dataesr/scientific-paragraphs-categorization huggingface dataset:
5); 6 images of neutral valence / low intensity (respectively rated between 4. 5-5. 5 and < 3. 5) (Fig  3 ). We will show these eighteen pictures one by one in a pseudo randomized order by ePrime software, in order to avoid two pictures of same valence to follow. Each picture will be shown for 10 seconds. After each picture, ePrime will show a smiling, a neutral and a sad face (respectively for positive, neutral and negative valences), at the same time and for 5 seconds; we will instruct each subject to indicate (with their right index finger) their own perception of the emotional valence of the picture. Again, this will be used to assess subjects' focus on the task in the MRI. If the subject does not choose within time, the task will go on, but this will be taken into consideration for data analysis.
Post-MRI examination After giving the participant a 15-minute break, the investigating psychiatrist will interview her. She will be asked to rate subjectively the emotional valence and intensity of each one of the 18 pictures of the emotional pictures viewing sequence, using the SAM scale created by the IAPS designers. This rating is from 1 to 9 for valence and intensity and assesses the cognitive part of the emotional experience of the subject. Finally, we will ask the subject for her general experience of the protocol and the psychiatrist will give her contact information in case she needs to talk again about it. We will not include these data in the analyses.
MRI data analysis Functional (expect to periodic oscillatory stimulations) and structural scans will be pre-processed and analysed using SPM software (Statistical Parametric Mapping, Version 12, Revision 7219, Wellcome Department of Cognitive Neurology, London, UK). Images will be automatically realigned to each other, and we will apply a correction for head-motion using a 6-parameters rigid body spatial transformation. Finally, we will normalize all images to the Montreal Neurological Institute (MNI) space, and smooth them using a Gaussian kernel ([6 6 6] FWHM) in order to improve signal to noise ratio. We will also use TAPAS PhysIO Toolbox  [38]  to denoise functional volumes. We will model the GE-EPI time-series using a first level general linear model  [39]  with regressors representing expected BOLD fMRI response to visual stimuli of task (colour arrangement or picture), separately for each session.
Realignment parameters are due to be added in the regression models to remove specific activation due to head movement (translations "right", "forward", "up" and rotations "pitch", "roll", "yaw") and we will use a 128 second high-pass filter to remove non-physiological slow signal shifts. We will calculate contrasted parameter estimate images (con-images) for each subject. Such images describe brain activation for every visual stimulus (emotional and go / no go paradigms). We will use CONN toolbox (www. nitrc. org/projects/conn, RRID:SCR_009550) for resting state analysis  [40]. This analysis is based on the identification of standard physiological functional neuronal networks ("default mode" network, motor network, sensory network, visual network, attention network). We will carry out this identification out without any preconception, by an analysis of independent components (MATLAB version 2017a and toolboxes).
We will assess connectivity forces with a Pearson correlation coefficient between each region concerned by the identified and compared networks, using a graph of this connectivity. The fMRI signal from periodic oscillatory stimulations will be compared using BrainVoyager QX (Version 2. 8. 0, Brain Innovation, Maastricht, Netherlands). Pre-processing will consist in a linear trend removal to exclude scanner-related signal, a temporal high-pass filtering applied to remove temporal frequencies lower than three cycles per run, and a correction for small interscan head movements by a rigid body algorithm rotating and translating each functional volume in 3D space. We will spatially align functional (unsmoothed) data with the highresolution anatomical volume which will be previously aligned to the AC-PC plane (automatic co-registration in BrainVoyagerQX, adjusted manually).
Subsequently, the pre-processed functional data will be exported and analysed using custom scripts written in MatLab. Following the same procedure as in  [41] , we will perform a Fast Fourier Transform (FFT) on the preprocessed BOLD time series to get the amplitude spectrum. Then, we will calculate the mean and standard deviations of the amplitude of the 40 frequency bins neighbouring the stimulation frequency (20 on each side, e. g. in  [42, 43] ). Lastly as in previous studies  [44] , we will calculate the signal-to-noise ratio (SNR) of the response at the stimulation frequency by converting its amplitude to a z-score using the computed amplitude mean and standard deviations from the neighbouring frequencies. This procedure will be applied to each voxel independently. With this procedure, we should obtain the magnitude of neural response at the stimulation frequency (0.
