dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the GEAR cycle The Crowd4SDG project organizes three one-year cycles of innovation, aimed at coaching teams of young social entrepreneurs through the steps of building a citizen science project. Each project tackles a challenge related to Climate Action or involves crowdsourcing tools that can generate data relevant for tracking progress towards the SDGs. The innovation methodology used follows a "GEAR Methodology" to coach teams through the innovation process required to develop new citizen science projects (Figure  1a ). Each GEAR cycle includes several phases of online coaching and in-person support: Gather, Evaluate, Accelerate and Refine. The Gather phase is promoted as a global crowdsourcing of ideas, called the Open17 Challenge (Figure  1b ), on the social network Goodwall.
Communication data A Slack workspace was used by the teams during the GEAR cycle as a means to communicate with other teams and with the organizing team. The data was extracted in JSON format using the export function available to the owners/admins of the Slack workspace. This allowed us to gather a data frame containing across all public channels the messages (post contents), their time stamp, sender, and channel it was sent in. The raw data was then processed to obtain mentions. A mention occurs when a Slack user types in a message the Slack username of a target user prefixed by "@". Each recorded mention has information on the source (who wrote the message), target (who is being mentioned) and the timestamp (when the message was sent). Slack also allows users to broadcast messages by citing all users in a channel or a workspace by using specific commands (@all, @here, @channel_name).
Survey data We used two types of surveys: those related to participant attributes (e. g. their background, country of origin etc), and those related to participant interactions (e. g. who they collaborated with, sought advice from etc). The initial survey was related to attributes only and was disseminated using a Google Form at registration to the Evaluate phase. We then disseminated 4 weekly surveys related to social interactions and activities using the CoSo platform  (Tackx et al. , 2021)  (Supplemental file 2: Figure  S1 ). The CoSo platform is designed to collect self-reported interaction data with a simple, reactive interface, and an analysis-ready database. To document their interactions, the users could select target users across all other participants and organizers. The interactions spanned prior ties in the first survey ("Which of these people did you know personally before?"), and on a weekly basis their advice seeking ("Who did you seek advice from last week?") and collaborations ("Who did you work with last week?").
Finally, prior experience was computed as the average answer to the question "Have you participated in data projects or contributed as citizen scientist to data production before?" (yes = 1 and no = 0) within each team. For communication, we leveraged the activity and interactions on Slack public channels. The Slack activity was assessed as the total number of messages posted by team members. For interactivity, we measure Slack interaction intra team as the number of mentions among members of a team, and Slack interaction organizing team as the number of mentions between members of a team with the organizing team. We counted mentions regardless of their directionality. For collaborations, we focused on the amount of collaborations within the teams, as well as the centrality of the teams within the advice network.
Team performance data To quantify team performance, we used the scores that teams obtained in their assessment by the jury and the Crowd4SDG organizing team (features from Figure  7  are in italics below). Performance was assessed through 4 team outcome metrics (crowdsourcing component, feasibility, relevance, novelty) as judged by a panel of experts selected by the Crowd4SDG consortium, and by 4 process parameters (project documentation, members attendance, commitment, and weekly evaluation) as assessed by the organizing team. More precisely, crowdsourcing was assessed using the mean score attributed to the question "Is there an effective crowdsourcing component?" (yes = 1 and no = 0). We measured the feasibility, relevance, and novelty by computing the mean score attributed by the jury on a scale from 0 to 5 to the questions "Feasibility: Is the project implementable with reasonable time and effort from the team?", "Novelty: Is the pitch based on a new idea or concept or using existing concepts in a new context?", and "Relevance: Is the solution proposed relevant to the challenge or potentially impactful?".
Team communication During the Evaluate phase, teams used a Slack workspace in order to discuss with other team members from their own or from another team, as well as with the organizing team. Since the challenge was fully conducted online, this workspace was a central repository for communications at the cohort level. We analyzed the data from the public channels of the Slack workspace to study the patterns of engagement of participants within and across teams, as well as with organizers. We first observed that the activity of the Slack workspace, measured by the number of posts per week, closely follows the phases of the GEAR cycle, with low activity outside of the phases (Figure  3a ). This might be due to the fact that teams would work solely during the program, or that they would synchronize on other communication channels outside of these phases, such as Whatsapp, e-mails, or private Slack conversations (Supplemental file 2: Figure  S4 ).
Comparison of the interaction networks The collected data allowed us to infer 4 interaction networks: communications from Slack mentions, and prior ties, collaborations, and advice seeking from surveys. When aggregated at the team level, these constitute a "multiplex" network, with the same nodes (the teams) having different types of links. Here we question whether these networks provide similar or complementary information to inform on team behavior. We show in Figure  6a  the networks at the team level. One can observe that the networks have similar densities (Figure  6c ), but different structures: the Slack mentions network is much more centralized than the surveyed interaction networks (Figure  6c ), indicating that Slack usage was mostly used to exchange with the organizing team who acted as a strong hub.
