dataesr/scientific-paragraphs-categorization huggingface dataset:
Ethics and dissemination This study protocol was reviewed and approved by CPP Ile de France VI-Groupe Hospitalier Pitié Salpêtrière on 14 April 2021 (ID RCB: 2021-A00009-32). The study was registered on ClinicalTrials. gov on 9 June 2021 (NCT04909970). Results will be published in peer-reviewed journals as well as presented and disseminated at scientific conferences.
Acknowledgements The authors would like to thank the  Reunion Island University Hospital  for their funding of the study and AcaciaTools for their proofreading services. The authors would also like to thank the nursing teams of the Geriatric and General Medicine departments, more specifically  Dr. Nolwen Kervella ,  Dr. Christophe Donikian ,  Thierry Hoarau ,  David Ferrere ,  Laurie Kichenapanaidou  and  Anne-Françoise Casimir  for their contributions, as well as  Marie Coudy , the hypnotherapy nurse.
Acknowledgments: Griet Glorieux. and  Stéphan Burtey  are beneficiaries of the European Union's  Horizon 2020 research and innovation programme  under grant agreement No [ 860329 ] (" STRATEGY-CKD "). Raymond Vanholder ,  Stéphan Burtey  and  Griet Glorieux  are members of the  European Uremic Toxins Workgroup (EUTox). Raymond Vanholder  and  Griet Glorieux  thank  Alain Goossens  ( VIB-UGent ) and  Anne De Paepe (UGent)  for the discussion and for sharing their expertise in relation to this review.
Patients This is a cross-sectional study involving 26 centers from the Genkyst Consortium in western France. [33] [34] [35]  ADPKD patients over 16 years of age were recruited from January 2010 to June 2019. ADPKD diagnosis was made by abdominal imaging, by employing the Pei criteria in the presence of a family history of ADPKD,  [36]  or following the identification of a proven pathogenic PKD1 or PKD2 mutation. In the absence of familial history or mutation identification, diagnosis required the presence of at least 10 bilateral kidney cysts. The patients' clinical data obtained during medical interviews at the time of their inclusion and from medical records were entered in a standardized clinical report form (CRF). For participants in whom previous history of IAs was reported in the CRF, medical records were reviewed on study sites to confirm the previous history of IAs and to collect additional information regarding aneurysm size (in millimeters), type (saccular versus fusiform), localization (anterior or posterior circulation) and management (monitoring, neurosurgical or neuroradiointerventional treatment).
Statistical analyses Statistical analyses were performed using IBM SPSS Statistics for Windows, version 23. 0 (IBM Corp. , Armonk, N. Y. , USA). The frequency of IAs detected in our cohort was estimated and reported with a 95% confidence interval (CI) constructed for the estimate. Baseline data are expressed as the median and standard deviation (SD) or range. Quantitative data were compared using the nonparametric Mann-Whitney test or Kruskal-Wallis test, depending on the number of groups. Qualitative data were compared using a t test or the chi-squared test. Survival without a diagnosis of IA and survival without ruptured IAs were analyzed using the Kaplan-Meier method. The differences between the survival curves were assessed using a log-rank test with a 0. 05 significance level. HNF1B (n=8), PKHD1 (n=1), SEC63 (n=2), PRKCSH (n=3), OFD1 (n=4), UMOD (n=2), ALG8 (n=6), ALG9 (n=1) , COL4A4 (n=2), COL4A1 (n=1)).
