dataesr/scientific-paragraphs-categorization huggingface dataset:
03) for CGS distribution compared with climate variables and HPD. We treated them as unchanging in future prediction as their reliable projections are not available. Actually, the use of static non-climate variables (e. g. , slope, soil type and land use/land cover) when predicting climate change impacts in SDM studies is widely accepted  (Peterson et al. 2002) , and previous work revealed that models including both static variables and dynamic variables perform better than or as well as those either masking or excluding the static variables  (Iverson and Prasad 1998, Stanton et al. 2012). Therefore, although we considered WTA and HIP as static in our study, the future range shifts of the species would be hardly modified. However, it should be noted that if a non-climatic variable is dominate for species distribution, such as HPD in our study, the future dataset should be used in future prediction to ensure model transferability.
Acknowledgements This study was financially supported by the  National Natural Science Foundation of China  (No. 51709187 ), the  Key Projects in Technology Innovation of Hubei Province, China  (No. 2019ACA154 ) and the  China Scholarship Council (CSC). The  Evolution et Diversité Biologique laboratory was supported by 'Investissementd'Avenir' grants (CEBA, ref. ANR-10-LABX-0025 ; TULIP, ref. ANR-10-LABX-41 ). We thank two anonymous reviewers for their constructive suggestions that greatly improved our work.
Acknowledgments This study was financially supported by the  National Natural Science Foundation of China  (No. 51709187 ), the  Key Projects in Technology Innovation of Hubei Province, China  (No. 2019ACA154 ) and the  China Scholarship Council (CSC). The ' Investissementd'Avenir' grants (CEBA, ref. ANR-10-LABX-0025 ; TULIP, ref. ANR-10-LABX-41 ). We thank two anonymous reviewers for their constructive suggestions that greatly improved our work.
This definition includes only objective criteria and is perfectly reproducible  [4]. However, recent studies and meta-analysis reported poor agreement between VAE, including ventilator-associated conditions (VAC), infection-related ventilator-associated complications (IVAC), or probable VAP (pVAP)  [5] [6] [7]. Few studies evaluated the agreement between VAE and VA-LRTI, including VAP and VAT. Although VAP and VAT are both associated with increased duration of mechanical ventilation and length of ICU stay, only VAP is associated with increased mortality rates  [8]. Thus, it is probably important to distinguish VAP from VAT. In addition, the recent ATS/IDSA guidelines on VAP recommended not treating VAT patients with antimicrobial, based on the low quality of the available evidence  [9]. Therefore, we conducted this retrospective analysis of prospectively collected data to determine the agreement between VAE and VA-LRTI, including VAP and VAT.
Data collection Data related to VAP and VAT episodes were prospectively collected. Data regarding mechanical ventilation (PEEP and FiO2) were automatically imported every hour in the patient management software ICIP ® (Philips Healthcare). Data from 2-h time slots were then retrospectively examined to determine the episodes of VAE. Other data such as body temperature, leukocytosis, antibiotic use, patient characteristics, aetiology of VAE episodes, duration of mechanical ventilation and hospitalization, and mortality were collected retrospectively from patients' computerized medical records.
VAE algorithm failed in identifying most of VAT and 73% of VAP episodes and should not be used to start an empirical treatment of VA-LRTI. Furthermore, the retrospective nature of VAE does not allow its use at bedside. In fact, to meet IVAC criteria, patients must have 2 days of stable oxygenation parameter and 2 days of worsening ventilatory settings after which a new antibiotic must be prescribed for at least 4 days. Our study has some limitations. First, it was performed in a single center, and its results may not be generalized to all ICU patients. Second, it was retrospective. However, all VA-LRTI were prospectively identified. Third, no data were collected on duration of antibiotic treatment before VA-LRTI, and VAE, neither on appropriateness of antibiotic treatment in patients with VA-LRTI.
