dataesr/scientific-paragraphs-categorization huggingface dataset:
Different DGI Distributions between Dyslexic and Non-Dyslexics Populations Before adding the DGI set of features to our ML experimental setup, we conducted some exploratory data analysis, to gain more insight into the distributions of the three handcrafted features, namely, the DGI, the mean duration, and the mean amplitude and their ability to predict dyslexia. Figure  3  presents the histogram of the retrieved segments from the seven painting datasets combined for the dyslexic population and non-dyslexic population. The left histogram corresponds to the non-dyslexic population, while the right histogram corresponds to the dyslexic population. The two histograms were computed using 100 bins, showing a statistical distribution with two main modes that correspond to the peak of micro convergence and micro divergence amplitude segments.
005°, and is approximately equal to the non-dyslexic population when considering the bins with an amplitude bigger than 0. 005°. Also, in Appendix D (Figures  A12-A18 ), we present the histogram of the retrieved segments when considering the seven paintings dataset separately. By analyzing the painting dataset separately, we found the same pattern. The Mann-Whitney U test results shown in Table  2  indicate statistically significant differences between dyslexic and non-dyslexic populations for all paintings. In addition, we present in the appendix, the DGI (Figure  A2 ), the duration (Figure  A3 ) ,and the amplitude (Figure  A4   3 , present the mean values of the disconjugacy velocity index for the dyslexic and the non-dyslexic population for each of the seven painting tests. The mean of the DGI over the dyslexic and non-dyslexic population is quite stable over the seven paintings dataset.
Hence one can try to predict dyslexia without considering the seven paintings separately. Also, in Appendix D (Figures  A12-A18 ), we present the histogram of the retrieved segments when considering the seven paintings dataset separately. By analyzing the painting dataset separately, we found the same pattern. The Mann-Whitney U test results shown in Table  2  indicate statistically significant differences between dyslexic and non-dyslexic populations for all paintings. In addition, we present in the Appendix B, the DGI (Figure  A2 ), the duration (Figure  A3 ), and the amplitude (Figure  A4 ) of each patient and for each painting test. Dyslexic populations are represented by a yellow dot and non-dyslexic by a blue dot. The two lines indicate the mean values of each of the two populations.
3 , present the mean values of the disconjugacy velocity index for the dyslexic and the non-dyslexic population for each of the seven painting tests. The mean of the DGI over the dyslexic and non-dyslexic population is quite stable over the seven paintings dataset. Hence one can try to predict dyslexia without considering the seven paintings separately. For the seven-painting dataset, the mean of the computed DGI for the non-dyslexic population is centered at 13. 9 with a standard deviation of 0. 72. For the dyslexic population, the mean of the computed DGI is centered at 27. 38 with a standard deviation of 0. 80 meaning that the variability of the DGI means for these particular seven painting studied is very low compared to the mean value. In addition, in Figure  4 , we present the variability of the DGI score over the seven paintings dataset for each individual.
The dyslexic population is represented using the red color and the non-dyslexic population is represented using the blue color. For the two populations, the variability of the DGI over the seven paintings is quite low. For the seven-painting dataset, the mean of the computed DGI for the non-dyslexic population is centered at 13. 9 with a standard deviation of 0. 72. For the dyslexic popula tion, the mean of the computed DGI is centered at 27. 38 with a standard deviation of 0. 80 meaning that the variability of the DGI means for these particular seven painting studied is very low compared to the mean value. In addition, in Figure  4 , we present the variability of the DGI score over the seven paintings dataset for each individual. The dyslexic population is represented using the red color and the non-dyslexic population is represented using the blue color.
ML Results: Predicting Dyslexia Using DGI For each of the seven painting datasets, and each of the logistic regression and RBF SVM models, the feature importance of the duration was similar to the DGI, whereas the feature importance of the amplitude was very low compared to DGI and the duration. In addition, the accuracy score didn't improve when considering the duration and the am plitude, in addition to the DGI to predict dyslexia. This is due to the high correlation o The DGI with the amplitude and the duration. Further details of the features' importance experiments, are presented in the appendix F. We considered only the DGI feature fo further analysis (analysis 2 and 3). Hence, for the two populations, the variability of the DGI is low for the different paintings; meaning that one can predict dyslexia using the DGI value without taking into consideration the identifier of the painting itself.
