dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses To explore associations between measures of trait impulsivity and the two indices of decisional impulsivity (at baseline and follow-up, respectively) and their relationship with brain functional activity, linear partial correlation analyses were performed using IBM SPSS, version 24, controlling for age, sex, IQ, and site. As we explored associations between three or two facets/indices of impulsivity and with brain functional activity at baseline and follow-up, respectively, false discovery rate (FDR)-corrected p values are reported to address issues of multiple testing. As the CGT was assessed only at baseline, no correlational analyses could be conducted for follow-up data. To assess changes in relationships from adolescence to young adulthood, linear mixed-effect models were fitted to the data using the nlme package in R software, version 3.
(65) ]. In addition, in our study, there was a focus on specific, a priori defined neural processes of interest, also with regard to the fMRI contrasts and ROIs selected. However, others might be relevant [e. g. (34) ]. Currently, this study should be seen as a starting point, and future studies extending these results are warranted. Furthermore, using confirmatory factor analyses, data were reduced by estimating predicted values. However, a higher validity of assessment might have been obtained by combining more than one measure per concept. Also, recent articles question the test-retest reliability of the fMRI blood oxygen level-dependent signal activity [e. g. (70) (71) (72) ]. The resulting discussion is especially relevant when interpreting findings from longitudinal multicenter datasets focusing on developmental processes, and these should be addressed in future studies.
ACKNOWLEDGMENTS AND DISCLOSURES This work was supported by the research consortium on ADHD,  ESCA-Life , funded by the  German Federal Ministry of Education and Research  (Grant No. FKZ 01EE1408E  [to TB]),  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (Grant No. LSHM-CT-2007-037286 ), Horizon 2020funded  European Research Council Advanced Grant STRATIFY (Brain network  based stratification of reinforcement-related disorders) (Grant No. 695313 ),  Human Brain Project  (Grant Nos. HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ),  Medical Research Council Grant c-VEDA (Consortium on Vulnerability to Externalizing Disorders and Addictions)  (Grant No. MR/ N000390/1 ),  National Institutes of Health (NIH)  (A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers) (Grant No.
R01DA049238 ),  National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London ,  Bundesministerium für Bildung und Forschung  (Grant Nos. 01GS08152 ,  01EV0711 , and  Forschungsnetz AERIAL 01EE1406A  and  01EE1406B ),  Deutsche Forschungsgemeinschaft  (Grant Nos. SM 80/7-2 ,  SFB 940 ,  TRR 265 , and  NE 1383/14-1 ),  Medical Research Foundation and Medical Research Council  (Grant Nos. MR/R00465X/1  and  MR/S020306/1 ),  NIH -funded ENIGMA (Grant Nos. 5U54EB020403-05  and  1R56AG058854-01 ),  Agence Nationale de la Recherche  (Grant Nos. ANR-12-SAMA-0004  and  AAPG2019-GeBra ),  Eranet Neuron  (Grant Nos. AF12-NEUR0008-01-WM2NA  and  ANR-18-NEUR00002-01-ADORe ),  Fondation de France  (Grant No. 00081242 ),  Fondation pour la Recherche Médicale  (Grant No.
Similar to the eGFP/mBeRFP pair, we imaged the mBeRFP/TRITC pair using either spectral or classical mode when the sample was illuminated with a 561 nm light source. The analysis of the spectral contamination revealed an absence of crosstalk between mBeRFP and TRITC for the settings used in this assay (Fig. S3C, D ). Thus, although mBeRFP can be weakly excited at 561 nm, its emission between 570 and 700 nm is close to the background when the laser power is low, suggesting that this fluorescent protein can be easily coupled with red-emitting FPs without disrupting either the observation or the quantification. In summary, these data showed that mBeRFP can be combined with green or red-emitting FPs or dyes without crosstalk. In addition, this tool could even allow the detection of five distinct channels using four light sources and only three tracks in classical confocal mode (Fig.
Design and Procedure The EU-GEI study Work-Package 2 (WP2) employed a case-control design collecting data with an extensive battery of demographic, clinical, social, and biological measures (Core assessment); psychological measures, and cognitive tasks. EU-GEI WP2 participants with complete Degraded Facial Affect Recognition (DFAR) task and Benton Face Recognition Test (BFRT) data were included in the current study. All the researchers involved in administering the assessments undertook a training organized by a technical working committee of the overall EU-GEI study at the beginning and throughout the study. Interrater reliability (0. 75) was assessed annually to warrant comparability of procedures and methods across sites.
