dataesr/scientific-paragraphs-categorization huggingface dataset:
Descriptive statistics and correlations between variables per group(Study 3b) M Advantaged M Disadvantaged Variable (SD Advantaged ) (SD Disadvantaged ) 1 2 3 4 1. Collective narcissism 4. 21 6. 16 - 0. 23*** 0. 31*** -0. 25*** (1. 69) (1. 46) 2. Ingroup identification 82. 64 87. 93 0. 22* - 0. 18*** -0. 15*** (24. 02) (20. 62) 3. Support for reactionary social movement 28. 81 29. 92 0. 03 0. 02 - -0. 06 (17. 91) (30. 16) 4. Support for progressive social movement 55. 03 77. 55 0. 34** 0. 24* -0. 11 - (34. 64) (26. 82) Note. Correlations above the diagonal refer to the advantaged group, correlations below correspond to the disadvantaged group. * p <. 05, ** p <. 01, *** p <. 001 at least two attention checks or who answered too fast were removed from the database (24% attrition rate). This time, the sample was rep- resentative of the US population in terms of age, education, gender, and income.
Background: Students are a vulnerable group for the indirect impact of the COVID-19 pandemic, particularly their mental health. This paper examined the cross-national variation in students' depressive symptoms and whether this can be related to the various protective measures implemented in response to the initial stage of the COVID-19 outbreak. Methods: Student data stem from the COVID-19 International Student Well-being Study, covering 26 countries during the first wave of the COVID-19 pandemic. Country-level data on government responses to the COVID-19 pandemic were retrieved from the Oxford COVID-19 Tracker. Multilevel analyses were performed to estimate the impact of the containment and economic support measures on students' depressive symptoms (n ¼ 78 312). Results: School and workplace closures, and stay-at-home restrictions were positively related to students' depressive symptoms during the COVID-19 pandemic, while none of the economic support measures significantly related to depressive symptoms.
Data Data stem from the COVID-19 International Student Well-being Study (C19 ISWS), which collected information on student wellbeing during the first wave of the COVID-19 pandemic in 133 HEIs in 26 countries. The C19 ISWS applied a stratified convenience sampling design. In a first step, HEIs were selected within countries, covering Western, Central-Eastern, Eastern, Northern and Southern European countries and including some additional high-and uppermiddle-income countries (Canada, Israel, South Africa, Turkey and the USA). Data collection took place between 27 April and 7 July 2020, with two-thirds of HEIs collecting the data within the first month of the initial launch. Within each HEI, the survey was active for at least 2 weeks, but a selection of HEIs prolonged this period. Respondents were recruited through direct emailing and social media and were asked to fill out an online survey.
Independent measures Data on policy interventions were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT). 14 The OxCGRT is very adequate for cross-country analyses and includes containment measures as well as economic support measures. 15 It closely resembles the data of the CoronaNet project. 16 The containment measures included (a) closures of schools and universities; (b) closures of workplaces; (c) cancellation of public events, (d) restrictions on social gatherings; (e) public transport closures, (f) stay-at-home regulations, (g) restrictions on internal movements (within a country); and (h) restriction on international traveling. The economic support measures included (i) income protection measures and (j) debt or contract relief measures for households (see Supplementary file S3 for details concerning the operationalization).
The impact of influential countries on the results was also estimated by deleting every country once from the analysis. 21 In addition, a logistic analysis was performed with the depression scale dichotomized (cut-off ¼ 9/ 24). 22, 23 The results of these analyses (see Supplementary files S7-S11) confirm the robustness of our findings. Data preparation and descriptive statistics were done in SPSS V R version 26, and the multilevel analyses were performed in MLwiN Version 3. 05.
The lack of a direct effect of the severity of the pandemic was also established for other outcomes, such as anxiety, protective health behaviors and supportive attitudes towards containment measures. 29, 30 ome limitations of this work should be addressed. First, the C19 ISWS made use of a convenience sample, both in terms of the selection of students, HEIs and countries. Sample sizes were small in some countries, or data were collected within only one HEI within certain countries. As a result, the C19 ISWS could be not representative of the entire student population. In addition, selection bias cannot be excluded, as it may be likely that students who experienced stress due to the COVID-19 pandemic were more likely to respond to our invitation to participate in the study, and students with a more disadvantaged socioeconomic background or limited access to the internet are generally less likely to participate in surveys.
