dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The different scales were shifted so as to all fall within a 0 to 100 range and subjected to a mixed modeling approach. This enabled us to account for missing values arising from the different velocities tested with the RTS in France and the cable robot in Hong Kong and to model a polynomial regression within subjects. Analyses were conducted in R (R Core Team, 2015) with the afex package  (Singmann et al. , 2019). Our dependent variable was the rating score averaged for each condition and participant. We normalized rating averages across conditions, but separately for each task, to a mean of 0 and a standard deviation of 1. Apart from removing potential differences in how participants used the 100-point scale across tasks, this normalization had no impact on the statistical results but facilitated the interpretation of beta coefficients returned by the models.
This method yields both F and p values for main and interaction effects. Associated effect sizes were estimated using the effectsize package  (Ben-Shachar et al. , 2020). Inclusion of the polynomial velocity effect meant that the Velocity effect comprised both a linear and a quadratic term that were each tested for significance. While a positive quadratic term indexed a convex relation, a negative quadratic term indexed a concave (potentially like an inverted u) relation between velocity and the rated sensory experience. The examination of linear and quadratic terms in follow-up analyses was done using FDR correction for multiple comparisons. In an effort to explore the relationship between pleasantness and the other rating constructs, we tested three additional models, one for each of these constructs.
MEG recordings Data were recorded using the Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD, Helsinki system), which comprises 306 sensors (102 magnetometers, 204 orthogonal planar gradiometers). Seven electrodes were used to record electrocardiographic (3 electrodes, ECG), and vertical and horizontal electrooculographic (4 electrodes, EOG) signals. A 3-dimensional Fastrak digitizer (Polhemus, USA) was used to digitize the position of three fiducial head landmarks (Nasal and Pre-auricular points). Four.
MEG preprocessing Data were preprocessed with the MNE-python toolbox  78  in accordance with accepted guidelines for MEG research. 79 Noisy MEG sensors were identified manually and interpolated using Signal Space Separation (SSS). The head position recorded at the beginning of each block was used to transform the signal to a standard head position by aligning head position across trials. Artifacts generated from eye blinks and heartbeats were isolated and removed automatically from raw MEG signals using Independent Component Analysis (ICA), also matching their activity with EOG and ECG signals. Prior to epoching, data were lowpassed filtered at 120 Hz and downsampled to 500 Hz. Cortical surfaces and inner and outer skull surfaces were reconstructed from individual MRI with Freesurfer (http://surfer.
nmr. mgh. harvard. edu) and individual binary element models (BEM) were estimated. Cortical surfaces extracted from FreeSurfer were projected to 5120 vertices with 4. 9 mm spacing per hemisphere. The inverse solution was computed using the dynamic statistical parametric mapping [dSPM; 80 ] inverse operator, with a loose orientation constraint (loose = 0. 2, depth = 0. 8) and a source covariance matrix estimated from the baseline activity. Subsequently, the cortical surface was parceled according to HCP-MMP1. 0 into combined whole-brain 46 functional labels,  40  as provided by MNE-python.
ACKNOWLEDGMENTS We thank the members of  UNIACT  for their logistical help on the recruitment of volunteers at NeuroSpin. We thank  Manuel Vidal  for his assistance in recording the original speech videos. We would like to thank  Joachim Gross  for insightful comments in a previous version of the manuscript and  Stefanos Sgardelis  for inspiring discussions. This work was supported by an  European Research Council-YStG -263584  to V. v. W. and an  Erasmus+  to N. C.
METHOD DETAILS Stimuli The experiment was written in MATLAB (The MathWorks, Inc. , Natick, Massachusetts, United States) with PsychToolbox (version 3. 0. 11). Audiovisual, visual and audio stimuli were extracted from videos of a female speaker pronouncing the syllables [ta] and [pa]. For each syllable, a video lasting 1320 ms (at a digitization rate of 50 Hz) was transformed into 66 frames (1 frame = 20 ms). The acoustic content was extracted as a wav file at 44. 1 kHz. Video and sound editing was carried out in Adobe Premiere Pro and Audacity, respectively. To reduce visual onset and offset responses, which would inevitably be caused by the sudden appearance and disappearance of the high-contrast faces on a black background, we modified the contrast of the first frame to produce five fade-in frames.
