dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis of variance data To compare the variances at which QR. pa expresses mig-1 in bar-1 mutants (Figure  5B ), we performed a Brown-Forsythe analysis of variance (ANOVA) test. Specifically, we made two comparisons (control versus bar-1 loss-of-function, and control versus ΔN-BAR-1) and therefore multiplied p-values by two to account for the Bonferroni correction. Position data were included if the mig-1 mRNA molecule number was greater than or equal to a threshold, and we performed the test for thresholds in the range 10 to 25. For the control versus bar-1 loss-of-function comparison, the Bonferroni-corrected p-values ranged from 0. 04 to 0. 5, with a mean value of 0. 2. For the control versus ΔN-BAR-1comparison, the Bonferroni-corrected p-values ranged from 5 × 10 -5 to 0.
Acknowledgements We thank the  Hubrecht Imaging Center  for technical support. This work was funded by the  Human Frontier Science Program  Grant  RGP0030/2016  to A. M. , M. A. F. and H. C. K. , and grants from the  Simons Foundation  ( 376198 ) and the  National Science Foundation  ( PHY-1945018 ) to S. G. and A. M. Some strains were provided by the CGC, which is funded by the  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). We are grateful to  Damien Coudreuse  and members of the  Korswagen and Galli  groups for helpful discussions and critical reading of the manuscript.
The second one is to use this model to implement a virtual clinical trial that identifies an improved TMZ protocol postponing the emergence of acquired TMZ resistance, while controlling tumor growth and reducing treatment toxicity. Several LGGs models have been previously developed  [17] [18] [19] [20] [21] [22]  but, to our knowledge, none of them have included the emergence of resistance considering the role of persister cells. Here we developed such a model and validated it by fitting longitudinal volumetric data from longitudinal MRI data of LGG patients showing evidence of acquired TMZ resistance. For each patient, we obtained a set of parameters describing the tumor behavior, what allowed us to create virtual copies of each real patient. Using those virtual twins, we conducted simulations to explore new TMZ administration schemes.
LGG patients imaging data Longitudinal imaging data from histology confirmed LGG patients who had been treated with TMZ were used to assess the goodness of fit of the ODE model, infer the values of the parameters and analyze modifications in the treatment scheme. Data were provided by three hospitals: Bern (Switzerland), Virgen Macarena (Seville, Spain) and Virgen del Rocı ´o (Seville, Spain). All patients signed informed consent and the study had been approved by the Institutional Review Boards of the hospitals. The authorization codes were: 07. 09. 72 (Bern University Hospital), 2158-N-19 (Virgen Macarena University Hospital) and 2158-N-19 (Virgen del Rocı ´o University Hospital). Data from a total of 91 patients were initially considered. Raw FLAIR 3D MRI studies of patients from Virgen Macarena University Hospital and Virgen del Rocı ´o University Hospital was processed as explained below.
Already processed volumetric longitudinal data from Bern University were used. Patients that showed evidence of acquired resistance to the treatment with TMZ were included in the study. This was assessed as a volumetric regrowth under TMZ treatment after a previous initial response to the treatment. Patients not responding to TMZ were excluded from the study. Patients who underwent surgery or other interfering therapies in the period of study were excluded. A total of 7 patients with grade-II gliomas (all male; median age 49 years with range 37-59 years) were selected. The final cohort consisted of four patients with oligodendroglioma and three patients with diffuse astrocytoma. Individual patient characteristics, longitudinal volumetric data and information related to TMZ treatment can be found in S1 File.
Tumor volume measurement Patients from Virgen Macarena and Virgen del Rocı ´o University Hospitals had fluid attenuated inversion recovery (FLAIR) 3D MRI sequences available. Tumor volume calculation was performed following the procedure described in  [35]. Digital Imaging and Communications in Medicine (DICOM) images were loaded into Matlab software (R2022b, The MathWorks, Inc. , Natick, Massachusetts) and were semi-automatically delineated using a gray-level threshold to identify the tumor region. A slice by slice manual correction of each segmentation was then done by a research team member (T. D. ) under the supervision of an image expert with six years of experience (J. P. B). Once tumor regions were identified and delineated, the tumor volume was computed by counting the number of voxels in the segmented tumor and multiplying by the individual voxel volume.
