dataesr/scientific-paragraphs-categorization huggingface dataset:
S5 , in the presence of H 2 O 2 ; other data not shown). Consequences of the P140-induced HSPA8 nuclear sequestration in heat-shocked cells. We have previously seen that in acute HS stress conditions, P140 peptide induces the sequestration of HSPA8 within the nucleus/nucleolus. Hence we wanted to determine whether this lack of return to normalcy might have any effect on cell behavior. First we examined the viability level of MRL/N-1 cells and their ability to grow. The percentage of propidium iodide positive (PI + ) cells measured during the post-HS recovery period remained low (<10%) and was not affected by the presence of P140 peptide (Fig. 2A, B ). This suggests that the integrity of plasma membrane of MRL/N-1 cells were treated and allowed to recover as in (A). Cells were collected at the indicated time points of the recovery phase and subjected to cell fractionation to isolate the nuclear and cytoplasmic fractions.
Quantification of fluorescence signals was done as described in Fig. S1B  in using Image J software (National Institutes of Health). Electron microscopy. The methods used have been described in details previously  15, 16. Freshly isolated PBMCs from 9-week-old MRL/lpr mice that received 100 µg biotinylated P140 or ScP140 peptides intravenously were collected 1 h post-injection, washed and fixed in 4% (v/v) paraformaldehyde and 0. 2% (v/v) glutaraldehyde in 0. 1 M phosphate-buffered saline (PBS) for 1 h at room temperature (RT). Cells were embedded in 1% (w/v) agarose, permeabilized with 0. 2% saponin for 10 min, washed and saturated with 2% (w/v) bovine serum albumin and 0. 2% (w/v) water fish skin gelatin (Sigma) in Tris-buffered saline. HSPA8 was revealed by using anti-HSPA8 rat monoclonal antibody (1:200; clone 1B5; Abcam) and goat anti-rat IgG conjugated to 15 nm-gold particles (Aurion); P140 and ScP140 were revealed with anti-biotin antibodies conjugated to 6 nm-gold particles.
1% (v/v) Triton X/PBS permeabilization buffer supplemented with 100 µg/ml RNase A (Sigma), at 37 °C for 15 min. 7-AAD (BD Pharmingen, 559925; 25 µg/mL) was added to stain the DNA, and samples were measured using a BD FACSCalibur flow cytometer (BD Biosciences) and analyzed using FlowJo software (Tree Star). qPCR. The mRNA of MRL/N-1 cells treated with P140 or ScP140 for indicated times and concentrations were extracted using RNeasy Mini kit (Qiagen, 75704) according to manufacturer's instructions. The first strand cDNA was synthetized from 200 ng of RNA using ImProm-II Reverse Transcriptase and oligo(dT) 15 (Promega, A3802 and C1101). Ten ng of cDNA were then amplified using Taqman Real-Time PCR Master Mixes (ThermoFisher Scientific, 10525395), followed by qPCR analysis of the cDNA amount using the StepOne TM real-time PCR system (ThermoFisher Scientific).
Structure depiction. The spacing filling and ribbon model of HSPA8 NBD (PDB: 3HSC) was depicted using PyMOL software. Statistical analyses. Statistical analyses were performed using GraphPad Prism version 5. 0. Depending on the number of samples that were included in the analyses, statistical significances were assessed using the parametric Student's t-test or the non-parametric Mann-Whitney's test when one parameter was included. Two-way ANOVA was used when two parameters were compared. Depending on the number of samples, non-parametric or parametric were used. P values less than 0. 05 were considered significant. In crosslinking experiments, the standard deviation (SD) values were calculated from the 9 ratios (3 independent experiments with three spots per experiment). The ratios were considered as significant if they were higher than a 3-fold change.
(C) Sequence, mass and MALDI-TOF signal intensity ratios of each peptide generated in the presence or absence of cross-linking. The SD values were calculated from the 9 ratios (3 independent experiments with three spots/experiment). The ratios were considered as significant (red) if they were higher than a 3-fold change. (D) Left: depiction of the space-filling model of HSPA8 NBD showing the ATP pocket (blue) and the BAG-1-interacting domain (yellow). Right: Ribbon illustration of HSPA8 NBD with the amino acid sequences of interest highlighted. The location of NLS (green) and NoLS (cyan) motifs are highlighted (PDB code access 3HSC). The peptides 88-101 and 272-298, whose detection by MALDI-TOF experiments is influenced by the presence of cross-linking, are shown in fuchsia pink in both panels.