ML Results: Predicting Dyslexia Using DGI For each of the seven painting datasets, and each of the logistic regression and RBF-SVM models, the feature importance of the duration was similar to the DGI, whereas the feature importance of the amplitude was very low compared to DGI and the duration. In addition, the accuracy score didn't improve when considering the duration and the amplitude, in addition to the DGI to predict dyslexia. This is due to the high correlation of The DGI with the amplitude and the duration. Further details of the features' importance experiments, are presented in the Appendix F. We considered only the DGI feature for further analysis (analysis 2 and 3). Figure  5  presents the accuracy of the two models (support vector machine and logistic regression). For the three analyses (for each of the ten painting datasets), the score on the test set was overall the same.
In other words, the generalization ability of each of the linear and nonlinear models has slightly improved when considering augmenting the sample size of the used dataset. Figure  5  presents the accuracy of the two models (support vector machine and logistic regression). For the three analyses (for each of the ten painting datasets), the score on the test set was overall the same. In other words, the generalization ability of each of the linear and nonlinear models has slightly improved when considering augmenting the sample size of the used dataset. The best accuracy was achieved when training in analysis 3, on the combined seven painting dataset with an accuracy of 89. 71%, a sensitivity of 92. 34%, and specificity of 86. 99%. Similarly, to the previous section, in Appendix F (Figures  A19  and A20 , Tables A6-A10), we present the full Tables of the different used metrics scores of the two models when trained on the DGI eye movement features in analysis 1 (Tables  A6  and A7 ), analysis 2 (Tables  A8  and A9 ), and analysis 3.
Sample Size Importance for AIDEAL To compare the generalization ability of each model when trained on AIDEAL deterministic based eye movement features, during each of the three analyses, for each model and each analysis, we aggregated the accuracy of the test set of each dataset by weighted means. The different scores are presented in Table  4. One can expect that the accuracy of each model would decrease when predicting dyslexia from the AIDEAL eye movement feature without taking into consideration the image at which each subject was looking, since the eye movement feature depends on the trajectory of the eye, which depends on the observed image. Hence, ignoring the observed will increase the complexity of the classification problem. However, the generalization ability of both the linear and nonlinear model, has increased when considering merging the data of the different painting datasets, from 75.
53% The best accuracy was achieved when training in analysis 3, on the combined seven painting dataset with an accuracy of 89. 71%, a sensitivity of 92. 34%, and specificity of 86. 99%. Similarly, to the previous section, in Appendix F (Figures  A19  and A20 , Tables A6-A10), we present the full tables of the different used metrics scores of the two models when trained on the DGI eye movement features in analysis 1 (Tables  A6  and A7 ), analysis 2 (Tables  A8  and A9 ), and analysis 3.
53% and 72. 85% in analysis 1 to 85. 88% and 83. 53% in analysis 2, and finally to 88. 24% and 89. 41% in the last analysis, respectively. This can be explained by the sample size since the size of the dataset used in analysis 3 was seven times bigger than the one used in analysis 1. If such dependence is not shown for DGI it is due to the very low feature space dimension.
Investigating the Complementarity of AIDEAL and DGI Based Features To investigate the complementarity of the two feature sets in predicting dyslexia on the dataset of analysis 3, we retrained the two models to predict dyslexia using the DGI feature in addition to the AIDEAL eye movement features. In Table  5 , we present the scores of the linear and nonlinear models when combining AIDEAL and DGI. When combining AIDEAL eye movement features and DGI features, we achieve an accuracy of 90. 0% and 87. 5%, a sensitivity of 90. 0%, and a specificity of 90. 0% and 85. 5%.
Summary of the Results When considering augmenting the dataset size by merging the seven painting dataset (the third analysis), we've found that both the deterministic approach with the AIDEAL eye movement features, and DGI based approach, can successfully predict dyslexia using both the linear and nonlinear models. Also, their predictability has increased when considering merging multiple datasets and ignoring the painting index feature. In addition, when combining the AIDEAL eye movement feature and DGI feature, we achieve a better accuracy using the RBF-SVM.