Polygenic Risk Scores The case-control genotyped WP2 EUGEI sample (N = 2169; cases' samples N = 920, controls' samples N = 1248) included DNA extracted from blood (N = 1857) or saliva (N = 312). The samples were genotyped at the MRC Centre for Neuropsychiatric Genetics and Genomics in Cardiff (UK) using a custom Illumina HumanCoreExome-24 BeadChip genotyping array covering 570 038 genetic variants. For genotype Quality Control, we excluded SNPs with minor allele frequency <0. 5%, Hardy Weinberg Equilibrium P < 10-6 , missingness >2%. For sample Quality Control, we excluded samples with >2% missing genotype, heterozygosity Fhet >0. 14 or <-0. 11, and those who presented genotype-phenotype sex mismatch or clustered with African ancestry in Principal Components Analysis (PCA) (N = 170). PCA was applied to genotype data to detect and correct our analysis for population stratification.
Sample Characteristics FEP patients and controls were included in the current study if data on both DFAR and BFRT were available. This led to a sample of 828 FEP patients and 1308 controls for the analysis. Patients were younger (mean age = 30. 9 ± 10. 6 vs. 36. 2 ± 13; t = 9. 9, P <. 001), with more men [61. 8% (512) vs. 47. 4% (620); χ 2 (1) = 42. 4, P <. 001], and more frequently from minority ethnic backgrounds (χ 2 (5) = 49. 3, P <. 001) compared with controls (table  1 ). The aforementioned differences are those expected when comparing psychotic patients with the general population. Table  1  also shows that IQ scores were lower in cases compared with controls.
The sequence-based computational method ProfileView  (Vicedomini, Bouly et al. 2019)  has been recently designed to address the functional classification of the great diversity of homologous sequences hiding, in many cases, a variety of functional activities that cannot be anticipated. ProfileView relies on two main ideas: the use of multiple probabilistic models whose construction explores evolutionary information in large datasets of sequences  (Bernardes, Zaverucha et al. 2016)   (Ugarte, Vicedomini et al. 2018)   (Vicedomini, Blachon et al. 2021)   (Fortunato, Jaubert et al. 2016 ) (Amato, Dell'Aquila et al. 2017) , and a new definition of a representation space where to look at sequences from the point of view of probabilistic models combined together. ProfileView has been previously applied to classify families of proteins for which functions should be discovered or characterized within known groups  (Vicedomini, Blachon et al.
Structural alignment of CrFBA3 to the protein data bank archive returned 470 matches, with highest similarities towards Arabidopsis thaliana glycolytic FBA (PDB ID: 6RS1, Qscore = 0. 88; RMSD = 0. 93 Å), with Plasmodium falciparum FBA (PDB ID: 4TR9, Q-score = 0. 87; RMSD = 0. 89 Å), and with Toxoplasma gondii FBA (PDB ID: 4D2J, Q-score = 0. 87; RMSD= 0. 95 Å). CrFBA3 is an α/β protein composed of eleven α-helices and nine β-strands folded as a TIM barrel  (CATH classification: 3. 20. 20) and numerated from H1 to H11 and S1 to S9, respectively (figure  1 ). Regions of the protein with the largest chain-to-chain differences are otherwise highlighted by the highest B-factors, and are mainly localized on the H2, H3, H4, H5 and H10 and H11 α-helices and the loops that connect them (figure  2 ).
Human FBA has been described to perform its lyase activity by forming a Schiff base between the substrate and the lysine in position 229 while being stabilized by the two positively charged amino acids (Arg148 and Lys146)  (Gamblin, Davies et al. 1991). The hypothesis of a similar catalytic mechanism between CrFBA3 and other FBA is comforted by superposing CrFBA3 structure to two FBA structures co-crystallized with their substrate in which amino acids from CrFBA3 are well superposed with those found in homologs from Homo sapiens FBA (HsFBA) (PDB ID: 4ALD) and Oryctolagus cuniculus FBA (OcFBA) (PDB ID: 1ZAI) with RMSD of 0. 813 Å and 0. 886 Å, respectively (figure  3 ).