ACKNOWLEDGEMENTS We thank the patients and families for involvement in the study. We would like to thank Genkyst/GeneQuest investigators:  Drs. Grall  A , Moal MC, Hanrotel C, Segalen I, Tanquerel T, Lanfranco L (Centre Hospitalier Régional Universitaire, Brest );  Morin MP ,  Le Pogamp P, Gie S, Rivalan J, Laruelle E, Richer C, Lorcy N, Golbin L, Terrasse M, Morice S, Brenier H, Michel A, Lavergne A, Tomkiewicz E (Centre Hospitalier Universitaire, Rennes); Gatault P, Merieau E, Barbet C, Buchler M, Golea G, Ghouti L, Gautard D, Sautenet B, François M, Fournier A, Baron C, Salmon C, Rabot N, Prat L, Valentin JF, Birmele B, Genest C, Goumard A (Centre Hospitalier Régional Universitaire, Tours );  Desport E, Thierry A, Touchard G ,  Belmouaz M ,  Javaugue V, Bauwens MA ,  Fride-Leroy F, Bouteau I (Centre Hospitalier Universitaire, Poitiers); Subra JF, Besson V, Cousin M, Sayegh J, Onno C, Maghakian MN, Demiselle J, Garnier AS, Planchais M, Guibert F (Centre Hospitalier Universitaire, Angers); Stanescu C, Le Cacheux P, Baluta S, Leonetti F (ⴕ) ,  Boulahrouz R, Ferrier ML ,  Freguin C, Simon A, Potier J, Coulibaly JM, Colombo A (Centre Hospitalier Yves le Foll, Saint Brieuc );  Dolley-Hitze T (Centre Hospitalier Broussais, Saint Malo) ; Michez E, Mandart L, Menoyo V, Pincon E, Muresan C, Durand PY, Wegner I (Centre Hospitalier de Bretagne Atlantique, Vannes); Metes I, Guyon-Roger T, Wehbe B, Siohan P, Drouet C (Centre Hospitalier de Cornouaille, Quimper) ;  Le Guillou A, Le Jeune M, Beillard G, Corlu L, Sawadogo T, Georgescu S (Centre Hospitalier de Bretagne Sud, Lorient); Jousset P, Latif R, Massad M (Centre Hospitalier du Centre Bretagne, Pontivy) ;  Jaulin JP ,  Couvrat-Desvergnes G, Querard AH, Ottavioli JN, Target N, Chapal A, Le Fur A (Centre Hospitalier Départemental de Vendée, La Roche sur Yon); Besnier D, Regnier-Le Coz S, Blanpain A, Durault S, Larmet D, Skandri A (Centre Hospitalier Georges Charpak, Saint-Nazaire );  Pouteau LM  ( Centre Hospitalier de Laval );  Labatut D  ( Centre Hospitalier de Niort );  Bachelet-Rousseau C, Delbes S, Pourreau F (Centre Hospitalier de La Rochelle) ; Mzoughi S (Centre Perharidy, Roscoff); Guillodo MP, Depraetre P, Strullu B, Chaffara E, Le Mee M (Association des Urémiques de Bretagne, Brest-Morlaix) ;  Terki N ,  Goulesque K  ( Centre de Néphrologie et de Dialyse d'Armorique, Brest );  Benarbia S, Dimulescu M, Rifaat M (Association des Urémiques de Bretagne, Quimper); Legrand D, Duneau G, Georges E (Association des Urémiques de Bretagne, Lorient); Babinet F, Lanoiselee S (ECHO, Le Mans); Savoiu C, Testa A, Oancea I, Coupel I, Parahy S, Lefrancois G, Hristea D (ECHO, Nantes) ; Briand E, Bugnon D (ECHO les Sables d'Olonne).
FUNDING The study was supported by a  National Plan for Clinical Research  (PHRC inter-regional GeneQuest,  NCT02112136 , ECLG), a grant by the  French Association for Information and Research on Genetic Kidney Diseases (AIRG France, ECLG) , a grant from  Otsuka Pharmaceuticals Industry (YLM) , and a  National Research Agency  grant ( ANR JCJC 2019 GENOVAS-PKD , ECLG).
Results On average, baboons required 2. 9 days to complete the 1,000 trials, with a mean of 339. 7 trials per day and a mean accuracy level of 92. 5% (SD=6. 2%). Incorrect trials were removed from the dataset (7. 8%). A recursive trimming procedure excluded RTs greater or smaller than 2. 5 standard deviations from the participant's mean for each of the nine possible positions (24. 4%) 1. RTs for each of the nine positions and for the 1,000 trials were divided into 10 Blocks of 100 trials.
Acknowledgements This work was supported by the  BLRI Labex  ( ANR-11-LABX-0036 ),  Institut Convergence ILCB  ( ANR-16-CONV-0002 ), the  CHUNKED ANR project  (# ANR-17-CE28-0013-02 ) and  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to  Laura Ordonez-Magro  for her helpful comments on a previous version of the manuscript.