Whole body plethysmography on adult mice Male mice were acclimatized to plethysmograph chambers (DSI Buxco ® Multi-function Bias Flow, Harvard Bioscience, Holliston, USA) for at least one hour during 4 consecutive days. On the day of the experiment, respiratory variables were recorded while mice were quiet (after 20 min in the chamber). Normocapnic (21%O 2 and 79% N 2 ) and hypercapnic (8%CO 2 , 21%O 2 , 71%N 2 ) gas mixtures were produced by a gas mixing device (ALICAT, RS-232 Multi Drop model BB9, Tucson, Arizona, USA; software Flowvision 1. 0. 16. 0) and delivered continuously at 0. 5 l. min -1. The recording session was as follows: 10 min in normocapnia followed by 10 min in hypercapnia. The plots were analyzed in order to evaluate the respiratory frequency (fR, respiratory cycles. min -1 ), tidal volume (VT) normalized as the ratio of VT divided by body weight (VT, ml.
As molecular sensors detecting H + and CO 2 changes are described as sensitive to an increased concentration of H + , we performed pH variation of artificial cerebrospinal fluid to mimic physiological consequences of an increase in CO 2  (7, 23, 24)   (25). Normal-pH artificial cerebrospinal fluid (pH 7. 4) and metabolic acidosis artificial cerebrospinal fluid (pH 7. 23) differed in terms of NaHCO 3 concentration (21 mM and 15 mM, respectively)  (15, 20, 22, 23). Electrical activity of the fourth cervical ventral nerve root (C4) was recorded using a suction electrode, filtered (300-1000 Hz), amplified (x10000 Grass P511 AC Amplifier), integrated (Dual channel integrator, University of Chicago), and digitized through a Spike 2 data analysis system (CED micro 1401; Cambridge Electronic Design) with a sampling frequency of 2500 Hz.
Respiratory-like rhythm (respiratory frequency, fR) was defined as the burst frequency recorded on the fourth cervical ventral nerve root for 1 minute (burst. min -  )  (15, 20, 22, 23). After surgery, preparations were routinely allowed to stabilize for 30 minutes in normal-pH free of drug (Figure  1 ). Consistent with previous reports about the respiratory effect of etonogestrel or other steroids and 5-HT drugs on newborn rodents less than 4 days old, we pooled data obtained from males and females  (14, 15, (26) (27) (28). Baseline values were defined as the mean value during the last 5 minutes of this stabilization period. Then, a given preparation was exposed to a given pharmacological protocol consisting of 20 minutes of drug under normal-pH conditions, followed by exposure to metabolic acidosis under the same drug for 30 minutes, followed by a 30-minute washout period.
Some sections were processed in parallel, but with the omission of primary or secondary antibodies. No labelling was observed in these conditions. All sections were mounted in sequential caudo-rostral order on silanized slides, air-dried, and coverslipped with EUKITT (Bio Optica, Milan, Italy). Sections were photographed with a digital camera (Leica DFC450C, Leica Microsystems, Heidelberg, Germany) coupled to a microscope (Leica DM2000; Leica Microsystems, Heidelberg, Germany), using the software Leica Application Suite (L. A. S V4. 5). c-FOS, 5-HT, and c-FOS/5-HT immunolabeled cells were counted by an investigator blinded to samples. Analysed medullary structures involved in elaboration or adaptation of the respiratory drive were localized using standard landmarks  (32, 33). c-FOS positive cells were counted in commissural and medial parts of the nucleus of the solitary tract (c/mNTS), in the ventrolateral medullary reticular nucleus (VLM), a neuronal column ventral to nucleus ambiguus, extending from pyramidal decussation to caudal edge of facial nucleus, that contains the ventral respiratory column in medullary raphe nuclei i.