Study participants This study used data from 7403 people who participated in the 2007 Adult Psychiatric Morbidity Survey (APMS) in England. Full details of the survey were previously published  [15, 16]. Briefly, this was a nationally representative survey of the English adult population (aged ≥ 16 years) living in private households. The National Center for Social Research and Leicester University performed the survey from October 2006 to December 2007 by using a multistage stratified probability sampling design with the sampling frame consisting of the small user postcode address file and the primary sampling units as postcode sectors. Participant information was obtained by face-to-face interviews during which some of the questionnaire items were self-completed (with use of a computer).
e. , marital status, obesity, smoking status, alcohol dependence, drug use, social support) explained less than 10% of the association. Thus, IQ likely has a direct effect on disability, or other mediators not assessed in this current study might be involved. For example, physical injuries may be an important mediator in the association between IQ and disability. One cohort study including more than 1,109,000 Swedish men found a 1-SD decrease in IQ in early adulthood associated with a 1. 15fold increased risk of any unintentional injury  [36] , and the prevalence of impairments and disabilities is high in survivors of severe physical injuries  [37]. The IQ-physical injury association may involve lower processing skills, lack of psychomotor coordination and unhealthy behaviors. Also, we believe that several unhealthy behaviors not available in the 2007 APMS dataset may be key players in the IQ-disability association.
We found that people with low IQ may be at particular risk for disability and ADL and IADL limitations. However, IQ is rarely assessed in the general adult population. Nevertheless, this score is frequently measured in youth with developmental problems and adults with psychiatric conditions. One key measure to prevent disability in individuals with low IQ is to intervene in education and allow these individuals to receive appropriate health-related information even if they do not attend high school or college. In addition, people with low cognitive abilities should be offered employment suited to their skills, and they should be regularly followed by occupational physicians. Longitudinal studies are needed to corroborate our results. Additional analyses are also needed to investigate potential influential factors that were not available in the dataset used in this study, such as physical injury, dietary behavior, and physical activity.
Finally, randomized controlled trials should be conducted to assess the effects of educational interventions on the risk of disability in people with low IQ. These educational interventions may be implemented at different levels (e. g. , individual, community) and via various methods (e. g. , booklets, face-to-face, Internet)  [41]. The use of nationally representative data and the large sample size are 2 strengths of this study. However, although these findings are of particular interest, the study has several limitations. First, verbal IQ, measured with the NART, was used as a proxy of intelligence, and other components (e. g. , numerical, spatial, logical) were not investigated. Second, disability was assessed with 7 questions about ADL and IADL only, and data on the type or severity of disability were lacking.
Conclusions We found a positive association between low IQ and disability in England, and socioeconomic status explained more than one-third of this association. Studies of longitudinal design are warranted to corroborate our findings, and future research should focus on additional potential factors involved in this association. Leicester, who were the principal investigators of this survey. We also thank the UK Data Archive, the National Center for Social Research, and other relevant bodies for making these data available. These bodies have no responsibility for this analysis or interpretation of this dataset. reference category was IQ 120-129. Disability was assessed with 7 questions about difficulties in activities of daily living and instrumental activities of daily living. Disability was used as a binary variable ("0" coded as "0" and "≥ 1" coded as "1").
[2]. For hair analysis, specific collected data were hair strand length, type of analysis (global or by segmentation), the presence of cannabis metabolites (11-nor-9-carboxy-Δ9-tetrahydrocannabinol [THC-COOH], Δ9-tetrahydrocannabinol [Δ9-THC], cannabinol [CBN], cannabidiol [CBD]) and their concentrations. Children were considered positive when Δ9-THC and THC-COOH values were over the limits of quantification (respectively, 50 pg/mg and 0. 2 pg/mg) as recommended by the Society of Hair Testing (SoHT). By applying the results of a preliminary study that determined the threshold of exposure intensity and significant systemic absorption  [3] , patients were divided in two groups: intense environmental exposure (Δ9-THC > 1 ng/mg) and low to moderate environmental exposure (Δ9-THC < 1 ng/mg). Laboratory analysis strategies -Urine drug testing could be performed by automated immunochemical (IC) methods as soon as urine samples were available.
