dataesr/scientific-paragraphs-categorization huggingface dataset:
Classical CD Plot of IDPs and Ordered Proteins We reproduced the double-wavelength plot using CD intensities at 200 and 222 nm wavelengths on the available data on proteins reported by  Uversky (2002) ,  Uversky (2003) ,  Uversky and Fink (2004) , as shown in Figure  1A. IDPs and globular proteins were separated with some overlap in the plot. However, when we completed this plot with all the proteins in our database, this picture has changed significantly (Figure  1B ). Although the newly added spectra of disordered peptides concentrated well on the previous disordered ones, the globular proteins covered a much wider space and even overlapped with the disordered region ruining the spatial separation. The CD spectra of those globular proteins that are located in the disordered region in the double-wavelength plot are similar to that of the disordered ones, despite their fully ordered globular structure (Figure  2 ).
Identification of IDPs Using Various Mathematical Models To develop a binary classification method (IDP vs. ordered structure) for an accurate and automatized IDP identification, we analyzed the CD spectra of our database using various mathematical models, such as decision trees with different number of branches (tree: simple and medium); support vector machines with different kernel functions [SVM: linear and radial basis function (RBF)]; k-nearest neighbor classification with Euclidean distance and three different numbers of nearest neighbors, a weighted distance function and a cosine distance metric (KNN: fine, medium, coarse, weighted, and cosine); and discriminant analyses with linear or quadratic decision surface including linear diagonal or quadratic diagonal models (discriminant: linear, quadratic, diaglinear, and diagquadratic).
These models are available in the MATLAB Classification Toolbox. As a starting point, we tested the performance of using the CD amplitudes at 200 and 222 nm wavelengths to identify IDPs using the 85 spectra collected from the literature based on Uversky's works  (Uversky, 2002; Uversky, 2003; Uversky and Fink, 2004)  as training set and using our entire database as test set (in a cross-validated manner). SVM-RBF was proven to be the best mathematical model providing 11. 1, 3. 5, and 8. 6% errors in identifying the ordered structures, disordered structures, in overall accuracy, respectively (see also Figure  1B ). In the next step, we tested the performance of all models using CD data at two wavelengths varying the wavelength values to find the best performing pairs as a function of the cutoff wavelength of the CD spectra.
, 2005; Woollett et al. , 2013; Micsonai et al. , 2021). The 89 spectra collected from the literature have obviously lower average quality, and this increases the error of the classification on them to 3. 3, 10. 9, and 8. 24% for disordered and ordered structures and for global error, respectively. These calculations were performed in a leaveone-out cross-validated manner using all available data as training dataset. Careful, noiseless experiments with correct baseline subtractions might give better accuracy than the average error found here. A real advantage of the KNN-cosine method with "212-217-225 nm" data is that it can be used for CD spectra recorded in the presence of strongly absorbing solutions such as the case of chemical denaturants (e. g. , urea and GdnHCl), or under crowded conditions if the spectrum can only be recorded down to ~210 nm.
Experimental Classification of Disorder vs. In Silico Predictions Numerous bioinformatics tools have been developed in the last decade to predict intrinsic disorder from the amino acid sequence  (Katuwawala et al. , 2019; Liu et al. , 2019; Necci et al. , 2021). Among them, AlphaFold2 was proven to be the most accurate method to detect disorder. Low values of the plDDT parameter (confidence) have been shown to be indicative of disordered regions  (Jumper et al. , 2021). AlphaFold2 and previous methods are useful when investigating large datasets, and high-throughput analysis is needed, and they indeed provide good statistics. However, in silico predictions always have a level of uncertainty and thus need experimental verification, especially when investigations are narrowed down and focus on a particular protein.
To confirm this statement, we analyzed the disordered proteins of our reference database by AlphaFold2 and found that several disordered chains were mistakenly predicted to be highly α-helical, such as α-synuclein, thymosin-α1, basic subdomain of the c-Jun oncoprotein, α-tubulin (fragment 404-451), β-tubulin (fragment 395-445), S21 protein from the 30S subunit of the E. coli ribosome, and artificial disordered peptides #1, 2, and 6. Moreover, computational methods, like AlphaFold2, can neither take the actual environmental conditions into account, such as pH, ionic strength, temperature, the presence of additives or crowding agents, the effect of protein concentration, intermolecular interactions, nor accurately calculate the effect of single mutations (unless the crystal structure was already solved and deposited in the PDB) and the effects of post-translational modifications (e.
