dataesr/scientific-paragraphs-categorization huggingface dataset:
This implies that if a participant answered correctly for a least one of the initial stimuli (100% or 90% or 80% male or /pa/) and one of the final stimuli (100% or 90% or 80% female or /ta/), he or she was considered unimpaired (see supplementary material 1 for repeated analyses using an incremental classification criterion). In normal healthy participants, this is achieved very easily. Taking the data from  (Pernet et al. , 2014) , 100% of controls (N=18, 9 males, 9 females) were unimpaired (figure  2 ). From the resulting classification in patients, the independence between the phoneme and gender categorization performance was tested for each group using a McNemar test with exact central probability  (Fay, 2010). Lesion to Symptom Mapping: Each participant received one or more CT or MRI scan from the National Health Service, and the scan the closest in time to the subjects' participation in this study was considered.
Sequencing and Bioinformatic analysis Sequencing of the 32 samples was performed on the HiSeq 2000 sequencer (Illumina) in single-end mode in order to have approximately 100 million reads of 50 bases per sample. Reads were cleaned of adapter sequences and low-quality sequences using an in-house program (https://github. com/baj12/clean_ngs). Only sequences at least 25 nt in length were considered for further analysis. TopHat (version 1. 4. 1. 1, default parameters, http://ccb. jhu. edu/ software/tophat) was used for alignment on the reference genome (hg19). Genes were counted using HTseq-count (parameters: -m intersection-nonempty, -s yes, -t exon)  [9, 10]. The PANTHER (Protein ANalysis THrough Evolutionary Relationships) (http://www. pantherdb. org/) Classification System was used to classify genes identified in our transcriptomics data according to their molecular function annotated using ontology terms  [11, 12].
Statistical analysis Cytokines. To determine whether differences between the CF and CTRL groups were statistically significant, we used Prism 6. 00 software (GraphPad Software, La Jolla, CA), as indicated in the figure legends. P values <0. 05 were considered significant. ANOVA was performed to compare quantitative variables across groups. To correct for multiple testing, we used either Bonferroni's method (comparisons of all groups) or Dunnett's method (comparisons of groups vs. a CTRL condition). RNAseq data. Counts were analyzed using R version 3. 0. 2 and the Bioconductor package DESeq2 version 1. 2. 9 (www. bioconductor. org) [13]. Data were normalized using DESeq2 and the default parameter. To estimate dispersion and test for differential expression, we used the default parameters (including outlier detection and independent filtering).
Activation of primary bronchial epithelial cells (hAEBCs) by P. aeruginosa After infection, the kinetics of IL-8 and IL-6 production were comparable for CF and CTRL cells (Fig 1A  and 1B , respectively). However, IL-6 and IL-8 concentrations differed significantly between CF and CTRL cells from 6 hours and at 8 hours postinfection, respectively. These data were obtained under experimental conditions in which P. aeruginosa infection did not alter the viability of CF or CTRL cells, as assessed based on LDH release (data not shown).
Results The auxiliary Tol-associated protein CpoB has a PBP1B-associated function To identify novel regulators of PG synthesis during cell division, we queried the E. coli chemical genomics database  (Nichols et al. , 2011)  for deletion strains whose growth responses closely resembled those of a strain lacking PBP1B (encoded by mrcB) across a range of drug and environmental stress conditions. This approach previously identified LpoB, the OM lipoprotein activator of PBP1B  (Typas et al. , 2010). ΔcpoB exhibited the second highest correlation with ΔmrcB (Figure  2A ; correlation coefficient = 0. 47, p < 10 -18 ), suggesting that CpoB may also be functionally associated with PBP1B. CpoB (YbgF) is encoded by the last gene in the tol-pal operon and binds TolA in vitro  (Krachler et al. , 2010) , but its deletion does not cause the severe OM integrity defects typically associated with tol-pal deletions  (Vianney et al.
, 2006). Strains for overexpressing PBP1B carried arabinose-inducible plasmid pBAD33-PBP1B. Strains for expressing YbgF in trans (for complementation of ΔcpoB) carried arabinose-inducible plasmid pBAD33-CpoB. E. coli wild type and mutant strains were arrayed and grown in a 1536-colony format (n ≥ 96 for each strain) on LB Lennox plates (20 g/L agar) at 37˚C, then replica-pinned robotically (Rotor HDA, Singer Instruments, Roadwater, UK) to LB Lennox and/or indicated condition plates. Assay plates were incubated for 8-14 hr at 37˚C and then imaged. Colony sizes were determined using in-house software. Images were collected using an Andor DC152Q sCMOS camera (Andor Technology, South Windsor, CT) and MicroManager v1. 3 software  (Edelstein et al. , 2010).