Statistical analysis Statistical analysis will include a descriptive analysis of all participants and a dedicated treatment for fMRI images, as presented hereinabove. We will describe quantitative variables by their means and standard deviations, quartiles as well as the maximum and minimum values and qualitative variables by the respective percentages of each element. The rating scales' scores will be calculated according to their authors' recommendations. Functional MRI data will be initially processed at individual level, and then at group level. We will create a map of significant differences, with risk corrected at whole brain level, and with a p-value lower than 0. 05. We will make separate individual statistical analysis, with a ttest comparing brain activity between emotional tasks and rest sequence, then a group analysis for each study group, and finally a statistical comparison between the three groups.
Human faces shown during MRI sequences As explained hereinabove, several sequences of the MRI acquisition involve showing series of human faces to the participants. Figs  3  and 4  show some of these faces and potential identifiable persons. Concerning Fig  3 , each image of the 18 we displayed during the visual emotional sequence, was selected either from the International Affective Picture System (IAPS) database or from the Nencki Affective Picture System (NAPS) database. As said in the IAPS designer's message  [45] , this database "provides normative ratings of emotion (pleasure, arousal, dominance) for colour photographs that provide a set of normative emotional stimuli for experimental investigations of emotion and attention". The baby face visible in the figure is one of these photographs.
Most of the face pictures in these databases are of actors or models, some of them from other databases from different countries. Some other faces pictures are from media (mostly newspapers) but back in that time, they were selected with anonymity criterion. These databases were used in several studies around the world, including previous studies involving functional brain magnetic resonance imaging. They cannot lead to identify anypatient. Concerning  Fig 4,  the images are from Dr Ekman's Pictures of Facial Affect (POFA) collection. These images are all face pictures of volunteer or professional actors and models. Similarly to NAPS and IAPS, they were used in several studies around the world, including brain fMRI and they do not show patient's faces.
The Q and U Bolometric Interferometer (QUBIC) is designed with particular attention to the limitation and control of systematics  [2]. See also in this series of papers  [3]  for the optics design,  [4]  for the cryogenics design, and  [5]  for the readout electronics. QUBIC uses the technique of interferometry which leads to the possibility of doing "self-calibration" in order to have exquisite control of instrument systematics  [6]. This technique also provides a method for doing spectral imaging. Bolometric Interferometry is the marriage of techniques bringing together the great sensitivity and large bandwith of bolometers and the instrumental control and high fidelity imaging of aperture synthesis. Using this innovative approach, any residual systematics in the data will be largely independent from those in other experiments, thus providing a uniquely powerful dataset in the context of the worldwide experimental effort.
This voltage is sent to an Analog-to-Digital Converter (ADC) board integrated in the Raspberry Pi mini computer which stores the data along with a timestamp for each sample. There are approximately 300 samples per second. A Command Line Interface written in python is used to configure the calibration source setup. This system accepts commands via socket and can be easily interfaced by the Graphical User Interface called QubicStudio. For the result shown in Figure  5 , the source was set at 150 GHz and was modulated with a square wave with 3 second period (0. 333 Hz) and a 33% duty cycle. Figure  5  shows the very clear signal in many of the TES detectors in the array. Figure  6  shows the signal seen on one TES together with the modulation signal measured by the power monitor. There is a very clear correlation between the two.
The signal in each case was measured by the RMS of the TES data in a given position while the source was modulated. The measurement was the source modulation amplitude together with the RMS of the noise, quadratically added. As a result the minimum value is dominated by noise. The maximum signal occurs when the HWP is between position 6 and 7. As polarization is selected before the horn array, the cross-polarization leakage is independent of the location in the focal plane. Therefore, the cross-polarization leakage is     expected to be the same for all TES. The example shown in Figures  10  and 11  are for TES#95 which has the best signal-to-noise for this measurement due to the pointing. The measured cross-polarization contamination was measured with the calibration source tuned to 150 GHz.