Generation of virtual patients In our mathematical model, each patient is characterized by a set of parameter values that includes initial tumor volume, patient-specific parameters, number of TMZ cycles received and fatal volume. Therefore, virtual patients can be generated by randomly assigning these sets of values using different statistical methods. First, the pre-chemotherapy tumor volume distribution was determined. We measured the volumetric data from MRI images of 32 patients at the time of diagnosis and determined the empirical probability distribution that they follow using KernelDensity function with a gaussian kernel from Scikit-Learn Python package, version 0. 24. 2  [40]. The obtained empirical Similarly, to generate patient-specific parameters we first studied the correlation between parameters of real patients (see Table  1 ).
Also, in clinical practice, tumor growth during treatment leads to its discontinuation. Consequently, with dosing intervals of 42 days the protocol would not be suitable anymore. Between all the patients, we found a mean rest period causing loss of control of 55 days, with a standard deviation of 17. 7 days. The threshold therefore depends on the specific patient. Since the goal was to obtain a general protocol improving the outcome for all patients, any scheme for which there is an increase in tumor volume for any single patient should be neglected. As a consequence, we did not consider any ID protocol that spaced doses over 42 days. Our results thus showed that spacing out each single dose between 7 and 42 days increases both OS and tumor shrinkage for all selected patients of our dataset.
Individual dose protocols show superiority in virtual clinical trial with fixed number of cycles In the previous analysis we intended to get as close as possible to the real situation represented by our patient's dataset. Since every patient received a different number of cycles, we reproduced the same behavior in the in silico patients by generating virtual cases which received a random number of total doses (within the data ranges). This number of doses was therefore different for each patient. However, demonstrating the efficacy of a proposal for a new standard by means of a clinical trial would imply delivering a pre-fixed and equal number of cycles to every patient. Therefore, we ran new clinical trials that fulfilled this criterion. For the ID14 and ID21 protocols, we set two different clinical trials to compare them to the C28 standard.
Therefore, as a consequence of the multiple mechanisms involved in the acquisition of TMZ resistance, and the fixed nature that many of them share, that phenomenon must be irreversible. Taking together all these evidences, we chose to model the resistant compartment V R as an irreversible state. Our model was validated through imaging data of LGG patients which had shown acquisition of resistance to TMZ. Semi-automatic segmentation on high quality FLAIR MRI images was used to obtain accurate longitudinal volumetric measurements. Indeed, accurate data acquisition is necessary to know exactly when the growth dynamics of a tumor changes, what is required to study the onset of resistance. The results from fits of volumetric growth to our model showed a strong dependency among several parameters.
As future work, it would be interesting to explore further the combined effect of chemotherapy and radiotherapy and propose optimum combined protocols. Further analyses on additional patient datasets are required to investigate whether tumor location, molecular type and combination of treatments may impact treatment response or resistance mechanisms. Moreover, a higher number of patients in the study would entail a better representation of the disease in our models. When the model was validated through patient data, we studied how modified protocols of treatment administration could improve the OS of patients by delaying the emergence of TMZ resistance. We chose to study schemes in which the doses were given individually with variable periods of rest between them. These periods were designed in multiples of weeks in order to keep the protocol simple to implement.
