dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Continuous variables were reported as means, medians, and interquartile ranges (IQR). To assess the differences of categorical variables between groups, the Pearson's chi-squared or Fisher's exact test were used, and for continuous variables, the Wilcoxon rank-sum test was used. Due to inherent imbalances in baseline patients and disease characteristics, further statistical analyses were performed in a stepwise fashion. First, to account for missing baseline data, which were assumed to be missing at random, multiple imputations using chained equations were performed. Fifteen imputed datasets were generated using predictive mean matching for numeric variables, logistic regression for binary variables, and Bayesian polytomous regression for factor variables  [31]. Second, a 1:1 propensityscore-matched (PSM) analysis was performed to reduce bias and adjust for the effects of the covariate imbalances.
A similar result was observed when GFP expression was studied by fluorescence microscopy. The imaging of multiple slides revealed that at 24 h. p. i. , ≈70% of HeLa cells were infected and expressed GFP (Figure  4f, g ). Exposing PSV particles to increasing concentrations of rGO-Ag reduced the fraction of eGFP-expressing cells to less than 20% (for 200 µg mL -1 rGO-Ag), indicating a significant loss of viral infectivity. In THP-1 cells infected with PSV/gfp for 30 h, ≈30% of the cells expressed GFP in the control samples, whereas less than 10% of cells were infected (Figure  4h, i ) when the PSV particles were exposed to 100 µg mL -1 of rGO-Ag. Altogether, our data showed that PSV incubation with rGO-Ag resulted in the potent inhibition of viral infectivity.
PSV particles were treated with 100 µg mL -1 of rGO-Ag or left untreated. The samples were centrifuged, and HeLa cells were infected with the collected supernatant for 2 h to allow the viral particles to enter the cells. The samples were fixed and imaged by confocal microscopy. PSVs expressing INsuperfolder green fluorescent protein (sfGFP) were detected as fluorescent spots in the cytoplasm of the infected cells. The number of detected viral particles was counted in the middle plane of the cells to avoid counting the PSVs at the cell surface. As shown in Figure  6d ,e, HeLa cells infected with nontreated PSVs contained on average 3. 8 cytoplasmic spots, whereas only 1. 25 viral particles were detected when the PSVs were preincubated with rGO-Ag (Figure  6e ). These data suggest that rGO-Ag effectively reduces viral entry into host cells.
Flow Cytometry Analysis: To quantify the GFP-expressing cell fraction, THP-1 monocytes were seeded in a 12-well plate at 2. 5 × 10 4 cells per well. An equivalent of 9 ng of p24 of PSV/gfp was incubated with rGO-Ag (100 µg mL -1 ), and the supernatants were prepared as discussed above, followed by cell infection. At 24 h post-infection, the cells were collected, fixed using 4% PFA for 20 min, and washed with cold PBS. Data acquisition was performed using a FACSCanto Flow cytometer (BD Biosciences) and analyzed using FlowJo software (version 10. 4). The median fluorescent GFP intensity was used to compare the different conditions in three independent experiments. SEM Microscopy: For SEM observation, the HIV-1 PSV particles were incubated in the presence of rGO-Ag (200 µg mL -1 ) for 1. 5 h.
The extracted RNA sample concentrations were determined using an ND-1000 NanoDrop spectrophotometer (Labtech, Palaiseau, France). 1 µg of extracted RNA was used for cDNA synthesis using the iScript cDNA synthesis kit (BioRad) according to the manufacturer's instructions. The primers for IL-6, IL-8, TNFα, and IL-1β were obtained from Eurofins Genomics, and the primer for IFN-α was obtained from Qiagen. The sequences are summarized in Table  S1 , Supporting Information. 50 ng of reverse-transcribed RNA were analyzed with Fast SyberGreen (Applied Biosystems) in triplicate using the StepOne plus realtime PCR system. qRT-PCR data were analyzed using StepOne Plus software. Target cDNA expression was quantified using the comparative ΔΔCt (delta-delta CT) method with 18S rRNA as an internal control (housekeeping gene).