Quantitation of periplasmic leakage and OM blebbing Strains were grown in LB Lennox broth with 1 mM ITPG to induce periplasmic mCherry expression. 1% arabinose and 10 μg/mL chloramphenicol were added for plasmid-bearing strains. Overnight cultures were diluted 1:200 and then grown in a 96-well microplate (Model #780271; Greiner Bio-One, Monroe, NC) for 2 hr before imaging. Phase-contrast images were segmented using a custom MATLAB software package called Morphometrics, as in  Ursell et al. (2014). The software is currently in beta version and freely available upon request (kchuang@stanford. edu); the release version will be made available on Bitbucket. Related scripts for measurement of periplasmic leakage (peripheral fluorescence) and outer membrane blebs/vesicles (fluorescent puncta) are also available from Bitbucket in the kchuanglab/ Gray_eLife_CpoB repository.
Cells were cooled on ice during UV illumination. Cell samples were separated by SDS-PAGE (8%), transferred to a nitrocellulose membrane (Bio-Rad Laboratories, Hercules, CA), and analyzed by western blot using monoclonal anti-polyhistidine peroxidase-conjugated antibody (1:4000 dilution; Sigma-Aldrich). Mass spectrometry and related data analysis (related to Figure  3  and Table  1 ) Excised protein bands were reduced with DTT, alkylated with iodoacetamide and in-gel digested with trypsin  (Shevchenko et al. , 2006). Nanoflow liquid chromatography coupled to mass spectrometry was performed on an Agilent 1200 nanoflow system (Agilent Technologies) connected to a MS LTQ-Orbitrap XL (Thermo Fisher Scientific). The samples were trapped on a 20 mm ReproSil-Pur C18-AQ (Dr. Maisch GmbH, Ammerbuch, Germany) trapping column (packed in-house, i.
d. , 100 μm; resin, 5 μm) with a flow-rate of 5 μL/min. Sequential elution of peptides was accomplished using an analytical column (Dr. Maisch GmbH; packed in-house, i. d. , 50 μm; resin, 3 μm) with a 35 min gradient of 10-38% buffer B (buffer A, 0. 1 M acetic acid; buffer B, 0. 1 M acetic acid, 80% [vol/vol] acetonitrile) followed by 38-100% B in 3 min, 100% B for 2 min. The flow rate was passively split from 0. 45 mL/min to 100 nL/min  (Nesvizhskii et al. , 2003). Nanospray was achieved using a distally coated fused silica emitter  (made in-house, o. d. , 375 μm; i. d. , 20 μm)  biased to 1. 7 kV. Mass spectrometer was operated in the data dependent mode to automatically switch between MS and MS/MS. The high resolution survey full scan was acquired in the orbitrap from m/z 350 to m/z 1500 with a resolution of 30.
000 (FHMW). The most intense ions at a threshold of above 500 were fragmented in the linear ion trap using collision-induced dissociation at a target value of 10,000. Peak lists were generated from the raw data files using the Proteome Discoverer software package version  1. 3. 339 (Thermo Fisher Scientific). Peptide identification was performed by searching the individual peak lists against a concatenated target-decoy database containing the E. coli sequences in the Uniprot database (release 2012_06) supplemented with a common contaminants database using the Mascot search engine version 2. 3 (Matrix Science, London, United Kingdom) via the Proteome Discoverer interface. The search parameters included the use of trypsin as proteolytic enzyme allowing up to a maximum of two missed cleavages.
Related image analysis Image analysis was performed as described  (van der Ploeg et al. , 2013). Briefly, individual cells were identified and analyzed using the public domain program Coli-Inspector, running under plugin ObjectJ written by Norbert Vischer (University of Amsterdam, https://sils. fnwi. uva. nl/bcb/objectj/), which runs in combination with ImageJ. Via this analysis, parameters including cell length, cell diameter (width) as a function of length, and integrated fluorescence as a function of length (fluorescence profile) were quantified for each cell. For each examined protein, fluorescence profiles of 3000-5000 individual cells were sorted according to cell length to generate profile maps (Figure  4B ), similar to demographs  (Hocking et al. , 2012). A cell diameter profile map (Figure  4B , bottom panel) was generated from imaged cells that were fixed only.