Seemingly subtle differences in, for example, breeding and husbandry protocols can have a significant impact on the interpretation of experimental outcomes. To date, there are no common data elements (CDEs) available for the generation and application of genetic rodent models for the study of epilepsy. CDEs provide a framework on which to improve the standardization of experimental design across laboratories. 11 This may improve the ability of different laboratories to better replicate experiments; validating, and ultimately increasing the ability to translate insights gained from genetic rodent models into the clinic. Furthermore, the use of "mining" rodent model databases to unravel complex disease mechanisms is becoming more common. 12 It is important that experiments are performed with standardized procedures, so that big database analyses can reveal relevant and robust outcomes.
4 he development of CDEs to standardize experimental protocols across different rodent models is therefore particularly timely. A significant issue faced in epilepsy research is reproducibility. In the era of "big data," consistency across laboratories has become even more imperative. 46 As highlighted in both the CRFs for engineered and spontaneous/ inbred genetic rodent models, there are many variables that can influence reproducibility. Both the CRFs produced here are considered to be "core," meaning that they were designed to provide researchers a means of defining the characteristics of a specific cohort of experimental animals. These are then used in conjunction with appropriate experimental CRFs that describe a specific procedure for a given individual animal (Figure  1 ). Although the CRFs are presented here as hardcopy forms, electronic versions, developed in the REDCap database management system will become available.
Funding information EJPRD SCN1A-UP!, Grant/Award Number:  ANR-20-RAR4-0001-01 ;  IDEX UCA-Jedi, Grant/Award Number :  ANR-15-IDEX-01 ;  Laboratory of Excellence "Ion Channel Science and Therapeutics ", Grant/Award Number :  ANR-11-LABX-0015-01 ;  National Health and Medical Research Council (NHMRC) , Grant/Award Number:  10915693  and  APP1141347 ;  NCATS, Grant/Award Number :  KL2TR002317 ;  NIA, Grant/ Award Number :  R01 AG067788  and  NCATS KL2TR002317 ;  NINDS, Grant/ Award Number :  RO1 NS091170  and  U54 NS100064 ;  US Department of Defense , Grant/Award Number:  W81XWH-18-1-0612  and  W81XWH2010848 ;  Monash University Central Clinical School ;  Abbe Goldstein/Joshua Lurie  and  Laurie Marsh/Dan Levitz  families;  Heffer Family  and the  Segal Family Foundations ;  American Epilepsy Society ;  Interdisciplinary Institute for Modeling in Neuroscience and Cognition.
It is known that dyslexics present eye movement abnormalities. Previously, we have shown that eye movement abnormalities during reading or during saccade and vergence testing can predict dyslexia successfully. The current study further examines this issue focusing on eye movements during free exploration of paintings; the dataset was provided by a study in our laboratory carried by Ward and Kapoula. Machine learning (ML) classifiers were applied to eye movement features extracted by the software AIDEAL: a velocity threshold analysis reporting amplitude speed and disconjugacy of horizontal saccades. In addition, a new feature was introduced that concerns only the very short periods during which the eyes were moving, one to the left the other to the right; such periods occurred mostly during fixations between saccades; we calculated a global index of the frequency of such disconjugacy segments, of their duration and their amplitude.
This study showed for the first time that eye movement abnormalities per se in the REMOBI tests, independently from reading, can successfully predict dyslexia and even the reading speed, with an accuracy greater than 81%, on a balanced dataset. The most predictive features were particularly, the velocity, the duration, and the disconjugacy. Moreover, the predictive capacity for categorizing dyslexics and non-dyslexics was better for the eye movement abnormalities from the meaningless reading text rather than from the meaningful text. During reading, eye movements are self-generated and are a kind of free exploration of the text where the tested person adjusts the rhythm of the sequence of saccades and fixations at will. While in the REMOBI testing, eye movements are the immediate responses to external stimulation (space and time) programmed with this device.
