GCTA's bivariate REML  63  and included 10 genetic PCs, age, and age 2 as fixed effects. The effect sizes of the aformentioned fixed effects were strongly correlated at each time point (ρ > 0. 6) and there were minimal differences in variance explained (< 5%). Heritability estimates at the two time points were plotted using circlize R package  64  , whereas the complete GCTA output, including genetic and environmental variance estimates, genetic and environmental covariances, and LRT p values for genetic correlation are provided in Supplementary Data 13.
Code availability The GWAS association screening was performed using the CMS approach. The code developed is available at: https://gitlab. pasteur. fr/statistical-genetics/runCMS/. The bivariate heritability analysis was performed using GCTA's bivariate REML https:// cnsgenomics. com/software/gcta/. Plotting of the gene-metabolites network was done using Cytoscape: https://cytoscape. org/. All others analyses and plots were done using the R software: https://www. r-project. org/.
Methods The analyses were performed using data from the Nutri-Bébé 2013 study, a cross-sectional food consumption survey. The design, recruitment procedure, methodology, and global results of this study have been reported in detail elsewhere  [2]. According to French law, no ethical approval was required for this study as it was conducted by an official polling institute, TNS Sofres, in full agreement with the guidelines laid down in the Declaration of Helsinki and the French data protection act, and additionally the current research did not involve invasive procedures or therapeutic interventions. The study was registered on ClinicalTrials. gov (NCT03327415).
Study Sample The survey was conducted from 3 January to 21 April 2013, by TNS Sofres, an official polling institute in full agreement with the guidelines laid down in the Declaration of Helsinki and the French data protection act. According to the French legislation ethical approval for this study was not required since the current research did not involve invasive procedures or therapeutic intervention. The study has been registered on ClinicalTrials. gov (NCT03327415). In brief, a randomly selected sample of 1184 healthy children was enrolled (Figure  1 ). From the 1035 non-breastfed children, 998 were included in the intake analysis  [2] , with 241 aged 12-23 months and 206 aged 24-35 months, selected for the current case-control study. The inclusion criteria for selection of children in the initial sample were that they were at least 1 year old, and either consumed YCF or did not consume any infant formula or YCF at all.
Data Analysis Food and drink intake were analyzed by the Centre de Recherche pour l'Étude et l'Observation des Conditions de Vie (CREDOC) (Research Centre for the Study and Observation of Living Conditions)  [2]. The foods consumed were described and grouped in food categories, according to the level of consumption. In addition to formula, YCF, milk and cheese (hard and semi-hard cheese, spreadable cheese, molten cheese), and other milk products were distinguished between smooth dairy products (yogurt, fresh cheese, Swiss type cream cheese) and milky desserts (cream, custard, mousse, pudding etc. ). Apart from potatoes, starchy foods included bread, pasta, semolina, rice, bulgur, quinoa, beans, peas, and lentils. In addition to ham, other delicatessen foods included pâté and sausages. The industrial ready meals included all the canned or frozen prepared dishes that can be used such as pizza, pies, "quiche", lasagna, ratatouille, meat-or fish-based dishes, etc.
Statistical Analysis The results are expressed as the mean daily intake (DI) ± standard deviation (SD) of each age group. The statistical analysis was performed using the SAS 9. 2 ® software (http://support. sas. com/documentation/installcenter/922/, Cary, NC, USA). The analysis of the quantitative data was based on ANOVAs and the Student's t-test. Differences were considered significant at p level < 0. 05. The relationship between the daily consumption of YCF and the daily intake of a nutrient was established using the Pearson correlation coefficient.
X-ray Structure Determination Crystallization of FPc and FPc 3 and the data quality have been described elsewhere  (20)  and details for C3bBbSCIN-FPc crystallization and data collection were also described  (12). A sulfur-SAD data set was obtained from two crystals of FPc 3 at the Petra III P13 beamline with a helium path inserted between the crystal and the detector. Crystals of the C3bBbSCIN-FPht 3-hFPNb1 complex were obtained by vapor diffusion against a reservoir solution containing 100 mM NaCl, 5% (w/v) PEG4000, 10 mM MgCl 2 , 100 mM Sodium Cacodylate trihydrate pH 5. 8. Crystals of hFPNb1 were obtained by vapor diffusion of 450 nL drops mixed in a 1:2 ratio with a reservoir containing 13. 1% (v/v) MPD, 13. 1% PEG1000 (w/v) 13. 1% (w/v) PEG3350, 100 mM Tris Bicine pH 8. 5, 33 mM CaCl 2 , 33 mM MgCl 2 , 4% (v/v) formamide, 114 mM FOS-Choline 8.
