dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantification of the Survival of C. elegans Neuronal Cell Populations Worms were fixed in PBS with 4% paraformaldehyde (PFA) before mounting on glass slides for visual inspection under a Leica DM2000 LED microscope equipped with a 60× oil immersion objective. The impact of treatments on neuronal survival was measured by visually quantifying the neuronal somas and, when specified, the dendrites of green fluorescent protein (GPF)-tagged populations of neuronal cells. Quantitative data were obtained by analyzing 30 worms per experimental condition, and the experiments were carried out in triplicate. Specifically, we quantified the number of dopaminergic somas in the head (CEP, ADE) and body (PDE). We also estimated the dendrite integrity from CEP neurons. NSM and ADF serotoninergic neurons and their dendrites were also counted in the heads of the worms.
Quantification of Immunosignal Intensity Image sections of the worm heads or bodies were acquired with a Leica TCS SP8 confocal microscope using a x63 oil immersion objective. Stacks of 30 images were acquired per worm using a 0. 25 µm Z step size. The intensity of the p-tau immunosignal in neurons from the region of interest (ROI) was quantified using FIJI software  [37].
Behavioral Analysis To evaluate the integrity and function of DA neurons, we used the basal slowing response (BSR) method  [45]. Briefly, upon completion of the test treatments, the worms were washed with the M9 medium and transferred onto a solid medium supplemented with or without food (bacteria). Then, the crawling velocity was monitored by tracking the worms on short movies (1 min, 7 fps) acquired with an AZ100M Nikon microscope. The image analysis was performed with the Imaris imaging software from Oxford Instruments (Abingdon, Oxfordshire, UK). To evaluate the effects of L-DOPA, 5 mM of L-DOPA (Sigma-Aldrich) was added to E. coli seeded agar plates 3 h before testing. Quantitative data for the behavioral assays were derived from three independent experiments, each with 30 worms analyzed per experimental condition.
CLD Is Toxic and Alters the Lifespan of Live Nematodes Then, we defined the experimental conditions to assess CLD toxicity in an in vivo model, the nematode C. elegans. Accordingly, we investigated the effects of chronic exposure to increasing CLD concentrations (0, 1, 5, 10, 50 and 100 µM) on the survival of young adult worms, at 20 • C. After 72 h of exposure to CLD, the treated worms exhibited a concentrationdependent mortality that gradually increased from 10 to 100 µM (Figure  5a ). For subsequent neurotoxicity experiments, a concentration of 15 µM of CLD inducing a mortality rate of approximately 40%, was most commonly used. In comparison, a concentration of 1 mM of MPP + used to model DA cell loss in C. elegans worms  [50]  a mortality rate of 60% under the present conditions. Note that quantitative data for subsequent neurotoxicity studies were obtained in the worms that remained alive and well-formed after exposure to CLD.
As expected, L-DOPA also restored a slowing behavior in cat-2 (e1112) mutant worms deficient in TH and consequently deprived of DA. This further validates that our BSR assay specifically detects changes in dopaminergic behaviors under the present experimental conditions. Basal slowing response of C. elegans worms exposed to CLD. Synchronized young adult worms from the control GFP and dat-1 (ok157) mutant strains were exposed for 3 days to 15 µM of CLD and then placed on a solid medium supplemented or not with food (bacteria) to evaluate the changes in velocity between these two conditions. When specified, L-DOPA (1 mM) was also added to the plates 3 hours before assessment. Velocity was also evaluated in cat-2 (e1112) mutant animals exposed or not exposed to L-DOPA (1 mM). Data values are expressed as means ± SEMs (n = 30 worms per condition and experiment, with three independent experiments performed by an investigator blind to the experimental conditions).
Velocity was also evaluated in cat-2 (e1112) mutant animals exposed or not exposed to L-DOPA (1 mM). Data values are expressed as means ± SEMs (n = 30 worms per condition and experiment, with three independent experiments performed by an investigator blind to the experimental conditions). *** p < 0. 001, vs. corresponding line, off food. ### p < 0. 001, vs. the corresponding line, off food treated with CLD. One-way ANOVA followed by Dunnett's test. Finally, we also tested the impact of CLD in the dat-1 (ok157) line, which lacks the DA transporter, as this transport system could possibly facilitate CLD accumulation in DA neurons, as it also does with other dopaminergic toxins, such as 6-OHDA and MPP +  [39, 47]. This was not the case here, however, as the BSR remained compromised in the dat-1 (ok157) mutant worms exposed to 15 µM of CLD.
