dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations of the study This mixed-methods study has several strengths. The combined use of quantitative and qualitative data brought robustness to the findings. The use of a nationwide healthcare database linked to an exhaustive cohort of patients with CKD who started dialysis allowed the precise reconstruction of their care trajectory and the thorough description of the healthcare professionals' presence in these trajectories. In-depth interviews with healthcare professionals working in different settings explained and enriched the observed trends that led to the description of their different forms of involvements in CKD care and their interactions. The quantitative analysis of prescription patterns and content supported this description. The findings presented here and their generalisation must be considered cautiously.
Acknowledgements The authors thank all REIN registry participants, especially nephrologists and data managers in charge of data collection and quality control. The dialysis centres participating in the registry are listed in the REIN annual report (https://www. agence-biomedecine. fr/Leprogramme-REIN). The authors thank  Elisabetta Andermarcher  for reviewing the English language. The authors thank all nephrologists and GPs who participated in this study for their time and contribution through the interviews.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded with a PhD grant from  University of Rennes 1. This study was also partly funded by a grant from the  French Agence de la Biomédecine  and from the  Fondation Paul Bennetot, groupe Matmut. The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. All authors were independent from these fund. All authors had full access to all of the data and can take responsibility for the integrity of the data and the accuracy of the data analysis.
Data collection Each allophone migrant participated in a single semi-structured interview with a researcher (MJ: MD, GP, Primary Care researcher, female). Each interview began by calling the TIS to be connected with an interpreter who speaks the migrant's mother tongue. The first few minutes of the interview were dedicated to ensuring that the interviewee understood the study context, and that they could withdraw their consent at any time. The interpreter then asked the participant to confirm orally that they consented to participate. Next, after discussing the circumstances of the participant's arrival in France, the interview focused on the participant's feedback concerning the use of the TIS during their medical consultation, and the impact of this service on the doctor-patient relationship.
Results A total of 32 people were identified and contacted to participate. Thirteen of these agreed to participate, but 3 of them failed to show up at the agreed time for the interview. Therefore thus, from September 2019 to January 2020, a total of ten interviews were held (i. e. one with each participating allophone patient). The interviews were held in a dedicated room at the accommodation centre, which was known to all participants, and was quiet and informal. The characteristics of the participants are detailed in Table  1. The average duration of the interviews was 34 minutes (range 23 to 63 minutes). Data from the interviews produced eight thought clusters related to the allophone migrants' perception of the TIS. As shown in Table  2 , these clusters were assembled to generate the following three major themes: (i) multi-level difficulties for allophone migrants in the healthcare pathway; (ii) the interpreter as the cornerstone of the doctor-patient relationship; and (iii) advantages and limitations of the TIS.
Data collection Quantitative study CF-patients had to fill out an online questionnaire. The study was advertised with the support of all the CRCM and the patient association "Vaincre la Mucoviscidose" via websites and social networks such as Facebook and Twitter, as well as via emails sent by the CRCM. Patients could access the questionnaire using a generic link available in the announcement of the study. The questionnaire was open from May 15 th , 2020 to June 11 th , 2020 and comprised the following parts: (1) Sociodemographic characteristics (gender, age, education, employment, marital status), (2) access to healthcare during lockdown defined as follows: cancellation or rescheduling of consultations (with the doctor or the physiotherapist) by the healthcare professional or by the patient, cancellation by the patient of telehealth, cancellation or rescheduling by the hospital or by the patient of hospitalisations (planned or not) and change in the route of administration of antibiotics (oral administration instead of intravenous), (3) Compliance to medical treatment and airway clearance as assessed by the adherence score (12).