Crystals of C3bBbSCIN-FPht 3-hFPNb1 were cryoprotected by soaking in reservoir solution supplemented with 25% glycerol whereas hFPNb1 crystals were cryoprotected directly in reservoir solution prior to data collection at Petra III. Native C3 at a final concentration of 8 mg/mL was added 1. 5 fold molar excess of hC3Nb1 and crystallized by vapor diffusion of 200 nL drops mixed in a 1:1 ratio with a reservoir containing 100 mM HEPES pH 7. 2, 1. 75% (w/v) PEG8000. The crystals were cryoprotected in reservoir solution supplemented with 30% glycerol prior to data collection at Petra III. All diffraction data (Supplementary Table  2 ) were processed with XDS  (23). The FPc 3 structure was determined by molecular replacement. Electron densities for the C3bBbSCIN-FPc complex  (12)  and the C3bBbSCIN-FPht 3-hFPNb1 complex (this study) calculated without contribution from FP models were improved by multi crystal averaging with phenix.
The structure of hFPNb1 was determined by molecular replacement followed by rebuilding and refinement. The final model of FPc was placed manually into the density modified maps of the C3bBbSCIN-FPc and C3bBbSCIN-FPht 3-hFPNb1 complexes. In the latter complex, clear omit density allowed the docking of the refined hFPNb1 structure. The resulting models of the two complexes were refined with phenix. refine using rigid body and TLS refinement, positional refinement under NCS restraints, and grouped B-factor refinement. Side chains at the FP-convertase interface were remodeled if the associated main chain was altered or clashes occurred, but to avoid bias side chains were not fitted in detail to point toward putative interaction partners. The structure of the C3-hC3Nb1 complex was determined using the coordinates of the C3 structure (PDB entry 2A73) with the C345c domain removed for molecular replacement in phenix.
phaser. The coordinates of hC3Nb1 (RCSB entry 6EHG) and the C345c domain (RCSB entry 2A73) were manually placed in Coot. The model was then refined against the reflections with rigid body, TLS and grouped Bfactor refinement in phenix. refine. In an iterative manner the structure was subjected to cycles of manual rebuilding in coot followed by positional, grouped B-factor and TLS refinement in phenix. refine. Prior to final refinement the structure was fitted to the electron density map using iMDFF as described in Croll and Andersen  (29). Diffraction data and atomic models are available at the protein data bank as entries 6RUS, 6SEJ, 6RUR, 6RU3, 6RUV, 6RU5, 6RV6 as specified in Supplementary Tables  1, 2.
Patient Data A patient with a FP deficiency was identified in the Complement Diagnostic Laboratory at the HEGP hospital in Paris (with informed consent from the patient's parents for the genetic analyses) using direct sequencing. Complement analysis was performed in the plasma of the patient and his relatives. A CH50 test to evaluate the activity of the classical complement pathway was performed on sheep IgG-opsonized erythrocytes. An AP50 test to evaluate the alternative pathway activity was carried out on rabbit erythrocytes. Plasma C3 and C4 were quantified using nephelometry (Dade Behring, Deerfield, IL, USA). The levels of FP were evaluated by ELISA. All analyses were performed according to the routine diagnostic procedures of the Complement Diagnostic Laboratory at the HEGP hospital in Paris.