, 2014). The concentration of CpoB injected ranged from 0. 05 to 0. 5 μM, or 0. 017-0. 167 μM assuming trimerization. Assays were performed in triplicate at 25˚C, at a flow rate of 75 μL/min and with an injection time of 5 min. The dissociation constant (K D ) was calculated by non-linear regression using SigmaPlot 11 software (Systat Software Inc. ). Continuous fluorescence GTase assays and measurement of TPase activity using radiolabelled lipid II substrate were performed as described previously  (Bertsche et al. , 2005; Banzhaf et al. , 2012)  with slight modification. Triton X-100 concentration varied, depending on constituent proteins, from 0. 04 to 0. 075%. Fold-increases in GTase rates were calculated against the mean rate obtained with PBP1B alone at the same reaction conditions, at the fastest rate.
Otherwise, method was as described in  (Bertsche et al. , 2006; Typas et al. , 2010). Antibody amounts for immunoprecipitation were optimized to capture all detectible target protein, as assessed by western blot of pellet and supernatant fractions. Pelleted material was extracted in 1/10 original volume SDS-PAGE buffer to concentrate the final IP fraction. Data-driven structural docking models (related to Figure  8A ) The docking models of CpoB/PBP1B/LpoB complex were built using HADDOCK2. 1 data-driven docking protocols  (Dominguez et al. , 2003)  and CNS1. 2  (Brunger et al. , 1998)  for the structure calculations. The initial coordinates of the CpoB, PBP1B, and LpoB molecules were taken from the PBP1B crystal structure (PDB code 3VMA)  (Sung et al. , 2009) , the CpoB crystal structure from the N-terminal domain (2XDJ) and the C-terminal TPR domain (2XEV)  (Krachler et al.
The docking was performed with default HADDOCK parameters including a clustering cutoff of 7. 5 Å. The HADDOCK score was used to rank the generated models (Lutje  Hulsik et al. , 2013). The generated docking models were analyzed within the Pymol software. BW25113 and BW25113 ΔcpoB were grown in 50 mL of LB to an OD 578 of 0. 3. The culture was then back-diluted 1:50 into 50 mL fresh LB and grown to an OD 578 of 0. 3. Cells were then cooled on ice for 10 min prior to harvesting 5 mL by centrifugation (10,000×g, 10 min, 4˚C). At this point, a viable count was also performed to determine the number of cells per milliliter of culture (see below). The pellets were resuspended in 100 μL TBS (Tris-buffered saline) buffer and lysed by addition of 100 μL SDS-PAGE loading buffer and boiling for 10 min.
3 × 20 μL samples of BW25113 lysate were resolved by SDS-PAGE along with purified CpoB standards (0, 1, 2, 4, 8, and 16 ng) loaded in 20 μL of BW25113 ΔcpoB lysate. CpoB was detected with specific antibody after western blot (Figure  5 -figure supplement 2). Images were analyzed using ImageQuant LAS4000 software, giving the chemiluminescence signal derived from CpoB bands over the background (the signal at the same position as CpoB in the 0 ng sample was used as the background). A standard curve was plotted using the known CpoB standards and the amount of CpoB in the BW25113 lysate samples was calculated. This was then converted to molecules per cell using the viable counts, which were performed as follows: Cells were serially diluted 10-fold in ice-cold LB. 100 μL of the 10 -5 and 10 -6 dilutions were plated on LB agar and grown overnight at 37˚C; colonies were then counted and the number of cells per milliliter of culture was calculated.
Fluorescent reporter genes have become widely used for monitoring gene expression in bacteria at high temporal resolution in a nonintrusive way  (Chudakov et al. , 2010; Giepmans et al. , 2006). The underlying principle is the fusion of a gene of interest and/or the promoter region driving its expression with a gene encoding a fluorescent protein (Fig. 1 ). A bacterial strain carrying the resulting reporter gene, on the chromosome or on a plasmid, emits a fluorescence signal proportional to the amount of reporter protein in the cell. When reporter strains are grown in a microplate, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The resulting data contain information on population-level gene expression that is highly valuable for such applications as the inference and analysis of regulatory networks in bacterial cells  (Berthoumieux et al.