Acknowledgments This study was supported by a public grant through the  national program Programme d'Investissements d'Avenir (PIA)  under the reference  ANR-18-RHUS-0004. This work is part of the  Federation Hospitalo-Universitaire (FHU) Saclay and Paris Seine Nord Endeavour to Personalize Interventions for Sepsis (SEPSIS). This work was also supported by  ANR PIA  funding, grant number  ANR-20-IDEES-0002.
Study Procedure and Participants We conducted a web-based self-reported cross-sectional questionnaire survey to collect data about sleep schedules and depressive symptoms. The questionnaire was built by psychiatrists and sleep physicians. The web-platform Framaforms was used to distribute the online-survey which was delivered in French language from April 2020, 9th to June 2020, 11th. At the time of the survey's distribution, a strict lockdown had been imposed in France. The population was required to stay at home, and all non-essential activities were prohibited since March 2020, 17th. To recruit a large number of participants, the survey was promoted via the Vinatier Hospital communication systems, social networking (Facebook, Twitter) as well as traditional media (local newspapers and radio programs).
Results Among the 2609 participants who responded to the survey, 96 individuals were excluded. Reasons for exclusions were 1) age below 18 (n = 18 participants); 2) shift in MSFsc >4 hours (n = 78 participants) as expected changes in MSFsc were estimated to be around 30 minutes with a SD between 1:15 and 1:45. 44, 45 This 4h value (>30 min + 2 SD) allowed to exclude outliers mainly due to misunderstandings (eg, we noticed that some participants had recorded bedtimes and wake-up times in 12-hour instead of 24-hour format), a table is provided with characteristics of these outliers (Supplementary Table  1 ); The final sample consisted of 2513 participants, among which 285 were excluded from the multivariate analysis because of missing data for the main outcome or at least one of the adjustment variables.
Importance: Prognosis and understanding of sleep disorders in rare genetic syndromes is limited, despite being a common complaint of caregivers. Rett Syndrome (RTT) is a rare, progressive neurodevelopmental disorder with problematic sleeping being a clinical feature yet inconsistencies exist in the literature. Objective: To examine the strength of evidence of a sleep disorder in RTT. To investigate the complaints reported based on a sleep disorders classification approach and to determine differences in rates per the RTT main clinical features. Data sources: PubMed, Web of Science, PsycINFO, Ebsco, Scopus, and Cochrane Library up to November 4 th 2021 with no time or language limitation (CRD 42020198099) were searched. Study selection: Original research published in peer-reviewed journals, with RTT clinical or genetic diagnosis reported and stating a sleep complaint with prevalence rate, were selected.
Data collection and analysis Study information, sample characteristics, and sleep-related data were extracted. Sleep complaints were sorted into subscales fitting the International Classification of Sleep Disorder (ICSD)  [ 28 , 29 ]  into problems related to: disorders of initiating and maintaining sleep (DIMS), sleep-wake transition disorders (SWTD), daytime excessive somnolence (DES), and disorders of arousal during sleep (DA). For the studies that did not specify the precise sleep complaint, it was labelled as "disturbed sleep ". This approach was similarly applied to studies reporting the number of individuals at or above "a sleep problem cutoff score ", given the sleep questionnaire used, in the absence of more detailed information. Both were categorized into a subscale "not otherwise specified (NOS) sleep problems ".
Statistical analysis Statistical analysis were conducted with Statistica TIBCO Software Inc (TIBCO, 2017) version 13 and Meta-analysis with Comprehensive Meta-Analysis version 3. 3. 070 (Biostat, Englewood, NJ). The heterogeneity between studies is described by I 2 (i. e. , I 2 = 0: no, 0 < I 2 ≤ 25%: low, 25% < I 2 ≤ 75%: moderate, I 2 > 75%: high heterogeneity). Tau-squared is the variance of the effect size parameters across the population of studies. The mixed effects model was chosen to estimate the pooled (prevalence rate, PR) and illustrated by forest plots with the 95% confidence intervals (95%CI). The z-value and p-value indicate whether the effect size is significantly different from zero. That is, in meta-analysis the null hypothesis is that all of the separate null hypotheses are true.