Acknowledgments: This research benefited from the equipment and excellent services of the cell culture, imaging and preclinical functional exploration facilities at  ICM. In particular, we thank  David Akbar  and  Claire Lovo  for technical support with the imaging studies. Some C. elegans strains were provided by the  Caenorhabditis Genetics Center at the University of Minnesota (St. Paul, MN, USA) , which is founded by  NIH office of Research Infrastructure Programs  ( P40 OD010440 ). We thank  BC Kraemer  ( University of Washington, Seattle, WA 98104, USA ) for the gift of the tau transgenic line.
Cells 2023 , 25 Figure 7. 2023257 Figure 7. Basal slowing response of C. elegans worms exposed to CLD. Synchronized young adult worms from the control GFP and dat-1 (ok157) mutant strains were exposed for 3 days to 15 µM of CLD and then placed on a solid medium supplemented or not with food (bacteria) to evaluate the changes in velocity between these two conditions. When specified, L-DOPA (1 mM) was also added to the plates 3 hours before assessment. Velocity was also evaluated in cat-2 (e1112) mutant animals exposed or not exposed to L-DOPA (1 mM). Data values are expressed as means ± SEMs (n = 30 worms per condition and experiment, with three independent experiments performed by an investigator blind to the experimental conditions). *** p < 0. 001, vs. corresponding line, off food. ### p < 0.
Cells 2023 , 25 Figure 8. 2023258 Figure 8. Chronic CLD exposure promotes p-tau expression in transgenic C. elegans worms. (a) Young adult C. elegans worm imaged with Nomarski optics. The black dashed rectangle defines the region of interest (ROI). (b) Synchronized young adult worms were treated or not treated with CLD for 3 days, and the living worms were recovered for fixation before being processed for the immunodetection of total tau (green) or p-tau (red) using BR19 and AT8 antibodies, respectively. Samples were then mounted for fluorescence imaging by confocal microscopy (x63 objective). Note that the immunosignal of p-tau (but not total tau) was dramatically increased by CLD within the anterior nerve ring. (c) Quantitative analysis of the p-tau immunosignal within the ROI. Data are means ± SEM (n = 50 worms per experiment, with three independent experiments performed by an investigator blind to the experimental conditions).
Search Methods We searched the PubMed online database up to January 23 rd , 2023 using the search term phrase, "Restless Legs Syndrome AND treatment. " After we found relevant articles, we then did another search where the name of the medication reported in the relevant articles was substituted in the phrase above as a way of capturing any relevant articles that may have been missed by the first search. We also supplemented our search with articles cited in the reference lists of articles picked up from our original literature search, when these secondary articles had not been picked up by the methodology above. One major caveat to our search is that earlier studies may have employed objective evidence for therapeutic effects such as polysomnography and actigraphy or adhoc scales to rate RLS severity.
The expected value of cycle quantification was around 13. The chip was placed into the IFC Controller, where 6. 3 nL of Sample Mix and 0. 7 nL of Assay Mix were mixed. Real-time PCR was performed on the Biomark System (Fluidigm) by BioControl to Grape platform (Bordeaux, France) with the following protocol: Thermal Mix at 50 (from 60 • C to 95 • C). Results were analyzed using the Fluidigm Real-time PCR Analysis software v. 4. 1. 3. (San Francisco, CA, USA) to control specific amplification for each primer. Then, the raw data of the qPCR were analyzed using GenEx software version 7 (MultiD analyses AB, Freising, Germany) in order to choose the best reference gene to normalize mRNA expression and to measure the relative expression of each of the 93 genes analyzed between groups. The best reference gene was found to be β-2 microglobulin (B2m) that was thus used for normalization of gene expression.
Subsequently, Fisher's LSD post hoc tests were conducted. All data related to the gut microbiota were subjected to a 1-way ANOVA with diet as the factor. Post hoc Fisher's LSD tests were performed when appropriate. Sequenced data were processed using the QIIME pipeline (v1. 9. 0) against the Greengenes (v13. 8) database. Bioinformatic analyses and assessment of gut bacterial diversity (both beta and alpha diversity) were conducted using the web interface of MicrobiomeAnalyst (MicrobiomeAnalyst 2. 0, https://www. microbiomeanalyst. ca/ (accessed on 10 June 2023), Canada). All data were presented as means ± SEM. Differences were considered significant when the p-value was less than 0. 05.
