dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical methods For data cleaning and statistical analysis, R software was used (R Core Team 2013) along with several additional packages. A descriptive analysis was performed in order to describe the main characteristics of the study participants, some numerical variables such as age and duration of ART were converted into categorical variables, frequency and proportions were reported. In order to manage missing values, a simple imputation was performed using the K-Nearest Neighbor  [20]  method with the value of five. Then, bootstrap method stepwise linear regression approach was done using the package caret  [21]. Variable selection for final linear regression model was done by stepwise AIC approach in order to find the subset of variables resulting in the best performing model, in a combination of forward and backward selection.
Some countries had lower number of patients and especially variables as homosexual relationship status or taking drugs and Viagra, which was a limit for the study. In addition, clinical data was self-reported by the participants, and a recall bias may have affected the data, as well it was difficult to establish temporal relationships between variables. There are behavioral variables that could not be modified by interventions, such as the care or advice of healthcare workers. Our data are from a cross-sectional study and they cannot claim the determination of causality, so they are limited to describing the associations.
Their aim is to provide responses to the changing needs of frail seniors, primarily by acting preventively and curatively to allow them to stay at home by providing support. Two major issues must thus be addressed in the smart digital platform conception: • Professional coordination: better communication in order to adapt services to the various and complex care situations. • Ethical management: use of health data in order to predict frailty aggravation. The scope of the PALOMA platform project therefore includes the support and the deployment of CRT by offering a global, interoperable and predictive digital tool (see Fig. 1 ). The digital solution has been designed to help elderly people maintain their independence and stay at home by improving the coordination of social, medico-social and health sector players in order to: • Provide better support for frail seniors, to preserve social and family networks by aggregating a set of existing but dispersed solutions.
Step 4: Use Case Design to Engage Discussion with the Ethical Committee In order to get a concrete representation of the potential risks of the smart digital platform, 4 use cases have been designed. They consist of 4 different potential types of data use by 4 types of elderly and professional personas (i. e. fictional character). Each of them has been designed with the help of healthcare professionals (1 gerontologist, 2 nurse coordinators in the RAIPS Project, 1 nurse coordinator in a nursing home). To perform the data use case design, several criteria have been described: -The persona story and way of life (personal characteristics, assistance plan, past medical history) -The inclusion in the smart digital platform (context of the persona data use) -The organizational activity of the digital platform simulation (what is happening? Who is responsible for what, when does the situation occur, and how are the roles organized? What is the platform doing and what kind of results does it provides? How does the professional in charge take care of the situation?) -Worst case scenario: simulation of what the worst could happen? -Ethical issues: what are the care values, rules or regulations at stake? These smart digital health use-cases were presented at HSTV Ethic Committee group on May 24, 2023.
Acknowledgments. The research was supported by the  Brittany IT Services and IT Consulting (GCS E-santé)  and the  Regional Health Authority (Agence Régionale de Santé de Bretagne). The authors would like to thank  Lise Delcourt , ARACT work organization engineer who helped designing the workshop. The authors would also like to thank the various HSTV's living lab community members,  Gérard le Goff ,  Lionel Jan ,  Martin Favre ,  Jean-Bernard Gauvin ,  Carole Zisa-Garat  for helpful discussion on the smart digital health coordination platform conception evaluation.
Cannabis is the fourth psychoactive substance to be legalized which are of far-reaching significance to the world. We analyzed data from the 2019 Global Burden of Disease Study (GBD) to estimate the incidence and prevalence of cannabis use disorder (CUD) and calculated the disease burden of CUD in 204 countries and territories and 21 regions over the past three decades. We reported disease burden due to CUD in terms of disability-adjusted life years (DALYs), age-standardized rate (ASR), estimated annual percentage change (EAPC), and analyzed associations between the burden of CUD and sociodemographic index (SDI) quintiles. Globally, the number of incidence cases of CUD was estimated to be increasing by 32. 3% from 1990 to 2019 and males are nearly double higher than that of female. DALYs increase 38.
