dataesr/scientific-paragraphs-categorization huggingface dataset:
We aimed to identify cognitive signatures (phenotypes) of patients suffering from mesial temporal lobe epilepsy (mTLE) with respect to their epilepsy lateralization (left or right), through the use of SVM (Support Vector Machine) and XGBoost (eXtreme Gradient Boosting) machine learning (ML) algorithms. Specifically, we explored the ability of the two algorithms to identify the most significant scores (features, in ML terms) that segregate the left from the right mTLE patients. We had two versions of our dataset which consisted of neuropsychological test scores: a "reduced and working" version (n ¼ 46 patients) without any missing data, and another one "original" (n ¼ 57) with missing data but useful for testing the robustness of results obtained with the working dataset. The emphasis was placed on a precautionary machine learning (ML) approach for classification, with reproducible and generalizable results.
The effects of several clinical medical variables were also studied. We obtained excellent predictive classification performances (>75%) of left and right mTLE with both versions of the dataset. The most segregating features were four language and memory tests, with a remarkable stability close to 100%. Thus, these cognitive tests appear to be highly relevant for neuropsychological assessment of patients. Moreover, clinical variables such as structural asymmetry between hippocampal gyri, the age of patients and the number of anti-epileptic drugs, influenced the cognitive phenotype. This exploratory study represents an in-depth analysis of cognitive scores and allows observing interesting interactions between language and memory performance. We discuss implications of these findings in terms of clinical and theoretical applications and perspectives in the field of neuropsychology.
Patients Fifty-seven drug-resistant patients with unilateral mTLE according to the ILAE committee report (Wieser for the ILAE Commission on Neurosurgery of Epilepsy, 2004) have been included. All patients underwent pre-surgical examination including clinical (neurological), neuropsychological and speech assessment, as well as electrical (video-EEG recordings) and brain structure (MRI) evaluations. Pre-surgical evaluation allowed identifying the hemispheric and regional localization of the epileptogenic zone (EZ). According to it, patients were separated in two groups, left (L-mTLE) and right (R-mTLE). Only 46 patients had no missing NPE data, 27 L-mTLE and 19 R-mTLE patients. Thus, we used two versions of our dataset: a "reduced and working" version (D': n ¼ 46 patients) without any missing data, and another one "original" (D: n ¼ 57, 24% more patients than in D'; 5% of missing values).
The rationale for using the "original" dataset with the missing data was to test the robustness of results. 2008) , as well as mental inhibition of irrelevant responses  (Stroop; Stroop, 1935). For all the tests mentioned above, the raw scores were standardized according to the patient's age. Except for the indexes from the WAIS-V (VCI and PRI), the raw performances were also corrected with respect to gender and sociocultural level. The standardization and normalization of scores was performed by the neuropsychologist with respect to the norms provided in respective manuals (Appendix S1). These scores were then expressed in terms of standard deviation from the norm (z scores). In total, 9 cognitive tests (IQ, VCI, PRI, DO80, verbal fluency, AMI, VMI, TMT and Stroop) have been used as features, to perform ML analyses.
Machine learning approach The first objective of this exploratory study was to assess the ability of NPE to predict the lateralization of epileptogenic zone in our population of patients (i. e. in other words, to classify categories of patients). The second objective was to identify the most discriminating scores for classification and determine their interactions. To this end, we performed several ML workflows including binary classification and feature selection. In practical terms, two parallel analyses have been conducted on the two versions of the dataset (i. e. , D and D') using two different algorithms: (a) a classical Support Vector Machine (SVM) algorithm  (Cortes and Vapnik, 1995)  with a Radial Basis Function (RBF); and (b) a state-of-the-art XGBoost algorithm  (Chen and Guestrin, 2016) , previously and successfully used by our team  (Torlay et al.
, 2017). We decided to use two different algorithms in particular to deal with missing values. A possible solution for dealing with missing values would have been to use an imputation method resulting in the computation of artificial data. However, the use of imputed data remains a matter of debate  (Jakobsen et al. , 2017). Therefore, we opted for XGBoost, an algorithm that deals with missing values. Both SVM RBF and XGBoost were applied on the reduced and working dataset (D 0 ), but only XGBoost was applied to analyze the original version of the dataset (D) that includes 5% of missing values. Our multi-algorithm approach also allowed to: (i) see if the results depend on the type of algorithms used; and (ii) perform supplementary analyses useful to verify the robustness of the results obtained in a different version of the dataset (D, including 24% more patients).
