dataesr/scientific-paragraphs-categorization huggingface dataset:
Approximately 1300 active participants complete assess ments in any 3-month period. Patients eligible for the SPIN COVID-19 cohort had a self-reported systemic sclerosis diagnosis (not confirmed by a physician), were at least 18 years old, and were fluent in English or French. Ongoing SPIN Cohort participants were invited to enrol by email and via notices during regular SPIN Cohort assessments. Recruitment announce ments were also posted via social media and through patient organisation partners. Potential participants accessed a Qualtrics (Provo, UT, USA) web portal for information and to consent. Ongoing SPIN Cohort participants provided their SPIN username to link COVID-19 Cohort data to demographic and medical data (person-level deterministic linking with 100% successful linkage). New participants provided demographic and disease-related information.
To minimise the possibility of bias from missing outcome data, we used multiple imputation by chained equations by means of the mice package to generate 20 imputed datasets, using 15 cycles per imputed dataset. Variables in the mice procedure included randomisation block, intervention group, number of intervention sessions attended, measures of all primary and secondary outcomes at baseline and post-intervention, age, sex, systemic sclero sis disease subtype, years since diagnosis, country (Canada, France, UK, Australia, other vs USA), and race-ethnicity (Black and other vs White). Pooled standard errors and associated 95% CIs were estimated by means of Rubin's rules. 25 o estimate average intervention effects among compliers (defined as attending ≥6 sessions), we used an instrumental variable approach to inflate intention-totreat effects from main models by the inverse probability of compliance among intervention group participants (complier-average causal effect analysis); 95% CIs were constructed via a cluster bootstrap approach, resampling at study randomisation block and participant levels.
Second, initially specified secondary out comes included stress and social interaction frequency; both were removed before collecting outcome data owing to concern about assessment length. Third, the COVID-19 Fears Questionnaire was not in the protocol because it was under development. Once validated, it was included as a secondary outcome. Fourth, the CSQ-8, which assessed intervention participant satisfaction, was added subsequent to the initial protocol. Fifth, post-hoc dichotomous and subgroup analyses were done (see appendix pp 11-21).
Analysis of management approaches for constipation was conducted on those individuals who completed all the monthly surveys (n = 1293 or 82% of enrolled), while the behavioral activity analysis was performed on those individuals who satisfied the previously described data density criteria (n = 756 or 48% of enrolled). Study participants were relatively young (mean age 36. 6), and mostly female (72. 8%), Caucasian (88. 8%) and well educated (69. 6% with at least a college degree). In terms of general management approaches, most participants (34%) reported a combination of lifestyle changes with waiting to see if symptoms improved. Only a small proportion of participants regularly used constipation medications (7. 7%) (Table  1 ). Fitbit data coverage was generally high for the enrolled participants (mean coverage 82.
DISCUSSION Despite some non-interventional studies suggesting that constipation and irregularity were associated with less active lifestyles  20, 21  , empirical investigations into this claim do not always produce consistent evidence. For example, whereas one surveybased study reported no relationship between physical activity and self-reported constipation among 1069 respondents  22  , two other large-scale survey studies of similar design but comprising nearly 40,000 and over 60,000 women found lower prevalence of constipation among women who reported some degree of physical activity versus a sedentary lifestyle  23, 24. Daily population refers to individuals who completed the daily surveys. Behavioral population refers to a subset of the daily population who satisfied data density requirements for the analysis of wearable activity metrics.
Columns 3-7 reflect changes with respect to the baseline measure. Symptom severity was rated on a 0-4 scale. * Indicates statistically significant result (q-values < 0. 05). the results to prior studies that use standard diagnostic criteria like the Rome  34. In summary, this study is the largest virtual study of activity and constipation using passively and actively collected PGHD with more than 100 million digital activity data points. The study allowed us to measure activity based associations with constipation and medication use at minute-level granularity over extended periods of time, which is not possible with conventional real-world approaches  35. The study findings were also consistent with prior research on the effects of physical activity on symptoms of constipation.