Molecular Dynamics Simulations As starting point for our simulations, crystal structures of C3 (PDB entry 2A73) and C3b (PDB entry 5FO79) were placed into a rhombic dodecahedron box, solvated with TIP3P water molecules containing Na + and Cl -ions at 100 mM, resulting in ∼371,000 atoms in the C3 model, and ∼449,000 atoms in the C3b model. The disulfide bonds present in the crystal structures were maintained. The CHARMM36m force field  (31)  was used for both protein and ions. Simulations were performed in the NPT ensemble (298K, 1 bar) using the V-rescale thermostat (32) with a 1 ps coupling constant, and the Parrinello-Rahman barostat with a 2 ps time coupling constant. A cut-off of 1. 0 nm for both the van der Waals interactions and the short-range electrostatic interactions, with long-range electrostatics treated using PME.
The Post-translational Modifications of FP Whereas, the disulfide bridge pattern in six thrombospondin repeats were those expected, the three cysteine bridges 32-56, 43-73, and 57-75 in the N-terminal TB domain differed from those annotated in UNIPROT. We therefore confirmed the disulfide bridge pattern by two orthogonal techniques. First, an anomalous Fourier map calculated from data collected at λ = 2. 48 Å (Supplementary Figure  1B ) confirmed the three disulfide bridges. Second, we also isolated the TB domain from the TB-TSR1-TSR2 fragment of FPc 3 by reverse phase chromatography and performed mass spectrometry analysis. This revealed ions representing disulfide-linked peptides confirming the observed disulfide arrangement (Supplementary Figure  1F ). One peptide obtained by trypsin digestion contained an ion of m/z 5112.
e. TIs were from 50 ms to 10000 ms). The TIs were chosen to assure maximum contrast among tissues from their corresponding sequences, the body region is found using a threshold method on the T 2 -weighted images (c). Then, the fit procedure is applied to obtain the relaxation maps and other different variables from the received signal model (d-e). These variables are then used to generate the synthetic T 1 -and synthetic T 2 -weighted images (f-g). Later, an independent process using the training data is used to generate tissues probabilistic maps, i. e. the probability of a voxel belonging to different tissues (h). At this point, all feature descriptors are normalized (i) to be used by the multi-class SVM classifier (j), which predicts the different classes (k). A 32-echo spin echo sequence was used to obtain the T 2 -weighted images.
Classification The tissue classification is performed using the multi-class linear SVM implementation from the libLinear software package  [19]. A One-vs-All method was used to solve the multi-class classification problem. It consists in developing for each class a binary classifier that separates that class from the rest of the data. A linear SVM kernel was chosen due to the lack of prior data suggesting the use of a non-linear SVM kernel (with our features), and due to the lower vulnerability for overfitting compared with non-linear kernels  [29]. The method then combines the classifiers for multi-class inference  [30].
Evaluation To evaluate the performance of the multi-class SVM classifier, a leave-one-out cross-validation was implemented. The overall database contains images from 22 subjects. We repeatedly trained our method on data from 21 subjects and tested on the remaining one. The reported cross validation accuracy is the percentage of data correctly classified pixel-by-pixel. By its very nature, the dataset is highly imbalanced with far more pixels from the fat, muscle and bone marrow classes than from the prostate, bladder and air classes. To avoid biasing the classification toward the larger classes, the number of voxels from the fat, muscle and bone marrow classes were randomly down-sampled to match the number of voxels in the prostate class. Since the bladder and air ROIs are smaller than the prostate, we kept every pixel from those classes.
The user, only required during the setup of the experiment, loads the probe (ScanAsyst-Fluid-Bruker, Santa Barbara, CA, USA) and does the setup and calibration in the dedicated well. The setup consists of the selection of the probe parameters, the selection of the tip location, and the alignment of the laser and photodetector, using the standard interface of the software. For the calibration, we currently use the standard touch calibration, which consists of calculating the deflection sensitivity and spring constant using force curves and thermal tune. For future experiments, calibrated tips and SNAP could be used  [13]. The PeakForce parameters, the sync distance and the PeakForce Sensitivity, were also measured at the selected PeakForce Frequency (here 1 kHz). The Force Volume trigger threshold was then set to 1 nm.