Acknowledgements We gratefully acknowledge  Tetsuya Kodama  ( Tohoku University, Japan ) for a gift of MRL/N-1 cells;  Marion Décossas  for electron microscopy experiments;  Christophe Macri  and  Nadine Ben  hamouda for their earlier contribution in the production of recombinant proteins and qPCR assays, respectively;  Elise Naudin  ( laboratory of Protein Chemistry, Institut de Science et d'Ingénierie Supramoléculaires ) for help in using the PyMOL software. This research was funded by the  French Centre National de la Recherche Scientifique, Région Alsace , the  Laboratory of Excellence Medalis  ( ANR-10-LABX-0034 ),  Initiative of Excellence (IdEx) ,  Strasbourg University, and ImmuPharma France. FW was a recipient of a post-doctoral fellowship from the  University of Strasbourg Institute for Advanced Study (USIAS).
Patients and Samples Plasma samples collected in ethylenediaminetetraacetic acidcontaining tubes were obtained from hospitalized patients with EVD treated at the ETCs in Guéckédou and Coyah. Clinical EVD diagnoses were confirmed by the EMLab, using reverse transcription polymerase chain reaction (RT-PCR) as described elsewhere  [9]. Frozen plasma samples were stored on site at -20°C, shipped on dry ice to the Bernhard Nocht Institute for Tropical Medicine (BNITM; Hamburg, Germany), and stored at -80°C in the biosafety level 4 laboratory. Patient data were obtained from EMLab and World Health Organization (WHO) databases. To increase the power of statistical analysis, study patients were retrospectively selected from the database according to the following criteria: similar numbers of patients in the groups of survivors and patients who died and comparable distributions of age and sex in both groups.
Fura2 was sequentially excited at 340 nm and 380 nm, and the emission monitored at 510 nm. Images were acquired with a cascade 512 EMCCD camera every 2 s using the Metafluor software (Roper Scientific). For each recorded cell, the intracellular calcium concentration [Ca 2+ ] i was estimated by measuring the F 340/380 nm ratio of fluorescence normalized to the maximal probe fluorescence measured when cells were perfused with the 10 Calcium + ionomycin solution. ω-agatoxin-IVa and ω-conotoxin GVIa were purchased from Smartox, Nitrendipine from Sigma-Aldrich. Resting calcium levels (''baseline'') were measured as the average fluorescence from the first 40 s of each recording. For KCl stimulation, for each cell analyzed we report the results of the mean of two maximal F 340/380 in two consecutive stimulations.
Multivariate Analysis of the Cell Morphology Parameters Baseline and KCl data were extracted and normalized to the maximal calcium value obtained for each cell with the 10 mM Calcium + ionomycin solution and combined to cell morphology parameters extracted from the images. Both cell morphology, normalized baseline and KCl data were then used for unsupervised analysis. Data were first log10 transformed, then mean-centered and scaled. Then, dimension reduction was performed using Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (tSNE), with perplexity parameter set to 40. K-means clustering was performed on the two-dimension tSNE projection and the optimal number of clusters was determined using the Gap statistic. Significance of the differences between continuous variable distributions was assessed using either Mann-Whitney or Kruskal-Wallis rank sum tests as appropriate.
Statistics The Kolmogorov-Smirnov test was used to assess the normality of the distribution of the datasets. To compare non-normally distributed data, two non-parametric tests were used: the Mann-Whitney test was applied to data of two unpaired samples, while the Kruskal-Wallis test was used to examine the significance of four unpaired groups. Data are expressed as mean ± SEM, and the P values (or adjusted P values) < 0. 05 were considered statistically significant. RT-qPCR analysis of mRNA expression were analyzed using ANOVA TWO WAY with Sidak's multiple comparisons post hoc test. The statistical analysis was performed using Prism Software 6-2 version (GraphPad Software, Inc. ).