** Increases are for a change in 1-point of symptom severity. • Self-reported use of at least one over-the-counter medication in the past 3 months. • Access to a Fitbit activity tracker capable of collecting steps, heart rate or sleep data that was connected to the Achievement platform and willing to wear the tracker daily during the day and at night for the entire study. • Willingness to respond to daily short questionnaires and report whether they experienced constipation symptoms the previous day. Potential participants were excluded from participation if they selfreported a diagnosis of Inflammatory Bowel Disease (Ulcerative Colitis and Crohn's Disease), irritable bowel syndrome, gastrointestinal malignancies, or chronic constipation. Potential enrollees into the study were determined by sending a screening questionnaire to interested participants.
Survey data variables Participants enrolled in the study completed three survey questionnaire types, each with different time frequencies. As described above, a single baseline survey was administered to potential participants to determine eligibility for the study, and to gather baseline demographic data as well as information about medication use and constipation symptoms. A monthly survey (every 4 weeks) was administered to enrolled participants (four surveys total per participant) asking them to describe self-reported management patterns for constipation and quality of life metrics. Lastly, a daily survey asking about constipation symptoms and medication use, started within 24 h of enrollment, was administered to every participant for the duration of the study (16 weeks or 112 days total).
Demographic data captured in the baseline survey include date-of-birth, gender, race and ethnicity, highest educational level, and comorbid conditions. Body mass index (weight in kg per meter  2  ) is computed from self-reported height and weight data. Age was determined by the date of birth as reported at the time of enrollment. In the monthly survey, participants were asked about management approaches on two dimensions in the prior month for symptoms of constipation/irregularity. The first dimension focused on general management approaches, which were categorized as Treat with medication immediately, Treat with lifestyle/dietary changes, Watchful waiting, or No treatment done. The second addressed the frequency of medication use, with options for Regular use of medications, As-needed use, and no-use of medications.
Patient generated activity tracker data The Achievement platform uses the Fitbit application programming interface (API) to pull data on steps, heart rate and sleep for each participant at minute level granularity (e. g. steps per minute, heart beats per minute, sleep states of asleep, restless or awake). The Fitbit API includes for each sleep state a binary (True/False) designation for the "main" sleep period of the day. Each activity data stream (steps, heart rate, and sleep) was then aggregated into a set of daily activity features engineered in collaboration with a medical domain expert. The features were designed to capture behavioral episodes that reflected a priori beliefs about the effect that constipation might have on these different types of activity. Table  5  shows a summary of the activity features along with a brief description.
A total of 38 daily activity features were engineered (24 steps, 9 sleep, and 5 heart rate features). Skewness and kurtosis features for steps and heart rate were computed for each daily distribution. Features over a specific duration (e. g. , 5, 6, 30, or 60 min) were determined with a sliding window of that duration over the entire day. The Sleep Efficiency score is provided from the Fitbit API and is determined by the proportion of time asleep (main sleep at night) to the time spent in bed. Analysis using PGHD (activity data and daily survey responses) was conducted on a subgroup of the study cohort with high density activity tracker data (the Behavioral Analysis Population). To define this subgroup, we followed a three-step process that has been previously used:  38, 39  1. Fitbit wear time for each participant computed based on available minute-level data, 2.
Comparison of means across different groups was conducted with Kruskal-Wallis (non-parametric) ANOVA tests or the Wilcoxon rank sum test when appropriate. For all the analyses, we adjusted p-values (q-value ≤0. 05 significant) for multiple comparisons with the Benjamini-Hochberg procedure for false discovery rate (FDR). All statistical tests were two-sided. Table  5. Fitbit derived behavioral activity features. Data streams from the Fitbit API were downloaded at minute-level granularity, and then aggregated into day-level features with input from a medical domain expert.
ACKNOWLEDGEMENTS We would like to thank the study participants. We also would like to thank  Sophie Maurel  and  Heike Siedler  from  Sanofi  who supported protocol development. The study was sponsored by  Sanofi-Aventis. Medical writing support was provided by  Evidation Health  and funded by  Sanofi-Aventis  in accordance with Good Publications Practice (GPP3) guidelines (http://www. ismpp. org/gpp3).