, 2018). At present, cannabis is the fourth psychoactive substance to be legalized after alcohol, tobacco, and coffee which are of farreaching significance to the world  (Degenhardt & Hall, 2012). It seems to increase the frequency and side effect of cannabis use, which leads to many adverse effects on public health  (Hall et al. , 2019; Hall & Lynskey, 2020). From 1990 to 2019, the burden of substance use disorders has increased, including alcohol, amphetamines, cannabis, and cocaine as substance use disorders  (Degenhardt et al. , 2018; Pan et al. , 2020; Whiteford et al. , 2013). In this research, we analyzed data from the 2019 Global Burden of Disease Study (GBD) to estimate the incidence and prevalence of CUD and calculated the disease burden of CUD in 204 countries and territories and 21 regions over the past three decades.
Data Source and GBD Estimation Method The GBD of CUD estimation process including case definition, input data, age and sex splitting, data adjustment, and modeling strategy is clearly defined in the appendix 1 of systematic analysis for the Global Burden of Disease Study 2019  (Vos et al. , 2020). Sources for the disease burden data of CUD can be explored using the online GHDx (Global Health Data Exchange) data source query tool (https:// vizhub. healt hdata. org/ gbd-resul ts/). We obtained incidence number, prevalence number, DALYs, ASIR, ASPR, and ASDR of CUD from 1990 to 2019 according to sex, 21 regions, and 204 countries and territories. Based on the SDI which combines information about the economy, education, and fertility rate of countries around the world, as a representation of social and economic development present in 2019, the 204 countries or territories were divided into five quintiles: low, low-middle, middle, high-middle, and high.
The general methods used in GBD 2019 are described in detail on the official website (http:// www. healt hdata. org/ gbd/ 2019), which also provides the SDI values from 1990 to 2019 at the global, regional, and national levels. To analyze global trends, we also assessed the trends of CUD according to the following age stratification used in  GBD 2019: < 20, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, and 80  plus years old which will generally be reflecting adult CUD, for adolescent combined into < 20 group. Incidence, prevalence, DALYs, ASIR, ASPR, and ASDR were extracted from the GBD database. EAPC was manually estimated based on its definition. In this article, we highlight the absolute value of incidence and prevalence, the variation of ASIR, ASPR, and ASDR stratified by SDI over time, as well as the EAPC value of three parameters above to be reported by splitting value into five intervals that based quintiles value.
Limitation There are some limitations to our study. First, data gaps, variable data quality, and uncertainty after modeling with these data will overestimate or underestimate the disease burden of CUD. For example, an important change in the estimated cause of death that restricts the use of all substances is the ICD code. Some countries have additional codes that allow more accurate causes of death to be attributed to specific substances, which leads to data uncertainty. Second, public health and data collection in some countries are relatively weak, and it is doubtful whether CUDs of disease burden are accurately reflected. Third, GBD uses the ICD-10 system to classify injuries and diseases. The introduction of DSM-V by the American Psychiatric Association included a shift from DSM-IV abuse and dependence to a category of use disorder, defined as mild, moderate, and severe in severity.
Moderate to severe substance use disorders in DSM-V may be higher than DSM-IV and ICD-10 dependence, which means that if DSM-V prevalence estimates are used, the estimated burden of substance use disorders may be higher  (Pan et al. , 2020). Fourth, more and more evidence showed that there is a causal relationship between cannabis use and traffic accidents, and whether accidental injuries are included in the disease burden of CUD is still controversial. Furthermore, we observed inconsistencies from similar studies. Besides the above limitations, we also found examples of significant differences in findings between regional studies and the GBD database. Based on the data from the GBD database, we can conclude that the incidence of CUD, prevalence, and DALYs in the European region have declined over the last three decades.