This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation (STD) of two Heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quanti cation of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena Scalp EEG and Post-Ictal Heart Rate Oscillations in Partial Epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, speci city of 95%, and an accuracy of 96. 4% whereas using acquisitions from the post-Ictal database, we achieved a sensitivity of 100%, speci city of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a speci city of 97% and an accuracy of 97.
5%. Furthermore, the proposed approach predicted 58. 7%, 57. 2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, speci city, and accuracy of 85%, 81%, 82% using our local database respectively, whereas using acquisitions take from the Siena Scalp EEG database, we achieved a sensitivity of 75%, speci city of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a speci city of 83% and an accuracy of 85%.
For that reason, several works tried to demonstrate and quantify the signi cant changes in ECG signal in the preictal period. A power spectrum analysis on 6 seizures taken from three patients (2 females, 1 male) in [8] showed a clear signi cant increase of the curve of the reciprocal HF-power just around the onset time of epileptic seizure. The authors used ECG data from 25 to 30 min pre-seizure up to 30 to 300 seconds post-seizure of ECG. Moreover, a random season of 30 min ECG acquisition not within 4 hours of seizures was used as a non-seizure control period. In fact, no signi cant changes were reported in this study for the LF, VHF, LF/HF, and LF/(LF + HF) frequency features analyzed. [14]  presents an investigation of the changes in the ECG in the pre-ictal period. In this study, four clustering techniques (K-means clustering, Agglomerative hierarchical clustering, Density-based spatial clustering (DBSCAN), and Expectationmaximization (EM) clustering) were used in order to quantify changes in the pre-ictal period using three-features combination extracted from 32 HRV features (linear and non-linear).
Moreover, they found that the following features in terms of predictability are LF/HF, pNN50, NN50, and RQA ENT. Other works on prediction of epileptic seizures based only on ECG such  [15] , where the authors used a set of 8 linear and nonlinear features ( which are: RRI, Mean HR, LF, HF, LF/HF, SD1, SD2 and SD2/SD1) extracted from the data taken from a public database  [16]  which consists of 11 seizures taken from 7 subjects. Besides, using the threshold technique, authors reported a prediction sensitivity of 86. 2%. In fact, a thresholding value was xed for each patient. They also reported that features likes mean HR, LF/HF, and SD2/SD signi cantly increased while the RRI signi cantly decrease. Moreover, most of the observed changes in the features occurred 15 minutes before the seizures.
In the work  [17] , authors studied the data taken from the public database  [16]. Moreover, to predict the epileptic seizure, authors used a threshold technique and SVM using temporal features and spectral features. The temporal features are the Hjorth features: activity, mobility and complexity where the spectral features are spectral entropy and means of absolute derivation. These authors reported that the threshold technique was better than the SVM for epileptic seizure prediction and reported a prediction performance of 94. 2% as accuracy, a sensitivity of 84. 1% and a speci city of 94. 5%. The work  [18]  studied 170 seizures taken from 16 patients where time and frequency domain features were computed. Adaptative threshold techniques were used and a threshold value was xed for each patient separately.
1%, false-positive per hour (FP/h) of 0. 49 in patients with epilepsy, and 0. 19 in healthy patients. This work aimed to study changes in the ECG signal in the pre-ictal period to explore the feasibility of creating an automatic epileptic seizure prediction approach, in which the result of the application of the statistical operator standard deviation (STD) on the computed HRV features is used as input for a thresholding technique in order to quantify the changes in the input pre-ictal ECG signal. The feasibility of our study was carried out using ECG acquisitions of a public and local databases consisting of 31 patients and a healthy public database consisting of forty patients. To address our objective (establishing the feasibility of our methodology to quantify the epileptic seizures), we focused exclusively on ECG acquisitions of epileptic patients from a public database.
The Siena Scalp EEG Database is free to use on the Physionet platform (https://physionet. org/content/siena-scalp-eeg/1. 0. 0/). The database consists of data acquired from 14 epileptic patients: 9 males (ages 36-71) and 5 females (ages 20-58), within the Unit of Neurology and Neurophysiology at the University of Siena, Italy. The data contains synchronized EEG-ECG recordings of pathologic patients acquired based on the use of EB Neuro and Natus Quantum LTM ampli ers, and reusable silver/gold cup electrodes. The electrodes used in the acquisition were arranged according to the international 10-20 system. During data acquisition, patients were asked to stay in bed as much as possible, either asleep or awake  [20]. All the data recorded were revised by expert clinicians according to the criteria of the International League Against Epilepsy.