Statistical Analysis To test the mediating effect of spatial ability, we performed mediation analysis using the pre-specified Model 1 of PROCESS macro  (Hayes, 2013). PROCESS application developed by  Preacher and Hayes (2008)  which is an SPSS procedure (PROCESS, v2. 13) that facilitates path analysis and mediation analysis by using ordinary least squares regression  (Hayes et al. , 2017 ). An analysis using 5,000 bootstrap samples with 95% confidence levels of the CIs was performed after mean-centering the continuous predictor variables. Three separate moderation analyses were performed in which spatial ability served as moderator variable and recall performance, hesitation time or subjective ratings of cognitive load were used as dependent variables. Significance was accepted for all analyses at the level of p ≤ 0.
Background: The gambling industry has developed many types of gambling on Internet in recent years. Gambling is a social activity for a majority of the world population, but problem gambling (PG) can emerge. The trajectories of gamblers from initiation to PG development are influenced by many variables, including individual and environmental variables and also variables linked to the gambling characteristics. Marketing has been reported to influence gamblers' perceptions and behaviors, but this is not as clear for digital marketing. Digital gambling marketing is broad, ranging from the marketing of gambling websites to communication and advertising on the social media and networks. The objective of this article was to fill this gap by conducting a systematic literature review in order to answer the following questions: (1) What are the strategies of digital gambling marketing? (2) What is the effect of this exposure on gambling representations, intentions and practices? Method: A systematic review was conducted following the PRISMA guidelines on Pubmed database (Medline) from February 2020 to March 2020 and.
Information Sources and Search Strategy From February 2020 to March 2020 existing papers published between January 2000and February 2020 were identified by searching the academic databases Pubmed (medline), and Scopus. The two authors drew up a list of agreed English keywords for the systematic search: ((("internet"[MeSH Major Topic] OR (communications[All Fields] AND media[All Fields])) OR ("social media"[MeSH Terms] OR ("social"[All Fields] AND "media"[All Fields]) OR "social media"[All Fields])) AND "gambling"[MeSH Major Topic]) AND ("marketing"[MeSH Terms] OR "marketing"[All Fields]), in title, keywords or abstract. The inclusion and exclusion criteria are presented in Table  1.
Study Selection and Data Collection Process The reviewers were the first two authors (MGL-KGM); they were researchers with previous experience in conducting literature reviews, and one of them had specific expertise in gambling disorders (MGL). The reviewers independently reviewed titles and abstracts, to ensure the reliability of the screening process. They then met to exchange their individual decisions and discussed their rationale for these decisions. Consensus was reached when the two reviewers agreed on article inclusion or exclusion. Full text articles for each included article were then collected, and screened by the two reviewers against the inclusion/exclusion criteria. The reviewers discussed any articles where a reviewer was unsure. Information extracted from the articles included: author names, year, and study location; journal, objective of the study, key results, key points of the discussion.
Strengths and Limitations This study focused on gambling, a growing public health concern, for which a preventive, therapeutic approach is needed. Twenty articles were selected following PRISMA guidelines among 64 identified initially. The analysis of these articles enabled identification of themes and characteristics of digital gambling marketing. One limitation is the focus on only two databases (Pubmed and SCOPUS), which could limit the results. In addition, the results of this review are subject to two biases limiting the generalizability of the data. There is firstly a cultural bias, in that a majority of studies concerned Australia or New Zealand. There is also a selection bias since a majority of the studies selected focused on digital strategies in sports betting. We did not include studies concerning gambling marketing on traditional media (television, radio, press).