Team performance Finally, we analyzed whether features of team composition, communication, teamwork and collaboration were associated with team performance at the Evaluate phase. The performance was measured using various features that can be grouped into two overarching categories: outcome, i. e. the evaluation of the project itself, and process, i. e. the assessment of engagement within the program (see Methods and Supplemental file 2: Figure  S6a ). Given the small number of teams from which we can compute an association with performance (14 data points), we use a correlation analysis with a soft significance threshold at p = 0. 1. We present the results of this analysis in Figure  7 , where we highlight that the quality of the outcome is generally associated with team profiles and communication activity from Slack, while the level of engagement in the program (process) as judged by the organizing team is associated with self-reported measures of collaborations and activity.
Conclusion One challenge that organizers of programs similar to Crowd4SDG face is to support with evidence their decision related to the formation of citizen science teams and their management, as well as the directions they give to participants to maximize the relevance of the data they generate, their ability to develop innovative solutions and eventually their impact on the problem they are addressing. Here we implemented a monitoring framework leveraging digital traces as well as self-reports to gather compositional and social interaction data during the course of the makeup of citizen science projects. This approach complements traditional outcome-driven metrics in the evaluation of Science  (Fortunato et al. , 2018)  by emphasizing the importance of the participation process  (Jaeger et al.
For each participant, we then inverted that model and obtained the value of internal noise (by exhaustive search between 0 and +5 std) that minimized the error between the observed and predicted values for that participant's consistency and bias. As in previous studies  9  , we estimated internal noise conservatively between [0; +5 std] in order to avoid unreliable estimates at large values, a known problem with double-pass procedures (see Appendix A). Internal noise values in the upper side of that range (e. g. illustrated in Figure  3  between 4. 8 and 5) may either correspond to true internal noise values, or to larger values for which we could not provide an exact estimate. Both of these analyses (internal representations and internal noise) were conducted using an opensource Python toolbox built for this purpose (https://github.
fMRI procedures Functional MRI data were collected using a Symphony/Sonata 1. 5-Tesla whole-body high-speed imaging device equipped for echo planar imaging (Siemens Medical Systems, Iselin, NJ) with a 12axis gradient head coil. We restricted head movement by using expandable foam cushions. After acquiring an automated scout image, field homogeneity was optimized by conducting shimming procedures. A high-resolution 3D magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence was gathered in a sagittal plane, with scan parameters as follows: repetition time/echo time/flip angle as 2. 73 s/3. 39 ms/7 • , respectively. Gradient echo functional images were gathered in 24 coronal slices angled perpendicular to the anterior commissure-posterior commissure line with slice thickness at 7 mm, skip 1 mm; voxel size, 3.
Data analysis. Functional MRI Image preprocessing and statistical analyses were performed using SPM8, a statistical parametric mapping software package to perform the functional region of interest (ROI) based analyses (http://www. fil. ion. ucl. ac. uk/spm/; Wellcome Department of Imaging Neuroscience, London, UK). Functional images of each participant were co-registered to their high-resolution structural MRI image, smoothed (4 mm), and spatially normalized with standard stereotactic space (Montreal Neurological Institute, MNI). Hypotheses were tested as contrasts in which linear compounds of the model parameters were evaluated using the t-statistics, which were then transformed into z-scores. We computed contrasts in each participant (the first level) and then used those contrasts in group analyses (the second level).
The first-level contrasts computed in each participant were: fearful vs. happy (all 3 blocks of fearful versus all 3 blocks of happy), and, to examine the change in activation across fearful face blocks, fearful block 3 vs. fearful block 1. We added translation and rotation motion regressors to the first-level statistical models to control for movement. In the second-level group analyses, we ran voxelwise one-group t-tests using the fearful vs. happy contrast, and the fearful block 3 vs. fearful block 1 contrast. We chose to use hypothesis-driven functional regions of interest (ROIs) following earlier studies with this same task  (Shin et al. , 2005) , which examined the brain activation in an independent sample of PTSD participants in response to fearful vs. happy facial expressions. Specifically, we extracted data from the following three functional ROIs in the amygdala (a sphere with a 4 mm radius centered on the following coordinates): x = -20, y = -8, z = -18; x = 22, y = 4, z = -14; x = 18, y = -6, z = -20.
We also extracted data from the following four functional ROIs in the rACC/vmPFC (a sphere with a 4 mm radius centered on the following coordinates): x = 14, y = 48, z = 8; x = 0, y = 46, z = -10; x = 16, y = 38, z = 22; x = -12, y = 52, z = -10. For each functional ROI, we extracted BOLD beta values per condition per subject from each region and used this information in correlational analyses to address our a priori predictions. Post hoc power analyses suggest good power (>0. 80) to detect an effect size of 0. 61. Based on z-scores, none of our variables of interest contained an extreme outlier (>3. 29). In exploratory analyses, we used separate independent-sample t-tests to determine whether the extracted ROI data differed between participants who completed vs. dropped out of treatment.