The obtained data are represented as the mean ± standard error of the mean (S E ). Statistical Analysis: Statistical analysis of microscopy imaging data was performed by using Origin 2015 software. For Figure  6  j,i, the box plot represents 25-75% of the data, the mean value is highlighted with a bold line, and the whisker represents the standard deviation (SD). The data were tested by a two-sided, Student's t-test. The sample size for each experiment is specified in the figure legend. Statistical analysis of RT-qPCR data was carried out using GraphPad software. Two-sided Student's t-test was performed on all datasets. In all cases, the difference was considered significant for p values ≤0. 05 (*), p ≤ 0. 01(**), and p ≤ 0. 001 (***).
Acknowledgements The Vpr-IN-sfGFP plasmid was developed by the group of  Prof. A. Cereseto  at the  Centre for Integrative Biology (CIBIO)  at the  University of Trento (Trento, Italy ) and was generously provided by  Dr. A. Francis  and  Prof. G. Melikyan  ( Division of Infectious Disease, Department of Pediatrics, Emory University School of Medicine, Children's Healthcare of Atlanta ). [42,67]The authors acknowledge  Dr. Eleonore Real  for optimizing the protocol for HIV-1 pseudovirus production. This work was supported by  Fond Régional de Coopération pour la Recherche Région Grand Est  ( EpiARN Project ) and by  Institut Universitaire de France (IUF). Y. M. is grateful for their support and provision of additional time to be dedicated to research. CIISB,  Instruct-CZ Centre of Instruct-ERIC EU consortium , funded by  MEYS CR  infrastructure project  LM2018127  and  European Regional Development Fund-Project  " UP CIISB " (No.
CZ. 02. 1. 01/0. 0/0. 0/18_046/0015974 ), is gratefully acknowledged for the financial support for the measurements performed at the  CF Nanobiotechnology. The authors acknowledge the assistance provided by the  Research Infrastructure NanoEnviCz , supported by the  Ministry of Education, Youth and Sports of the Czech Republic  under Project No. LM2018124  and  CzechNanoLab Research Infrastructure  supported by  MEYS CR  ( LM2018110 ). This work was supported by  ERFD  " Multidisciplinary research to increase application potential of nanomaterials in agricultural practice " (No. CZ. 02. 1. 01/0. 0/0. 0/16_025/0007314 ). S. M. was supported by  Erasmus + Internship.
10  MeV n G4,He =. In simulated acquisitions, the energy deposited in the five-stage detector is first converted to ADC including their spatial variation (see section 6. 2 of  Dedes et al (2020) ). We differentiate between two types of datasets-an ideal one which refers to the data obtained by the virtual tracking detector, and a realistic one that refers to the data measured using the silicon strip trackers and the five stage energy detector. Using the event ID of each simulated particle, which is identical for both datasets, the ideal and the realistic data were further merged to a third semi-realistic dataset which consists of ideal energy values but realistic tracking positions and directions. This dataset was used to investigate the impact of measurement errors of the tracking detector on spatial resolution.
Spatial resolution Figure  2  compares the spatial resolution of protons and helium ions obtained from the reconstruction of the ideal, semi-realistic and realistic datasets of the resolution phantom. The Hann filter was not applied to the reconstruction and a pixel size of 0. 1 mm was used (and additionally 0. 25 mm to study pixel size effects on the ideal data). In all cases, the resolution was lowest at the phantom center and highest at the edge of the phantom. Since MLP errors vanish at the phantom's surface, at the largest radius the ideal results for both ions converged within uncertainty to a limit governed by the pixel size. This can be appreciated when comparing ideal results with 0. 1 and 0. 25 mm pixel sizes. For the 0. 1 mm pixel size, spatial resolution at the center was found to be 0.
59 lp mm -1 for protons and 1. 21 lp mm -1 for helium in the ideal case, and 0. 48 and 0. 86 lp mm -1 in the realistic case. The resolution ratio of helium ions to protons in the ideal 0. 1 mm pixel size case was found to be   2. 0 at the center of the resolution phantom, and approaches unity towards the edge of the phantom. The ratios of the realistic dataset were 1. 8 at the center and 1. 4 at the edge. The semirealistic dataset exhibited good agreement with the resolution of the realistic dataset, confirming the expectation that the tracking detectors govern the resolution performance of the scanner  (Krah et al 2018 , Khellaf et al 2020). The visual difference between the reconstructions of the ideal and realistic datasets of the resolution phantom is shown in figure  3  for both (a) protons and (b) helium ions.