5 4. 5 3. 8 200 KNN-cosine 212 217 225 4. 7 1. 5 3. 8 SVM-linear 202 205 231 7. 2 2. 5 5. 7 SVM-RBF 206 212 229 5 7. 5 5. 7 Discr-quadratic 201 211 215 6. 6 3. 8 5. 7 KNN-fine 212 215 227 3. 9 10 5. 7 205 KNN-cosine 212 217 225 3. 3 7. 4 4. 6 SVM-RBF 206 212 229 5 7. 4 5. 7 KNN-fine 212 215 227 3. 9 9. 9 5. 7 a Algorithms showing the least errors using three wavelengths (WL1, WL2, WL3) for classification as a function of the cutoff wavelength are presented. For training dataset, for a given wavelength triplet, all proteins' spectra that covered those wavelengths were used. Frontiers in Molecular Biosciences | www. frontiersin. org May 2022 | Volume 9 | Article 863141.
Automated detection of ARISA fragments was carried out using an ABI Prism 3100 genetic analyzer (Applied Biosystems). ARISA fragments in the range 390-1400 bp were determined using Genescan analytical software 2. 02 (ABI), and the results analyzed adopting the procedure described by  Danovaro et al. (2006) , which included standardization of fluorescence among samples, elimination of "shoulder" and non-replicated peaks, and cut-off criterion. According to available literature  (Danovaro et al. 2006; Hewson and Fuhrman 2006) , we have set the accuracy in sizing at ± 3 bp for ARISA fragments lesser than 700 bp, ± 5 bp from 700 to 1000 bp and ± 10 bp for greater than 1000 bp.
Tasks accessing individual differences in inhibitory control capacity In line with  Barzykowski, Hajdas, et al. (2021, p. 14) , we used the Stroop task (MacLeod, 1991; Chuderski, Taraday, Nęcka, & Smoleń, 2012) and the Eriksen flanker task  (Eriksen & Eriksen, 1974)  to measure participants' inhibitory control capacity. These tasks were created using Inquisit Web software (Millisecond software) and were used in both the online and laboratory-based sessions. While the Inquisit-web protocol is described in more detail in  Barzykowski, Wereszczyński et al. (2021) , we briefly describe it below. The Stroop task consisted of four colour words (red, green, blue, and yellow) in Polish, displayed in one of these four colours (e. g. , the word red could be displayed in red, green, blue or yellow). Participants were instructed to judge the colour of the 'ink' of the word as quickly as possible without paying attention to the meaning of the word by pressing a key corresponding to the colour of the ink.
Acknowledgements The present study was supported by a grant from the  National Science Centre, Poland  ( 2015/19/D/HS6/00641 ) to  Krystian Barzykowski. In addition, the writing of the present paper was supported by the Bekker programme from the  Polish National Agency for Academic Exchange  (no. PPN/BEK/2019/1/00092/DEC/1 ) and the  National Science Centre, Poland  ( 2019/35/B/HS6/00528 ) to  Krystian Barzykowski. We thank  Marcin Draszczuk  for IT help in preparing the  Involuntary Memory Programme.
Subjects and Housing Conditions We observed twenty-five captive red-capped mangabeys housed at the Station Biologique de Paimpont (University of Rennes 1, France). Subjects were 10 females and 15 males, including 3 juveniles, 14 middle-aged, and 8 old adults (Table  1 ). They lived in social groups, from two to 13 individuals, which have been stable from at least 8 months at the moment of data collection. Mangabeys were housed in outdoor-indoor enclosures of different sizes (from 8 to 26. 4 m 2 for indoor enclosures, 14. 7 to 37. 2 m 2 for outdoor enclosures, and height from 2. 5 m to 4. 4 m) and could move in and out freely, using connecting tunnels. The temperature of indoor enclosures was set at 22 • C. Enclosures were enriched with perches made of wood and metal, on which swinging chains and hessian ribbons were attached.