Separations were performed using a 100 x 2. 1 mm Kinetex C18 core-shell 2. 6 µm column (00D-4462-AN, Phenomenex) equipped with an Ultra Security Guard (AJ0-8782, Phenomenex) as a precolumn. The mobile phase (70 mmol/L potassium phosphate containing 0. 1 mmol/L EDTA, 6. 6 mmol/L octane sulfonate, 3. 1 mmol/L triethylamine, 10% methanol, pH adjusted to 3. 12 with 1 mmol/L citric acid, filtered), was pumped at a flow rate of 0. 4 mL/ min. Analytes were detected at an oxidation potential of 800 mV versus the reference electrode. Chromatograms were acquired at a rate of 10 Hz using Lab Solutions software for 17 minutes per sample. All the samples had to be diluted 3-fold in the extraction solution to properly quantify 5-HIAA. Concentrations of analytes were determined by comparison of chromatographic peak areas with calibration curves derived from a mixture of synthetic standards.
Statistics Data were analyzed with GraphPad (GraphPad Prism9, San Diego, California, United-States). Normality of data distribution for fR, VT and VE, for cells immunolabelled c-FOS, 5-HT, c-FOS/5-HT, PHOX2B, c-FOS/PHOX2B, for concentrations of 5-HT, 5-HTP and 5-HIAA, and for 5-HT/5-HTP and 5-HIAA/5-HT ratios were assessed using the d'Agostino and Pearson omnibus normality test. Depending on whether the distribution was normal or not, data were expressed as mean ± standard deviation or median and interquartile range [Q1; Q3] and parametric or non-parametric tests were performed: within a group of preparation, in paired conditions, paired t-test, Wilcoxon signed rank test, one-way analysis of variance, or Friedman test followed by Dunn's or Benjamini, Krieger and Yekutieli's multiple comparison test; between two groups, in unpaired conditions, unpaired t test, Mann & Whitney test and two-way analysis of variance followed by Benjamini, Krieger and Yekutieli's multiple comparison test in unpaired conditions; between more than two groups, one way analysis of variance or Friedman test followed by Benjamini, Krieger and Yekutieli's multiple comparison test.
Funding Work was supported by  Inserm ,  Sorbonne University, Legs Poix (Chancellerie des Universiteś de Paris , grant to LBo and FC),  Association Francaise du Syndrome d'Ondine (AFSO , grant to LBo),  Fondation du Souffle  (grant to LBo and FC) and  Agence Nationale de la Recherche  ( ANR , grant to LBo, ProgestVentil;  ANR-15-CE17 ). AC received funding from the  French ministry for Research and Education.
Data collection Two independent authors (RMR and ST) screened the references based on title and abstract using the reference software, Rayyan. Each duplicate article was identified and deleted. Selected articles underwent a full-text assessment. Disagreements were resolved by a third author (SRE). Throughout the inclusion process, a predesigned Excel spreadsheet was used to collect data about exclusion criteria, study design, participants, intervention, comparator, quality assessment, and outcomes. When results were not reported, we attempted to contact the study's authors to obtain the relevant missing data. The number of participants and denominator were extracted for dichotomous outcomes; whereas sample size, mean ± standard deviation (SD), and median [interquartile range (IQR)] were extracted for continuous outcomes.
The results of the meta-analysis were presented as forest plots. Statistical heterogeneity was assessed using Higgins I 2 value. [10]  Heterogeneity was considered low ( I 2 < 30%), moder-ate ( I 2 = 30-< 60%), substantial ( I 2 = 60-< 75%), and considerable ( I 2 ≥ 75%). When more than 10 articles were included in the meta-analysis, funnel plots together with Egger test  [18]  were used to assess the risk of publication bias, if present. Sensitivity analysis was performed to probe influence factor (number of included studies ≥ 10). When sufficient data were reported, subgroup analyses were performed on (1) health professionals receiving educational interventions, (2) baseline measures used as a standard of care, (3) overall bundle compliance exceeding 70% or not, (4) study quality, (5) study design, (6) geographical area, and (7) VAP diagnostic criteria.
Limitations This systematic review and meta-analysis had certain limitations that should be considered when interpreting the results. First, high heterogeneity was observed among studies, which could be due to high variation in study design, applied care bundles, standard care, and patient population. In future studies, consideration of interventions that have proven to be effective for VAP prevention for inclusion in the ventilator bundle may be important. Second, the sample size was relatively small, and the study was underpowered to perform several subgroup analyses (by type of ICU, nurse-patient ratio, isolated quality improvement interventions, and others). However, other biases, such as selection biases and poor methodological quality, can also explain this limitation. Third, although a large amount of information was recorded, data on many endpoints were incomplete, because they were reported in a format that could not be extracted and assessed.