This is computed as if the horn plane and the focal plane were parallel. Simulations are also performed in Maynooth with GRASP (a commercial optical modelling software) and MODAL (an 'in-house' software developed and maintained by Marcin Gradziel at Maynooth) [see 3, for details about optics modelling]. While both software packages model systems using the technique of Physical Optics, there are differences in how systems are implemented which make them complementary. MODAL can calculate the coupling of beams from free-space to feedhorns, while GRASP considers feedhorns to be point sources. However, GRASP can describe surfaces using a grid of points which permits the modeling of real measured mirror surfaces, and then using the Physical Theory of Diffraction, GRASP can model mirror edge effects.
The two packages can exchange data through common file formats which permits the analysis of a complete optical system, using the best package for each subsystem and then combining the results. They can also be used independently and compared as a verification of modelling results. A model was created in GRASP to match the MODAL model. This model plots the interference pattern of any horn baseline. The comparison of the output of the models for a given baseline is shown in Figure  16  as plotted by our Python-based QUBIC simulation software using the output files from the MODAL simulation model. The result shows that MODAL and the GRASP simulations are in agreement. Additional baselines were tested and also had matching patterns. Having these two independent methods in agreement is a good check that the horn setup, instrument data, software, and simulations are working as expected.
The entire beam map measurement was preprogrammed in a script executed by the QUBIC instrument control software QubicStudio. The result of the beam mapping measurement campaign is a series of maps for each TES pixel in the detector array. This is 244 maps for each frequency for a total of 1220 maps. Figure  18  shows example maps for TES#93 (ASIC 1) at three frequencies. The main lobe and secondary lobes are clearly visible and match well with theory. The secondary lobe locations depend on frequency while the main lobe is always at the same place. Figure  19  shows a cut across the beam maps of Figure  18. The secondary lobes are closer to centre as frequency increases, as expected. This is a key feature of the bolometric interferometer which makes spectroscopic imaging possible.
Acknowledgments QUBIC is funded by the following agencies. France:  ANR  ( Agence Nationale de la Recherche ) 2012 and 2014, DIM-ACAV ( Domaine d'Intérêt Majeur-Astronomie et Conditions d'Apparition de la Vie ),  CNRS/IN2P3  ( Centre national de la recherche scientifique/Institut  national de physique nucléaire et de physique des particules),  CNRS/INSU  ( Centre national de la recherche scientifique/Institut national et al de sciences de l'univers ). Italy:  CNR/PNRA  ( Consiglio Nazionale delle Ricerche/Programma Nazionale Ricerche in Antartide ) until 2016,  INFN  ( Istituto Nazionale di Fisica Nucleare ) since 2017. Argentina:  MINCyT  ( Ministerio de Ciencia, Tecnología e Innovación ),  CNEA  ( Comisión Nacional de Energía Atómica ),  CON-ICET  ( Consejo Nacional de Investigaciones Científicas y Técnicas ).
Data and analyses Data on national HTA decisions were searched in the public domains of the national HTA agencies (Table  S1 ) and the EUnetHTA Network (eunethta. eu/abouteunethta/eunethta network/) between April 1, 2020 and November 12, 2020. Two authors (MO, RS) searched independently for the reports, translated the reports into English by an online machine (deepl. com) if none of the authors was fluent in the respective language and then extracted data. If both authors could not find the respective information, a third author (GF) performed an additional search. The selection of nine countries from the European Union was based on the availability of at least one HTA report with a clear recommendation for one of the three case medicines for an individual country. This criterion was chosen to test the efficiency of our search strategy for HTA reports in the public domain and to test whether the respective country has a functioning and transparent HTA system in place.
