dataesr/scientific-paragraphs-categorization huggingface dataset:
0% to 18. 9% (Table  1 ), significantly improving the correlation. Integrating the fraction of other immune cell types further decreased the residual unexplained variability but not significantly so (Table  1 ). This suggested that immune cells other than macrophages do not significantly influence the partitioning of MXF. The absolute distance between ROIs and lesion periphery did not correlate with MXF signal intensity, which is not surprising given the highly variable thickness of the cellular rim (Figure  3B  and ). The validation dataset was then used to confirm the predictive value of the model equation. We found that the model could predict ROIspecific MXF abundance using histiocyte fraction, relative distance from lesion margin and necrosis content (Figure  4B  and Figure  3 -figure  supplement 4D, E ).
MALDI-MSI analysis MALDI-MSI acquisition was performed MALDI LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) with a resolution of 60,000 at m/z 400, full width at half maximum. Imaging data was acquired in full scan mode to maximize sensitivity and drug peak identities were confirmed by acquiring several MS/MS spectra directly from the dosed tissues. Instrument parameters were tuned and optimized using spiked fluoroquinolone drug standards on stainless steel plates and control mouse lung tissue. Limits of detection (LOD) were assessed as described previously  (Prideaux et al. , 2015a). The lower limits of detection were 100 fmol or 750 ng/g for LVX, GTX and MXF. For LVX, GTX and MXF analysis, spectra were acquired in positive mode across the mass range m/z 300-500.
Relative quantitation of fluoroquinolones within granuloma compartments Relative quantitation of MXF, LVX and GFX within caseum and cellular granuloma areas was performed using ImaBiotech Software Quantinetix (v 1. 7, Loos, France), following the workflow shown in Figure  1 -figure supplement 1. Areas of interest for each tissue type (lung, cellular rim, caseum) were delineated by first aligning and superimposing the MS image over the optical scan of the tissue (acquired prior to MALDI matrix deposition). The MS image layer was made transparent and the areas were drawn based upon the optical scan (Figure  1 -figure supplement 1D) and by referral to an adjacent H and E-stained tissue section (Figure  1 -figure supplement 1C) as a guide rather than the MALDI ion map to avoid bias in region selection.
High-resolution relative quantitation of MXF ions in 35 regions of interest (ROI) Thermo ImageQuest software (v1. 01) was used to export MXF pixel intensity datasets into '. imzML' format. The dataset was loaded in SCiLS Lab MVS (version 2018a Core, Bruker) and normalized to MXF-d4 [M + K] + signal (m/z 444. 1638 ± 0. 003). A map of normalized MXF [M + K] + signal (m/z 440. 1380 ± 0. 002) was generated and 35 regions of interest (ROIs) were drawn with 'polygonal region' tool, with the objective of covering the full range of relative signal intensity (Figure  3 -figure  supplement 2A ). The normalized MXF signal intensities of every pixel within each ROI were exported from SCiLS. Signal intensities of these different pixels are represented in Figure  3C. SCiLS' MXF image with area of interest was saved and loaded into Photoshop and aligned with H and E as described below, using the ion map of m/z 362.
Calculation of intracellular drug concentration All intracellular drug uptake data are expressed as a ratio of intracellular to extracellular drug concentration (IC/EC). The starting concentration of drug added to the macrophages was used as extracellular concentration. The intracellular concentration was calculated using the drug concentration of the cell lysate determined by LC-MS/MS analysis (described below) and adjusting for the number of cells and cell volume of each particular cell type. Macrophage and foamy macrophage volume was estimated at 755. 5 mm 3 per cell as measured in a previous study  (Chen et al. , 2018) ; neutrophils volume was estimated at 363 mm 3 per cell  (Niemiec et al. , 2015) ; lymphocyte volume was estimated at 173 mm 3 per cell  (Chapman et al. , 1981)  and epithelial cell volume was estimated at 1670 mm 3  (Jiang et al.
5 mm) using a reverse phase gradient elution. All gradients used 0. 1% formic acid in Milli-Q deionized water for the aqueous mobile phase and 0. 1% formic acid in acetonitrile for the organic mobile phase. Multiple-reaction monitoring of parent/daughter transitions in electrospray positive-ionization mode was used to quantify the analytes. The compounds were ionized using ESI positive mode ionization and monitored using masses MXF (402. 2/358. 1), LVX (362/318. 5), GFX (376/261. 2), and Diclofenac (296/215). Sample analysis was accepted if the concentrations of the standard and quality control samples were within 20% of the nominal concentration. Data processing was performed using Analyst software (version 1. 6. 2; Applied Biosystems Sciex). NZW control plasma treated with K 2 EDTA was obtained from Bioreclammation and used to build standard curves.