Participants This exploratory study was conducted with 360 emerging adults aged 18-25, mainly in the Paris region and the Grand Est region of France. The recruitment period lasted 3 weeks, in February 2022. Table  1  presents the socio-demographic data of the participants (see "Tables"). To summarize, most participants were in their very early adulthood (M = 21. 2, SD = 2. 2), with a majority of women (77. 6%). A large proportion of the participants were students in a higher education institution (n = 300), particularly in the private field (n = 228). The sample of participants was randomly coming from three sources. The students at the higher education institution received oral information about the research during the welcoming week (September) and an email to solicit participation in February.
However, to ensure the fidelity and validity of the measures, the analyses have been extended to take into account the factorial dimension. There was one dependent variable: emerging adults' wellbeing. The independent variables were emotional regulation, attachment style, and assertiveness. Using cluster analysis, we created three attachment groups (secure, anxious, and avoidant). There were 97 participants with an anxious attachment, 134 with an avoidant attachment, and 122 with a secure attachment. The socio-demographic data collected were gender, age, the institution of study, professional activity, socio-professional categories of parents, consumption (cigarettes, cannabis, alcohol, online money gambling, or video games), perceived abuse or neglect, and the effect of the COVID-19 pandemic.
Data analysis Regarding the data analyses, correlations were made between the dimensions of well-being, emotion regulation, attachment style, and assertiveness. Multiple linear regressions were performed to better understand the weight of emotional regulation, attachment style, and assertiveness on the well-being of emerging adults. With the three attachment groups resulting from the cluster analysis, ANOVAs were performed to compare the means of attachment styles. Post-hoc ANOVAs were performed, considering some socio-demographic variables (level of professional activity, feeling of having been impacted or not by COVID, cigarette and cannabis consumption), as well as emotional regulation. We also performed an a posteriori comparison of our sample with a clinical sample regarding emotional regulation.
Normality and fidelity consistency analysis The statistical analyses were performed using the Jasp software. Given the sample size, it was possible to consider using parametric statistics. The Kurtosis and Skewness indices were between -2 and 2 for most variables. Therefore, they followed a normal distribution. It was then possible to produce parametric statistics, except for cannabis consumption. Fidelity analysis showed that three scales had good internal consistency, with Cronbach's alpha ranging from 0. 70 to 0. 95 (well-being: α = 0. 89; attachment: α = 0. 70; emotional regulation: α = 0. 86;  Tavakol and Dennick, 2011). However, the assertiveness scale and the attachment subscales had a Cronbach's alpha of less than 0. 70 (assertiveness: α = 0. 53; avoidant attachment style: α = 0. 37, anxious attachment style: α = 0.
A posteriori comparison of emotional regulation with the threshold values of a clinical population In this study, it was assumed that emerging adults' lack of emotional awareness and emotional clarity would be negatively related to their well-being. Hallion et al. (2018)  conducted a validation study of the Difficulties in Emotion Regulation Scale (DERS) with adults suffering from emotional disorders. Compared to this clinical sample of 427 adults diagnosed with one or more DSM-5 disorders  (Hallion et al. , 2018) , our sample of emerging adults had high emotional regulation scores, possibly signaling a dysregulation, as well as high scores for lack of emotional awareness, difficulty with goal orientation, and limited access to emotion regulation strategies (see Table  8 ). As the database of Hallion and colleagues' research was not available, it was unfortunately not possible to compare the score of the two independent populations (the population of the present study and that of Hallion et al.
Literature In literature since 2010, mean lengths of stay go from 116. 3 days [6] to 17 years  [37]. The wide range of results is due to the variety of legal systems and service provisions across countries, and to methodological differences in sampling and data collection. Indeed, length of stay can be measured in different ways  [19] : -Admission sample: completed length of stay collected for all patients admitted during a speci c period; -Discharge sample: completed length of stay collected for all patients discharged during a speci c period; -Census sample: length of ongoing stay collected for all patients hospitalized at the census date. Along with descriptive data, some studies analyzed factors associated with long stay. Table  1  summarizes factors associated with long stays, as well as thresholds chosen to de ne « long stay », in studies published since 2010.