The utility of integrating L-systems inside mechanical structures can be validated via numerical simulations using different FEA software such as ANSYS Workbench and COMSOL Multiphysics. Indeed, numerical simulations of bioinspired mechanical structures and composites have proven the effectiveness of emulating nature in its aspects and shapes  [64] [65] [66]. Despite all the research works based on L-systems in the aforementioned domains, there is still a lack in the application for biomimetic lightweight structures with high stiffness in AM. To address this challenge, this paper proposes a novel computational design method based on L-systems generated and distributed along the PSLs' directions. By considering a rough design space, multiple load cases and boundary conditions are investigated.
PSLs Extraction The next step of the bioinspired design method (Step 2 of Figure  1 ) aims to determine and extract PSLs exhibiting load-case-dependent trajectories  [73, 74]. In the 2D design space, only two PSL directions (denoted as first and second PSL directions) are significant. According to Daynes et al. [42] , PSLs are free of shear stress since they are aligned with the principal stress trajectories. The numerical construction of the PSLs can be performed via the use of mathematical models  [27, 41, 71]. In this research work, numerical simulations, using a solid mechanics formulation, are conducted on the 2D design space via the commercial software COMSOL Multiphysics ® 4. 3a (COMSOL Inc. , Stockholm, Sweden). Using the load case and the boundary conditions expressed in Figure  2 , PSL's trajectories are computed in streamline forms (see Figure  3 ).
Finite Element Analysis and Design Regions Decomposition Numerical simulations are then conducted on the six L-systems structures via CO SOL Multiphysics ® 4. 3a software. A beam elements formulation is used for the calcu tions in this step of the proposed method. Similar to the 2D rough geometry, the L-s tems' structures are all clamped at their lower boundary and subjected to different load types. For example, CL-1 and CL-2 are subjected to a compressive load of 60 N. Structu SH-1 and SH-2 are subjected to a shear load of 60 N, and structures CL-SL-1 and CL-SL-2 are subjected to a compressive load of 60 N and a shear load of 60 N (see Figure  6 ). In order to reach the objectives of light weight and increasing stiffness by emulating natural aspects, it was necessary to decompose the structures into two regions (see Figure  7 ).
Objective: The aim of this paper is to propose an extended translation of the MeSH thesaurus based on Wikipedia pages. Methods: A mapping was realized between each MeSH descriptor (preferred terms and synonyms) and corresponding Wikipedia pages. Results: A tool called "WikiMeSH" has been developed. Among the top 20 languages of this study, seven have currently no MeSH translations: Arabic, Catalan, Farsi (Iran), Mandarin Chinese, Korean, Serbian, and Ukrainian. For these seven languages, WikiMeSH is proposing a translation for 47% for Arabic to 34% for Serbian. Conclusion: WikiMeSH is an interesting tool to translate the MeSH thesaurus and other health terminologies and ontologies based on a mapping to Wikipedia pages.
Results The WikiMeSH tool has been written in Python 3X and calls the Wikipedia API through the dedicated endpoints depending on the languages. Overall, the WikiMeSH tool was able to map a Wikipedia page for 18,191 MeSH descriptors (61. 14%), which means that less than 39% of the MeSH descriptors have no Wikipedia links. 15,268 MeSH descriptors obtained a link based on their preferred term and 2,923 based on a synonym. The average number of detected Wikipedia languages per MeSH descriptor is 17. 57 ± 34. 80 (min-max: 0 -300). The average number of added languages thanks to WikiMeSH is 17. 61 ± 34. 75. Among the top 20 languages of this study, seven are not already officially present in the MeSH thesaurus translations: Arabic, Catalan, Farsi (Iran), Mandarin Chinese, Korean, Serbian, and Ukrainian.
For these seven languages, WikiMeSH is proposing a translation for 13,959 out of 29,754 MeSH descriptors (46. 91%) for Arabic, 12,039 for Farsi (40. 46%), 11,945 for Mandarin (40. 16%), 10,839 for Ukrainian (36. 43%), 10,770 for Catalan (36. 20%), and 10,103 for Serbian (33. 96%). The WikiMeSH results are integrated into the crosslingual terminology server HeTOP  [4].