In this way we identify the most contributing and stable features of the classification but also if they are sufficient to separate on average our two mTLE populations. The selection stability was assessed using two metrics (frequency and phi: Φ) computed on the 1000 iterations, giving an idea of the robustness of the results obtained by the feature selection. Once the feature selected we have redone a binary classification on the reduced and working version of the dataset (D 0 ) restricted to the selected features. C. Re-test step of the ML results obtained with D'. We used here the original version of the dataset D (24% more patients but 5% of missing values) to assess the classification performance of the 9 features of interest as well as on the restricted dataset composed of the same features selected in the previous step of feature selection (step B).
Since this dataset is composed of missing values, only XGBoost was used to compute the classification performances. This stepalthough made on a not completely independent datasetallows estimating the robustness and the generalization of the results obtained in the two first steps (A and B). D. Workflow of the partial dependence plot and model interpretation. The goal of the PDP is to observe how the selected features values change the prediction or interact with each other. The PDP was made on the dataset D 0 , restricted to the selected feature. The values of a chosen feature F are changed in small steps between the min and max values of F; whereas all the other features are kept unchanged. In this way we could estimate the effect of F on the classification (PDP 1 dimension). The same procedure could be done on 2 features in order to estimate the interaction between these two feature, on the prediction (PDP 2 dimensions).
Binary classification Many assumptions used in a learning algorithm (such as the Radial Basis Function kernel of Support Vector Machines (SVM-RBF) or the L2 regularizers of linear models (used in our feature selection workflow) assume that features are standardized. If not, the estimator may be unable to learn correctly. The reduced and working version of the dataset (D 0 ) was hence standardized. Since XGBoost is not based on these assumptions, we did not preprocess the original version of the dataset (D). The goal was to predict the lateralization of mTLE patients (left or right) based on our neuropsychological measures of interest. In other words, we aimed to train a model (supervised learning) to assign correctly a patient to one of two classes, left or right (binary classification) based on a series of features.
The algorithm uses the labeled data as the training set and its prediction performance is subsequently measured by using the unlabeled data as the validation set. A special attention has been paid on the generalization ability of the machine learning workflow. We used a classical 10-fold cross-validation (CV) scheme repeated 100 times and an inner CV in each training fold to do a grid search for hyperparameters. Those CV were stratified, i. e. , samples were randomly chosen in order to get always the same ratio of left and right epileptic patients in folds. To quantify the quality of predictions, we chose two widely used performance measurements: the Area Under the Curve of the Receiving Operator Curve (AUROC) and the balanced accuracy (BAcc). The AUROC of a classifier is equivalent to the probability that it will rank a randomly chosen positive instance higher than a randomly chosen negative instance.
Model interpretation (PDP analyses) Model-agnostic methods are not specific to ML  (Ribeiro et al. , 2016)  but allow the interpretation of any model. This type of method offers the possibility to explore how state-of-the-art algorithms work rather than to be limited to directly interpretable but less-performing models like regressions or simple decision trees. We have chosen to use Partial Dependence Plot (PDP) in order to explore the way in which the values of selected features change the prediction and how these features interact. In practice, the PDP builds the model by averaging features with the exception of a chosen feature F and measures changes in prediction for different values of F (Fig. 1 , Panel D). Taking into account that the working version of the dataset (D') was restricted to the selected features, we randomly performed a 5-fold cross validation with a mean AUROC of 95%, by using the XGBoost algorithm.