55, p = 0. 03 (Figure  1C ) and positively correlated with SPRINT change scores when controlling for SPRINT pretreatment scores r(13) = 0. 63, p = 0. 01. Finally, an extracted BOLD signal change in the right amygdala (MNI; x = 18 y = -6 z = -20) was negatively correlated with CGI-S percent improvement scores r(14) = -0. 62, p = 0. 01 and CGI-S change scores when controlling for pre-treatment CGI-S scores r(13) = -0. 65, p = 0. 01. Thus, lesser amygdala activation and greater vmPFC activation in response to fearful vs. happy facial expressions were associated with greater improvement following PE. No significant associations were found between pre-treatment symptom severity (SPRINT, CGI-S scores) and our ROI data (ps > 0. 11).
Fearful block vs. fearful block contrast BOLD signal change in one of the three amygdala ROIs and none of the four rACC/vmPFC ROIs was significantly correlated with symptom-related outcome measures. Specifically, BOLD signal change in the left amygdala (MNI; x = -20, y = -8, z = -18) across blocks of fearful facial expressions was negatively correlated with SPRINT percent improvement scores r(14) = -0. 62, p = 0. 01 (Figure  2 ). These extracted values were also negatively correlated with SPRINT change scores when controlling for SPRINT pre-treatment scores r(13) = -0. 55, p = 0. 03. Thus, a greater decline in amygdala responses from the first to the last fearful facial expression block was associated with greater symptomatic improvement. No significant associations were found between pre-treatment symptom severity (SPRINT, CGI-S scores) and our ROI data (ps > 0.
Inclusion and Exclusion Criteria of Participants Participants were recruited according to the following inclusion criteria: young subelite middle-distance runners under 18 years of age from the same training center with the same anthropometric data (difference not significant), nutrition, load training, and recovery techniques. The exclusion criteria were a history of musculoskeletal injuries and disorders, significant difference in anthropometric data, smoking, and consumption of alcohol or the regular use of medication for any reason. The participants were required to avoid any physical activity for 48 h before the day of the test and were excluded from the study if they were uncomfortable with any aspect of the experimental protocol.
Results A total of 50 young athletes volunteered to participate in this study. After a diagnosis of malocclusion, 13 athletes (5 females and 8 males) without malocclusion formed the control group (CG) and 37 athletes (21 females and 16 males) with malocclusion formed the experimental group (EG). The anthropometric data in the control and experimental groups were similar (age: p = 0. 46; BMI: p = 0. 76), as shown in Table  1. The values for the MAS, VO 2max , heart rate, SAP, DAP, LBP, and LAP in the control and experimental groups are summarized in Tables  2  and 3. After analyzing our data, we found that the differences in the athletic performance and biomarkers were not statistically significant between the athletes with malocclusions and those without malocclusions, as summarized in Figures  2 3 4 5.
Data analyses. The data were analysed using R (version 4. 1. 0) and R Studio software (version 1. 3. 1093). We first verified that our multisensory integration task succeeded in showing the typical effects of the tactile stimulation delay on reaction times (RT) in each of the three stimuli used (see Supplemental Materials for procedure and results). Extent of the different spaces. To determine the individual extent of RS, PS and IPS, we averaged for each participant and each stimulus the distance of the visual stimulus at the time of the response in the reachability judgement task, and in the discomfort and comfort distance judgement tasks, respectively. The extent of MIS was determined by identifying the farthest distance at which the bimodal trials induced facilitation effects as compared to the unimodal trials in the visuotactile integration task (see Supplemental Materials for detailed procedure).
Background Cystic fibrosis (CF) is a rare genetic disease for which systematic newborn screening has been implemented in France for the past 20 years. 1  The CF care pathway is organised by specialised paediatric CF centres and adult CF centres (in French: CRCM-"Centre de Ressources et de Compétences Mucoviscidose") within the rare disease network,  2  and then by transplant centres when lung transplant is indicated. The French CF Registry lists patients followed in CF centres and transplant centres, records their health data, and publishes an annual data report on the evolution of their health status. According to the data from 2021, 7513 patients were registered and followed in the 45 CF centres, of whom nearly 62% were adults (4576), and 955 adults had undergone lung transplantation (representing nearly 21% of all adult patients).
Acknowledgements Kathy Sabadosa ,  Karen Homa ,  Pamela Mertz  from the  Cystic Fibrosis Foundation  for their contribution and support to the design of the study, their participation in project meetings and discussions about the results of the  US Patient and Family Experience survey. Vaincre la Mucoviscidose  for their support, including financial subsidies and their contribution to the recruitment of the patient and parent co-researchers.