It remains to be seen whether the DGI feature is powerful in any eye movement condition, including oculomotor tests such as saccade and vergence tests, or including free exploration of narrative paintings or more banal everyday images. To investigate how the DGI feature is powerful in the saccade and vergence tests, we applied the same method described in analysis 1, but on the saccade and vergence dataset present in the previous analysis  [6]. We found that DGI can predict dyslexia from the saccade dataset with an accuracy of 75%, and from the vergence dataset with an accuracy of 80%. The full metrics scores on the test set, when training the SVM and the Logistic regression are presented on Tables  6  and 7. Hence, the very high predictability of the DGI is lost, which confirms that the power of the paintings chosen, is responsible for the relevance of the DGI; we predict that paintings without depth illusion or complexity would provide less predictive power to the DGI.
A current study with classic paintings, e. g. , Le tricheur by Georges de La Tour seems to confirm such a prediction. In contrast to this literature that focuses on the detection of binocular or conjugate micromovements here  [12] [13] [14] [15] , we focus on micro-movements in the opposite direction for the two eyes. Also, in the microsaccade literature, data cited above are mostly coming from a fixation task during which the subject has to fixate a target for several seconds typically 15 s. Moreover, we do not limit ourselves to a particular waveform such as saccade type, the movement can be rapid or drifting. Also, the analysis is done on traces of the eyes continuously during a free exploration task that is self-triggered. In a way this analysis is less deterministic. Yet, the only hypothesis would be that the visual features of the paintings Brain Sci.
Conclusions In this paper we developed a new algorithm to compute a new descriptor that we called the disconjugacy global index (DGI), then we investigated the complementarity of such features with AIDEAL based features to predict dyslexia. We evaluated the generalization ability when the models were trained on three different conditions, where we had a different sample size and different variance for each feature. In the first analysis, each model was trained on the seven paintings dataset separately and on the Alouette reading test. Also, in the second analysis, each model was trained on two groups of painting. Finally, in the last analysis, each model has been trained on the entire seven paintings datasets. For each for the three analyses, we used AIDEAL based features and the DGI feature computed from eye movements to predict dyslexia, in the third analysis we also considered the complementarity between the two sets of descriptors, and we found that we achieved the best score in term of accuracy, sensitivity, and specificity.
We found that when combining the seven paintings dataset, we can predict dyslexia with an accuracy of 89. 71%, a sensitivity of 92. 34%, and a specificity of 86. 99% when using the DGI feature. Similarly, we found that we can predict dyslexia with an accuracy of 89. 41%, a sensitivity of 88. 89%, and a better specificity of 90% when using the AIDEAL based. Finally, we found that by using the A eye movement features with DGI, we can predict dyslexia with an accuracy of 91. 21%, a sensitivity of 90. 0%, and a specificity of 92%.
Patents Zoï Kapoula has applied for patents for the technology used to conduct this experiment: REMOBI table (patent US8851669, WO2011073288); AIDEAL analysis software (EP20306166. 8, 7 October 2020; EP20306164. 3, 7 October 2020-Europe). Patent application pending EP22305903. 1. Author Contributions: A. E. E. H. conducted feature extraction and the machine learning analysis, and co-wrote the manuscript; L. M. W. conducted the experiments, analyzed the data, and co-wrote the manuscript. V. S. F. G. supervised the machine learning analysis; T. P. participated in the machine learning analysis and provided comments to the manuscript. Z. K. designed the study, developed the algorithms for the physiological data analysis and co-wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Pre-Processing and Feature Extraction3. 1. Eye Movement Descriptors Based on the AIDEAL Velocity Threshold MethodFor each individual and each test, and each of the above-cited features, the AIDEAL software provides the mean value (based on 10 to 20 trials), the standard deviation, the coefficient of the variation (standard deviation/mean × 100), and the total number of trials used for each descriptor. The eye movement features computed are:  Amplitude: the amplitude of the movement (in degrees), i. e. , the change in eye position between the onset and the offset of the eye movement; the onset and offset of eye movement being determined by velocity threshold criteria as described above using AIDEAL software.  Duration of the phasic component: the time of execution of the movement (in ms), i.