33 ± 0. 75 min) of recording per individual, and 52. 22 h of observations overall. Sessions were scheduled randomly, and then equilibrated so that all monkeys were observed at all times of day (from 9:00 a. m. to 6:00 p. m. ) and at all feeding periods (before or during the first feeding/between two feedings/after or during the second feeding). BORIS v. 6. 0. 6 software  [72]  was used to point each event of manual gesture and each social interaction involving the focal individual. We then coded parameters and behaviors associated to each gesture occurrence (i. e. , the signaler and receiver identities, the type and characteristics of gesture, the social context, and relative position of interactants).
The model quality was verified by checking for the absence of data overdispersion, the independence of model residuals, and the absence of fixed factors collinearity (R packages {RVAideMemoire} and {performance}). Secondly, we tested the presence of an overall gestural laterality in our captive redcapped mangabeys. To assess gestural laterality at the individual level, for each individual that produced at least 6 manual gestures, we performed binomial tests to compare the proportion of right-and left-hand gestures to a theoretical proportion of 0. 5. For each of these subjects, a handedness index (HI) was also computed using the following formula: HI = (R -L)/(R + L), where R corresponds to the number of gestures produced with the right hand and L with the left hand. This index, between -1 and 1, is commonly used to assess laterality  [90] , as its sign reveals the potential bias direction: toward the right if it is positive, and the left if it is negative.
Patients and Procedure Patients included 60 veterans suffering from PTSD with nightmare complaints treated at five French military teaching hospitals between November 2020 and November 2021, and recruited for a larger ethically approved study evaluating sleep in PTSD (ClinicalTrials. gov Identifier: NCT04581850). Patients were recruited via flyers and clinician referrals. All patients were veterans suffering from combat-related PTSD [with PTSD Checklist Scale (PCL-S) > 44 and a clinical diagnosis of PTSD]. Exclusion criteria included medical history of chronic psychiatric disorders, identified sleep disorders, and neurologic disorders prior to trauma. After providing written informed consent for use of their data in this study, patients completed questionnaires for approximately one hour. Patients were asked to keep a sleep diary over the course of 1 week.
Data Analysis All analyses were conducted using JAMOVI for Windows. A test-retest analysis was performed on 54 patients who completed two evaluations (1 month apart). As was done in the preliminary validation of the original English version of the TRNS, a test-retest was conducted on quantitative items considered important for nightmare-related constructs (nightmare frequency, nightmare severity, number of nights with nightmare, fear of sleep, sleep latency, feeling restfulness after awakening, and depression upon waking). 17 We also added analyses of other quantitative items: total sleep time, duration of sleep before nightmares occur, and time to fall asleep again after a nightmare. For the Phelps questionnaire, a test-retest was conducted on the four translated items. All 60 patients were included in the validation process.
2020 ) (Table  1 ). For the current analysis only individuals with no missing data for any variables of interest (CID-12, RMET, and, in case of patients, PANSS) were included in the analyses. Healthy participants with no known history of psychiatric or neurological disorders were recruited via online advertisements and included in the study as a control group. Controls were matched head-to-head to patients with regard to age (SCZ: 33. 52+/-8. 23; HC: 33. 80+/-6. 78 t(276)=. 310 p=. 76) and sex (45F/94M in each group). Procedure: During the behavioral assessment session participant completed Communicative Interaction Database explicit recognition task  (Manera et al. 2015)  and Reading the Mind in the Eyes task  (Calder et al. 2002)  as proxies of either lower-level social perception abilities and higher level social inference abilities.
6 While there is still a growing body of research on the consequences of the pandemic on students' mental health, studies assessing the long-term impacts are rarer. However, the direct (infections, hospitalizations, deaths) and indirect (economic crisis, difficulties in accessing care, isolation) consequences of the pandemic are likely to induce a very long-lasting mental health crisis,  7, 8  and recommendations invite monitoring the mental health of populations over the next few years. 9 The present study used data from the third measurement time (T3) of the COSAMe survey, conducted 15 months after the beginning of the COVID-19 pandemic to (1) measure the prevalence rates of self-reported mental health symptoms (stress, anxiety, depression, PTSD, and suicidal thoughts) and (2) identify factors associated with mental health outcomes.
Study Design and Population The study used data from the repeated cross-sectional university-based survey COSAMe, which consisted of 3 measurement times: T1, during the first lockdown (April 17 to May 4, 2020); T2, 1 month after the lift of the first lockdown (June 15 to July 15, 2020); and T3, 15 months after the start of the pandemic (July 21 to August 31, 2021). At each time, the French Ministry of Higher Education, Research, and Innovation requested the 82 universities to send an email to their students (target population, approximately 1 600 000 students) asking them to participate in the survey by completing online self-administered questionnaires. Due to the heterogeneity of sanitary measures from 1 country to another, the study only included students residing in France during the first lockdown.