We formulate the estimation problems in the classical framework of regularized linear inversion  (Bertero, 1989; de Nicolao et al. , 1997; Wahba, 1990) , which gives access to a range of powerful tools for robust estimation. Contrary to the related work of  Bansal et al. (2012)  and  Porreca et al. (2010) , we consider not only the inference of promoter activities, but also of growth rates and protein concentrations. Moreover, no restrictions are imposed that limit the practical applicability of the approach. We propose efficient procedures for the implementation of the methods and show by means of an in silico simulation study under realistic conditions that they perform better than the indirect approaches usually encountered in the experimental literature. The algorithms have been implemented in a Python package and are also accessible through a web application.
, 2005). Reconstructing these transient gene expression profiles from the data is highly important for increasing our understanding of the functioning of the underlying regulatory networks, but is difficult to achieve. To the best of our knowledge, the methods and computer tools presented in this paper provide the most comprehensive solution for analyzing reporter gene data available to date. Although the application has focused on fluorescent reporter gene data, the methods are directly applicable to the analysis of luminescent reporter gene data or other time-series gene expression datasets. In addition, the gene expression models underlying the methods are valid not only for bacteria but also for higher organisms.
For simulated data shown in Figure  4A , the results in panel B show that at the beginning of the experiment, when the absorbance signal is low, the growth rate estimation is highly unstable. Additional numerical experiments, shown in Supplementary Section S5, indicate that increasing the smoothing parameter to reduce the variance of the estimates introduces a strong bias on the estimate, reflecting the wellknown variance-bias trade-off. We conclude that the proposed linear inversion method performs better than this indirect approach. Notice that the estimation of growth rate and initial volume also leads to a denoised estimation of the population volume by Equation (  5 ): c a V ¼ H w c a V 0 b l ; ( 15 ) which will be used in the next sections for the estimation of promoter activity and protein concentration.
Software for applying linear inversion methods As they rely on few assumptions and require virtually no hand-tuning, the linear inversion methods presented above are suitable for routine treatment of reporter gene data obtained in microplate experiments, which generate a huge quantity of measurements (typically 10 4 À 10 5 data points). The linear inversion methods were implemented in the Python package WellFARE, relying on the scientific libraries NumPy and SciPy  (Jones et al. , 2001). In addition, the package provides utilities for parsing data files and removing outliers from the absorbance and fluorescence signals. The WellFARE package is available under an LGPL license, but has also been integrated into a web application called WellInverter, which provides a user-friendly access to the linear inversion methods through a web browser (Fig.
, 1997). The methods described in this article are made available as a Python package and can also be accessed through a user-friendly web application. Other tools for the analysis of reporter gene data are WellReader  (Boyer et al. , 2010)  and BasyLICA  (Aı ¨chaoui et al. , 2012). Although the Matlab program WellReader uses the indirect approaches from de Jong et al. (2010), BasyLICA is based on the use of Kalman filters, which also directly estimate quantities of interest from the reporter gene data by a Bayesian approach. In comparison with BasyLICA, WellInverter estimates not only promoter activities but also protein concentrations from the data, and bases the inference of these quantities at a given time t not only on past data, at time-points preceding t, but on the entire dataset. In addition, WellInverter uses regularization based on GCV to avoid handtuning.
In all cases, whole blood was collected using Li-heparin as an anti-coagulant and maintained at room temperature (18-25°) until processing. The cells were stained using commercially available monoclonal antibodies (Table  S1 ), according to the operating procedure developed as described herein. The standardized protocol is provided (Supplementary Protocol #2). We evaluated three red blood cell lysis reagents to optimize the staining protocol: BD FACS lysing solution (BD Biosciences, ref. (Miltenyi,. The semi-automated staining was performed using the Evo-150 liquid handling system (Tecan). A detailed script for the semi-automated sample processing is provided in (Online Supplementary Data File #1, http://www. milieuinterieur. fr/en).
Data analysis and statistical methods Flow cytometry data were generated using MACSQuantify™ software version 2. 4. 1229. 1 and saved as. mqd files (Miltenyi). The files were converted to FCS compatible format and analyzed by FlowJo software version 9. 5. 3. Statistical graphs were prepared with the R Software version 3. 0. 1 (Ref. : R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www. Rproject. org/), using the ggplot2 graphical package version 0. 9. 3. 1  [16].