A. binary classification and feature selection When using the reduced and working version of the dataset (D 0 ) and all the nine neuropsychological features, we get an average AUROC XGBoost ¼ 88. 2% and AUROC SVMÀ RBF ¼ 88. 9% and an average BAcc XGBoost ¼ 77. 39% and BAcc SVMÀ RBF ¼ 76. 26% (cf. Fig. 2 , Panel A for an example of the performances distributions obtained using SVM; Appendix S3 for all the distributions). This high level of performance clearly shows the ability of the complete NPE to predict epilepsy lateralization. The feature selection approach (cf. Fig. 2 , Panel B) shows a remarkable quality of stability, b Φ ¼ 93. 2% with k ¼ 4 features selected on average and a very good performance level (AUROC XGBoost ¼ 89. 7% and AUROC SVMÀ RBF ¼ 85. 9%; BAcc XGBoost ¼ 76. 08% and BAcc SVMÀ RBF ¼ 76.
41%). The four selected features were language and memory scores with respective frequencies of: VMI ¼ 100%, AMI ¼ 99. 5%, Semantic Fluency ¼ 98. 8% and Phonological Fluency ¼ 96. 4%. We obtained an excellent level of prediction when we measured again the performance with the same 100-times repeated 10-CV on the 4 selected features, we get AUROC XGBoost ¼ 90. 2% and AUROC SVMÀ RBF ¼ 86%; BAcc XGBoost ¼ 77. 70% and BAcc SVMÀ RBF ¼ 77. 68%. In the re-test step, when using the original extended version of the dataset (D; 24% more patients), the performance remained very good despite the number of missing values: AUROC XGBoost ¼ 82% with all scores and 84% with the selected ones (BAcc XGBoost ¼ 75. 16% and BAcc XGBoost ¼ 74. 04%, respectively).
Modulations by clinical data On average, patient groups were matched regarding their demographical and clinical data. They did not differ significantly in terms of age (t(55) ¼ À 0. 27, p ¼. 8); handedness (t(55) ¼ À 0. 51, p ¼. 6); educational level (t(55) ¼ À 0. 58, p ¼. 6); hippocampal asymmetry (t (55) ¼ À 0. 49, p ¼. 6); age of seizures onset (t(55) ¼ À 0. 44, p ¼. 7); duration of epilepsy (t(55) ¼ 0. 13, p ¼. 9); seizure frequency (t(55) ¼ 0. 59, p ¼. 5); and number of antiepileptic drugs (t(55) ¼ 0. 89, p ¼. 4). Only two regressions between the clinical factors and the 4 selected scores from the feature selection step were significant at p <. 05. We observed a weak but significant effect of the HS asymmetry on AMI (F (52) ¼ 12. 45, p <. 001, R 2 ¼ 0. 19) and VMI (F(52) ¼ 11. 95, p <.
, 2007). ML approaches by contrast, estimate the predictive power and give an insight of the results' stability, allowing to address these issues. An interesting recent ML study conducted by  Frank et al. (2018)  had also provided some answers regarding the prediction of the seizure focus in TLE patients. By using different ML algorithms on a dataset restricted to language and memory tests (without IQ and executive functioning evaluations) they consistently found better-than-chance The closer you get to 0 on the y-axis, the more likely you are to be an R-mTLE patient; the closer you are to 1, the more likely you are to be an L-mTLE patient. To take the example of a 'clear' feature, VMI, the more negative the values, the clearer the classification as R-mTLE becomes. Conversely, the more positive the values (above 0), the better the probability of being correctly classified L-mTLE when the patient is actually L-mTLE.
Other studies have demonstrated similar atypical patterns regarding the cerebral functioning of memory  (Haag and Bonelli, 2013). These unusual patterns of brain processing previously identified in patients could constitute a possible origin of the behavioral disruption we observed for these cognitive functions in particular. Next to the memory deficits usually reported in mTLE patients  (Bell et al. , 2011; Brissart and Maillard, 2018; Hoppe et al. , 2007; Tramoni-Negre et al. , 2017) ; one of the most frequently described language impairments of mTLE is the ability to name an object, including the well-known "tip of the tongue" phenomenon. Naming impairment would be more important in patients with L-mTLE than those with R-mTLE  (Stemmer and Whitaker, 2008). Surprisingly, in our patient dataset, the naming score (DO80) was not considered as an important feature to differentiate groups of patients (feature selection results, Fig.