05-10 ng/mg for THC, CBD and CBN) and intra and inter-assay accuracies below 15% for the all compounds. This method has already been published  [4]. Statistical analysis -For statistical analysis, data were entered in Microsoft Excel tables (Microsoft Corp, Redmond, WA). The analysis was performed with StatView 5. 1 (SAS Institute Inc. , Cary, NC) and EpiInfo 6. 04fr (VF, ENSP-Epiconcept, Paris, France). In the descriptive analysis, data are presented as mean ± SD, median with extreme values, or with 95% confidence intervals where appropriate, unless otherwise indicated. To compare qualitative variables, a χ2 test (Mantel-Haenszel) was used and a 2-tailed Fischer's exact test if the expected value was <5. For quantitative independent variables, a paired student t test was used. A nonparametric Kruskall-Wallis test was performed in case of non-normal distribution.
Statistical significance was considered at p < 0. 05. Ethical and regulatory considerations -The data recorded during this research were subject to electronic processing at the Toulouse University Hospital Pediatric Emergency Department (PED) in accordance with law no. 78-17 of January 6, 1978 regarding Data Processing, Data Files and Individual Liberties, amended by law 2004-801 of  August 6, 2004. At the time of the study, toxicological analysis of hair strands were excluded from the French bioethics law and did not require approval from an ethics committee, parental acceptance or non-opposition. Results -Main characteristics -During the study period, 41 pediatric patients were consecutively enrolled. The male/female ratio was 1:1. 4 (male, n=17). The mean age was 1. 5 ± 0. 5 years; the mean weight was 10.
Unprecedented crisis situations require that design solutions be based on concrete knowledge derived from inclusive scientific debates and problem understanding grounded in reality. In the following we use the French case, to critically interrogate the effectiveness of the design and the adequacy of smartphone contact-tracing apps and argue that: • Solutionist approaches  (Morozov, 2013)  lead to inadequate specifications and irrelevant data collection due to partial understanding and recognition of the problem (especially transmission paths of COVID-19). • The dominant epidemiological approach and inadequate design of the French Stop-COVID app indicate an alienation from nature of disease transmission and from social reality when borders reopen. • The unfortunate outcome (ineffective design), misunderstandings of the transmission paths and pan-European interoperability issues of apps can be partly explained by the government's use of coercive tactics in their urgency to act and CONTACT Frantz Rowe Frantz.
, 2019). In France, e-government initiatives launched during the COVID crisis, focused on online communication, an online form for administrative control during the lockdown, and the Stop-COVID contact-tracing app. While the European Data Protection Board pleaded in favour of developing a pan-European coordinated action plan for contacttracing apps, European countries adopted various stances, with some governments in favour of a decentralised, distributed, and privacy-preserving approach and others such as the French government, favouring a partially centralised approach  (Rowe, 2020a).
Unlockdown and post-crisis phase This phase began on May 11 when the first step of unlockdown took place. However, effective management of the unlockdown required planning for crisis containment and for a second outbreak. The Stop-COVID app was announced as an essential measure to "limit the virus diffusion and identify future chains of infections" (Health Minister, May 13). Key for the Stop-COVID app approval in this period were the debates in Parliament and Senate on May 27, encouraged the day before by a favourable recommendation of the Stop-COVID app by CNIL (Administrative Authority for Data Protection and Privacy) revising their April 15 findings concerning data privacy risks. The new CNIL finding stated that the design of the app would not lead to discrimination, privacy, or security leaks.
Assessment of Stop-COVID App On assessing the app from the perspective of Mingers and Walsham (2010) four criteria we found the following. Efficacy: From a pragmatic perspective the Stop-COVID app is only half efficacious. The system works, but does more than what it is supposed to do even with too narrow specifications for contact (1 metre distance and >15 min); it collects more data than originally claimed by the government and goes beyond the expectations of stakeholders. Ethicality: In the French context, the app and the way it is publicly debated, fuel dilemmas and the privacy paradox  (Rowe, 2020a). Consistent with the ranking comparing other smartphone warning apps on data privacy (see Appendix A), the French app does not meet good standards and cannot be considered ethical due to lack of transparency of its code and the way its characteristics are communicated to the public.