The Nanoscope software is, by default, able to realign the photodetector and perform the engagement, here in PeakForce mode. The script then switches to Force Volume mode and a low-resolution (here 64 pixels squared) Force Volume is captured and used as a survey scan. Although we are only interested in imaging the sample, Force Volume was chosen on this large but low-resolution scan because its speed depends mostly on the resolution (1 curve per pixel) whereas imaging modes are mostly limited by the scan size, since they need to track the sample during raster-scanning. Raster-scanning at high-speed on large distance can also be damaging for the sample. Detection. The survey scan is analyzed automatically. In the cases of obviously aberrant scans, i. e. when sudden steps or an abnormally large number of "hits" are detected, the position is dropped and the scan moves on to the next survey area.
Such aberrance might be due to contaminants or defects in the area or to contamination of the cantilever during scanning. Otherwise, the data is flattened at the first order to remove the tilt. A height threshold is defined, below the expected size of bacteria, here at 200 nm higher than the background. Iteratively, the local maximum of the flattened image is taken, as long as it stands higher than the threshold. A square mask of the size of the target (defined earlier, here 2 μm squared) is centered on this point. The mask is repositioned on the center of mass of its intersection with the data above the threshold. This location is stored and the area falling under the mask is excluded for future calculation. The procedure is then reiterated until all data falls below the threshold. Scan. Once the positions have been detected, the script switches the system back into scan mode, in PeakForce-QNM, although we did not use the QNM channels in the analysis.
Analysis After the experiment, the scans were quickly visually checked on Nanoscope Analysis to remove those showing no bacteria or of which the bacteria part was clearly negligible. Further checks were added in the analysis to ignore files for which the bacteria occupied less than 5% of the usable area of the scan. The remaining scans were deemed of "good quality". Having a quick manual filtering is a step that we consider important to guarantee the soundness of the data and that takes negligible time. This filtering aims at removing obvious artifacts and empty scans based on the "qualitative" experience of the user, while the automated filtering removes border-line cases based on quantitative criteria. In the case of a perfectly characterized system whose quality is guaranteed, the manual filtering could be dropped to lead to a fully-automated process.
This implies being able to afford a few percents of potentially corrupted data in the analysis. Most of the filtered-out files would likely be removed by the analysis, but a few might still be analyzed and yield outliers in the data, which can still be visually checked post-analysis. In order to illustrate the interest of gathering such an important number of scans, we observed a few parameters on each sample using the aforementioned Python library. For each scan, the height image was smoothed by a 2D Gaussian filter with sigma = 3 pixels (1 pixel = 15. 625 nm in our conditions). The background level was defined as the minimum of the smoothed image. The area of interest was defined as everything that was 300 nm higher than the background and for which the error signal was higher than -5 nN (the SetPoint value).
Multi-Sample To quantify these differences, we extracted height and roughness on all the gathered data for the three conditions in an automated manner. As shown in  Thus, the height enables the control to be differentiated from the other two conditions. On the other hand, the roughness allows for the differentiation of the gentamicin-treated sample. Height and roughness could then be used to create a 2D scatterplot (Fig  6C ). This plot clearly illustrates the clustering of the three conditions over these two parameters. A Welch's t-test was used to highlight the significance of the results (values in  Fig 6). The differences between the control and the gentamicin-treated sample, although visually subtle, clearly appear in the numbers with an overwhelmingly high significance (p-value < 10 -150 ).
This work was supported by  ANR  ( Agence Nationale de la Recherche)  ( ANR-10-EQPX-04-01 ,  FL) Feder  ( Fonds europeen de developpement regional)  ( FEDER 12001407 , FL),  France. Bruker Nano Surfaces  provided support in the form of salaries for authors A. D. and P. D. W. as well as technical help. The  ANRT  provided support in the form of salaries to A. D. , the  University of Lille  provided support in the form of salaries to V. D. and the  CNRS  provided support in the form of salaries to F. L. , but did not have any additional in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Finally, siRNA targeting ER chaperon (GRP94), lectins involved in transfer of ERAD-substrate to the retrotranslocon (OS-9, XTP3-B) or components of the retrotranslocon itself (SEL1L, HRD1) did not increase significantly the expression level of mutant or WT-CALR. In order to test whether the ERAD machinery might be affected in CALR-mutated MPN patients, we analyzed the expression of select ERAD genes in the dataset generated by Rampal et al. [26]. In accordance with the mechanism of CALR mutant degradation, we observed a significant deregulation of the expression of different ERAD components such as EDEM3, CANX, DNAJC10, SEL1L, VCP and different components of ubiquitin ligase complex in MPN samples, as seen in Figure  5E.