Sample The treatment of blood samples has a large impact on cytometry data  [15]. One of the biggest considerations was analysis of fresh blood as compared to freezing samples and batching analysis. To assess potential differences in results obtained by immunophenotyping of fresh whole blood versus thawed PBMC in our experimental conditions, we compared results from eighteen healthy donors. We did not observe a major difference in B cell frequencies (Figure  S2A , upper panel) or of CD19 mean fluorescence intensity (MFI) values (Figure  S2A , lower panel). Lower frequencies of CD3 + lymphocytes and a significantly lower expression level of CD3 were observed in frozen/thawed PBMCs compared to whole blood (Figure  S2A ). While the frequency of CD4 + and CD8 + lymphocytes was similar in thawed PBMC and whole blood (Figure  S2A , upper panels), we noted higher and more variable MFI values of CD4, and lower expression of CD8β in PBMCs as compared to fresh whole blood samples (Figure  S2B , lower panels).
Staining protocol Fresh whole blood samples were washed to eliminate soluble antibodies and other molecules that may interfere with staining. The duration of antibody incubation and staining temperature was evaluated (data not shown). Since blocking of FcR did not have a significant impact on the results (data not shown), it was not included in the staining protocol. The staining was followed by red blood cell lysis. We tested three red blood cell lysis solutions and identified the BD solution to be the most efficient, with reagents from other suppliers being either less efficient (Miltenyi) or slower in achieving RBC lysis (eBioscience) (data not shown). Additionally, we tested different criteria that impact lysis conditions and the reproducibility of the results: duration of incubation in lysis solution, use of mixing and the implementation of a wash step.
Cytometer Initial testing of panels was performed using an LSRII cytometer (BD) equipped with 4 lasers (488 nm, 405 nm and 630 nm and 658 nm). The design of our study (four 8-color cytometry panels) required an instrument that enables automatic acquisition from 96-well plates, the acquisition of absolute cell counts and at least 3-lasers. We compared two cytometers that corresponded to these criteria and that were commercially available at the beginning of the study (initiated in 2012): the BD FACSVerse and Miltenyi MACSQuant. The cytometers were evaluated based on their hardware, sample acquisition performance, software robustness, quality control and post-installation support provided by the suppliers  (Supplementary Table  2 ). Although the hardware characteristics, quality control and the concept of the software were comparable between the two systems, MACSQuant showed significant advantages concerning the software robustness and performance.
Data analysis We selected the FlowJo software to analyze data. In order to improve standardization of analysis, we created analysis templates for each panel. A template consists of the gating strategy specific for the given panel, including a pre-defined table with parameters selected for statistical analysis. Magnetic gates were applied for the brightest and most clearly defined antigens to minimize bias introduced by manual repositioning of gates. Identical gate coordinates were selected to gate on the same cell populations across the four panels. The results obtained for each of the samples were verified by an operator prior to final validation. In order to minimize bias introduced by subjective analysis by different individuals, a given panel was analyzed by the same individual for all samples.
Shown are absolute cell numbers (median value) of selected cell populations per 1 μl of blood. For each immune cell population identified, the intra-panel coefficient of variation (CV) was calculated. In instances where cell populations could be identified by two different panels, both data are reported. The cell numbers of each indicated cell population obtained by PMN, T and DC panels were calculated upon normalization of the total cell number obtained by each panel to the total cell number as quantified by the lineage (Lin) panel. typical starting gates of FSC/SSC, and directly queried fluorescent markers, using FSC/SSC when needed to exclude doublets or dead cells (Figs. 2 3 4 5 ). The FlowJo templates created for and used in the study are provided (Online Supplementary Data File #2, http://www.
To provide a stable reference, we utilized commercially available stabilized blood, analyzed over a period of one month. These data showed reproducible results with CVs in the range of 2. 0-5. 3% (Fig. 8B  and C ). The percentage of each analyzed cell population was within the range of values indicated by the manufacturer (Fig. 8B ). The total cell numbers obtained, however, were under the expected value, and consequently so were the total numbers of each analyzed cell populations (Fig. 8C ). This difference may be explained by additional washing steps included in our protocol, which are not used to set the reference values indicated by the supplier of the stabilized blood. Another factor contributing to the difference may be the utilization of different cytometric platforms or the use of beads for cell enumeration, as compared to a volume based calibrator built into the cytometer itself.