However, the study by Manthey et al. expressed the fact that cannabis use and related problems in Europe, estimated using data from the European Monitoring Center for Drugs and Drug Addiction (EMCDDA) survey, have increased over the last decade  (Manthey et al. , 2021). The prevalence of CUD on GBD data is estimated and adjusted based on any type of cannabis use including originally reported cannabis use, regular (ie. weekly) cannabis use, and cannabis dependence. The different trends between two research have questioned the CUD estimates. This may be due to the fact that CUD data from statistical models is smoothed estimates and that GBD input data has some inherent problems. Some studies  (Manthey & Rehm, 2019; Xia & Huan, 2017)  have also questioned the consistency of selected GBD estimates, and therefore, the CUD prevalence estimation procedures need to be revisited and improved.
Group 1: WT mice treated with saline and sacrificed at P8; Group 2: WT mice treated with saline and sacrificed at P90; Group 3: Magel2-KO mice treated with saline and sacrificed at P8; Group 4: Magel2-KO mice treated with saline and sacrificed at P90; Group 5: Magel2-KO mice treated with OXT and sacrificed at P8; Group 6: Magel2-KO mice treated with OXT and sacrificed at P90; Group 7: WT mice treated with saline and tested for USVs at P8; Group 8: Magel2-KO mice treated with saline and tested for USVs at P8; Group 9: Magel2-KO mice treated with OXT and tested for USVs at P8. The day of birth was considered postnatal day 0 (P0). A post-hoc power calculations was performed on the autoradiography datasets recently published  (Bertoni et al. , 2021). Such analyses was run with the G * Power 3.
1 software (RRID:SCR_013726, University of Düsseldorf). 1 The effect size f was derived from the available datasets (1. 83 for DG and 1. 66 for dCA2/CA3), and transferred into a post-hoc ANOVA (Fixed effects, omnibus, one-way) test with set number of groups = 3, total sample size = 9 and alpha error = 0. 05. We then calculated the effective power achieved in  Bertoni et al. (2021)  for the DG and the dCA2/CA3, which were 0. 97 and 0. 93, respectively, thus indicating that 3 mice/group are sufficient for this type of analysis.
Densitometric gray level were measured using NIH ImageJ software. To obtain single ROI values, the gray level of the film   (Franklin and Paxinos, 2007) , are highlighted for the most representative section. A color code was used to identify the different areas analyzed: violet: Pir, piriform cortex; emerald green: MPOA, medial preoptic area; blue: LS, lateral septum; brown: VMH, ventral medial nucleus of the hypothalamus; three values of olive green for MeA, medial amygdala; CeA, central amygdala; BLA, basolateral amygdala; orange: dCA2/CA3, dorsal and vCA2/CA3, ventral field CA2 and CA3 of the hippocampus; pink: DG, dentate gyrus; red: PVT, paraventricular thalamic nucleus. Within each representative autoradiographic section, whose border color follows the color code reported above, the ROI used for acquisition of the data is depicted in red.
Ultrasonic vocalization (USVs) Briefly, the mother and litter were left to habituate to the testing room for 30 min, then P8 pups were separated from the mother and gently transferred to a new cage on a heating pad (37 • C). After 5 min, each pup was transferred in an anechoic box (54 × 57 × 41 cm; Coulbourn instruments, PA, USA) and USVs were immediately recorded for 300 s by an ultrasonic microphone (Avisoft UltraSoundGate condenser microphone capsule CM16/CMPA, Avisoft bioacustic, Germany) sensitive to frequencies of 10-250 kHz. Recordings were done using Avisoft recorder software (version 4. 2) with a sampling rate of 250 kHz in 16-bit format. Data were analyzed for the total number of calls using Avisoft SASLab software.
Statistical analysis All graphs have been created and datasets analyzed with GraphPad Prism ver. 8. 0. 2 (GraphPad Software, Inc. ). Molecular data were analyzed by analysis of variance (ANOVA). In particular, for the combined analysis of age, sex and genotype, for each brain area a three-way ANOVA was applied, followed by Tukey's multiple comparisons post-hoc test. Similarly, for the analysis by region of the combination of age, sex and OXT treatment, three-way ANOVA was applied, followed by Tukey's multiple comparisons post-hoc test. For treatment efficacy, two-way ANOVA followed by Tukey's multiple comparisons post-hoc test was applied, to account and correct for multiple testing. Adjusted p-values of p < 0. 05 were deemed significant. USVs data were analyzed by One-way ANOVA followed by Tukey's multiple comparisons post-hoc test.