Variance versus spatial resolution The relation between water phantom central ROI variance at a fixed dose (set to 5 mGy) and spatial resolution is shown in figure  5  where we generated a set of reconstructions from the realistic dataset of the water and the resolution phantom, each at a different W Hann. We can appreciate that without Hann windowing, helium has both higher spatial resolution and variance than protons. At equal resolution, and above approximately 0. 6 lp mm -1 , helium yields better variance than protons. The ratio of spatial resolution of helium ions to protons at the equal-area insert was 1. 5 when no Hann filter was applied and 1. 4 for W Hann = 1. For W Hann = 0. 2, the cutoff frequency dominates spatial resolution, leading to the same resolution for both ions. Without Hann windowing, the variance of helium was 2.
The increase of the dose ratio is below 2%. Table  1  shows the dose ratio at equal variance within the ROIs for ideal and realistic datasets. The ratio is defined as the helium dose divided by the proton dose required to reach the same variance in the ROI in a given phantom. The ideal dose ratio of helium ions to protons was found to be 1. 3 for the water phantom and the center of the brain of the head phantom, and 2. 1 for the head phantomʼs nasal cavity. For realistic data, the dose ratio of helium ions to protons was 3. 7 (water phantom), 3. 8 (head nasal cavity) and 3. 1 (head center of brain) without the ΔE-E filter. Applying the ΔE-E filter reduces the dose ratios to 2. 9, 2. 9 and 2. 8 respectively. Table  1  additionally reports the pCT and HeCT doses required to achieve the reference variance for each phantom in realistic simulations.
RSP accuracy. The RSP accuracy is shown for protons and helium ions in figure 9. Both particles slightly underestimate the actual RSP value within a relative error range of 1%. Helium shows a slightly worse accuracy of MAPE He = 0. 29% than protons with MAPE p = 0. 19% for the ideal dataset. For the realistic dataset, we have MAPE He = 0. 67% and MAPE p = 0. 59%.
Discussion The analysis of the two different types of datasets allows disentangling uncertainty contributions from the measurement processes and from statistical limitations of particle imaging. Specifically, we found that the uncertainty of the position and direction measurements, which is higher for the semirealistic and realistic datasets than for the ideal dataset, contributes importantly to the achievable spatial resolution as expected from  Krah et al (2018)  and  Khellaf et al (2020). This effect is more prevalent towards the resolution phantom's periphery while at the center the effect is more pronounced for helium ions than for protons. The spatial resolution as represented by the spatial frequency of the MTF at the 10% level is inversely proportional to the paramter σ of the error function fit (see equation (4)).
8. The above observations further agree well with the model introduced by Collins-Fekete et al (2021) who determined a limiting spatial resolution introduced by multiple Coulomb scattering (MCS) of 0. 57 lp mm -1 for protons and about 1. 15 lp mm -1 for helium ions at an energy of 200 MeV/u when crossing 20 cm of water (see figure  3  of Collins-Fekete et al 2021). This corresponds to the central spatial resolution values of the resolution phantom when reconstructing the ideal dataset (see figure 2, 0. 59 lp mm -1 for pCT and 1. 21 lp mm -1 for HeCT). For the reconstruction of the realistic dataset, our values deviated partially from Collins-Fekete et al (2021), namely by 0. 096 and 0. 29 lp mm -1 for protons and helium ions respectively, probably because that study did not account for specific tracker uncertainties.
The reason for choosing small pixels for the analysis of the spatial resolution (section 3. 1) was to avoid limitations on the attainable spatial resolution which might be introduced by larger pixel sizes. However, this is only relevant for reconstructions of the ideal dataset. For realistic reconstructions, the decrease of the spatial resolution over the whole phantom is low for protons when increasing the pixel size from 0. 1 to 0. 25 mm. Similarly, the Hann filter leads to only minor further changes of the proton resolution. At the same time, the Hann filter helps to reduce some of the increased variance values in the pCT images that were introduced by choosing the small pixel size (see variance reduction by a factor of 7 in each image pixel with W Hann = 1 in figure  6(d) ). Therefore, the chosen parameters for the reference proton reconstructions (0.
25 mm pixel size and W Hann = 1) can be considered as a well-balanced choice of the reference parameters for the comparison of protons to helium. The model of Collins-Fekete et al (2021), which describes the noise at the center of a water cylinder and models effects from attenuation losses of primary particles in an idealized detector, predicted a 36% dose increase at the same signal-to-noise ratio (SNR) for helium ions compared to protons. This value is close to what we obtained for the ideal dataset in case of homogeneous phantom regions, the ROI of the water phantom (27% dose increase) and the center of the brain (33% dose increase), at equal image variance. However, the value is different for inhomogeneous regions like the ROI of the nasal cavity (109% dose increase). These results are independent of the additional smoothing caused by the Hann filter since the image variance is always decreased by the same factor when W Hann is equal for both particle types (figure  6 ).