Study characteristics Table. 1 contains the 23 papers selected for this meta-review ( Fig. 1  ), and marked (asterisks) are the 19 papers usable for analyses. Their publication date ranged from 1988 to 2020, and data reflect samples from 1985 to 2019, yet for ten papers this information was not available. Data from five research groups are included: 26. 3% Italy, 21. 1% for both United Kingdom and Australia, and 15. 8% for both USA and Netherlands. For two studies, we presumed female gender  [ 24 , 32 ] , while 13 studies reported only female data. In four studies separate data for males and females were reported ( n = 2) or not ( n = 2). The number of studies reporting data per age-groups are: 0-7 years = 10 studies, 8-12 years = 8 studies, 13-17 years = 9 studies and 18 + years = 9 studies.
Information about the genotype was not always provided, resulting in 11 on MECP2 , five studies on CDKL5 , and three studies on FOXG1. Mutation types were reported in 11 studies but further analysis could not be pursued. Nine studies reported the type of clinical diagnosis. Most studies used diagnostic criteria by Neul ( n = 4), but also Hagberg either or not combined ( n = 4) and per Andreas Rett personally ( n = 2), others were Ariani & Guerrini criteria and RTT clinical workgroup. This meta-review represents a total sample size of 4298 (min. 2 to max. 926) with an age-range from 0. 3 to 57. 2 years old, and including 18 males. Data sources were from clinic ( n = 6), Australian Rett Syndrome Database (ARSD, n = 3), InterRett ( n = 2), others were: International CDKL5 Disorder Database (ICDD), Rett Networked Database (RND), Rett Aid or Elternhilfe für Kinder mit Rett-Syndrom in Deutschland e.
MECP2 samples Including only female data with confirmed MECP2 demonstrated a significant pooled PR for DES (79. 6%, moderate heterogeneity), involving sleepy during daytime (85%) and napping (77. 2%) ( eFigure 4 ). Several other sleep complaints are significant ( eFigure 4 ): night waking (81. 9%), nightly unrest (77%), night laughing (66. 3%), night screaming (40. 1%), night terror and sleep talking (each 17. 8%) and sleep walking (4. 3%). In 61. 7% of individuals with MECP2 , problematic sleeping was reported (NOS). Again, by including also male data some differences were noted ( eFigure 4 ). No subscale was significant. Furthermore, night laughing, napping and sleepy during the day are no longer significant in the mixed gender groups. Alternatively, difficulty falling asleep (60. 3%) and difficulty waking (31.
Likewise, and as supported by the quality assessment, studies could improve their reporting of RTT sample characteristics allowing the generation of more precise prevalence rates of aggregated data regarding genetic or other clinical characteristics of RTT. Lastly, given a rare disorder sampling bias from cases collected by potentially overlapping databases, in terms of timeframe or through convenience sampling, might further blur results. The need for a clear sleep research objective is even more demonstrated by the "problematic sleeping " item or "NOS " prevalence rates. That is, our PR remains within the same high range across analyses, whereas the PR's reported in the literature display a very large spread, that is from 10% to 90%  [ 32 , 34 , 38-40 ]. The majority of studies reviewed focused on sleep behaviors assessing DA and SWTD.
That is, caregivers may adjust to the circumstances of nightly poor sleep of the family, yet an outspoken call for therapeutic management of sleep disorders is voiced  [46]. Regarding gender, and in the realm of a neurodevelopmental disorder affecting principally girls, such that boys with MECP2 mutations usually die prenatally  [47]  , our meta-review could not pursue analysis in boys separately. Moreover, too often data reported mixed gender sample percentages, hence our current approach shows but a handful datasets differing between gender samples. Our approach is far from ideal but obviously advocates for more detailed reporting of RTT genetic and clinical characteristics. Next, albeit those three genes share common pathogenic processes thus causing similar phenotypes, CDKL5 and FOXG1 only play a partial role in the pathway mediating MECP2 functions.
We extracted some information alike but could not pursue phenotyping due to scattered data. Subsequently, also a confirmation bias might be present. That is, the incomplete or inconsistent reporting of genes, clinical characteristics or associated features, has certainly complicated the clinical picture displayed in this meta-review. The process of sampling over time, or the unsystematic collection and sharing of sleep data potentially further introduced a sampling bias as well. A potential measurement bias, might be exemplified by the mixed reporting of samples with (un)confirmed genotypes. Similarly, specific sleep questionnaires in the included studies here were rarely used or only applied in the small sample size studies  [ 34 , 35 , 51 ] ; for example, such as the Sleep Disturbance Scale for Children (SDSC) (i.