Cellular Analysis Our imaging data clearly show the heterogeneity of the neuronal types present in primary neuron cultures (Figures  1A-C ). Cells differ in size, shape, resting intracellular calcium levels and maximum calcium entry upon KCl stimulation. We wondered whether the absence of FMRP could have different impacts on calcium homeostasis in different cell types. The Fura2 fluorescence ratio and the shape analysis of the ROIs were simultaneously quantified by an ImageJ lab-made macro giving the shape descriptors for each ROI (area, roundness, solidity, circularity). Roundness reflects how circular a ROI is, while solidity and circularity indicate how soft (high scores) or rough (low scores) are the contours of the region. We then performed an unsupervised multivariate analysis (Supplementary Figures  S2A, B ) to group cells according to their size, shape and calcium homeostasis parameters (baseline levels, maximum calcium levels upon KCl stimulation) identifying four distinct and homogeneous groups of cells (Supplementary Figures  S2C-H ).
Therefore, we can only speculate that the increased presence of Cacna1a mRNA in light and medium fractions reflects an increased translation of this mRNA in Fmr1-KO mice; 4. The working model describing the putative role of FMRP in the regulation of N-and P/Q-type VGCCs at the plasma membrane (Figure  6 ) awaits a molecular mechanism and therefore is speculative. It nevertheless may be considered as a starting point for future analyses. (INSERM); Centre National de la Recherche Scientifique (CNRS); Agence Nationale de la Recherche: ANR-12-BSV4-0020, ANR-12-SVSE8-0022 and Fondation pour la Recherche Médicale (FRM) DEQ20140329490 to BB; Investments for the Future, through the LABEX SIGNALIFE program: #ANR-11-LABX-0028-013, Fondation Jérome Lejeune and ANR-15-CE16-0015 to BB and SM; FRM-ING20140129004 to BB and TM; FRAXA Foundation to TM.
Comparison study The comparison study was performed according to an internal protocol. For that purpose, samples (N = 100) were assayed using Cobas c513 and two routinely used HPLC analysers (Variant II equipped with a Dual Kit and D-100, Bio-Rad Laboratories, Hercules, USA). Haemoglobin A 1c values were compared by Passing Bablok regression and Bland-Altman analyses, using samples with HbA 1c values distributed across the linearity range  (10, 11). Samples with Hb variants or elevated values of carbamylated Hb (cHb) or labile HbA 1c were excluded from the comparison study. All comparative assays were performed within a 48 hour-period, and samples were kept at + 4 °C until analysis. The interpretation of data was based on the results of Passing Bablok regression analysis, which allows the determination of constant and proportional errors, and Bland-Altman analysis in which the proportion of outliers had to be lower than 5%.
Acknowledgments This study was supported by  Roche Diagnostics  which provided the Cobas c513 analyser as well as the corresponding reagents, quality control samples, and blood samples containing Hb variants, in the frame of a contract established with the  University Hospital of Reims  and the  University of Reims Champagne-Ardenne. The authors would like to thank  Dr Z. Djerada  for his help in statistical analyses.
The size of the box has been chosen such that the results are free from finite size effects. An example of calculation of the Green's function G(y, y ) ≡ G(y, z = 0; y , z = 0) is shown in Supplementary Fig. 3  for illustration. The Schrödinger equation is discretized using a simple finite difference scheme and solved using the Kwant package 4. Solving the sequence of Supplementary Eq. (  7 ), (  8 ) and (  9 ) for an input density ρ results to a new density ρ out. The self consistency is reached when ρ = ρ out (see Supplementary Figure  5  for an example of convergence of our iterative procedure). The self-consistent solutions are obtained using a Newton-Raphson scheme 5. We use the following parameters: effective mass m = 0. 067 m e , dielectric constant = 12 0 and a fixed dopant density of n d = 3.
ACKNOWLEDGMENTS We would like to dedicate this article to the late  Frank Hekking  who has been very implicated in the initial stage of the project. We would like to acknowledge fruitful discussions with  D. Carpentier ,  P. Degiovanni ,  M. Filippone  and  T. Martin. S. T. acknowledges financial support from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sk lodowska-Curie  grant agreement No. 654603. A. L. and A. D. W. acknowledge gratefully support of  DFG - TRR160 ,  BMBF -Q. com-H 16KIS0109 , and the  DFH/UFA   CDFA-05-06. C. B, T. M. and X. W. acknowledge financial support from the  French National Agency (ANR)   in the frame of its program ANR Fully Quantum Project  No. ANR-16-CE30-0015-02  and  QTERA  No. ANR-15-CE24-0007-02. C. B. and T. M. also acknowledge the  SingleEIX Project  No.