Funding Funding was provided by the French Institute for Public Health Research (IReSP) for the entire project in the frame of the call for project "AAP General 2018" (Decision IReSP 07/16/2019): €197 320. Funding was provided by the patient association Vaincre la Mucoviscidose (VLM) for the training of the patient and parent co-researchers (Decision VLM 06/22/2019): €9,360.
V2R Natural Mutations Over 260 mutations have been reported to date on hV2R (the Human Gene Mutation Database at the Institute of Medical Genetics, Cardiff, UK) and recently reviewed  [9]. These mutations are associated with either loss or gain of function associated with human diseases. Recent V2R structures are helping to understand the physiological role of their mutations and, hopefully, to design drugs that can counteract their effects. For example, in most class A GPCRs, D136 3. 49 and R137 3. 50 interact with each other through an ion lock The V2R, when coupled with Gs, induces cAMP production. Interestingly, there is a direct contact with the side chain of R137 3. 50 , which is part of the ionic lock motif and the free carboxylic acid function of the Gs C terminus. This was never observed before between a GPCR and a G protein of any family (Gs, Gi, Go or Gq).
V2R Natural Mutations Over 260 mutations have been reported to date on hV2R (the Human Gene Mutation Database at the Institute of Medical Genetics, Cardiff, UK) and recently reviewed  [9]. These mutations are associated with either loss or gain of function associated with human diseases. Recent V2R structures are helping to understand the physiological role of their mutations and, hopefully, to design drugs that can counteract their effects. For example, in most class A GPCRs, D136 3. 49 and R137 3. 50 interact with each other through an ion lock and are key points in the balance between active and inactive states. In the V2R, R137C or R1373L missense mutations induce constitutive activity toward Gs coupling. This activity, inducing water retention and hyponatremia, can be explained from a structural point of view.
AVP-like Toxins The vasopressin-oxytocin family of peptides is of very ancient lineage and found in organisms as diverse as hydra and man. AVP-like toxins have been found only in cone snails and named conopressin (Table  1 ). A total of 15 conopressins are classified in the ConoServer database (https://www. conoserver. org/ accessed on 8 March 2023), which have been de-scribed since 1987  [62]. Most pharmacologically characterized conopressins show activities on V1a/b receptors as agonists or antagonists. Conopressins strictly conserve the AVP cysteine residues in positions 1 and 5 to keep the cyclic structure. The hydroxyl group of the AVP TYR2 is rarely conserved in conopressin. The AVP PHE3 is never conserved in conopressins but is occupied by hydrophobic residues, which are probably compatible with the hydrophobic pocket of the V2R binding site.
Study design Altogether 15 countries/areas took part in the first ICOSS study with a cross-sectional survey collecting data online in each country in their native language between May and August 2020. The methods of the first ICOSS and the development of the questionnaire have been described elsewhere  [4, 16, 18]. Briefly, the first ICOSS questionnaire comprised 106 items (50 questions) on sociodemographic variables, sleep patterns, sleep disorders, psychological symptoms and quality of life  [16]. The survey was completed anonymously on a web survey platform on a University website or in the public domain. After gathering data on important sociodemographic variables (i. e. age, gender, marital status, ethnicity, education, profession, working status, confinement, and economic situation), the survey included questions on EDS, EQS, and fatigue.
In addition, EQS was defined as long sleep with either EDS or fatigue (or both). The presence and severity of these four symptoms were asked retrospectively at one time point "before" and "during" the pandemic period. The increase, decrease, and stability of these symptoms during the pandemic was computed by subtracting the response during the pandemic by the prepandemic value. Only those respondents with complete answers to these four questions were included to the analytic sample and no imputations for missing data were used. The following information was also collected with estimates for the periods before and during the pandemic: The use of sleeping pills (hypnotics by prescription)", the quality of life and health using the 5-item World Health Organization Well-Being Index (WHO-5)  [20] , and depressive and anxiety symptoms by the Patient Health Questionnaire-4 (PHQ-4)  [16, 21, 22].
Statistical analysis The normality of the distributions were tested using the Shapiro-Wilk test. Means and standard deviations are given for the normally distributed variables and medians and interquartile ranges are reported for non-parametric variables. Meta-analyses were used to compute occurrences of EDS, ETFS, EQS, and fatigue in different countries. To strengthen the results, we used randomeffects models in all tests. For occurrences and rates, the 95% confidence limits are given. Logistic regression analyses were conducted by weighting of data by the number of inhabitants in the country/area of interest and by the number of responders in that country. Different countries were used as strata. Observations were used as sampling units and non-parametric statistics were applied with proportions, and 95% confidence intervals were calculated.