23 At T1, we measured acute distress using the Impact of Events Scale-Revised. Bivariate analyses were conducted to test the association between outcomes and covariates using χ 2 tests, and multivariate logistic regression models identified risk factors of reporting at least 1 poor outcome (suicidal thoughts, PTSD, stress, depression, or anxiety) at T3. Then, similar models were conducted for each outcome. All explanatory variables were included except age due to collinearity with the year of study. Associations between risk factors and outcomes were presented as odds ratios (ORs) and 95% CIs. Data analysis was performed using R version 3. 6. 1 (R Project for Statistical Computing). The level of significance was set at. 05, and all tests were 2-sided.
Addictive structural characteristics of gambling products and related messages Pictogram Definition reworded for laypeople: "This pictogram regards a gambling game. …" Unlimited temporal access to the game (24/7 venue, online…) "… with an access unlimited in time (24/7, online). " Messages promoting ease of gambling (including bonus and rapid cashout messages) "… for which advertising messages or instructions encourage gambling (e. g. , 'safe first bet, ' bonus, rapid cash-out). " Table  1. Final 10 pictograms and their reworded definitions for laypeople. Test of the impact of the pictograms and their definitions in the general population. Design and population. We conducted an online survey in a sample of 900 laypeople from the French general population over 18 years old. The sample was randomly selected from a large probabilistic panel in Europe that provides robust, representative survey data not limited to individuals who are actively seeking online surveys to complete, thus limiting the risk of sample bias.
In parallel, French-speaking experts of the Delphi panel (n = 25) were also asked to assess the addictiveness of the same 14 gambling products, according to their expertise and personal judgement. We considered the experts' opinion as the data with the highest level of evidence and the "true rating" for the analyses. Data collection. Data collection included the following: age, sex, employment status, academic level, and place of residency. Gambling habits were also collected: last 12 months' use of offline and online lottery, scratching, horse race betting, sport betting, poker, casino games, or none, as well as scores on Problem Gambling Severity Index of the Canadian Problem Gambling Index (PGSI). Desire to gamble was measured as "decreased" (-1), "no change" (0), or "increased" (+ 1). Addictiveness of each of the14 French current gambling products was assessed on a 5-point Likert scale from 0 (not addictive) to 4 (highly addictive) responding the question "After seeing these pictograms/slogan/[], how addictive do you think the following game is?".
Statistical analyses. Descriptive analyses were conducted for all variables by group. We used the following operational definition of agreement: reliable raters agree with the "true" rating of addictiveness, determined by the mean rating of the experts for each of the 14 gambling products. The three groups of laypeople were considered to be three raters, and mean addictiveness was calculated by group for the 14 gambling products. The primary outcome was therefore the 2-2 groups-experts inter-rater coefficient of addictiveness on the 14 gambling products (mean of each group for each of 14 products , as calculated by the intra-class coefficient (ICC); Two-Way Random-Effects Model, R package "irr", "icc", twoways, agreement, average; Refs. 27, 28. We hypothesized that PictoGRRed pictograms and their definitions would increase the agreement between laypeople and experts, where the experts' opinion was considered the "true rating"; more precisely, the Pictogram group-expert ICC would be higher when the Pictogram and expert groups were compared than when the Slogan/Control groups and the expert group were compared.
The secondary outcome was the 2-2 between-group difference in the increase in gambling desire, with a chi-square test. Secondary analyses were performed on the subgroup without problem gambling (PGSI total score < 5). Statistics were performed with R software version 3. 6. 3. A flowchart is provided in Fig. 2. Ethics. The PictoGRRed study was approved by the Local Nîmes Hospital IRB (Approval Number 0. 10. 03). All experiments were performed in accordance with relevant guidelines and regulations.