5 M of ammonium sulfate with 2 µl drops of protein/precipitant solution ratio 1/1 equilibrated against 1 mL of reservoir solution. Protein concentration was brought to 2. 6 mg/mL to avoid formation of clustered crystals and monocrystals were grown in a solution of 5 % isopropanol and 2 M ammonium sulfate. Crystals were flash frozen in liquid nitrogen for diffraction experiment at Proxima-2 beamline of SOLEIL synchrotron (Saint-Aubin, France). A 99. 41 % complete dataset at 2. 36 Å resolution was obtained from 3600 images and indexed in the C2 space group, integrated, scaled and converted with XDSME  (Legrand 2017). Structure was phased by molecular replacement with PHENIX  (Adams, Afonine et al. 2010)   (Adams, Afonine et al. 2011)  PHASER-MR  (McCoy, Grosse-Kunstleve et al. 2007 ) using a search model obtained from the Phyre2 server  (Kelley, Mezulis et al.
Calvin-Benson cycle, such as C. reinhardtii FBA3, from paralogs involved in other chemical pathways (FBA1, FBA2, FBA4). The C. reinhardtii FBA3 protein sequence (UniProt code Q42690) contains only one protein domain which encompasses the most conserved part of the protein. It is the large Pfam domain Glycolytic PF00274. In order to generate ProfileView functional tree for the 2601 homologous FBA sequences and including all C. reinhardtii FBA paralogous sequences, we devised the following pipeline:(1) Selection of the sequences for the construction of the probabilistic models To generate the ProfileView library of probabilistic models for the Glycolytic PF00274 domain, we downloaded the Pfam "full" alignment (Pfam version 34https://pfam. xfam. org/family/Glycolytic). We favored this set of sequences because of their relatively limited number of gaps (high sequence length homogeneity), hand curation and uniform sampling of homologous sequences on their phylogenetic tree.
Acknowledgements We thank  Mirko Zaffagnini  for critical reading of the manuscript. This work was funded by  ANR  grants  CALVINDESIGN  ( ANR-17-CE05-0001 ),  CALVINTERACT  ( ANR-19-CE11-0009 ) and LABEX DYNAMO (ProjetIA- 11-LABX-0011 ). We thank the  Institut de Biologie Physico-Chimique  ( CNRS FR 550 ) for access to crystallization facility. We acknowledge SOLEIL for provision of synchrotron radiation facilities at beamlines Proxima-1, Proxima-2, and SWING.
NMJ visualization On a selected area of Gastrocnemius lateralis, series of acquisition and reconstruction were performed at higher magnification (10X, Fig. 3 ). This method allows to visualize very bright and well-defined junction organization in all the imaged volume, whatever their orientation (Video S9). Such resolution is enough to clearly identify and study characteristic pretzel-like shape of NMJ, an ellipse with several internal segments. Interestingly, NMJ in FDB muscle were estimated to be two to three times smaller compared to other studied muscles. Supplementary data related to this article can be found at https:// doi. org/10. 1016/j. toxicon. 2022. 04. 003.
1  and Table  1 ). Below, we use a multiple regression approach to further disentangle the variables. The oldest 9-year-old group (23 children, 115 ± 6 months-old, 12 boys and 11 girls) or the group of advanced reader had three complete years of reading experience (80 words/min in average. Their data were previously published in  Monzalvo et al (2012). Children were also tested on several cognitive skills that have been proven to be important for the acquisition of the reading abilities: Phonological awareness was tested with a French standardized test (EVALEC) consisting of the deletion of the first syllable in 10 trisyllabic words, then of the first phoneme in 12 CVC words, and finally in 12 CCV words  (Sprenger-Charolles et al. , 2005). Rapid automatic naming (RAN) of pictures was measured.
Effect of age and reading expertise on brain activity: In order to disentangle effects related to reading from those related to age on brain activity, we conducted two distinct whole-brain regression analyses. First, to investigate the effect of reading acquisition while controlling for age, we focused only on the two groups of 6-year-olds (total n=37) who had approximately the same age but different expertise in reading. We computed analyses with reading performance (number of words read in one minute) as a regressor and with age as a regressor of non-interest. Second, conversely, to examine the effect of age on brain activity, we analyzed the data from all 6-and 9-years-old (total n=60; age range 59-130 months) and entered age as a regressor of interest and reading performance as a regressor of non-interest.