As a result, based on the current available data, our PR's remain pooled effect sizes on crude groupings. We moreover cannot ignore the localness of the studies, and potential overlap of datasets, approaches or samples; hence multisite well-designed studies are needed. We conclude that in individuals with RTT poor sleep is prevalent. That is, they appear to have a disrupted maintenance of sleep (or wakefulness) state. As a result, a wide variety of simple to complex sleep behaviors might be displayed. These may interrupt the nightly rest of the family or relevant others.
Acknowledgements We would like to thank  Dr. Jenny Downs  for providing reprints and  Chris Vanden Houten  for the administrative help. We are appreciative of the financial support (No. 201806180064 ) for Miss Xinyan ZHANG (XYZ) provided by  China Scholarship Council (CSC). Miss Zhang 's is a doctoral student who did the preliminary groundwork for this paper. We would like to dedicate this paper to  Mrs. Lietaer Caroline , former president of  Rett Syndrome Europe  for her unwavering support, and each of the parent organizations.
(Glu203Gln)]; FOXG1 (2): NM_005249. 3 [c. 256delC;p. (Gln86ArgfsX106) AND 2 Mb 14q12deletion (28 780 663-30 780 833; hg19), de novo] Leven et al. United Female (286) 0-6: 55 TYPICAL (287) Sleeping NOS Rett Aid or 09/2017- Poor (2020) Kingdom Male (1) 7-12: 54 Questionnaire Elternhilfe 12/2017 observational, [40]  * 13-18: 51 for Children für Kinder cross- > 18:127 with mit Rett- sectional, Neurological Syndrom in prospective and Deutschland study other Complex e. V Diseases (SNAKE) Genetic etiology (n): No information Frullanti Italy Unknown:1007 > 5 TYPICAL (806 ) MECP2 (949): Ariani [48] , Clinical profile NOS Rett 03/2017 Poor et al. (twins included) A TYPICAL (201) classic (804), CV Guerrini Networked observational, (2019) (24), ESV (5), [56] Database cross- [32]  * PSV (54), (RND), a sectional, atypical (62) registry of retrospec- CDKL5 (32): 13 European tive ESV (31), countries study atypical (1) https://www.
rettdatabasenetwork. org FOXG1 (26): CV (22), classic (2), atypical (2) Genetic etiology (n): MECP2 (949): C-Term (101), Early truncation (93), Large deletion (72), p. R106W (31), p. R133C (62), p. T158M (102), p. R168X (80), p. R255X (106), p. R270X (62), p. R294X (63), p. R306C (67), other (110); CDKL5 (32): Early truncation (7), Late truncation (9), Missense mutation (10), Large deletion (6); FOXG1 (26) Merbler United Female (9) 9. 4 ± 4. 2 TYPICAL (7), MECP2 (8) CSHQ NOS Local parent Good et al. States of [1. 7-17. 1] ATYPICAL (2) MECP2 -related support observational, (2018) 51  * America disorder (1) network in cross- the Midwest sectional, of United prospective States of study America Genetic etiology (n): MECP2 (9): p. P152R (1), p. R168X (2), p. R255X (1), p. R106W (1), p. A131fs (1), Exon 4 deletion (1), Deletion between exon 3 and 4 (1), p.
K144X (1) Mori et al. Australia unknown All MECP2 (140); Trevathan Rett Syndrome NOS Australian 2002 2006 2009 Good (2018) [25] In 2002: 132 In other (19); and Moser Behaviou r Rett 2011 observational, 2006: 140 In no pathogenic [57] Q uestionnaire Syndrome cohort, ret- 2009: 168 In variant (39) (applicable Database rospective 2011: 160 for age 2- (ARSD), study 5yrs) + Hag- registry berg and since 1993 Skjeldal [8] ( continued on next page ).
AIR: Italian Association for RTT; ARTT-NOS: Atypical RTT-not otherwise specified; BIRSS: British Isle RTT Survey; C: Clinical diagnosis; CDD: CDKL5 Deficiency Disorder; CDKL5 : Cyclin dependent kinase-like 5; CSHQ: Children's Sleep Habit Questionnaire; CV: Congenital variant; DA: disorder of arousal; DES: disorder of excessive somnolence; DIMS: disorder of initiating and maintaining sleep; ESV : Early seizure onset variant; F: female; FOXG1 : Forkhead box G1; G: Genetic test; ICDD: International CDKL5 Disorder Database; ICSD: International Classification of Sleep Disorder scale; M: male; MECP2 : Methyl CpG binding protein 2; MEF2C : Mads box transcription enhancer factor 2; NOS: non-specified otherwise; PSV: Preserved speech variant; RND: Rett Networked Database; RTT: Rett Syndrome; SDB : sleep-disordered breathing; SDSC: Sleep Disturbance Scale for Children; SNAKE: Sleeping Questionnaire for Children with Neurological and other Complex Diseases; SWTD: sleep-wake transition disorder.