Apparatus and procedure The experiment was created using the online experiment builder LabVanced  [19]  and displayed on participants' computer screens (we allowed Mac, Windows and Linux operating systems, but tablets and smartphones were disabled). Before the start of the experiment, a consent form was displayed on the screen to inform participants that their data would be recorded anonymously. If they accepted to participate, the instructions were presented on the screen and were followed by 10 practice trials. The main experiment was composed by 720 trials, with a different random order for each participant. Each trial started with a fixation cross in the middle of the screen for a duration of 250 ms. Following a 200 ms blank screen, the prime sequence was rapidly presented (200 ms) followed by another blank screen for 100 ms (i.
e. a 300 ms SOA). The target sequence remained on the screen until the participant indicated whether the target sequence was grammatical or ungrammatical by pressing the 1 The hypotheses and methodology were pre-registered on the Open Science Framework (OSF) repository (https://osf. io/ba4nw). 2 1000 simulations were run based on a pilot study of 16 participants to determine the necessary number of participants. The results of the power analysis showed that with 69 participants, power was 80% to observe the main effects on which the current study focuses. We decided to collect data from 80 participants (see pre-registration form) to cover for any issues with online experiments and in order to increase power above 80% as suggested by Brysbaert  [17]. Some more participants were tested than the pre-registered 80 because we were expecting a number of participants to be dropped due to, for instance, poor accuracy or not finishing the experiment.
Analysis We used linear mixed-effects (LMEs) models to analyse response times (RTs), with items and participants as crossed random effects (including by-item and by-participant random intercepts)  [20]  and with random slopes  [21]. Generalized (logistic) linear mixed-effects (GLMEs) models were used to analyse error rates. The separate models for the repetition and transposition factors 3 were fitted with the lmer (for LMEs) and glmer (for GLMEs) functions from the lme4 package  [22]  in the R statistical computing environment. We report regression coefficients (b), standard errors (s. e. ) and t-values (for LMEs) or z-values (for GLMEs) for the Relatedness factor (i. e. priming effects). Fixed effects were deemed reliable if |t| or |z| > 1. 96  [23] , and the corresponding values are presented in bold font.
Response time Prior to the RT analysis, we removed trials with incorrect responses (5. 30%) and trials with RTs beyond 2. 5 s. d. from the grand mean (3. 38%). The remaining dataset was composed of 29 600 observations. Condition means are reported in figure  2. For grammatically correct targets, there was a significant effect of Relatedness in the repetition condition (b = 0. 13; s. e. = 0. 006; t = 20. 64), with faster RTs to targets following related primes (M = 1456 ms; 95% CI = 19. 06) compared to unrelated primes (M = 1588 ms; 95% CI = 20. 15). There was also a significant effect of Relatedness in the transposition condition (b = 0. 02; s. e. = 0. 006; t = 4. 13), with faster RTs to targets following related primes (M = 1505 ms; 95% CI = 20. 89) compared to unrelated primes (M = 1524 ms; 95% CI = 18.
Error rates The dataset for the error analysis consisted of 32 352 observations. Average values per conditions are reported in table 2. For the grammatically correct targets, there was a significant effect of Relatedness in the repetition condition (b = 0. 28; s. e. = 0. 11; z = 2. 42) with more errors in the unrelated condition (M = 4. 96; 95% CI = 0. 58) compared to the related condition (M = 3. 92%; 95% CI = 0. 79). The effect of Relatedness was not significant in the transposition condition (b = 0. 09; s. e. = 0. 11; z = 0. 84). For the ungrammatical targets, the effect of Relatedness was not significant for either the repetition condition (b = 0. 08; s. e. = 0. 13; z = 0. 60) or the transposition condition (b = 0. 04; s. e. = 0. 10; z = 0. 48).
These syllables were grouped into three pairs (/fɐn/-/jɐu/, /fɐn/-/wɐi/ and /jɐu/-/wɐi/), which had equal chance to occur. The discrimination task contained 12 blocks in total, which corresponded to the combination of the two tone types, the three stimulus types and the two testing ears. Within each block, the sequences of stimulus presentation were randomized. The same pairs were repeated six times (3 × 6), and different pairs were repeated three times (6 × 3), creating equal numbers of same and different pairs in each block. Each block lasted about 2-2. 5 min and the whole discrimination task took about 25 min. Participants were asked to take a five-minute break every three blocks. The tasks were programmed with E-prime 1. 0 (Psychology Software Tools, Pittsburgh, PA). All the tasks were conducted in a soundproof booth in the Speech and Language Sciences Lab at the Hong Kong Polytechnic University.