Sample Participants (all genders, older than 18 years, no other in-or exclusion criteria) were recruited using a snowball sampling approach started on 22 March 2020 via the social media, mailing lists, and a general media campaign. Data collection via the SurveyMonkey platform (www. surveymonkey. de) was anonymous. Participants gave informed consent electronically. The study was approved by the ethics committee of the State Medical Board of Rhineland-Palatinate, Germany, and was conducted in accordance with the Declaration of Helsinki. There was no initially planned sample size and the survey was paused on 8 July 2020, when recruitment had been low for several weeks. In the beginning, the survey questionnaire was available in English and German only; by 1 April, ten further languages had been added, and by 20 April, a total of 24 languages were available (Supplement 1).
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement numbers  777084  ( DynaMORE  project) and  101016127  ( RESPOND  project),  Deutsche Forschungsgemeinschaft (DFG  Grant  CRC 1193 , subprojects  B01 ,  C01 ,  C04 ,  Z03 ), the  German Federal Ministry for Education and Research (BMBF)  as part of the  Network for University Medicine  under Grant number  01KX2021  (CEOsys and EViPan projects), and the  State of Rhineland-Palatinate ,  Germany (MARP program, DRZ program ,  Leibniz Institute for Resilience Research). A. R. is supported by  Studienstiftung des deutschen Volkes. We thank  J. Timmer  for critical comments;  H. Burger ,  A. Dimitrov ,  C. Rohr ,  T. Steppat ,  S. von Paleske , and  E.
RNA expression by Fluidigm microfluidics arrays One microgram of total RNA was obtained from each brain area as described in  Dinel, Andre, et al. (2014)  and was reverse transcribed with   Then the raw data of the qPCR were analyzed using GenEx software (MultiD analyses AB, Freising, Germany) in order to choose the best reference gene to normalize mRNA expression and to measure the relative expression of each 93 genes analyzed between groups. The best reference gene was found to be β-2 microglobulin (B2m) that was thus used for normalization of gene expression.
Statistical analysis All data are expressed as means ± SEM. Statistical analyses were performed using GraphPad Prism software. Corticosterone secretion was compared by a 3-way ANOVA test with diet, stress and time as between factors followed by a Fischer's LSD post-hoc. Gene expression were compared using a 2-way ANOVA test (with diet and stress as factors) followed by a Tukey post-hoc test comparison when appropriate. Principal component analysis (PCA) was performed, using RStudio software. PCA summarizes data into principal components (PC) that explain the maximum of the data variance. These PCs are uncorrelated linear combinations of the initial variables which can be interpreted as pattern. PCA provides the correlation between each variable with the PC and a PC score for each individual.
In absence of a dictionary, this requires exactly the kind of bottom-up approach we have outlined. Multiple searches over Latin texts databases (for words such as furor, ira, iracundia, etc. ) yield a mass of data; each passage has to be individually analyzed by the researcher. For those reasons, it is important that the results of analyses of the metaphorical layer of Latin's semantic 21 An overview of the history of Latin lexicography from antiquity to the twentieth century is provided by  Krömer (1990). For a criticism of this model, see  Wharton (2011). 22 Cf. Short (2013b: esp. 398-400). system be stored in a way that makes them permanent, easy to access and reusable. They should also be cumulative, so that each new study builds on top of existing ones and adds to them, scaling up our knowledge and our capabilities of research.
With this goal in mind, we have conceived the idea of a Lexicon of Latin Metaphor, or Lexicon Translaticium Latinum. This is a collaborative international Digital Humanities project aimed at developing an online, extensible, open-access lexicon of metaphors in the Latin language. Rather than reproducing the format of dictionaries in print, this instrument will try to capture large-scale, deeply entrenched and highly conventionalized metaphoric and metonymic patterns that organize meanings pervasively throughout Latin. 23 In other words, it will be able to represent the phenomena we have been investigating in this paper. In joining the idea of open scholarship, we maintain that not only the results, but also the data itself of our ownand hopefully other scholars'corpus-based research may be conveniently stored in the Lexicon.