Introduction 'Open EFSA' aspires both to improve the overall quality of the available information and data used for its scientific outputs and to comply with normative and societal expectations of openness and transparency  (EFSA, 2009 (EFSA, , 2014a)). In line with this, the European Food Safety Authority (EFSA) is publishing three separate but closely related guidance documents to guide its expert Panels for use in their scientific assessments  (EFSA Scientific Committee, 2015). These documents address three key elements of the scientific assessment: the analyses of Uncertainty, Weight of Evidence and Biological Relevance. The first topic is the analysis of uncertainty. This current opinion provides the scientific principles, background and methods to guide how to identify, characterise, document and explain all types of uncertainty arising within an individual assessment for all areas of EFSA's remit.
The guidance document further indicates the types of qualitative and quantitative methods that can be used to weigh and integrate evidence and points to where details of the listed individual methods can be found. The weight of evidence approach carries elements of uncertainty analysis that part of uncertainty which is addressed by weight of evidence analysis does not need to be reanalysed in the overall uncertainty analysis, but may be added to. The third guidance document (EFSA Scientific Committee, 2017b) provides a general framework to addresses the question of biological relevance at various stages of the assessment: the collection, identification and appraisal of relevant data for the specific assessment question to be answered. It identifies generic issues related to biological relevance in the appraisal of pieces of evidence, in particular, and specific criteria to consider when deciding on whether or not an observed effect is biologically relevant, i.
e. adverse (or shows a positive health effect). A decision tree is developed to aid the collection, identification and appraisal of relevant data for the specific assessment question to be answered. The reliability of the various pieces of evidence used and how they should be integrated with other pieces of evidence is considered by the weight of evidence guidance document. EFSA will continue to strengthen links between the three distinct but related topics to ensure the transparency and consistency of its various scientific outputs while keeping them fit for purpose.
Uncertainties in how evidence is used and combined in assessmente. g. model uncertainty, or uncertainty in weighing different lines of evidence in a reasoned argumentare also important sources of uncertainty. General types of uncertainty that are common in EFSA assessments are outlined in Section 8. In the context of risk assessment, various formal definitions have been offered for the word 'uncertainty'. For chemical risk assessment,  IPCS (2004)  defined uncertainty as 'imperfect knowledge concerning the present or future state of an organism, system, or (sub) population under consideration'. Similarly, EFSA PLH Panel (2011) guidance on environmental risk assessment of plant pests defines uncertainty as 'inability to determine the true state of affairs of a system'. In EFSA's previous guidance on uncertainties in chemical exposure assessment, uncertainty was described as resulting from limitations in scientific knowledge  (EFSA, 2007 ) while EFSA's BIOHAZ Panel has defined uncertainty as 'the expression of lack of knowledge that can be reduced by additional data or information'  (EFSA BIOHAZ Panel, 2012).
The case study examples were developed using information contained in the EFSA (2008) statement and other information cited therein, including a previous US FDA assessment  (FDA, 2007). Where needed for the purpose of the examples, additional information was taken from EFSA (2012a,b, c) opinion on default values for risk assessment or from EFSA's databases on body weight and consumption, as similar information would have been available in other forms in 2008. The EFSA (2008) statement was produced in response to a request from the European Commission for urgent scientific advice on the risks to human health due to the possible presence of melamine in composite food products imported from China into the EU. The context for this request was that high levels of melamine in infant milk and other milk products had led to very severe health effects in Chinese children.
6 mg/kg. Milk chocolate frequently contains 15-25% whole milk solid. Higher amounts of milk powder would negatively influence the taste of the product and are unlikely in practice; therefore, the upper end of this range (25%) was used in the worst-case scenario of EFSA  (2008). Data on consumption of Chinese chocolate were not available. The high level consumption of chocolate used in the exposure estimates in the EFSA statement were based on the EU average annual per capita consumption of chocolate confectionary of 5. 2 kg (equivalent to an average EU daily per capita consumption of 0. 014 kg). The average daily consumption was extrapolated to an assumed 95th percentile of 0. 042 kg per day, based on information in the Concise European Food Consumption Database. In estimating melamine intake expressed on a body weight basis, a body weight of 20 kg was used for children.