Nonlinear Mixed Effect Modeling (nonMEM) correlation analysis The entire dataset included 5053 observations (pixel intensities) from 35 ROIs. Each ROI contained between 48 and 429 pixels. Data points below the limit of quantitation (BLOQ) were set to 'zero'. Non-linear mixed effect modeling was applied, where two levels of random effects were implemented: a first level describing within-ROI variability and a second level of mixed effects describing between-ROI variability. All variability terms were assumed to be log normally distributed, where the median and variance terms were estimated. First, a constant baseline model was developed (the 'base' model) where the typical value of drug abundance is the median value of pixel intensity across all ROIs, and the between-ROI variability term represents the deviation of each individual ROI compared to the typical value.
Internal validation of the covariate model To validate the approach, we also randomly split the dataset into a training and validation set. The data points were categorized into low, medium or high MXF abundance, to ensure that low, medium and high ranges of abundance were well represented in both sets. Within each category, data points were randomly split (2/3 and 1/3) and assigned to either the training or validation set. The training data set included 3277 measurements from 24 ROIs and the validation data set included 1776 measurements from 11 ROIs. The model was internally validated by predicting MXF abundance (both median value and within ROI variability) using ROI-specific measurements of histiocytes, distance.
Additional information Funding Funder Grant reference number Author  National Institutes of Health   U01-HL131072  Veronique  Anne Dartois National Institutes of Health   R01-AI111967   Veronique Anne Dartois Bill  and  Melinda Gates Foundation   OPP1174780   Veronique Anne Dartois The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Patient population From November 2014 to November 2016, 109 consecutive, stable, symptomatic patients with positive exercise stress test (downsloping ST segment depression <1 mm) or perfusion imaging (reversible perfusion defect in at least 2 out of 17 myocardial segments) were referred for diagnostic invasive coronary angiography (ICA) and screened for possible eligibility in the WATERDAY trial. Information regarding patient age, gender, weight, medical history, risk factors (family history of CAD, hypertension, diabetes, smoking or hypercholesterolemia), medication, blood pressure, heart rate and symptoms were recorded. Among these patients, 45 were enrolled in the WATERDAY study (clinicaltrials. gov unique identifier NCT02278497) and underwent FFR measurement. Inclusion criteria were: patients between 18 and 80 years old, with angiography-proven CAD, affiliated to a social security system, who have been informed of the study and gave informed consent.
PET image analysis After Fourier rebinning, emission sinograms were corrected for random coincidences and dead time and then reconstructed with the PET manufacturer's attenuation and scatter correction using filtered backprojection (Hanning filter, cut-off 8 mm). MBF was quantified using the Carimas 2. 4 software (Turku PET Centre, Turku, Finland)  [7]. After reorientation of the data to the standard short axis, the software automatically searches for ventricle borders and locates initial regions of interest (myocardium, left and right ventricles), which can be modified manually. Based on this segmentation, stress and rest MBF (mL/min/g) were calculated using a single compartment model  [8]  and computed for the whole left ventricle (LV) and for each coronary territory (LAD, LCx, RCA).
Three MBq/kg was then injected at a rate of 1-2 cm 3 /s using an automatic injector (Nemoto, Tokyo, Japan) and flushed by 30 mL of saline to ensure consistent delivery of a tight bolus. Regadenoson (400 μg) was then administered, followed by the injection of 9 MBq/kg of 99m Tc-sestamibi at peak hyperemia. Rest-stress dynamic acquisitions were completed within 75 min. Data were rebinned into 32 frames consisting of 21 × 3-sec, 1 × 9-sec, 1 × 15-sec, 1 × 21-sec, 1 × 27-sec and 7 × 30-sec frames. An ordered subset expectation maximization (OSEM) algorithm was used for image reconstruction with 4 iterations and 32 subsets. The procedure for dynamic CZT-SPECT imaging is summarized in Fig. 2.