Namely, the phonological dorsal and the semantic ventral pathways, as well as a posterior parietal and hippocampus sub-circuit that is assumed to serve as a mediator between general language representations and other cognitive systems such as the episodic memory  (Vandenberghe et al. , 2013). The disruption of these communication streams, due to recurrent and refractory epileptic seizures originating in mesio-temporal structures and in the hippocampus in particular, could consequently have a double impact on both language and memory. One limitation of our study is the size of our dataset, especially after sub-grouping, which could be problematic in ML. However, we have paid a special attention to the generalization of the results as well as to limiting the risk of overfitting. Namely, we used a multi-algorithm approach with a cross-validation and a re-test procedure performed in an extended version of the dataset.
This "re-test" procedure on a second version of the dataset (D), including an additional quarter of patients, is not optimal since the sample used is not completely independent. However, as "the use of different tests results in different outcomes, which cannot be directly compared"  (Helmstaedter and Witt, 2012) , collecting a completely independent sample of patients presenting a one-sided diagnosis of mTLE and with the same cognitive evaluation would be a real challenge. We therefore proposedfor information purposesthis complementary and auxiliary analysis as an indicator of the robustness of the classification performances when adding additional patients. As mentioned above, we paid a particular attention to the homogeneity of our sample. Some factors could influence the relations observed between the cognitive signatures and the location of the epilepsy.
There were no significant differences between our two groups of patients on demographic data such as age, manual laterality or education level on average (Table  S1 ). Similarly, the patients included in this study were clinically matched (no differences in clinical data such as duration of epilepsy, frequency of seizures, number of antiepileptic drugs, and hippocampal asymmetry). Nevertheless, these factors can have a transversal impact on cognition, independently of the patient groups. For example, TLE patients with hippocampal sclerosis (HS) have been found to have worse naming performance than those without HS and the volume of left hippocampus has been found to significantly predict verbal fluency and naming ability  (Alessio et al. , 2006). We have also found a significant and negative modulation of the hippocampal asymmetry on the cognitive data, but mainly on memory scores (AMI and VMI as well, Fig.
Conclusion The NPE is efficient to help clinicians in predicting the location/ lateralization of the EZ. The cognitive tests used in this study are overall very sensitive and relevant in the discrimination of the two populations of mTLE patients. We observed different cognitive profiles according to the epilepsy location. Language (semantic and phonological fluency) and memory (WMS IV auditory and visual memory index) scores were the best predictors regardless the version of the patients' dataset used (i. e. the reduced or original version). Interestingly, some cut-off points have been identified beyond which the prediction increases with greater certainty. Finally, we found complex and interesting interactions between language and memory scores. Some projects, as the European project E-PILEPSY, have the objective to harmonize practices and set standards for the NPE in epilepsy surgery  (Vogt et al.
Funding This work has been funded by the  French program  " AAP GEN-ERIQUE 2017 " run by the  "Agence Nationale de la Recherche ", grant "REORG" [grant number   ANR-17-CE28-0015-01 ]; and by  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir" program  [grant number  ANR-15-IDEX-02 ].
Optimal growth temperatures and temperature limits were assigned to the sequences by matching GenInfo identifiers with genus and species annotations in BacDive database. Sequences without a match were excluded from further analysis. The remaining sequences were sorted into four temperature groups: psychrophiles (growth temperatures below 21°C), mesophiles (21°C-45°C), thermophiles (46°C-65°C), and hyperthermophiles (over 65°C). All temperature groups, except mesophiles, were manually sorted to exclude sequences originating from organisms with growth temperature ranges spanning more than two temperature groups. This process yielded 128, 5,811, 93, and 24 sequences from psychrophiles, mesophiles, thermophiles, and hyperthermophiles, respectively. The amino acid frequencies were calculated at each position in the MSA for all temperature groups using Prophecy  (Rice, Longden et al, 2000)  and using the Glt Ph sequence as a reference.
1 mM L-Asp, as required, and subjected to a freeze/ thaw cycle. All smFRET experiments were performed on a home-built prism-based total internal reflection fluorescence (TIRF) microscope constructed around a Nikon Eclipse Ti inverted microscope using passivated microfluidic imaging chambers  (Munro, Altman et al, 2007; Juette et al, 2016). The samples were illuminated with a 532-nm laser (Laser Quantum). LD555P and LD655 fluorescence signals were separated using a T635lpxr dichroic filter (Chroma) mounted in a MultiCam apparatus (Cairn). Imaging data were acquired using home-written acquisition software and scientific complementary metal-oxide-semiconductor (sCMOS) cameras (Hamamatsu). Labeled Glt Ph variants in proteoliposomes were surface-immobilized via a biotin-streptavidin bridge on PEG-passivated microfluidic imaging chambers functionalized with streptavidin.