Genotyping and Imputation Analysis was conducted on the existing Illumina Immunochip Array data consisting of 2,565 IIM patients  [1]. Clinical subgroup analysis was conducted on PM (n=903), DM (n=817), JDM (n=508), IBM (n=252) and patients with anti-Jo1 autoantibodies (n=311). Controls were taken from the same pool of controls, but matched with cases 4:1 based on principal components analysis (PCA) coordinates  [8]. Variants were imputed with the Michigan Imputation Server using the Haplotype Reference Consortium panel (HRC) (Version r1. 1 2016). The HRC consists of 64,940 haplotypes of predominantly European ancestry. Poorly imputed genotypes (r 2 <0. 5), SNPs deviating from Hardy Weinberg Equilibrium in controls (p<0. 001) and those with a low MAF (MAF<0. 01) were removed. After stringent SNP quality control (QC), we analysed 1,648,116 variants, of which 120,734 were directly genotyped SNPs.
Functional Analysis We report the functional effect of the most strongly associated SNP in the locus from dbSNP's predicted functional effect. GARFIELD analysis (GWAS Analysis of Regulatory and Functional Information Enrichment with LD correction) was used to characterise the cellular and regulatory contribution of the associated variants  [10]. 95% credible SNP sets were calculated using the 'gwascredible-sets' package implemented in LocusZoom. js [9]  and annotated with functional information from public databases including GTEx expression and splicing QTLs, and regulatory information from the UCSC genome browser.
Discussion By using imputation to identify and fine-map genetic associations in IIM we found three new genome-wide associations in the combined IIM cohort: STAT4, SDK2 and LINC00924. Conditional analysis revealed evidence of independent associations in the STAT4 region. For the whole IIM group and clinical subgroups, the HLA region is the most significant genetic risk factor. The strongest genetic risk in this region was for the anti-Jo1 subgroup, despite a sample size of only 331 patients. In the PM subgroup, we report one novel genome-wide association with NAB1. This is the first time this dataset has been used for genome-wide imputation, and the first time it has been stratified by adult and juvenile-onset myositis subgroups, and by individuals with anti-Jo1 autoantibodies. We defined an associated region by proximity to the nearest gene.
Study Design and Ethical Considerations The current study used an exploratory cross-sectional survey design. Individuals with tinnitus who were seeking online psychological treatment (i. e. , internet-based cognitive behavioral therapy for tinnitus; ICBT) by registering to three separate clinical trials (ClinicalTrials. gov registration numbers NCT04004260, NCT04335812) were included  [16] [17] [18]. Ethical approval (IRB-FY17-209 and IRB-FY20-200-1) was obtained from the Institutional Review Board at Lamar University, Beaumont, TX, USA.
Data Collection The data were gathered pre-intervention using a secure platform for online questionnaires consisting of demographic and tinnitus-related questions, standardized patientreported outcome measures (PROMs), and response to free association tasks. The standardized PROMs included Tinnitus Functional Index (TFI  [19] ) as a measure of tinnitus distress, Generalized Anxiety Disorder-7 (GAD7  [20] ) as a measure of anxiety, Patient Health Questionnaire-9 (PHQ-9  [21] ) as a measure of depression, Insomnia Severity Index (ISI  [22] ) as a measure of insomnia, and the EQ-5D-5L VAS scale  [23]  as a measure of general health-related quality of life. In addition, participants were asked to provide answers to a free association task about tinnitus and health. In this task, participants were prompted to come up with five words or phrases that spontaneously come to their mind when they think about their tinnitus and to write them in the order or importance (i.
Data Analysis The data were analyzed using various qualitative (content analysis) and quantitative (i. e. , Chi square analysis, similarities analysis, and prototypical analysis) methods which are detailed below. However, a more detailed description of these methods can be found in our earlier publications  [12, 26, 27]. The similarities and prototypical analysis were conducted using the open-source text analysis software, IraMuTeQ. It is noteworthy that the studies on social representations generally apply multiple types of analyses to get multidimensional understanding of the data  [12]. Content analysis: The response to free-association task were analyzed using qualitative content analysis  [32]. This involved the grouping of similar words (e. g. , anger, rage, hate) into a category (e.