Dynamic SPECT image analysis Dynamic imaging data and corresponding perfusion information were analyzed using commercially available Corridor 4DM software (v2015, INVIA, Ann Arbor, MI, USA). LV endocardial and epicardial surfaces were algorithmically estimated from summed myocardial images beyond the 2-min mark. A midwall surface was divided into 460 polarmap sectors, where LV myocardial tissue time activity curves (TACs) were nearest-neighbor-sampled at the center of each sector across all time frames. Global and regional TACs (LAD, LCx, RCA) were averaged from the polarmap sector TACs. The MFR analysis used blood sampling by averaging a 3D box-shaped ROI within the LV/left atrium (LA) blood pool, localized in the LA in the short axis and centered at the basal valve plane along the long axis, across all time frames.
Comparison of dynamic CZT SPECT vs. PET. The inter-observer (DA, CN) reproducibility of MBF and MFR measurements was excellent (r = 0. 92). The software (Corridor 4DM) offered the possibility to manually correct the positioning of the VOI in case of failure of the automatic processing and we applied it in two cases essentially by drawing VOIs in the LA. Table  3  shows all myocardial blood flow and reserve quantification for the whole LV and for vessel territories assessed by CZT-SPECT and PET in 30 patients. As expected, pharmacological stress significantly increased MBF in both modalities. CZT-SPECT yielded higher stress and rest MBF compared to PET for global, and LAD and LCx, territories but not in RCA territory. Nevertheless, MFR was similar in global, and each vessel territory, for both modalities.
1%. However, MFR yielded much better diagnostic value than stress MBF (AUC: 0. 96 vs. 0. 77). SPECT is less accurate than PET for absolute quantification (MBF), but this limitation may be overcome by using the ratio of values from acquisitions performed in a very similar setting (MFR). Previous studies exploring quantification of MBF with planar scintigraphy in humans  [26] [27] [28] [29] [30]  and with dynamic    [33, 34]. Using CZT-SPECT in a pig model of rest and transient occlusion at stress, Wells et al. [34]  analyzed the data using a one-tissue compartment model with attenuation and scatter correction and reported a good correlation to microsphere flow values. Ben-Haim et al. [1]  have assessed the technique in 95 patients with stable CAD. Factor analysis was used to generate right and left ventricular blood pool TAC which served as an input function in a two-compartment model, where MFR was calculated from K1 ( 99m Tc -sestamibi uptake) values at stress over rest and K2 ( 99m Tc-sestamibi washout) was set to zero.
Study participants This post-hoc analysis included data from individuals with T2DM from LONG TERM (NCT01507831)  [14]. T2DM was diagnosed based on medical history. LONG TERM recruited individuals with hypercholesterolaemia who were on MTS therapy plus or minus other lipid-lowering therapies (LLTs) but who had LDL-C levels above pre-specified goals. MTS therapy was defined as atorvastatin 40e80 mg, rosuvastatin, 20e40 mg, or simvastatin 80 mg daily (or lower doses with an investigator-approved reason for using a lower dose, e. g. , intolerance). LONG TERM recruited individuals with heterozygous familial hypercholesterolemia (HeFH) or non-familial hypercholesterolemia at high cardiovascular risk. Exclusion criteria included fasting TGs >400 mg/dL (4. 5 mmol/L). Randomization was 2:1 to alirocumab 150 mg Q2W or placebo, administered subcutaneously, for a double-blind period of up to 78 weeks.
Efficacy and safety analysis Efficacy and safety data were compared between individuals with T2DM (defined based on medical history) with and without MDL. MDL was defined in this analysis as TGs !150 mg/dL (1. 7 mmol/L) and LDL-C !70 mg/dL (1. 8 mmol/L) at baseline. Efficacy endpoints included the percentage change from baseline to Week 24 in calculated LDL-C and other lipids, and changes in LDL-C over time up to 18 months. Lipid levels were determined by a central laboratory using standardized methods. In the primary study, LDL-C levels were calculated using the Friedewald equation  [16]  at all time points. LDL-C was also determined via betaquantification at baseline and at Week 24, and was determined by beta-quantification (rather than calculation) if TG levels were >400 mg/dL (4. 5 mmol/L); however, LDL-C values derived by betaquantification were not included in the analysis of calculated LDL-C.