Maps were built to represent anxiety, depression scores and concern and constraints about COVID-19; median scores were used in each geographic area. A map of the prevalence of COVID-19 in the general population in France at time of the questionnaire survey was built as a reference to visually compare scores between geographic areas, taking into account the prevalence of COVID-19 in these areas. All significance tests were two-tailed, and the threshold for statistical significance level was set to 5%. All analyses were performed with Stata software (v16. 0 StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC. ) and R software (R Core Team, R Foundation for Statistical Computing, Vienna, Austria, 2020).
Acknowledgments We are very grateful to the patients who took time to fill in questionnaires or undergo interviews. We are grateful to  National Clinical Research Programme (PNRC)  and the  Clinical Research Centre (CRC) of the CHIC  for their valuable help in the elaboration of the questionnaires. We thank  Vaincre La Mucoviscidose , the  Filière MUCOVICIDOSE-CFTR , the  CHIC communication department  and all the  CRCMs in France  for their support and their assistance in the distribution of the questionnaires. We are grateful to  Cécile Hoffart  for clinical research coordination assistance,  Martine Torres  (manuscript) and  Sequoya Frey  (verbatims) for English editing assistance.
The real-time forearm inclination and joint misalignments between the human and the robot were taken into account to accurately estimate g comp  (48). C. Definition of the main kinematic and EMG parameters analyzed in the present study for an example movement in the -1g condition. Fast pointing movements are typically characterized by velocity profiles that mainly consist of one acceleration phase and one deceleration phase. Here, the temporal structure of movement was characterized by the rtPV parameter, as it is known to be sensitive to gravity  (9). The represented EMG data are the envelope of the filtered, rectified and normalized signal recorded during -1g motions. Regarding EMG data, we systematically subtracted the tonic activity (blue line) from rectified EMGs and analyzed the resulting phasic EMG patterns.
Motor patterns change quickly and significantly with respect to the local gravity field In Experiment 1, participants (N = 22) performed 6 consecutive blocks of fifteen 45-degrees upward movements in each of the 1g, 0g, -1g conditions. The order of the conditions was randomized. Analyses conducted on the first 10 movements of participants involved in Experiment 1 allowed to exhibit that a quick adaptation occurred during the very first movements after which a plateau was reached and the participants could reliably execute the task. In 0g and -1g the main kinematic parameters did not differ from the grand-average value computed from the last five blocks, which means that a plateau was attained after a couple of movements (see supplementary ). As a consequence, all the subsequent analyses were conducted on data averaged across all the blocks (excluding the first 5 trials of block 1), and we focus hereafter on the changes induced by different gravity conditions on the average motor patterns.
Kinematic and electromyographic recording An optoelectronic tracking system (10 Oqus 500+ cameras, 179 Hz; Qualisys, Gothenburg, Sweden) was used to record the position of nineteen 10 mm and one 3 mm reflective markers placed either on the participant or on the robot. Seven markers were placed on the participant: shoulder (acromion), elbow (epicondyle and epitrochlea), middle of the forearm, wrist (styloid process of the radius), base of the index proximal phalanx and tip of the index finger (the 3 mm marker). The other markers placed on the robot were used during the identification process. The EMG activity was recorded with bipolar surface electrodes (Wave Plus, Wireless EMG, 2000 Hz; Cometa, Bareggio, Italy). The QTM interface (Qualisys, Gothenburg, Sweden) allowed recording synchronously kinematic data and EMG activity.
Kinematics Three-dimensional position data of the marker taped on the tip of the index finger was used to assess the human kinematics. Data from the other reflective markers was used as a control. Position data was filtered (low-pass Butterworth, 5 Hz cutoff, fifth-order, zero-phase distortion, "butter" function from the "scipy" package) before differentiation as in previous studies  (28, 48). The computed kinematic parameters were defined as in Fig. 2B. The threshold for segmenting movements was set at 5% of PV in agreement with previous studies  (17, 19, 48) , which allowed defining MD and movement amplitude. PV and PA were respectively defined as the maximum value of the velocity and the maximum positive value of the acceleration reached during each movement. In addition to these absolute parameters, the relative time to peak velocity (rtPV) was computed as the ratio between the time elapsed from the movement onset to PV and the duration of the movement.