But are extreme attitude holders indeed more likely to resist change and ossify in their views than moderates? Mapping out individual developmental trends in political views is important for understanding the role of extreme and moderate opinion holders in polarization trends. Yet, existing research on temporal patterns in polarization has used pooled cross-sectional data that do not track the same people over time. In the current studies, we used longitudinal data surveying the same individuals for over a decade to provide a clearer view on the evolution of extreme attitude holders. Since the ultimate purpose of political discourse is influencing policy choices, we focus on attitudes related to public policies. The picture of extreme attitude holders as being less likely to change than moderates might seem appealing.
Methods Study 1. Study 1 examined the effect of attitude extremity on attitude change over time. We used the General Social Survey (GSS), a large-scale nationally representative data on attitudes about a variety of policy and socio-political issues collected from American adults over a period of 6 years. We compared attitude change exhibited over this time by people who held a more extreme (e. g. , extreme support or extreme opposition) attitude towards policy issues to more moderate attitude holders. For purposes of replicability, we adopted the Exploring Small, Confirming Big analytic strategy  34  , which involves randomly splitting the data into an exploratory part used to develop hypotheses and a confirmatory part used to test these hypotheses in out-of-sample data. Pre-registration is at https://osf.
io/h67bk. We used the three panel datasets collected within the General Social Survey, GSS  35  Each panel dataset consists of unique GSS participants who have been invited to join the panel study in 2006, 2008, or 2010. Each dataset includes three waves separated by a two-year time lag (e. g. , for participants who joined in 2006, wave 2 was in 2008 and wave 3 was in 2010; for participants who joined in 2008, wave 2 was in 2010 and wave 3 in 2012 etc. ). The datasets include multiple questions assessing attitudes towards different policy and socio-political issues. For the present analyses, we selected all attitude items that were measured in all three waves using a continuous scale, which is necessary for assessing extreme vs. moderate stances about an issue. Since all three panel datasets used the same design (2-year lag) and measures, we combined them into one (n = 6067).
All attitude items included response options "Don't know" and "No answer" that we coded as missing (only 0. 1% to 3. 4% of respondents selected one of these options). We removed cases that had missing values on either all of the attitude items or who participated only in the first wave, resulting in the final dataset of n = 4668. Following Exploring Small, Confirming Big analytic strategy  34  , we randomly split the dataset into an exploratory and a confirmatory (or hold-out) part. Twenty percent of the data constituted the Exploratory Sample (n = 933, M age wave 1 = 47. 38, SD age wave 1 = 16. 99; 431 male, 502 female based on information provided by participants) and 80% constituted the Hold-Out Sample, used for confirmatory analyses (n = 3,735, M age wave 1 = 47. 75, SD age wave 1 = 17.
To visualize this interaction, we plotted the trajectory of attitude change over time experienced by individuals with high, moderate and low attitude values at baseline. Finally, as a robustness test, we also tested whether classifying participants by general political ideology yields similar results to those seen using specific socio-political issues. The GSS dataset measures ideology on a scale ranging from 1 (extremely liberal) to 7 (extremely conservative) with labels attached to all scale-points (i. e. , 2 = liberal; 3 = slightly liberal; 4 = moderate, middle of the road, etc. ). We used this item to conduct this test. Data distribution was assumed to be normal but this was not formally tested. Study 2. Study 2 used a different set of attitudes, data from a different country (Netherlands), a larger sample ( ~13,000 individuals), and longer time span (up to 13 years).
Pre-registration is at https://osf. io/8ytev. Because this dataset contains 13 waves of surveys, it provides another opportunity to mitigate concerns about regression to the mean: Due to random measurement error inherent to all surveys, some attitudes measured as extreme in time 1 may indeed belong to extreme attitude holders or, alternatively, may reflect a particularly large measurement error. If the latter, then what appears to be moderation in time 2 might simply be a regression to the true attitude of the opinion holder due to a smaller measurement error at subsequent measurement points. The dataset in Study 2 helps here by having numerous survey waves (13 waves vs. only 3 waves in Study 1), which increases the number of measurements and thus reduces measurement error across all of them  38.