Data collection Data was obtained from the computerized medical records of the clinic. As we were looking for an association between mortality and sociodemographic and medical variables, in accordance with the literature  [12, 19, [26] [27] [28] , we collected: -sex, age; -occupational status classified as: student, in employment, unemployed, disabled status; -method of the suicide attempt; -previous suicide attempt, previous suicide attempt by violent means, history of psychiatric hospitalisation; -main psychiatric diagnosis: the diagnoses were coded according to the International Classification of Diseases, 10th revision (ICD-10) as recorded in the individual's medical file at the end of hospitalisation by the psychiatrist in charge of the person. They were categorized into three classes: schizophrenia (ICD-10 F2 code: schizophrenia, schizotypal and delusional disorders), mood disorders (F3 code) and 'Other diagnoses'.
This third group included personality disorders, anxiety disorders, behavioral disorders or substance use disorders. -substance use (any substance use reported by individuals, whether or not they met the substance use disorder criterion); -nature of injured body parts in the suicide attempt: head or face, chest, abdomen, spine, pelvis, arms, legs (as in Omi et al. [29] ); -length of stay in acute care after the suicide attempt; -length of stay in the transdisciplinary unit; -clinical limitations in arm mobility at discharge from the transdisciplinary unit, limitations in leg mobility at discharge, sphincter disorders at discharge; -treatments at discharge: antidepressants, neuroleptics, mood stabilizers, nociceptive pain treatments, neuropathic pain treatments. Vital status was ascertained in 2021 by searching the French national mortality databases between 2011 and 2019.
Statistical analyses The statistical analyses were performed using R Studio software (version: 1. 2. 5033). To describe the sample, the data was expressed as frequencies and percentages for nominal variables and as means and standard deviations for continuous variables. Given the low levels of missing data per variable (≤7%), we decided not to impute missing data. Log-rank tests were used to identify factors associated with mortality. Considering the number of tests, the differences were considered significant at p < 0. 002 (27 tests: 0,05/27 = 0. 002). A Cox regression model was then used to assess predictors of mortality in a multivariate analysis. We first calculated the crude mortality rate. To compare the observed mortality with the expected mortality, the SMR was used. SMR is the ratio of observed deaths to expected deaths, where expected deaths are calculated for a typical area with the same age and gender mix during the period studied.
Limitations First, this study is based on a small sample. However, this sample was an opportunity to study a relatively large cohort of people who had attempted suicide by violent means, a fairly rare method of deliberate self-harm. It was a clinical population hospitalised after a suicide attempt in a specific psychiatric ward with mixed psychiatric and physical rehabilitation approaches. Most had very severe injuries and could not be representative of all people who attempt suicide by violent means. In addition, the stays in acute care and in the transdisciplinary unit were long and this could influence mortality or suicide risk in ways we cannot predict. Data collection was retrospective, from clinical records, without standardized interviews for psychiatric diagnoses. We were not able to collect certain data that could have influenced suicide and all-cause mortality, such as social factors (family environment or social integration).
Funding This publication was made possible by  Qatar University Emergency Response  Grant ( QUERG-CMED-2020-2 ) from the  Qatar University  and  NPRP12S-0310-190276  grant funded by  Qatar National Research Fund  (a part of  Qatar Foundation ). All statements made here are the sole responsibility of the authors. P. M. H. is funded by an  Investigator Grant  from the  National Health and Medical Research Council (NHMRC) of Australia  ( 1175134 ). P. D was supported by the grant  NSFC  No. 82000392. Open Access funding provided by the  Qatar National Library.
Following the recent European directives highlighting the need to increase energy efficiency in the European Union, this work aims to show the possibility of using Non-Intrusive Load Monitoring (NILM) techniques to improve energy audits by estimating the compressed air leakage from a dataset of a tertiary building. The first step towards the reduction of energy consumption is performing an energy audit, in which a detailed analysis of the energy performance is executed. This analysis usually uses on-site measured data by the auditors. However, the time available for these measurements is limited and may not include some modes of operation. One example of that is the quantification of compressed air leaks. This task can be performed by estimating the flow rate during a no compressed air consumption period.