The significance level of the corrected p-value was set at p < 0. 05. All statistical analyses were performed using custom Python 3. 8 scripts and the Pingouin package  (87). Given the number of participants and conditions in Experiment 3, only quantitative comparisons between the model predictions and the average experimental data were provided there. Mean absolute errors between the model and the experimental data for rtPV, and Pearson correlation coefficients were computed to quantify the agreement between the model and the data when varying gravity incrementally.
Here, the temporal structure of movement was characterized by the rtPV parameter, as it is known to be sensitive to gravity (9). The represented EMG data are the envelope of the filtered, rectified and normalized signal recorded during -1g motions. Regarding EMG data, we systematically subtracted the tonic activity (blue line) from rectified EMGs and analyzed the resulting phasic EMG patterns. This phasic activity is also known to be sensitive to gravity (20). Negative phasic activity is referred to as inactivation and indicates periods when the EMG activity of a muscle is below the tonic level that would be required to counteract gravity (in shaded red). Positive phasic activity, referred to as activation (in shaded green), is responsible for net accelerations in the gravity field.
Community-based health insurance (CBHI) has been implemented in many low-and middle-income countries to increase financial risk protection in populations without access to formal health insurance. While the design of such social programmes is fundamental to ensuring equitable access to care, little is known about the operational and structural factors influencing enrolment in CBHI schemes. In this study, we took advantage of newly established data monitoring requirements in Senegal to explore the association between the operational capacity and structure of CBHI schemes-also termed 'mutual health organizations' (MHO) in francophone countries-and their enrolment levels. The dataset comprised 12 waves of quarterly data over 2017-2019 and covered all 676 MHOs registered in the country. Primary analyses were conducted using dynamic panel data regression analysis.
Specifically, we aimed to explore the association between different levels of scheme enrolment and:  Performance refers to the extent to which CBHI schemes succeed in increasing enrolment equitably, while sustainability refers to the schemes' capacity to be financially viable over time. Our analysis leveraged a novel, rich panel of quarterly administrative data that covered all 676 MHOs registered in Senegal over 3 years (12 waves between 2017 and 2019). Such a nationwide panel of activity data at the MHO level is unique in SSA. The use of administrative data for secondary analysis is relatively new in the social sciences  (Connelly et al. , 2016) , including in health policy and systems research  (Hung et al. , 2020; Masefield et al. , 2020). Such data, which are not originally generated for research purposes-routinely collected for registration, transaction or record-keeping by administrative systems  (Elias, 2014) -have several advantages.
First, they usually provide larger sample sizes than surveys  (Card et al. , 2010). As is the case in this study, they may even cover the entire population of interest. Second, they may provide information difficult to collect (e. g. information on groups or entities reluctant to participate in surveys or information less likely to be disclosed to a primary researcher;  Goerge and Lee, 2002). Third, they can provide necessary information more quickly and at a lower cost than conducting a new survey, which is especially relevant in low-income settings  (Hung et al. , 2020; Masefield et al. , 2020). Yet, administrative data also present some challenges for academic research. Access is often rendered difficult by legal and ethical issues  (Stevens and Laurie, 2014) , and datasets typically require substantial management efforts-such as cleaning or restructuring-before analysis.
We are not aware of the existence of similar microdata elsewhere in SSA. We collated all reports from the start of 2017 to the end of 2019, which amounted to 12 waves of quarterly panel data covering all functioning MHOs in the country. Of the 676 MHOs registered in Senegal, 22 were identified as having discontinued their operations before the studied period and were therefore not included in the analysis. In addition, we excluded the 26 MHOs located in the departments of Foundiougne and Koungheul. MHOs in these two departments are subject to a special regime of governance that translates into a different implementation of the CBHI scheme through so-called departmental health insurance units (Unités Départementales d'Assurance Maladie; UDAM)  (Bossyns et al. , 2018). The UDAM system is a model of larger-scale insurance units at the department level (rather than the community level, on which this study focuses), with specific features: (a) centralized scheme management at the department level; (b) advanced technical assistance to MHOs; (c) advanced degree of professionalization and (d) longer-term financial and technical partnerships with external development partners.