In this way, we aim to produce a collaborative and incrementally useful tool. Here, we present the outline of the project, in the hope that fellow scholars consider contributing to the development of the Lexicon and other similar collaborative instruments. Concretely, what is the Lexicon Translaticium Latinum? The prototype database we are using so far implements the technical design of the MetaNet Project, an electronic repository that contains records for hundreds of attested conventional and imaginative metaphors in English. 24 For instance, the metaphor ANGER IS FIRE that manifests itself in English expressions like she was doing a slow burn or he was breathing fire, is represented in the MetaNet as a record of key-andvalue pairs with a general format where each field registers some theoretically relevant datum about the conceptual metaphor (Figure  3 ).
The MetaNet-like specifications we have presented so far provide the general foundation of the Lexicon, but precise lexical data are needed to flesh out the whole enterprise. For example, we have to answer questions such as: What concepts are used to express the idea of 'anger' as an 'opponent'? And what words, specifically, express them? For this reason, we have built the Lexicon on top of a new Latin WordNet, based on a now-defunct endeavor by Stefano Minozzi. 25 A WordNet is a semantic database that represents lexical meaning in terms of synsets, i. e. sets of terms that in a given context can be used as synonyms. For example, all the words denoting 'anger' (ira, furor, iracundia …) would be part of the same synset 'the state of being angry', labeled by a unique ID number (in this case, #10110870).
The same applies to all the words designating an 'opponent' (#07064973). Thus, our database depicts metaphors as conceptual relations between a source synset ('opponent') and a target synset ('the state of being angry'), quite independently of the concrete realization of this metaphor in the texts, which may vary as we have seen. In this way, we can alert the user as to the fact that a metaphor is a conceptual link, instantiated by different lexical items (armiferos … furores, iracundiam … hostem, ira … invasit). However, it should also be noted that not all the words meaning 'anger' or 'opponent' are actually used in the extant Latin texts to express the metaphor ANGER IS AN OPPONENT. Thus, we also specify which lemmas are used in a given metaphorical sense, and which are not. Over the course of time, the Lexicon will contain more and more information about the most common metaphors in Latin.
Users will be able to search for a 25 A WordNet for Latin had originally been developed by Stefano Minozzi and includes over 40, 000 words that cover the archaic through classical periods of this language  (Minozzi 2008). Because the WordNet does not presently distinguish between literal and figurative senses, the database has been rearchitected to accommodate the encoding of metonymic and metaphoric senses of words. Two other major changes have been introduced: these regard etymological information, and diachronic semantic changes occurring in the history of the language, thus adding a chronological dimension to the database. These sets of data seem in fact particularly relevant to investigating the metaphorical structure of Latin. concrete source domainsay, FIREto discover the abstract target domains that are structured in terms of this concept in Latin: LOVE, SEX, WAR, ANGER, GRIEF, and so on, along with representative examples of these metaphors drawn from the Lexicon and from literature.
2; ChiaraFedriani wrote Sections 3, 4. 1, 4. 2, 6; Irene De  Felice wrote Sections 4, 4. 3, 4. 4, 6; William M. Short wrote Sections 5, 6. 2 "The Lexicon of Embodied Experience in Latin" is a two-year project (2019-2021) financed by the University of Genova within a "Curiosity Driven" funding call for researchers aged under 40 (https://lexelat. unige. it). 3 The project is directed by William M. Short at the University of Exeter and aims to create a comprehensive lexico-semantic database of the Latin language (https://latinwordnet. exeter. ac. uk/lexicon).
In 2011 the European Society for the Study of Tourette Syndrome (ESSTS) published its first European clinical guidelines for the treatment of Tourette Syndrome (TS) with part IV on deep brain stimulation (DBS). Here, we present a revised version of these guidelines with updated recommendations based on the current literature covering the last decade as well as a survey among ESSTS experts. Currently, data from the International Tourette DBS Registry and Database, two meta-analyses, and eight randomized controlled trials (RCTs) are available. Interpretation of outcomes is limited by small sample sizes and short follow-up periods. Compared to open uncontrolled case studies, RCTs report less favorable outcomes with conflicting results. This could be related to several different aspects including methodological issues, but also substantial placebo effects.