1) Background: The Vaccine Coverage Rate of influenza remains low and omnichannel efforts are required to improve it. The objective was to evaluate the feasibility and outcomes of a QR Code nudging system in outpatient departments. (2) Methods: The study was performed in 6 departments ensuring ambulatory activities in a French university Hospital between November and December 2021. By scanning QR codes, users accessed anonymously to the ELEFIGHT ® web app, which provides medical information on influenza and invites them to initiate a discussion about influenza prevention with their physicians during the consultation. (3) Results: 351 people made 529 scans with an average reading time of 1 min and 4 s and a conversion rate of 32%, i. e. , people willing to engage in a discussion. (4) Conclusions: The study suggests that direct access to medical information through QR codes in hospitals might help nudge people to raise their awareness and trigger their action on influenza prevention.
" The visitor has the choice to accept the cookie or not, but in either case, he has access to the same content. The visitor is always ANONYMOUS. For this reason, no personal information is requested. ELEFIGHT ® pilot study was carried out at Edouard Herriot Hospital, an 850-bed University Hospital in Lyon, Hospices Civils de Lyon, Lyon, France, between November 8 and 10 December 2021, in the departments ensuring ambulatory activities for adult patients, i. e. , general internal medicine, oncology, nephrology, rheumatology, geriatrics, and pharmacy. Separate QR codes were printed on the posters installed in the waiting rooms of these departments. A specific QR code was also developed for HCWs reserved spaces, with the same content as for patients. Once a week, a clinical research associate (CRA) went to the waiting rooms (except in pharmacy) to explain the ELEFIGHT ® pilot study to patients and encourage them to scan the QR code with their smartphones.
The study was planned to start at the same time as the official French National flu campaign. The launch day was kept unchanged for our study, but the National campaign had been unexpectedly moved forward. After scanning the QR codes, the ELEFIGHT ® web app invites the users to initiate a discussion about influenza vaccination with their respective physicians during the consultation-by offering them a list of potential questions they would like to ask after clicking on the call-to-action button "Talk to my doctor. " The following data were computerized anonymously in real-time: (1) Number of users who scanned the QR code; (2) Bouncing rate (users who closed the web app right after the opening); (3) Average connection time to the web app; (4) Number of clicks on the " Talk to my doctor" button, and (5) Conversion rate: number of users who clicked the "Talk to my doctor" button divided by the number of users who scanned the QR code and stayed on the web app.
Results Between November 8 and December 10, 2021, 351 people (219 patients and 132 HCWs) realized 529 scans of QR codes with an average conversion rate of 32%. The number of scans across time is described in Figure  1. had been unexpectedly moved forward. After scanning the QR codes, the ELEFIGHT ® web app invites the users to initiate a discussion about influenza vaccination with their respective physicians during the consultation-by offering them a list of potential questions they would like to ask after clicking on the call-to-action button "Talk to my doctor. " The following data were computerized anonymously in real-time: (1) Number of users who scanned the QR code; (2) Bouncing rate (users who closed the web app right after the opening); (3) Average connection time to the web app; (4) Number of clicks on the " Talk to my doctor" button, and (5) Conversion rate: number of users who clicked the "Talk to my doctor" button divided by the number of users who scanned the QR code and stayed on the web app.
Another observation concerns older adults who are rarely equipped with a smartphone at all or one suitable for reading QR codes. In addition, we confirmed that perception of QR code technology and its usage could Vaccines 2022, 10, 1591 6 of 7 be affected by certain demographic factors such as age  [11]. Therefore, efforts might be made to facilitate access to the web app, regardless of age or other patient determinants, e. g. , tablets available for public access in the waiting rooms. The conversion rate of 32% in this ELEFIGHT ® pilot study supports that nudging through QR codes for increasing patient awareness about influenza and Flu vaccination is feasible in hospital settings. This rate may be partly accounted for by (1) demographic differences in sentiments about influenza risks across participants due to the absence of influenza epidemics over the last two years, (2) differences in the patients' perception of the risks associated with influenza, (3) emotional and social fatigue about vaccination and risks related to viral infectious disease caused by COVID-19 pandemic discussions, and (4) technical problems (i.