Data were analyzed by three-way ANOVA, followed by a Tukey's multiple comparisons post-hoc test. *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. When a comparison was approaching statistical significance, the corresponding p-value was reported on the appropriate graph. Datasets and detailed statistical analyses are reported in the Supplementary Table  1. down-regulation of OXTR levels (VMH and PVT) below those observed in WT animals. In CeA, dCA2/CA3, PVT as compared to those observed in WT females, the final effect of the OXT was a down-regulation of OXTR levels below those observed in WT animals (Figure  5B ).
Each histogram represents data expressed as mean + SEM of multiple datapoints collected from three animals. Filled bars are used for vehicle (Veh) treated Magel2-KO, striped bars are used for oxytocin (OXT) treated Magel2-KO; blue is used for males of both treatment groups and ages, red for females of both treatment groups and ages. Data were analyzed by Three-way ANOVA, followed by a Tukey's multiple comparisons post-hoc test. *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. When a comparison was approaching statistical significance, the corresponding p-value was reported on the appropriate graph. Datasets and detailed statistical analyses are reported in the Supplementary Table  2. (Hammock and Levitt, 2013) , revealing a peak of OXTR expression in the 2 nd postnatal week, which parallels maximal synaptogenesis and experience-dependent plasticity.
Funding This work was supported by the  Fritz Thyssen Foundation  grant  10. 16. 2. 018MN  to BC and FM and  Telethon Foundation  grant  GGP19103A  to BC. FSa was the recipient of a Ph. D fellowship from the  French-Italian University, Bando Vinci 2017. AB was the recipient of a fellowship from the  Foundation for Prader-Willi Research (USA). VG was the recipient of a post-doctoral Fellowship from  Fondazione Umberto Veronesi. CP was the recipient of  CNR predoctoral fellowship  funded by the  Telethon Foundation  (grant  GGP19103A  to BC).
During the first year of the COVID-19 pandemic, the Methods Sub-Group of the WHO COVID-19 Social Science Research Roadmap Working Group conducted a rapid evidence review of rapid qualitative methods (RQMs) used during epidemics. The rapid review objectives were to (1) synthesize the development, implementation, and uses of RQMs, including the data collection tools, research questions, research capacities, analytical approaches, and strategies used to speed up data collection and analysis in their specific epidemic and institutional contexts; and (2) propose a tool for assessing and reporting RQMs in epidemics emergencies. The rapid review covered published RQMs used in articles and unpublished reports produced between 2015 and 2021 in five languages (English, Mandarin, French, Portuguese, and Spanish).
We searched multiple databases in these five languages between December 2020 and January 31, 2021. Sources employing "rapid" (under 6 months from conception to reporting of results) qualitative methods for research related to epidemic emergencies were included. We included 126 published and unpublished sources, which were reviewed, coded, and classified by the research team. Intercoder reliability was found to be acceptable (Krippendorff's α = 0. 709). We employed thematic analysis to identify categories characterizing RQMs in epidemic emergencies. The review protocol was registered at PROSPERO (no. CRD42020223283) and Research Registry (no. reviewregistry1044). We developed an assessment and reporting tool of 13 criteria in three.
II. Methods The Methods Subgroup of the WHO COVID-19 Social Science Research Roadmap Working Group conducted this rapid review from October 2020 to February 2021 in a process that included serial parallel review processes and 15 virtual research and coordination meetings. We investigated the framing and application of rapid qualitative methods during recent epidemics, with an emphasis on the COVID-19 pandemic. We identified data collection tools, research questions, research capacities, analytical approaches, and strategies that were employed to speed up data collection and analysis in their epidemic and institutional contexts. The review objectives were as follows: 1. To synthesize the implementation and uses of RQMs in epidemic emergencies, their content, contexts in which they are used, research questions to which they are applied, research capacities required, and how they speed up data collection and analysis 2.