As recommended by  Braun and Clarke (2006)  this stage included systematically reading through the entire data set. This was carried out by three researchers. One researcher generated the codes, and a further two researchers independently reviewed the codes and the fit of the codes to the data. Once the codes were agreed and the data had been fully reviewed, themes were identified independently by each researcher and responses from participants were described as relating to a theme. Reflexive thematic analysis was used to identify patterns of meaning in the data and group similar themes into wider, super-ordinate themes. Three thematic maps were created to determine the most relevant themes and to highlight the relationships between them  (Braun & Clarke, 2006). Themes that were not related to our central three super-ordinate themes were discarded at this stage.
They include very little neurophysiological prior, and rather trust the (potentially noisy) EEG data recorded during the calibration phase. In this paper, we hypothesise that, to be more effective, these methods should take into consideration some constraints that may need to be identified. Indeed, are all properties of the features or classifiers learned from BCI data equally likely to be associated with good performances in practice? If not, what properties are more often associated with superior decoding performances? Would enforcing such properties in machine learning BCI algorithms lead to better decoding performances in practice? In this paper, we aim at answering these questions for a specific machine learning BCI algorithm: the frequency band selection algorithm of Blankertz et. al  [11].
Dataset We used the data of fifty-five subjects (N=55) out of the sixty subjects from the experiment in  [36]  (29 women; age 19-59; X =29; SD=9. 318). This dataset is named Dataset A in the rest of the paper. We discarded five subjects due to technical issues during the BCI recording session (e. g. , a missing calibration run, or a mislabelling of EEG channels that resulted in an erroneous calibration). The main goal of this previous experiment was to assess the impact of the interaction between experimenters' and participants' gender on MI-BCI performances. Each subject participated in one MI-BCI session of 2 hours, comprising two three-minute recordings in resting state condition (one with eyes open and one with eyes closed ) followed by six 7-minute blocks (a. k. a runs) of trials in which the subject had to perform MI-tasks, i.
EEG Recordings and Signal Processing To record the EEG signals, 27 active scalp electrodes, referenced to the left ear, were used (Fz, FCz, Cz, CPz, Pz, C1, C3, C5, C2, C4, C6, F4, FC2, FC4, FC6, CP2, CP4, CP6, P4, F3, FC1, FC3, FC5, CP1, CP3, CP5, P3, 10-20 system). EEG signals were measured using a g. USBAmp (g. tec, Austria), sampled at 256 Hz and processed online using OpenviBE 2. 1. 0 [20]. In order to discriminate the MI tasks from EEG data, participant-specific spectral and spatial filters have been optimized on the data of the first two runs (calibration runs). First, the participants' MDFB was selected using the heuristic algorithm proposed in  [11]  (see  Algorithm 1). It selects the frequency band the power of which, in the sensorimotor channels, maximally correlates with the class labels.
Objective In order to investigate a possible causation between BCI performances and characteristics of the MDFB, we designed a constrained version of the MDFB selection algorithm that enforced i) an MDFB width larger than 3. 5Hz and ii) an MDFB mean value under 16Hz. We used it in an online experiment specifically designed for this study (Dataset B). We then compared the online performances obtained for subjects in this experimental group, Dataset B (using the constrained algorithm) with online performances of matched subjects in a control group, named Dataset A*. These subjects were selected from Dataset A and were then provided with the unconstrained, i. e. , original algorithm.
Analyses Our objective was to compare the MI-BCI performances obtained with a constrained vs. unconstrained MDFB algorithm. Therefore, we selected in Dataset A*-B only the participants whose MDFB characteristics differed in both conditions. The following heuristics were used to select the participants: m constrained m unconstrained -1 ≤ 0. 10 (1) where m constrained is the MDFB mean selected by the constrained algorithm and m unconstrained is the MDFB mean selected by the unconstrained algorithm. d (l constrained + l unconstrained ) -d ≥ 0. 60 (2) where l constrained is the MDFB width selected by the constrained algorithm, l unconstrained is the MDFB width selected by the unconstrained algorithm, and d is the width of the range of frequencies that are common to the MDFB obtained by both algorithm (e.