Specific sources of uncertainty (and related types of uncertainty) Assessment/ subassessment Assessment inputs Exposure assessment Maximum concentration of melamine in milk powder Ambiguity (missing information) (1) Accuracy and precision of measurements (  2 ) Sampling (  3 ) Assumptions (  6 ) Extrapolation  (8)  Unknown accuracy of the method used to measure melamine  (1, 2)  491 batches from 109 companies (3) Used maximum measured value 2,563 mg/kg as proxy for the maximum actual value  (6, 8)  Extrapolation from infant formula to milk powder  (8)  Maximum concentration of milk powder in chocolate Assumptions (  6 ) Extrapolation  (8)  Assumed 25%, based on information about industry practice for chocolate produced in EU (  6 ) Extrapolation from EU chocolate to Chinese chocolate  (8)  Maximum daily consumption of Chinese chocolate Ambiguity (1) Accuracy and precision of measurements (  2 ) Sampling (  3 ) Assumptions (  6 ) Extrapolation  (8)  Estimates based on data for chocolate confectionery  (2, 3, 6)  Accuracy of per capita consumption data unknown  (1, 2, 6)  Representativeness of consumption data unknown (  8 ) Used an estimate of 95th percentile daily consumption as proxy for maximum actual value (  8 ) Extrapolation from daily average to 95th percentile based on a different database  (6, 8)  Extrapolation from chocolate overall to Chinese chocolate  (8)  Body weight Assumptions (  6 ) Default value of 20 kg for children  (6)  Table A.
Descriptive expression of uncertainty Descriptive methods characterise uncertainty using only verbal expressions, without any defined ordinal scale, and without any quantitative definitions of the words that are used. Short summary of contribution to uncertainty analysis: 'Exposure of children could potentially exceed the TDI by more than threefold, but it is currently unknown whether such high level scenarios occur in Europe' (Contribution to output of uncertainty analysis). This is an abbreviated version of part of the conclusion of the EFSA (2008) statement: 'Children who consume both such biscuits and chocolate could potentially exceed the TDI by more than threefold. However, EFSA noted that it is presently unknown whether such high level exposure scenarios may occur in Europe'. The  EFSA (2008)  statement also includes descriptive expression of some individual sources of uncertainty that contribute to the uncertainty of the assessment conclusion: 'There is uncertainty with respect to the time scale for the development of kidney damage' and 'In the absence of actual data for milk powder, EFSA used the highest value of melamine'.
Melamine example Descriptive narrative is the main method that was used to express uncertainties in the EFSA (  2008 ) statement on melamine. The summary of the statement includes the following phrases, in which the words indicating the presence of uncertainty have been italicised: 'There is uncertainty with respect to the time scale for the development of kidney damage'. 'In the absence of actual data for milk powder, EFSA used the highest value of melamine. ' 'Children who consume both such biscuits and chocolate could potentially exceed the TDI by more than threefold. However, EFSA noted that it is presently unknown whether such high level exposure scenarios may occur in Europe'. Many further examples can be identified within the detailed text of the EFSA (2008) statement. Strengths 1) Intuitive, requires no special skills (for assessors proficient in the language used for the assessment).
g. Mastrandrea et al. , 2010). Applicability in areas relevant for EFSA Some EFSA Panels have used ordinal scales that are described as scales for uncertainty, but defined in terms of evidence (e. g. type, amount, quality, consistency) and the level of agreement between experts. In a joint opinion in 2010, the Animal Health and Animal Welfare Panel (AHAW) and the BIOHAZ Panel defined three levels of uncertainty associated with the assessment of the effectiveness of different disease control options of Coxiella burnetii, the causative agent of Q-fever (EFSA, 2010a,b). • 'Low: Solid and complete data available; strong evidence in multiple references with most authors coming to the same conclusions, or considerable and consistent experience from field observations'. • 'Medium: Some or only incomplete data available; evidence provided in small number of references; authors' or experts' conclusions vary, or limited evidence from field observations, or solid and complete data available from other species which can be extrapolated to the species being considered'.
• High uncertainty (H): Scarce or no data available; evidence provided in unpublished (unverified) reports, or few observations and personal communications, and/or authors'/or experts' conclusions vary considerably. The group members were asked to use the above scale to assess three selected sources of uncertainty (content of melamine in milk powder, Chinese chocolate consumption of European children and appropriate health guidance value for melamine), by expert judgement, and also to assess the combined impact of these three sources of uncertainty on the uncertainty of the assessment conclusion. The evaluation was conducted in two rounds, with the scores from the first round being collated on-screen and discussed before the second round. This allowed assessors to adjust their scores in the light of the discussion, if they wished.