The present study uses the data from another study  [5] , in which dyslexic and nondyslexic teenagers spontaneously explore a selection of paintings. The study focused on free exploration eye movements, but outside of text and reading contexts. Would eye movement abnormalities show up even in ludic conditions? The authors have chosen specific types of paintings, amongst others Op-Art from Bridget Riley, creating the illusion of visual movement in-depth. Also, paintings from Escher and Magritt, because of their multiple impossible spaces or hidden spaces, or because of contradictions between image and text  [6]. According to these authors, pictorial movement perception and pictorial space perception would be connected with theories and controversies concerning increased creativity in dyslexia.
e. , during the saccades or the post-saccadic fixations or the resting period between large movements. The motivation behind developing such scores is to capture the reaction of the different subjects to the 3D illusion optics present in the seven paintings in terms of motion or space composition complexity. The results demonstrate that when considering the seven painting dataset separately, the predictive power of the features derived from AIDEAL depends on the type of painting, while the DGI is more universal and predictive no matter what the painting is. However, as soon as the dataset size grew, the ability of AIDEAL, based on deterministic eye movement features, to predict dyslexia became more competitive. Further, the complementarity between the two sets of features is investigated.
Participants The data used for the ML analysis is taken from published studies, please see Ward and Kapoula for full details regarding methods and materials  [1, 2, 5]. In brief, eye movements were recorded during painting exploration in 46 dyslexic adolescents (18 females, 28 male; mean age 15. 52, SD 2. 45) and 41 non-dyslexic adolescents (20 female, 21 male; mean age 14. 78 +/-2. 44) recruited from schools in Paris. Dyslexic adolescents were diagnosed in specialized medical centers and were admitted to their schools based on their dyslexia diagnosis. Typically, diagnosis by these centers includes multiple testing which confers a diagnosis of visual, phonological dyslexia, mixed dyslexia, etc. We, therefore, did not have children self-identify their condition. Both dyslexic and non-dyslexic adolescents had no known neurologic or psychiatric abnormalities.
Ocular Motor Tests Painting Tests The data concern eye movements while viewing modern art painting selected because of their specific visual psychophysical attributes: three Op Art paintings from Bridget Riley, providing a strong illusion of visual motion-in-depth, and on the other hand, four paintings from Escher or Magritt with the high complexity of space construction, e. g. , bistable spaces or image text contradictions. The reason for choosing such paintings was the hypothesis that dyslexics known to show more unstable fixations in-depth (diverging or converging post-saccadic drifts) would be perceptually more sensitive to the illusion of movement in depth as fixation instability could contribute to the genesis of such an illusion. On the other hand, dyslexics would have a higher capacity in analyzing complex space compositions, or contradictory images.
Painting Tests The data concern eye movements while viewing modern art painting selected because of their specific visual psychophysical attributes: three Op Art paintings from Bridget Riley, providing a strong illusion of visual motion-in-depth, and on the other hand, four paintings from Escher or Magritt with the high complexity of space construction, e. g. , bistable spaces or image text contradictions. The reason for choosing such paintings was the hypothesis that dyslexics known to show more unstable fixations in-depth (diverging or converging post-saccadic drifts) would be perceptually more sensitive to the illusion of movement in depth as fixation instability could contribute to the genesis of such an illusion. On the other hand, dyslexics would have a higher capacity in analyzing complex space compositions, or contradictory images.
Eye Movement Descriptors Based on the AIDEAL Velocity Threshold Method For 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. e. , the time between the onset of the movement and its offset.
Model Fitting We followed the same procedure presented in  [4]  to train a support vector machine(SVM) with a radial basis function RBF kernel and a logistic regression model to predict dyslexia based on eye movement descriptors extracted from the seven paintings dataset. In the first analysis, we considered the seven datasets separately. In this analysis, for each dataset, all its samples were collected when each subject was observing the same painting. Hence, the different DGI scores were supposed to be easily comparable. However, the sample size was low (92 samples for each dataset). In the second analysis, we grouped the seven paintings datasets into two groups. By aggregating the painting 1, 2, and 3, datasets into group 1, and painting 4, 5, 6, and 7 into group 2. Hence, the sample size of each dataset has increased from 92 to 276 and 368 for the first and second dataset, respectively.