Statistical Analysis Data analyses were performed using the Statistica (Stastsoft France, Maison Alfort, v7. 1) software. The values are expressed as mean ± standard error of mean. Correlations were done using Bravais-Pearson analyses. Comparisons between groups were performed using Pearson's chisquare test for variables with several modalities and using t-test for the quantitative data or nonparametric Kruskal-Wallis analyses as they did not have a normal distribution. For the PTSD group, we characterized patients according to PTSD diagnosis and severity. Three groups were defined according to the PCL5 score: a group with a score under 33 (group with minimal PTSD; Minor PTSD), a group with a score under the median of the PCL5 score of our population (group with moderate PTSD; Moderate PTSD), and a group with a score above the median of the PCL5 score of our population (group with severe PTSD; Gr Severe PTSD).
| Searching The Web of Science (Core Collection) database was used to identify articles containing the search strings presented in Table  1. Web of Science was searched for articles with titles, abstracts, keywords, and "keywords plus" (additional relevant but overlooked keywords not listed by the author or publisher). The initial search was already F I G U R E 1 A conceptual framework of anticonsumption research [Color figure can be viewed at wileyonlinelibrary. com] informed by the inclusion and exclusion criteria "Language" and "Type of Literature" (Table  1 ). The latter included article, abstract of published item, and review. At this stage, 383 results were identified.
Statistical analysis Due to the nested nature of these data, a hierarchical linear modeling (HLM;  Raudenbush and Bryk 2002)  approach was performed to test the hypotheses using SPSS, version 18 (SPSS Inc. , NY). HLM allows a correct estimation of parameters when data are hierarchically structured with repeated measurements (at level 1) nested within individual (at level 2). HLM is the preferred method for this type of data, as it accounts for shared variances due to multiple observations within the same participant (i. e. , non-independence). To examine the moment-to-moment relationships between teachers' state-based experiences and their motivating style, cross-lagged multilevel modeling was used  (Raudenbush and Bryk 2002) , which allowed for examination of whether teachers' subjective feelings predicted the next moment of their motivating style (controlling for the immediately previous moment of motivating style), and also whether motivating style predicted the next moment of subjective feelings (controlling for the immediately previous moment of subjective feelings).
The experimental protocol was approved by INSERM and the local research ethics committee (Comité de protection des personnes Ile de France III-Project CO7-28, N° Eudract: 207-A01125-48), and it was carried out in accordance with the Declaration of Helsinki. The participants provided informed written consent and were compensated for their participation. General procedure. First, participants were recruited thanks to an online advert, which consisted of a short description of the study and included an internet link which directed participants to a Qualtrics survey. Due to the potentially stressful nature of our paradigm and after discussion with the referent medical doctors of our laboratory, we requested that participants fill in, online and anonymously, the French versions of State-Trait Anxiety Inventory Spielberger  75  (STAI) and Post-traumatic stress disorder Checklist Scale 76 (PCLS), at least one week before the experiment.
Participants were asked to indicate the seat they would like to occupy, maintaining fixation on the cross displayed between the faces throughout the trial. In order to make their choice, participants had to left-click on the mouse, move the cursor from the bottom center of the scene and release the click on the chosen seat. The cursor was automatically re-centered at every new trial. Participants were required to make spontaneous choices and were informed that there were no correct or incorrect responses. Importantly here, the number of neutral, fear and anger trials was equal for safe and threat blocks. Data from the task are not presented here, being part of another study (Beaurenaut et al. unpublished). To resume, participants performed a free action-task and were either exposed to screams at any time during threat blocks and to no screams during safe blocks.
Recordings were performed with LabChart 7 software, at a sampling rate of 1 kHz, with the recording range set to 40 μS and using initial baseline correction ("subject zeroing") to subtract the participant's absolute level of electrodermal activity from all recordings (devices and software from ADInstruments). Finally, as low-pass filter of 0. 05 Hz has been proposed as an appropriate filter to eliminate any noise as well as the phasic component of the signal (see  80  ), we applied this filter to isolate the tonic activity of the skin conductance signal and to minimize the phasic influence of screams during threat blocks on the skin conductance activity. Performing the analyses on raw data signal revealed similar results as those performed on filtered data (see Fig. S3 ).