3. 3 Pre-Processing and Feature Extraction3. 1. Eye Movement Descriptors Based on the AIDEAL Velocity Threshold MethodFor each individual and each test, and each of the above-cited features, the AIDEAL software provides the mean value (based on 10 to 20 trials), the standard deviation, the coefficient of the variation (standard deviation/mean × 100), and the total number of trials used for each descriptor. The eye movement features computed are:  Amplitude: the amplitude of the movement (in degrees), i. e. , the change in eye position between the onset and the offset of the eye movement; the onset and offset of eye movement being determined by velocity threshold criteria as described above using AIDEAL software.  Duration of the phasic component: the time of execution of the movement (in ms), i.
Selection of CSF samples and clinical study design For the analytical validation of the Nf-L ELISA and evaluation of the primary calibrators, routine CSF samples (Biomnis) available at the biobank of ADx were used. For these routine CSF samples, only age and gender data were available with no additional clinical information or cognitive scoring. For clinical validation, a cohort of 60 CSF samples was used. This cohort contained three diagnostics groups (FTD, AD, and patients with subjective cognitive decline (SCD)) with every 20 samples in each group. The AD biomarker profile data (tau and amyloid-beta levels) and the paired MMSE scores (ranging from 6 to 30) were available for these patient samples. The clinical diagnosis of the samples was available from the AUMC biobank according to previously established diagnostic criteria for AD, FTD, and SCD.
The authors have performed the following tasks: (1) Research A. Immunization strategy for antibody generation B. Characterization of antibodies C. Analytical validations of immunoassay D. Clinical validations of immunoassay E. Planning and designing mass spectrometry experiments F. Calibrator characterizations using mass spectrometry. (2) Providing clinical samples and clinical data. (3) Analytical and clinical data analysis. (4) Manuscript interventions A. Writing the first manuscript draft B. Editing and formatting C. Review and critique. S. D. : 1(B, C, D), 3, 4(A, B, C); N. D. : 1(A, B, C), 3, 4(B, C); D. J. : 1(A, B), 4(C); Y. P. : 2, 4(C); S. V. : 2, 4(C); S. C. : 1(E, F), 4(B, C); M. Q. : 1(E), 4(C); C. H. : 1(E), 4(C);C. T. : 2, 4(C); E. V. M. : 1(A), 3, 4(A, C) Funding: The research discussed herein has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 860303.
Author Contributions: The authors have performed the following tasks: (1) Research A. Immunization strategy for antibody generation B. Characterization of antibodies C. Analytical validations of immunoassay D. Clinical validations of immunoassay E. Planning and designing mass spectrometry experiments F. Calibrator characterizations using mass spectrometry. (2) Providing clinical samples and clinical data. (3) Analytical and clinical data analysis. (4) Manuscript interventions A. Writing the first manuscript draft B. Editing and formatting C. Review and critique. S. D. : 1(B, C, D), 3, 4(A, B, C); N. D. : 1(A, B, C), 3, 4(B, C); D. J. : 1(A, B), 4(C); Y. P. : 2, 4(C); S. V. : 2, 4(C); S. C. : 1(E, F), 4(B, C); M. Q. : 1(E), 4(C); C. H. : 1(E), 4(C);C. T. : 2, 4(C); E. V. M. : 1(A), 3, 4(A, C) Funding: The research discussed herein has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 860303.