On the other hand, the variance of the input space has increased. The paintings datasets were grouped based on their visual psychophysics similarity of the painting table, hence the three datasets of the Op Art paintings from Bridget Riley were grouped into group 1, whereas the other four paintings from Escher and Magritt were combined into group 2. Finally, we've followed the same logic of analysis 2, by combining the seven datasets into one big dataset that contains 92 × 7 observations, to have a more reliable score of the generalizability of our models. By merging the seven datasets into one big dataset, the test size has increased from 20 samples to 140 samples. In this analysis we investigated the ability of each feature set (Aideal eye movement feature and the DGI features) to predict dyslexia without having the information about which painting each participant was looking at when eye movements were recorded.
During the three analyses, we used cross-validation (five-fold). At each fold, the model is fitted to the train set, and its generalization ability is evaluated on the test set. We report the average test performance on the five folds. At each fold, the train set, and test set were first normalized by subtracting the mean of the training set then dividing by the standard deviation of the training set. Since the size of the dataset did not allow for the creation of a representative validation set, we do not perform any hyperparameter tuning and use the default values of the different models in their scikit-learn implementation. To evaluate our ML algorithms, we used three metrics: accuracy, sensitivity, and specificity. A more complete version of the procedure is presented in  [4].
Predicting Dyslexia Using AIDEAL Based Features of Saccades and Fixations Figure  2  presents a summary of the accuracy of the two models (support vector machine and logistic regression) when using AIDEAL based features to predict dyslexia on the ten datasets. In addition, in Appendix E (Tables A1-A5), we present the full tables of the different metrics scores used for the two models when trained on the AIDEAL eye movement features on analysis 1 (Tables  A1  and A2 ), analysis 2 (Tables  A3  and A4 ), and analysis 3. Finally, in Appendix C (Figures  A5-A11 ), we present the distribution of each of the three features and for each of the 7 paintings datasets.
Predicting Dyslexia Using AIDEAL Based Features of Saccades and Fixations Figure  2  presents a summary of the accuracy of the two models (support vector machine and logistic regression) when using AIDEAL based features to predict dyslexia on the ten datasets. In addition, in Appendix E (Tables  A1-A5 ), we present the full Tables of the different metrics scores used for the two models when trained on the AIDEAL eye movement features on analysis 1 (Tables  A1  and A2 ), analysis 2 (Tables  A3  and A4 ), and analysis 3. Finally, in Appendix C (Figures  A5-A11 ), we present the distribution of each of the three features and for each of the 7 paintings datasets. In the first analysis, when considering painting separately, for all seven of the painting datasets, AIDEAL-based eye movement features were able to predict dyslexia.
Except for the first dataset (painting 1), there was no significant difference between the performance of the linear model and the non-linear model. The best performances were achieved when trained on the painting 2 dataset, with nonlinear SVM, with an accuracy of 86. 25%, a sensitivity of 82. 5%, and a specificity of 90%. On the other hand, the worst model in terms of the generalization ability was the logistic regression when trained on the painting 1 dataset. In the second and the third analysis, where we've grouped multiple datasets to augment the sample size of each dataset. The accuracy has improved to 85. 88% using the SVM model. Finally, the best performance was achieved when merging all the seven datasets, without considering the nature of the painting that was observed during the dataset acquisition process.
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. In the first analysis, when considering painting separately, for all seven of the painting datasets, AIDEAL-based eye movement features were able to predict dyslexia. Except for the first dataset (painting 1), there was no significant difference between the performance of the linear model and the non-linear model.
The best performances were achieved when trained on the painting 2 dataset, with nonlinear SVM, with an accuracy of 86. 25%, a sensitivity of 82. 5%, and a specificity of 90%. On the other hand, the worst model in terms of the generalization ability was the logistic regression when trained on the painting 1 dataset. In the second and the third analysis, where we've grouped multiple datasets to augment the sample size of each dataset. The accuracy has improved to 85. 88% using the SVM model. Finally, the best performance was achieved when merging all the seven datasets, without considering the nature of the painting that was observed during the dataset acquisition process. This improvement can also be explained by the sample size that has been multiplied by seven when considering combining all the seven painting datasets.