Imaging experiments were performed in 20 mM Hepes/Tris buffers at pH 7. 4 containing 5 mM β-mercaptoethanol and an oxygen scavenger system comprised of 1 U/ml glucose oxidase (Sigma), 8 U/ml catalase (Sigma), and 0. 1 % glucose (Sigma). Experiments under symmetric apo conditions were performed in buffer supplemented with 200 mM KCl using proteoliposomes loaded with buffer containing 200 mM KCl. Under non-equilibrium transport conditions, imaging buffer was supplemented with 200 mM NaCl and 0. 1 mM L-Asp. For symmetric saturating Na + /L-Asp conditions, the internal liposome and imaging buffers contained 200 mM NaCl and 0. 1 mM L- Asp. To inhibit protein dynamics, liposomes containing 200 mM NaCl were incubated in imaging buffer containing 200 mM NaCl and 10 mM D,L-TBOA. Data were collected using 10-or 100-ms averaging time.
Single-molecule fluorescence trajectories were selected for analysis in SPARTAN  (Juette et al, 2016)  implemented in MATLAB (MathWorks). Trajectories were corrected for spectral bleed-through from donor to acceptor channel by subtracting a fraction of the donor intensity from the acceptor (0. 168). Acquired traces were selected for analysis using the following criteria: a single catastrophic photobleaching event, ascertaining Glt Ph labeling with a single LD555P-MAL and LD655-MAL label; over 10:1 signal-tobackground noise ratio; over 5:1 signal-to-signal noise ratio; FRET lifetime of at least 5 s; and a maximum of 1 donor blink per trajectory. FRET trajectories were calculated from LD555P and LD655 intensities, I D and I A , respectively, using E FRET = I A /(I D + I A ). Population contour plots were constructed by superimposing E FRET from individual trajectories and fitted to Gaussian distributions in Prism (GraphPad).
Dwell-time distributions and transition frequencies were obtained by idealizing E FRET trajectories in SPARTAN  (Juette et al, 2016). State survival plots and dwell-time histograms were fitted to double or triple exponentials and the probability density functions  (Sigworth & Sine, 1987) , respectively. Analyses of transition frequencies, dwell times, and survival plots for individual trajectories were performed using custom-made scripts implemented in MATLAB (MathWorks). State survival plots of individual trajectories were fitted to single exponentials or double exponentials if R 2 increased by at least 5 %, and the time constants differed at least fivefold. Fits were only performed when traces contained at least 5 visits to the OFS or the IFS. Mean dwell times were used otherwise. For illustration purposes, state survival plots of selected molecules were refitted in Prism (GraphPad).
(Adams, Grosse-Kunstleve et al, 2002)y hanging-drop vapor diffusion method. Crystals were cryo-protected in reservoir solution supplemented with 30-35% PEG 400. Diffraction data were collected at Advanced Light Source beamline 8. 2. 2. Diffraction data were indexed, integrated, and scaled using HKL2000(Otwinowski & Minor, 1997). Initial phases were determined by molecular replacement in Phaser (McCoy, Grosse-Kunstleve et al, 2007) using 2NWX as the search model. The model was optimized by iterative rounds of refinement and rebuilding in Phenix(Adams, Grosse-Kunstleve et al, 2002)and Coot.
Acknowledgements We thank  Roger Altman  for preparation of smFRET chambers,  Zhou Zhou  for dye formulations,  Eva Fortea  and  Emma Garst  for assistance with crystallization trials, and  Lucy Skrabanek  for R scripts used in bioinformatics sequence analysis. We thank  Julia Chamot-Rooke  for continued support. We are grateful for the support of  NINDS  ( R37NS085318  to O. B. and S. C. B. ) and  AHA  ( 19PRE34380215  to H. D. C. ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant agreement MEMDYN  No  660083  (to G. H. M. H. ).