For the realistic datasets, the dose ratios however increase since both phantom simulations include energy straggling and nuclear interactions within the five-stage energy detector (see table  1 ). The impact of nuclear reactions of the helium ions on the dose ratio can be reduced by applying the ΔE-E filter, but the dose ratio from the ideal dataset cannot be fully recovered. The image variance is proportional to the variance of the particle range and inversely proportional to the number of particles detected. After traversing the center of the water phantom, approximately 86% of the primary protons and 71% of the primary helium ions (figure 2. 7 of Volz 2021) remain and may be used for the image reconstruction where 4 times more protons need to traverse the ROI for the same image dose. The variance of range straggling is inversely proportional to the mass at the same particle range (Durante and Paganetti 2016) and therefore for helium ions a factor of 4 less than protons.
Acknowledgments We gratefully acknowledge the support of the U. S. pCT collaboration throughout this project, in particular  Robert Johnson  and  Mark Pankuch. This work was supported by the  German Research Foundation (DFG)  project # 388731804  'Fluence modulated proton computed tomography: a new approach for low-dose image guidance in particle therapy' and the  DFG's Cluster of Excellence Munich-Centre for Advanced Photonics (MAP). Additional funding from the  Zusatzfinanzierung 2019 für DFG-Sachbeihilfen  from the  Forschungsdekanat of the Faculty of Medicine  of the  LMU Munich  is acknowledged.
The recovery experiments in human urine were performed as follows. A given volume of the urine sample was diluted in the blank-supporting electrolyte placed in the electrochemical cell. A known amount of QCT was then added. Each recovery rate was calculated by comparing the results obtained before and after the addition of the analyte, using DPV as described above. The electrochemical manipulations were performed using an OrigaStat Potentiostat-Galvanostat (OrigaLys ElectroChem SAS, Rillieux-la-Pape, Rhône, France) controlled with the OrigaMaster 5 software, whose cell consists of three electrodes: a reference electrode (Ag/AgCl/KCl 3 M, Metrohm), a stainless-steel counter electrode and a glassy carbon working electrode. A GENESYS 10S UV-visible spectrophotometer was used to collect the UV-visible spectra of the QCT at a maximum absorbance of 373 nm.
This work was supported in part by the  European Union  through the  European Regional Development Fund , in part by the  Ministry of Higher Education and Research , in part by the  Région Bretagne , in part by the  Département des Côtes d'Armor and Saint-Brieuc Armor Agglomération  through the CPER Projects 2015-2020 MATECOM and SOPHIE/STIC and Ondes and in part by the  Telekom Malaysia Berhad  under Grant  MMUE/220012.
Moreover, discriminate alternatives based on close scores could not be adequate  [41]. Thus, to sum up considering these findings, we argue that an ''adaptable'' (or flexible) DSS based on MCDM methods must allow DM to interact with data in order to: 1. define the decision matrix, i. e. select the criteria to be considered within the alternative's database; 2. give relative weight to the considered criteria based on DM's perception of priorities and integrate these preferences in an aggregation mechanism. There are many methods that could be implemented to model and aggregate DM priorities, but most of them are complex and time consuming with numerous data. Weighted sum mechanisms are interesting approaches given its simplicity; 3. consider the uncertainty and inaccuracy of the DM's preferences.
Conclusion and perspectives This paper presents a MCDM ranking solution including an algorithm coupled with an interactive UI, which allows to rank a defined number of alternatives considering a set of criteria. The main objective is to propose a decision-support system for designers who have to choose a solution/technology, while considering conflicting criteria, and uncertainty, within a NZEB refurbishment process. The proposition takes into account the notion of importance between criteria, and granularity to differentiate solutions within a criteria, in order to facilitate an exploration of the solution space and to take an informed decision. The influence of the importance and granularity parameters have been explored with a sensitivity analysis, as well as the impact of the distribution of input data on the algorithm response.