Skin conductance level (SCL) processing. The SCL corresponds to the tonic activity of the skin conductance signal. The physiological signal was processed using Labchart 7 and Matlab. For each participant and for each block, we obtained (a) one SCL value corresponding to the averaged signal over the 1-min baseline at the start of the block (see Fig. 1b ), and (b) one SCL value corresponding to the averaged signal over task performance (M Duration of one block = 4 min, SD = 30 s). The instruction phase (Condition + Readying-see Fig. 1b ), between 1-min baseline and the beginning of the free action-task was not included in the averaged signal. Moreover, for threat blocks only, the phasic activity induced by the distress screams (6% of threat block trials) was excluded from the averaged signal.
Statistical analyses. All statistical analyses were carried out using JASP Software (JASP Team (2017), JASP (Version 0. 8. 5. 1) [Computer software]). The corresponding tables of results are available in Supplementary materials. Repeated-measures ANOVAs. For each experiment and for both the physiological variable (SCL) and the subjective reports of anxiety, we ran two-way repeated-measures ANOVAs with Condition (Threat vs. Safe) and Time (Blocks 1 to 5) as within-subject factor. We applied the Greenhouse-Geisser correction to correct for deviations from the assumption of sphericity (the corrected P corrected and the GG-ε are reported) and Bonferroni correction for the post-hoc tests (Pbonf). Effect sizes (partial eta-squared, η 2 p ) are reported together with F and p values. To better examine if SCL and subjective anxiety changed over time, we looked at threat vs.
Acknowledgements This work was supported by  FRM  Team  DEQ20160334878 ,  Fondation ROGER DE SPOELBERCH, INSERM, ENS , the  French National Research Agency  under Grants  ANR-10-LABX-0087   IEC ,  ANR-10-IDEX-0001-02 PSL* , and  ANR-17-EURE-0017  FrontCog. Author GD is indebted to the  British Academy  for financial support as part of the  Newton International fellowship scheme  ( NIF 171514 ). We thank  Jorge Armory  for sharing his distress screams and the  Perception et design sonores team (Institut de Recherche et Coordination Acoustique/ Musique, Paris, France)  for their assistance. The authors also thank  Michèle Chadwick  for carefully proofreading this manuscript.
Statistical analysis Comparison of fecal bacterial density between groups at different time points during the protocol and lean and fat masses between groups at Day 7 and 26 were performed using Kruskal-Wallis test with Dunn's correction. Comparison of bacterial and fungal taxa levels quantified by qPCR were performed using one-way ANOVA with Tukey's correction. Comparison of fecal moisture content between groups at different time points during the protocol were performed using oneway ANOVA with Holm-Sidak's correction. Statistical analyses were performed with GraphPad Prism 6 (GraphPad Software, San Diego, USA). Principal components analyses were computed with MATLAB (version R2018a, The Mathworks, Inc. ; Natick, USA). way ANOVA with Tukey's correction). In both experiments, the weak decrease in fecal bacterial density for mice receiving antibiotics by oral gavage once per day is correlated with an hyperproliferation of the Escherichia/Shigella taxon.
Analysis of Survey Results Analysis of survey results consisted mainly of analyzing students' scores. For the knowledge survey, scores were summed to calculate students' pre-test and post-test scores. Mean scores were calculated for the group of all students, by curriculum and discipline, and by theme of the questions. Statistical analysis was then performed using R software v. 3. 6. 1 (Foundation for Statistical Computing, Vienna, Austria). [40]. First, descriptive statistics (e. g. , mean, median, standard deviation) of scores and student descriptors (e. g. , age, agroecology background) were calculated. Next, equivalence tests with paired data were used to calculate the change in scores between the pretest and post-test. Finally, Pearson correlation coefficients and one-way analysis of variance were calculated to assess the significance of observed differences.