Then, we used the LASSO as explained above to predict the MI-BCI performance of the left-out subject. We repeated this process 1000 times and stored each mean absolute error to obtain the distribution of the chance prediction performance. Then, we sorted those values in descending order and the 99 th and 95 th percentiles were used to identify the chance level for the mean absolute error for p =. 01 and p =. 05 respectively. statistical model to select the most relevant algorithm for each subject For this analyse, we used M=20 pairs of subjects from Datasets A* and B (see Table. 1). Subject A52 paired with subject B06 were removed as their online performance were exactly the same in the two conditions (i. e. , with constrained and unconstrained algorithm). This subject pair was thus not informative to predict which algorithm to use.
For each cross-validation, we compared the online performances of subjects from Dataset A* (all using the unconstrained algorithm) with performances obtained using the algorithm (either constrained or unconstrained) automatically selected by the decision tree model. We used the Wilcoxon signed-rank test to compare the two groups of performances. We also built a classification tree trained on all 20 subjects from Datasets A*, for interpretation purposes, in order to determine the critical values of a that could give information about which algorithm to use.
Conclusion In this study, we used the data from two different experiments to study the impact of the MDFB selected by an optimization algorithm during the calibration of an MI-BCI system. The objective was to determine if there was a relationship between characteristics of the chosen MDFB and subjects' online performances (i. e. , classification accuracy), the causality of their relationship, if any, and if we could find a parameter that could be used to refine the algorithm to optimize it further. Our analyses suggested that there is a correlation between the selected user-specific frequency band characteristics (i. e. , the mean value and the width of the MDFB selected) and the classification accuracy. To study a possible causality link between them, we added constraints to the algorithm, enforcing characteristics associated to higher performances (based on our first results), and used it in an experiment specifically designed for this study (Dataset B).
We then compared the online performances obtained for subjects in this experimental group, Dataset B (using the constrained algorithm) with online performances of matched subjects in a control group Dataset A* from Dataset A (using the unconstrained, i. e. , original algorithm). This study did not allow us to conclude on a causality link. Indeed, while average online performances obtained with the constrained algorithm were slightly higher than those obtained with the unconstrained algorithm, this difference did not reach significance. Finally, in a last part we were able to predict the MI-BCI classification accuracy of users using parameters extracted from the MDFB optimization algorithms. To build our models, we added three parameters in addition to the MDFB width and mean. We modeled each correlation signal as a Gaussian function of the frequency and used the parameters of the Gaussian model as predictors (i.
MDFB mean and width of subjects from Dataset B using the unconstrained (i. e. , original) algorithm. Then, each subject was paired with a subject from Dataset A with similar MDFB mean and width. This enabled us to compare online the effects, if any, of the constrained algorithm (from Dataset B) for pairs of subjects with similar MDFB with the original algorithm. In the end, in Dataset A* we included 21 subjects (11 women; age: 20-57 year-old; X =29; SD=10). Table. 1 shows selected MDFB characteristics and the mean classification accuracy for subjects in Dataset B and Dataset A*. Data for experimental and control groups.
Tourette syndrome is a neurodevelopmental disease in which clinical manifestations are essentially present during childhood and adolescence, corresponding to one of the critical development phases. However, its consequences on the daily lives of young patients have been insufficiently investigated. Here, we aimed to investigate this using a statistical text mining approach, allowing for the analysis of a large volume of free textual data. Sixty-two adolescents with Tourette syndrome participated in an interview in which they discussed their daily life (i) in school, (ii) at home, and (iii) with strangers, (iv) the aspect of Tourette syndrome which caused the most difficulty, and (v) their thoughts regarding their future as adults. Following data pre-processing, these corpora were analyzed separately using the IRAMUTEQ software through factorial correspondence analysis to identify the most commonly recurring topics of each corpus, and their relations with clinical features.
The main difficulty corpus was directly related to comorbidities of Tourette syndrome. Daily life at home was correlated with executive functioning. Difficulties at school were related to a higher severity of tics. Thoughts regarding future daily life were worst for the youngest patients and were correlated with executive functioning and a higher depression score. Taken altogether, our results highlighted that social stigma was a pervasive topic among our corpora. From a clinical standpoint, tic severity was especially related to difficulties at school, while comorbidities had a high impact on social daily living and cost for managing both tics and symptoms of comorbidities. Trial registration clinicaltrials. gov/ct2/show/NCT04179435.