With the present study we tested the hypothesis that the changes we observed previously could be related to cell-specific transcriptional differences of cellular "state," rather than constitutively expressed "trait" markers in diverse brain areas, including hippocampus, amygdala and hypothalamus. To this end, we defined groups of genes belonging to specific cell types based on single cell sequencing data from the Allen Brain Atlas (Figure  2 ). We also assessed the expression of genes known to be regulated by GR activation, and genes related to oxidative stress pathways. Additionally, we explored possible alterations in neuronal populations in areas that showed changes in the marker gene NeuN.
| Single cell data resources, metrics, and processing The single cell data is based on the 10Â scRNA-seq dataset published by the Allen Institute for Brain Science and publicly available at https://portal. brain-map. org/atlases-and-data/rnaseq/mousewhole-cortex-and-hippocampus-10x. The data was clustered, and cluster names were assigned based on the Allen Institute proposal for cell type nomenclature (https://portal. brain-map. org/explore/ classes/nomenclature). The topology of the taxonomy allowed to define regions of interest, cell classes (glutamatergic, GABAergic, or non-neuronal) and subclasses. 26, 27 The metadata was used to subset cells of the hippocampus region from the gene expression matrix. The newly obtained matrix was processed and analysed in R v3. 6. 1 according to the Seurat v3.
| Glucocorticoid target genes GR activation was tested by measuring the mRNAs coded by Fkbp5, Sgk1, Tsc22d3 (commonly referred to as Gilz), and Crh. Fkbp5 mRNA expression showed significant genotype effects with p <. 01 in all brain regions (Figures  3A-E ). Except for the cingulate cortex, significant genotype effects (p <. 01 in all cases) were also found for Sgk1 mRNA (Figures  3G-J ). For Tsc22d3 mRNA, significant genotype effects were found in CC (p <. 3R-T ), compared to wild-type. This differential regulation by glucocorticoids is in agreement with previously published literature. 30 gether, these data confirm long lasting responsiveness of known GR target genes to the long-term glucocorticoid overexposure in AdKO 2. 0 mice. respect to wild-type. In CA3, the decrease was more pronounced in male AdKO 2.
| Cellular activity Basal cellular activity was assessed by measuring expression of immediate early genes Fos and Arc. Of note, while these genes are routinely interpreted as markers for neuronal activity, basal Fos mRNA expression was highest in oligodendrocytes in the Allen Brain Atlas data (Figure  2 ). Significant sex effects on Fos expression were found in PVN (p =. 03), CA3 (p =. 02) and Amy (p =. 01) (Figures  9A-C ), all describing an increased expression in females compared to males; in addition, significant factor interactions were also found in CA3 (p =. 01), and Amy (p =. 02) (Figures  9B, C ) indicating expression levels in female AdKO 2. 0 mice higher than the other groups. For Arc mRNA a significant genotype effect was found in PVN (p =. 04) (Figure  9D ), based on an expression in AdKO 2.
0 mice (Factor: Interaction). The complete mRNA expression dataset is shown in Supporting Information figures, while for readability, selected panels are shown in the individual figures in the main text. Although the total number of sacrificed animals was 28 (n = 7, for each of four groups: male wild-type, male AdKO 2. 0 , female wild-type, and female AdKO 2. 0 ), different numbers of samples were lost in the dissection stage. Comparisons of gene expression in the different brain areas, and of immunohistochemical labelling were made with the respective number of remaining samples for every case.
0 mice, with relation to wildtype, in CC (p <. 01 and p =. 02, respectively) (Figure 8F,J). Also, Gad1 was significantly higher in AdKO 2. 0 male mice in Cg (0. 046) and DG (p =. 047) in comparison to wild-type (Figures 8I,K). The complete dataset of the expression of the three referred markers in all brain regions is displayed in Figure S6A-U.
ACKNOWLEDGEMENTS We would like to thank  Dr Elly Hol  for discussion of our data, and  Dr Elise P. Gomez-Sanchez  for kindly providing MR monoclonal antibodies. This work was supported by  CONACyT  (grant number  440584 ) and  Agence Nationale pour la Recherche  (grant numbers  ANR-14-CE12-0007-01  and  ANR-18-CE14-0012-02 ).
AdKO 2. 2 0 mice (Factor: Interaction). The complete mRNA expression dataset is shown in Supporting Information figures, while for readability, selected panels are shown in the individual figures in the main text. Although the total number of sacrificed animals was 28 (n = 7, for each of four groups: male wild-type, male AdKO 2. 0 , female wild-type, and female AdKO 2. 0 ), different numbers of samples were lost in the dissection stage. Comparisons of gene expression in the different brain areas, and of immunohistochemical labelling were made with the respective number of remaining samples for every case.