The panel started in 2008 and at the time of writing has accomplished 13 waves (last wave in 2021). Panel participants are asked to respond to short surveys (referred to as 'modules') on different topics monthly, such that each survey (or module) is repeated annually. We used the module "Politics and Values" that included multiple questions assessing participants' attitudes towards different policy and socio-political issues. Our analyses included all policy and socio-political attitude items that were repeatedly assessed in the module. The entire dataset included We included the attitudes towards a variety of policy and socio-political issues, ranging from support of euthanasia to gender egalitarianism to multiculturalism. The attitudes items, measures, response options and internal consistencies are provided in Supplementary Table  5.
Some of the items included the response option "Don't know" that was coded as missing. We used the same analytic strategy as in Study 1. Finally, as a robustness test, we tested whether classifying participants by general political ideology yields similar results to those seen using specific socio-political issues The LISS dataset measures ideology on a scale ranging from 0 (left) to 10 (right), and we used this item to conduct this test. Data distribution was assumed to be normal but this was not formally tested. Study 3. Study 3 tested whether people hold a lay belief in stubborn extremists (i. e. , a belief that extremists are less susceptible to attitude change than moderates) in an experiment. Participants were presented with the attitudes of different target persons on various policy issues.
05. We have not analyzed any data until collection was completed. This study was pre-registered at https://aspredicted. org/KPG_Q49. We recruited 300 participants from Cloud Research and 17 failed the attention check, resulting in N = 283 (M age = 46. 00, SD age = 13. 73; 136 women, 146 men, 1 other based on information provided by participants). Compensation was $0. 48. This was a 2(Attitude: moderate vs. extreme) x 2(Stance: oppose vs. support) x 3(Policy issue: affirmative action, wealth redistribution, gay marriage) mixed design, with the first two factors manipulated between and the third factor manipulated within subjects. We were interested in the effect of the first factor (Attitude: moderate vs. extreme) and varied the other two factors (stance and policy issues) to increase the generalizability of the findings.
Higher values indicate greater decrease over time. Supplementary Fig. 3  plots the relationship between attitude similarity and attitude change, showing that it is strongly positive (n = 72 issue pairs, r 70 = 0. 82, p < 0. 0001, 95% CI[0. 73, 0. 88]). This means that similar attitudes exhibited the same change pattern whereas dissimilar attitudes exhibited different change patterns, arguing against regression to the mean or a measurement error. To account for the possibility that the bounded scales in datasets influenced the effects, we also re-did our regressions using Tobit models that allow obtaining unbiased coefficients in the presence of floor or ceiling effects  40. The results (Supplementary Tables  3  and 4 ) support the conclusions drawn from other analyses. Beyond the Tobit analyses, notice that the general results of this study (and Study 2 as well) also weaken the possibility that the results are an artifact of the bounded measurement scale.
15,561 participants. We retained the cases with valid values on attitude items in at least 2 waves (n = 11,570). As in Study 1, we randomly split the data into the Exploratory sample (20%) and Holdout sample (80%). The Exploratory dataset consisted of 2260 participants (M age wave 1 = 42. 26, SD age wave 1 = 18. 86; 1053 male, 1207 female based on information provided by participants). The Holdout sample consisted of 9310 participants (M age wave 1 = 42. 31, SD age wave 1 = 17. 92; 4208 male, 5102 female based on information provided by participants).
The protocols given to the participants should be assessed and tested before being handed out or may even be co-constructed with participants. v2. 9. 21-EN Maintaining a link between a dataset and the participant who produced it (while retaining anonymity) makes it possible to construct these indicators and identify the less reliable contributors, including malicious practices where data are deliberately tampered with (fake data). Exclusion rules can be put in place for such participants. There are different levels of openness when managing the sharing of the project data: -data sharing restricted to the researchers; -data sharing restricted to the participants and the researchers in compliance with regulatory constraints (GDPR 1 , health data); -public open access to the data during or after the project.