Team 1 and Team 2 set no limits on geographic boundaries. Team 3 researched Spanish and Portuguese language publications in the South/Central American region, noting that there had been no prior reviews of RQMs in epidemics conducted in South/Central America and that a full search for the entire world would produce more sources than could be evaluated during a rapid review. All other searches were set to cover 2015 to the present to include major recent epidemic emergencies, namely Ebola and Zika) (See S1 File for search strings). The teams worked independently on translation of search terms, database selection, and manual inclusions, and met weekly to ensure coherence and consistency.
B. Search databases and strategy Search strategy. Initially, six-person Core Group tried to use English search terms from a previously published study  [3]  but could not successfully replicate the resource extraction because we did not use all the same databases, notably for our non-English language searches, and because the prior study offered a "sample search strategy" that was not replicable across all databases due to their varied constructions. We then sought input from expert peers in the larger Methods Subgroup to develop new common search terms, translated into five languages. After initial testing of the search terms in English and translation and testing to other languages, all research teams implemented the search using a shared translated search thread in 3-5 database searches.
Extracted data included full citation and detailed textual descriptions of the key methodological dimensions in the papers, including study objectives, tool(s) used, time frame of the study (initially designated six months or less from study conception to reporting, but subsequently, delineated as data collection completion because sources lacked clarity about study duration), training required to conduct the study, recruitment procedures, ease of data collection and analysis, community engagement and reporting of results back to communities and stakeholders, and impact of the study. To extract data from the included articles, teams used a pre-defined template developed by the six Core Group members (S2 File). The Core Group developed a 25-point template to extract data on the creation, contexts, implementation, and impact of RQMs in epidemics.
Contextually, the numerous demands for RQMs in the COVID-19 pandemic also influenced Core Group attention to specific data extraction points, for instance, a tool's capacity to identify social vulnerabilities or to be repurposed in other epidemic settings. A single reviewer extracted the data; the language team leader read the source and reviewed the data for accuracy and completeness. Again, disagreements about coding were resolved by discussion within individual teams and within the core group to reach consensus. In Phase 2, the 11-member Methods Subgroup analysed and reviewed code sheets, leading to iterative refinements in coding and classification (S3 File). A second round of coding was conducted to clarify analysis regarding the following coding criteria: tool selection, training, rigor and replicability of research methods, analysis techniques, and study design.
In the second round of coding, expert researchers reviewed abstracts and first coding for each source and read the entire publication or consulted the original language team for additional information. In our final round of extraction, we entered all data into MS Excel to identify and organize key features of selected RQMs. This tabulation of features included the range of objectives, timing of studies, types of epidemics, peer-reviewed publication or grey literature, locations, tools and associated measures to enhance rapidity, analytical approaches, and impact of the study, among other features. These assessments provided the basis for synthesizing our global comparison of all included studies. To ensure that all Methods Subgroup members coded in a consistent and coherent fashion, we evaluated intercoder reliability (ICR) by using an online tool, ReCal OIR ("Reliability Calculator for Ordinal, Interval, and Ratio data")  [11].
E. Data analysis and synthesis We employed thematic analysis to identify the categories characterizing RQMs in epidemic emergencies, to assess RQM adaptability to specific contexts and yield robust qualitative evidence, and to evaluate impact. Notably, many identified RQMs lacked sufficient descriptive detail regarding methods (see Results). These thematic categories thus constituted key indicators of the quality of RQM studies in epidemic emergencies. In the COVID-19 pandemic context, the Methods Subgroup sought to extend our analysis and apply our expertise so that RQM users could draw from our rapid review and to design and implement RQMs in epidemic emergencies, to evaluate their quality, and to engage in transparent reporting. To that end, the Methods Subgroup grouped and reorganized identified themes into chronological order and subsequently, through extended collective reflection and discussion, elaborated the 13 key criteria and three domains of our RQM assessment and reporting tool.