Population We included all inpatients, men and women, at the census date of January the First 2020. We speci cally chose this date to minimize any potential in uence of the Covid-19 crisis on our results. During the admission process, patients were provided with the opportunity to complete an opposition form if they wished to refuse data collection for research purpose. Lengths of stay were calculated as the difference between census date and date of admission. This census sample method allowed us to consider patients who have very weak perspective of discharge, which is consistent with the purpose of this research. Conversely, admission samples and discharge samples only consider patients whose stay has been completed. Census sample method also provides a good snapshot of all hospitalized patients at one given time.
Statistics We rst conducted a descriptive analysis of long stay patients, then we compared in univariate analysis characteristics of long stay patients to the rest of the sample. Due to the small sample size (n=60), the number of variables that could be analyzed was limited. Consequently, we selected the most relevant variables for our study, and comparisons were avoided for variables with too few observations. To control for potential cofounding factors, a logistic regression multivariate model was developed. We used R 3. 6. 3 software on Windows to conduct analysis. No missing data processing was necessary.
Study Flow and Data Collection The study comprised three visits, a screening visit (V1) and two study visits (V2 and V3), separated by a washout period of at least 5 days. A maximum of 45 days following V1 was allowed for the completion of the two kinetics (Fig. 1A ). At V1, sociodemographic data, cardiovascular parameters (blood pressure and heart rate), and general medical history were recorded by the investigator. Blood samples (2 × 5 mL) were collected for standard assays (blood count and platelets, glucose, creatinine, urea, liver tests, thyroid stimulating hormone (TSH), and C-reactive protein). If the results were within normal ranges, the volunteer was included, and randomization performed to determine the order of allocation of study products for each study visit. Foods rich in melatonin or tryptophan (a precursor of melatonin) should be restricted for 2 days before and during each study visit.
The intra-and inter-assay coefficients of variation are 9. 5% and 12. 5%, respectively, for a concentration of 50 pg/mL. For the 6-SMT assay, the detection limit is 4 pg/mL and the intraand inter-assay coefficients of variation are 8% and 12%, respectively, for a concentration of 100 pg/mL. Samples from the same subject were processed in a single assay run to overcome the inter-assay variability of the technique. Quality controls were added to each series. Raw data processing (radioactivity counting) was performed using the Rialisme software.
Data were recorded using the eNNOV Clinical system and analyzed using SAS software version 9. 4. Mean C max , T max , T 1/2 , and AUC values obtained for both forms were compared using a Wilcoxon signed-rank test for paired samples. The same test was used to compare mean melatonin and 6-SMT concentrations at each time point versus basal concentrations. The threshold for statistical significance was defined as alpha risk of 0. 05. The percentage of subjects who experienced at least one adverse event is presented. The frequency and nature of adverse events are presented for each form according to the imputability to the product, evolution, and therapeutic decision.
Baseline Characteristics of Volunteers Fourteen men were included in the study. Mean ± standard deviation (SD) age was 33. 8 ± 8. 3 years, ranging from 20 to 45 years. Mean BMI was 24. 0 ± 0. 8 kg/m 2 (median [range]: 24. 3  [22. 3-24. 9] kg/m 2 ), and blood parameters were within normal ranges. Volunteers had satisfactory quality of sleep (mean ± SD VAS score of 7. 8 ± 0. 7). In two volunteers, only one of the two kinetics was performed (the subjects did not come to the second study visit), therefore the pharmacokinetic parameters were obtained in 13 subjects for each galenic form, with no missing data for assays and calculations. Safety results are reported for the 14 volunteers.
Background: It is unclear whether cognitive skill deficits during childhood carry risk for suicide attempt or mortality later in adulthood at the population level. We conducted a systematic review and meta-analysis of population-based studies examining the association between childhood cognitive skills and adult suicidal behavior, namely attempt and mortality. Method: We systematically searched databases for articles then extracted study characteristics and estimates on the association between childhood cognitive skills (i. e. , IQ or school performance at age ≤ 18 years) and later suicide attempt and mortality. Random-effect meta-analysis was used to quantify this association across all studies with available data. Results: Twenty-three studies met the inclusion criteria and suggest an association between lower childhood cognitive skills and increased risk of suicidal behavior.