Identification of a Network Involved in the ERAD-Dependent Degradation of CALR Variants We then collated all the information gathered in our siRNA screen, the available databases, and the literature. This identified a CALR closed network containing the EDEM proteins, as seen in Figure  6A , blue nodes, the CALR homolog CANX, as seen in Figure  6A , orange node, and the thioredoxin domain containing proteins DNAJC10 and TXNDC11  [27] , as seen in Figure  6A , green nodes. Based on this, we first tested whether siRNA-mediated silencing of TXNDC11 impacted on the expression of CALR mutants. This showed that TXNDC11 expression attenuation led to a stabilization of both del52 and ins5 variants, as seen in Figure  6B. As such, together with the results obtained in Figure  5 , this shows that specific thioredoxin domain containing proteins might be involved in the degradation of CALR variants, with a preferential impact on the ins5 mutant.
These results show that the expression of CALR variants is dependent on a proximal ER quality control system, most likely including EDEM3, TXNDC11, and DNAJC10, which, when deregulated in MPN, might lead to sufficient stabilization of the proteins to yield constitutive MPL activation. 5 , available databases (https://string-db. org/; https://thebiogrid. org/; https://www. ebi. ac. uk/intact/), and literature. (B) HEK293T cells were transfected with siRNA directed against TXNDC11 or a scramble control. Twenty-four hours later, cells were transfected with pcDNA6-CALR plasmids. Twenty-four hours later, cells were lysed before analysis by western blotting. CALR proteins were detected via their Myc epitope and actin was used as a loading control. The expression of the different forms of CALR secondary to TXNDC11 silencing was expressed as in Figure  5D.
Interestingly, we found that the expression of ERAD genes was selectively altered in CALR variants expressing MPN using existing datasets  [26] , thus confirming a pathophysiological link between ERAD and CALR variants. This concept was reinforced by the presence of complexes containing CALR variants and selective ER quality control and ERAD components. A recent study also described an increased interaction of mutant CALR proteins with Endoplasmic Reticulum Quality Control (ERQC) actors  [45]. As such, one might propose a model in which the combined expression of CALR variants and an altered ERAD network might concur with the development of a myeloproliferative phenotype. In such a model, a strategy aiming at restoring the properties of the altered ERAD pathway might in turn promote the degradation of CALR variants and decrease the pro-oncogenic burden.
, Hercules, CA, USA). Membranes were incubated with the indicated primary antibodies and appropriate Horseradish Peroxidase (HRP)-coupled secondary antibodies. Immunoreactive proteins were detected via chemoluminescence with a LAS-3000 Camera (Fujifilm, Tokyo, Japan) or Amersham Hyperfilm MP autoradiography films (GE Healthcare, Chicago, IL, USA). When quantification was performed (using ImageJ software), only data obtained with the camera were used, allowing a larger linear range and the detection of saturated signal. Secreted proteins were precipitated from conditioned media with trichloroacetic acid (10% v/v). The pellets were then washed twice with 80% acetone and resuspended in Laemmli buffer before analysis by Western blot.
Gene Expression Microarray Analysis and Statistical Selection Affymetrix raw microarray data of 93 patients with MPNs (28 PV, 47 ET, 18 MF) and 11 age-matched normal donors, deposited in Gene Expression Omnibus (GEO) database with the accession number of GSE54644, were retrieved and their gene expression values were background corrected, log2 transformed, and then quantile normalized using Robust Multiarray Averaging (RMA). Linear Models for Microarray Data (LIMMA) was applied to identify any differentially expressed genes between CALR mutants (21 samples) and their matched normal samples. Differentially expressed genes with p-value ≤ 0. 05 and False Discovery Rate (FDR) ≤ 0. 1 were highlighted as statistically significant. Microarray, differential expression and exploratory data analysis were performed using the open-source R/Bioconductor programming environment.