Statistical analysis For statistical analyses, the percentage changes from baseline in LDL-C, non-HDL-C, apo B, and HDL-C were analysed using a mixedeffect model with repeated measures as previously described  [14, 20]. TGs and Lp(a) were analysed by multiple imputation to handle missing data, followed by robust regression. The intentionto-treat (ITT) population was used for efficacy analyses, which included all data irrespective of adherence to study treatment. Achievement of lipid goals was analysed by multiple imputation to account for missing data, followed by logistic regression using ontreatment analysis and was assessed in the modified ITT population (including only on-treatment lipid data). Interaction p-values (comparing the difference in percentage change from baseline with alirocumab vs placebo, in individuals with and without MDL) were derived using the same models as for the primary analyses, and are provided for descriptive purposes only.
Acknowledgments The authors thank the patients, their families, and all investigators involved in this study. The following people from the study sponsors provided editorial comments on the manuscript:  Michael Howard ,  MBA ,  Corinne Hanotin, MD , and  Catherine Domenger, MD  ( Sanofi ), and  Robert Pordy, MD ,  Carol Hudson, MS,  and  Rita Samuel, MD, MSc  ( Regeneron Pharmaceuticals, Inc. ). Medical writing support under the direction of the authors was provided by  Nila Bhana , MSc, of  Prime (Knutsford, UK ), supported by  Sanofi  and  Regeneron Pharmaceuticals, Inc. , according to Good Publication Practice guidelines (Link). The sponsors were involved in the study design, collection, analysis, and interpretation of data, as well as data checking of information provided in the manuscript.
Molecular Docking. The molecular docking study of isolated phytochemicals, compounds 1 and 2, from H. radicata (shown in Figure  1 ), were carried out to recognize the molecular interaction of the phytochemicals (1 and 2) within the active site of cyclooxygenases COX-1 (PDB ID 4O1Z) and COX-2 (PDB ID 5F19) by captivating X-rays crystal structure data of enzymes from protein data bank (PDB)  [7]. Molecular docking analysis essentially involves the selection and grounding of suitable protein and ligand, following by the screening of docking output and their interaction. Both ligands and the receptors were prepared distinctly. The protein structure of both COX-1 and COX-2 was independently preprocessed using structure preparation wizard  [7] , and omitted hydrogen was also added. The hydrogen bonds inside the proteins were also optimized and monitored by the minimization of protein with default setup of MOE (Molecular Operating Environment).
Statistical Analysis. Results are expressed as mean ± SEM. One-way ANOVA (analysis of variance) was used for a comparison test of significant differences among groups, followed by Dunnett's multiple comparison posttest. A level of significance ( < 0. 05 or 0. 01) was considered for each test. The results of activities were also presented as mean ± SEM, and their statistical analysis was carried out using the GraphPad program (GraphPad, San Diego, CA, USA).
For the present case of cross-linked polymer networks, this will require a detailed understanding of non-equilibrium interfacial effects that find their origin at a scale smaller than the distance between cross-linkers. The observed time-dependent surface tension suggests, as for thixotropy, the possibility that the surface tension actually depends on a microstate variable that remains yet to be identified. Future work on surface effect in reticulated polymer network will have to focus on the micro-physics of the problem. We acknowledge discussions with J. Eggers and A. Pandey. This work was financially supported by the ANR grant Smart and ERC (the European Research Council) Consolidator Grant No. 616918. which the surface curvature could change due to bulk rheological effects is typically more than a factor 10 larger than the optical resolution limit.
Leterme et al. (2014)  explained that in spite of being an important tool, geochemical modelling suffers from the lack of well characterized sites. Actually, the counterpart of being able to reproduce detailed reactive mechanisms is that those models may require an important input dataset, including site-specific parameters. Even if determined, the values may suffer from variability and heterogeneities. As an alternative, we propose building a model to reproduce the results of a less complex column experiment. The model itself would include all the complexity governing MeHg fate, including methylation/demethylation reactions, the formation of aqueous complexes and gaseous species and surface complexation reactions. Johannesson and Neumann (2013)  conducted a comprehensive set of measurements in groundwater along a 13 km flowpath located within a confined aquifer in southeastern Texas, USA.
Their biogeochemical model was able to highlight the main mechanisms (mineral dissolution and sorption onto oxide surfaces) responsible for the speciation of total Hg. The aim of this study is to test geochemical modelling in a more controlled context like the column experiment performed by  Hellal et al. (2015). The geochemical calculations on Hg fate can then be somehow constrained or compared with respect to those experimental results. In addition, taking advantage of the analyses performed by  Hellal et al. (2015) , the calculations are especially focused on methylmercury fate (methylation/demethylation and transportation), allowing testing of the methylation/demethylation rates available to date in the literature. Before comparing the calculations with the experimental results, we include a critical selection for MeHg complexes, consistent with the database built up by  Leterme et al.