A. AMBIENT TESTS 1) MEASUREMENT SETUP The measurement setup includes the fabricated test antennas in section III, a 60 cm-long 50-coaxial cable, and a spectrum analyzer, model FieldFox N9918A from Keysight, as presented in Fig. 15. A standard personal computer (PC) with a dedicated LabVIEW program controls the spectrum analyzer (SA) for automated power acquisitions.
2) ACQUISITION The embedded LabVIEW software on the PC allows setting some of the key measurement parameters, defined as follows: frequency band: 0. 6 -3 GHz, Number of points: 401, sweep mode: continuous, amplitude type: power (dBm), video bandwidth (VBW): 10 kHz, resolution bandwidth (RBW): 10 kHz, Attenuation: 0 dB, and Preamplifier: Off. Under these settings, the SA's noise floor decreases to around -90 dBm, which is decent for measuring low-power signals in the ambient environment. It is worth mentioning that any maximum power tracking function is deactivated during measurements to get ambient power's "real" time-varying behavior. The system stores the received power every 10 seconds to reach 50 measured samples with each test antenna. Each sample includes all the frequency points. All the antennas are measured on the same support the exact day between 10 -12 am before changing the location.
, 2002). The Paganin filter with length 30 µm is applied to the projections. The missing data is replaced by the mean grey level of the rest of the data (Fig. 6a ). In parallel-beam CT, the reconstruction field of view can be widened by moving the rotation axis away from the beam centre and performing the acquisition over 360 •  (Stock, 2008). The first half turn provides the projection of one half of the sample; the second half turn gives the rest. Then, those half projections are stitched to get back in the standard (axis-centred) tomographic geometry. Their relative positions are determined pixel-wise from the known acquisition geometry. Indeed, a more accurate positioning would have imposed interpolating one side of the projection, which is computationally expensive and useless because the Nyquist resolution is two times the pixel size.
Acknowledgements  L. Turpin 's PhD was supported by a convention CIFRE implying  Association Nationale de la Recherche et de la Technologie  and  Safran Ceramics. Safran group and Direction Générale de l'Aviation Civile  are acknowledged for funding this work through  PRC MECACOMP. In-situ experiments were carried out at PSICHE beamline at synchrotron SOLEIL (Saclay, France) and were part of proposal 20181639.
Electron microscopy To perform elementary microanalyses and nanostructural observations by SEM (scanning electron microscopy) coupled with EDS (energy dispersive X-ray spectrometry), the samples were polished mechanically down to 1/4 µm with diamond suspensions and then ion-milled with the Fischione 1060 at 5 KV and with an argon gun at tilt angles of 5 and 2 •. To ensure a good conductivity on the sample surface and maintain a good image quality for SEM observations, the plugs were coated with platinum. To assess the rock heterogeneity, a quantitative mineralogical map was acquired on a FEI Quanta 650 electron microscope equipped with two energy dispersive X-Ray spectrometers (EDS Bruker X-Flash) and combined with the mineral identification software package Maps-Nanomin. The interpretation method used in the Nanomin software was previously calibrated with quantitative data based on X-Ray diffraction and X-ray fluorescence  (Fialips et al.
, 2018). Additional SEM observations at different length scales were then performed locally to qualitatively identify the different types of porosity in the organic matter and in between the minerals following the classification proposed by  Louks et al. (2012). To quantify the porosity by image processing from the nanometric scale up to the micrometric scale (typically in an equivalent diameter range from 10 nm to a few micrometers), large-area imaging was performed on a Zeiss Crossbeam 540 electron microscope. Backscattered electron images were acquired at low acceleration voltage (5 KV) and a current beam of 100-200 pA to cover a representative area of 480 µm × 240 µm at a resolution of 5 nm (pixel size). Segmentation of organic matter and pores was then performed on filtered images using the Ilastik software.
The classification of the different types of porosity (mineral vs. organic matter hosted pores), performed using the software Visilog, was then carried out using current image processing based on Figure  1. Visualization of the microstructure and the arrangement of mineral grains versus organic matter using SEM imaging at different length scales from 20 µm to 100 nm (from top to bottom). The successive zoomed-in views focus on the meso-and macroporosity in the organic matter. mathematical morphology, where the pixels describing the pores neighboring the organic matter are detected after a dilation operation. The mineral hosted pores were then deduced by subtraction. From the image, the surface of each pore is extracted, and whatever its shape, a diameter is calculated for an equivalent disk.