These specificities create two challenges for our analysis. First, due to major structural differences, the performance of MHOs based in UDAM departments cannot be assessed through the same lens as regular MHOs. Second, due to centralized management, some variables of interest are unavailable at the MHO level in the UDAM departments. Finally, we excluded 10 MHOs for which enrolment was reserved for members of professional guilds. The performance of these MHOs cannot be directly compared to those of regular MHOs due to restricted enrolment conditions. As a result, the sample for analysis included 618 MHOs located in 43 departments in all 14 regions of Senegal. Further description of the dataset is provided in Appendix, p. 3. Other variables used for data analysis, i. e. control variables at the community, health district, departmental or regional level, were collected from publicly available databases and reports published by the National Agency of Statistics and Demography and the Ministry of Health and Social Action of Senegal (Appendix, p.
g. supermarket transactions), GPS tracking data and-although a little less present in the mainstream discussions-administrative data  (Connelly et al. , 2016). For example, it has recently been argued that administrative data generated by non-governmental organizations (NGO) 'present an important but largely unrealised opportunity to provide new research insights in critical areas, including the evaluation of health policy and programmes'  (Masefield et al. , 2020). Such data promise to open up new research perspectives in low-income settings. Their volume and immediacy represent significant untapped potential for secondary analysis, especially when resources are limited or no other data source is available (e. g. information on groups that are hard to reach)  (Masefield et al. , 2020). As shown in this study, administrative data enable us to build datasets that cover the entire population of interest, which is often not possible with survey data.
Moreover, while conducting panel surveys can be costly for researchers, administrative data open up the opportunity to build panel datasets rapidly and at a reduced cost. Facilitating the construction of panel data may promote the study of behavioural and social change processes, which is highly valuable for evaluating social programmes and developing (better-targeted) policies  (Card et al. , 2010). To better exploit these resources, further collaborations need to be fostered between data owners, such as governments and NGOs, and researchers  (Masefield et al. , 2020). These collaborations would particularly benefit the development of research programmes to inform health policies in low-income countries and for marginalized groups, improving health and reducing health inequalities. They may also help enhance data quality, inducing the broader use of administrative data for research purposes.
Acknowledgements We thank  Makoto Tobe  and  Shotaro Togawa  of the  Japan International Cooperation Agency (JICA)  for facilitating the implementation of the research project. We acknowledge the support of  Ndèye Maguette Gueye Ndiaye ,  Tiguidanké Gassama Deme ,  Magor Sow ,  Arona Mbengue , and  Fatoumata Baldé  of the  National Agency for Universal Health Financial Protection of Senegal (ANACMU)  for data collection and interpretation of the findings. We finally thank  Valéry Ridde  of the  French National Institute for Sustainable Development (IRD)  for very helpful discussions.
Therefore, the ontogenetic changes of PVN are selective to a cluster of OXT cells located in the antero-posterior area. Quantitative analysis further indicates that a specific growth appears at P7 in the anterior cluster, while the number of OXT-Ir cells present in the posterior cluster remains stable over time (Figure  5B ). In striking contrast, AVPcell-density based processing does not show such developmental micro-organization (data not illustrated). We then used the distribution of fluorescence intensity as an indirect indicator of protein expression levels  (Coffman and Wu, 2012; Verdaasdonk et al. , 2014). For each identified cell, the fluorescence value was estimated based on the voxel maximum intensity (Imaris Software, Bitplane, Oxford Instrument). Because the fluorescence intensity levels are not directly comparable between separate acquisitions, each fluorescence value was systematically Z-transformed for each subject within the region of interest (Figures  5D  and S8 ).