(2003) ,  Loux (2007)  and  Skyllberg (2012) , for various chemical systems, at 25°C. Loux (2007)  are especially important, resulting from an extrapolation to infinite dilution of large experiment datasets. Alderighi et al. (2003)  have measured, by calorimetry, the heat exchanged during various complexation reactions involving methylmercury. From these measurements, we were able to calculate the entropy of complexes, which are reported in Table  1. Actually, they provided Sr for reactions involving CH3Hg + as primary specie. In order to use these measurements, we have considered the reaction: H Hg CH CH Hg 3 aq 4,         (2) It was converted into an isocoulombic equilibrium using Ca++ and Ca(OH)+ species: O H Ca Hg CH CaOH CH Hg 2 3 aq 4,             (3) The third law entropy was calculated considering the one term approximation method from  Gu et al.
(1994)  and entropies from the Thermoddem database  (Blanc et al. , 2012). The result allows obtaining the third law entropies from  Alderighi et al. (2003)  measurements. The whole results are reported in Table  1. In addition to the aqueous complexes we have included a gas phase, Hg(CH3)2,g which represents an extreme stage of the methylation process  (Thomassin and Touze, 2003).
(2) CH3HgSH CH3Hg + + HS -= CH3HgSH 14. 50 (2) (CH3)2Hg 2CH3Hg + = (CH3)2Hg + Hg ++ 13. 00 (1) CH3HgCO3 - CH3Hg + + HCO3 -= CH3HgCO3 -+ H + -4. 23 (2) CH3HgHCO3 CH3Hg + + HCO3 -= CH3HgHCO3 2. 60 (2) CH3HgSO4 - CH3Hg + + SO4 --= CH3HgSO4 - 2. 64 (2) (CH3Hg)2OH + 2CH3Hg + + H2O = (CH3Hg)2OH + + H + -2. 15 167. 62 (2), (3) (CH3Hg)2S a CH3Hg + + CH3HgS -= (CH3Hg)2S 20. 32 (2) Hg(CH3)2,g Hg ++ + 2CH4,aq = Hg(CH3)2,g + 2H + 8. 82 306. 00 (4) (1) Erni (  1977  For the selection, we followed the approach promoted by  Stumm and Morgan (1996)  where the methyl group is represented by the CH3 -anion. This formally identifies the methylated complexes and easily separates the inorganic and organic chemical system when required. The selection was integrated in the Thermoddem database  (Blanc et al. , 2012)  in order to use it with geochemical codes GWB  (Bethke, 2004)  and PhreeqC  (Parkhurst and Appelo, 1999).
A first test of the database consisted in calculating the production of methyl complexes which would arise from the speciation model detailed in Table  1. After  Leterme and Jacques (2012) , the amount of MeHg in waters in contact with soil systems is usually close to 2%. A speciation calculation is conducted, using PhreeqC-2 with the above database and considering a solution with [NaCl] = with 10 -3 M and [Hg] = 10 -9 M. This amount was reached applying two different corrections: -by modifying reaction (2) equilibrium constant from log10K = 3 to 2. 5 when reactions from Table  1  a written with CH4,aq as primary specie -or by modifying CH4,aq = CH3 -+ H + equilibrium constant from log10K = -46 to -52. 5 when reactions from Table  1  a written with CH3 -as primary specie. Actually, we did not expect the relation between organic and inorganic carbon to be driven by thermodynamic equilibrium.
The correction considered in the first test is still of small extent, especially because the value originally given by Erni (1977) was calculated and not measured and the uncertainty can be larger in that case. In addition, the fact that the results obtained for MeHg speciation may depend on the way the database is written is to be underline. For the rest of the calculations, we no longer consider the equilibrium between organic and inorganic reduced carbon. However it is important to note that when the database uses CH4,aq or CH3 -complexes as primary specie for the methylation reactions, this allows considering also the methylation for other metals like Sn or Pb  (Stumm and Morgan, 1996) , jointly with Hg methylation. The database in Table  1  was also tested using activity diagrams of which examples are displayed in Figure 1  The database set up previously is now tested by modeling the MeHg rate obtained experimentally by  Hellal et al.