Limitations of the study In this study we focused on the organization of OXT and AVP cells and their postnatal functional expression. However, to understand how such organization is integrated into multiple hypothalamic and extrahypothalamic networks it is necessary to focus on OXT and AVP projections, which was not addressed in this study. Although the light-sheet imaging approach has been optimized for capturing the intact rodents' brain, further development is still needed to efficiently quantify, map, and compare OCT and AVP projections density within the entire brain. Handling, detecting, and analyzing such large datasets would require methodological innovations, such as advanced automated detection algorithms based on deep learning technology, coupled with rigorous supervisions. The implementation of other strategies such as viral vector tracing approaches with cell type-specific reporter could help in this direction.
For hypothalamic scanning (from the anterior commissure to the median eminence), in coronal plane, a step size corresponding to the x and y pixel size was used to obtain approximatively cubic voxels: X:1. 89; Y: 1. 89; Z: 2 (optical zoom of 1. 6) for adult samples; 1. 51mm 3 ( optical zoom of 2) for P14 and 1. 21 mm 3 (optical zoom of 2. 5) for P0, P3 and P7 samples. For region quantification, cellular details were obtained using a most zoomed acquisition onto the regions of interest, resulting in voxel size of 0. 485 mm 3 , 0. 604 mm 3 , or 0. 75 mm 3. For all specimens, 2048 3 2048 pixel resolution, and 16-bit images were collected, resulting in datasets ranging from 30 to 150 Gigabytes (depending on voxel size, crop size, and number of channels used). Pilot studies dedicated to the imaging parameters settings revealed that a combination of excessive exposure time, and laser power can lead to a cross-reactivity of adjacent fluorophores, where the specific labeling expressed in the 488 nm wavelength for example could also be seen in the 568 nm channel, resulting in false-positive costaining.
Cell detection, cell quantification and region identification Detection and quantification of OXT-and AVP-Ir cell bodies was realized in the entire brains (both hemispheres) using the spot detection module from Imaris software (version 9. 5, Bitplane, Oxford Instrument). As a first step, cell diameter was set to 10 micrometers for automatic detection. This parameter was sufficient to avoid automatic detection of large vesicles. In a second step, manual verification and validation for each data sets were realized to discard any false positive or dismissed signals (i. e one spotted cells instead of two). Following detection, regions of interests (ROI) were defined based on cell density. For the hypothalamic nuclei, we first identified the Paraventricular nuclei (PVN) as the ROI#1, as this region contains the highest number of OXT and AVP cells localization in histological papers.
In ROI#9 (BNST), cells were located above the anterior commissure. From the hypothalamic scanning, we obtained a list of nine regions containing OT and AVP cells, whose Cartesian coordinates in x, y, z were extracted to perform atlas registration and molecular volume estimation. We performed additional acquisitions for each identified ROIs to reach a higher resolution for detailed cell count within each region. Cell count was done at a resolution of 0. 48 mm 3 in both hemispheres. As no difference was noted between left and right hemispheres, datasets were pooled together, and expressed as the total number of Ir cells.
1. Anatomical landmarks. In order to validate and better define the location of the different pre-identified ROIs, each data acquisition was transposed into the reference atlas using the PVN and SON occupancy volumes as reference anatomical landmarks. First, for the adult animals, the molecular volumes occupied by the OXT-Ir and AVP-Ir cells were combined and symmetrized with respect to the sagittal plane. Then, atlas registration was processed using rigid transformations (only scale, position and rotation were estimated) using custom MATLABMatlab code to align the obtained volumes within the PVN and SON volumes of the Allen Brain Mouse Atlas. PVN and SON were chosen as our internal anatomical reference landmarks because they are the two regions with the best signal-to-noise ratio and the OXT-Ir and AVP-Ir neurons are sufficient to estimate the full volume of these two regions.