Local data: The local database consists of 14 ECG acquisitions taken from 13 patients aged between 8 and 42 years old. Experimental data were selected from the F-TRACT database of spontaneous video-SEEG seizures (research protocol INSERM IRB 14-140). The patients gave their consent to undergo invasive recordings and peripheral recordings as part of a presurgical evaluation of their drug-resistant epilepsy. SEEG/ECG recordings were performed using a video-EEG monitoring system (Micromed, Treviso, Italy) that allowed for simultaneously recording up to 256 monopolar contacts. Sampling rate was either 256 or 512 Hz, with an acquisition band-pass lter between 0. 1 and 90 Hz or between 0. 1 and 200 Hz respectively, depending on ampli er capacities at the date of recordings. Data were acquired using a referential montage with reference electrode chosen in the white matter.
Data manipulation To study changes in the ECG signal in the pre-ictal phase, we took 1 hour of ECG signal before seizures and 5 min after seizures. If the acquisition does not contain at least 1h of signal before seizure, we use all the provided pre-ictal in our work. Moreover, We only used data from the patients where at least one readable ECG acquisition is available. In fact, the Siena scalp database was made only for epileptic EEG analysis. For that reason, some of the ECG acquisitions are unreadable where they can contain a lot of noise, especially from 20 min before the seizure's onset which allow to study the variation of the input ECG signal in the pre-ictal period. The existence of such noises is due to either patient movements or bad connection of the ECG electrodes with patients skin.
From the Siena scalp database, we were able to study only acquisitions from 11 patients after deleting all unreadable and very noisy ECG acquisitions. From the 11 patients, only 34 seizures were studied after getting rid of the unreadable acquisitions. In addition, Patient PN16 was excluded from this study because both of his seizures were recorded during sleep. From the Post-Ictal database, all the seizures were used in our study. Moreover, in order to improve our analysis, we also used inter-ictal segments in our study taken from all the studied patients. We extracted 8h of pre-ictal period from the Post-Ictal database, 29 hours from the Siena scalp EEG database and 14 hours from the local database. The heart rate takes some time after seizures to back to the basic signal rate. The rst ve minutes after seizures are considered as post-ictal instabilities and we do not take it into consideration.
In our work, the inter-ictal period will be considered from at least 5 min after the onset. And also, inter-ictal segments are considered before the one hours signal in front of the onset which is the pre-ictal segments. In addition, we only extracted inter-ictal segments where at least 20 min of continuous signal so we can analyze all changes in the segments. Moreover, to maximize the number of segments of the inter-ictal period, the whole inter-ictal signal extracted was divided into sub-segments of 20 min length each to obtain 24 inter-ictal segments from the Post-Ictal database, 87 inter-ictal segments from the Siena scalp EEG database and 63 inter-ictal segments from the local database.
Results And Discussion In order to measure performance of our proposed approach, pre-ictal and inter-ictal data taken from both epileptic databases were tested. From the Siena scalp EEG database, we were able to extract 34 seizures taken from 11 patients studied. Moreover, none of noisy or in-sleep acquisitions were taken into consideration. Besides, all of the 10 seizures from the Post-Ictal database were analyzed. In addition, we used 8h of the inter-ictal signal taken from the Post-Ictal database and 29h of inter-ictal signal taken from the Siena scalp EEG database. The whole inter-ictal signal extracted was divided into sub-segments of 20 min length each to obtain 24 inter-ictal segments from the Post-Ictal database, 87 segments from the Siena scalp EEG database and 63 inter-ictal segments from the local database.
As all the analysis were extracted from the Post-Ictal database, we present in the following table our results:  As can be seen in Table  4 , our proposed approach achieved a highest value of sensitivity and speci city compared to the other works. However, the proposed approach in  [17]  achieved a better accuracy than our approach, where the work  [15]  did not mention his accuracy. Moreover, the proposed approach also showed a prediction latency much better than both approaches seen in the stat of arts. However, none of the two works have speci ed in his proposed methodology the length of the pre-ictal and inter-ictal segment studied compared to us. Besides, no information was mentioned about the number of inter-ictal segments used to measure the performance of their approaches. We also compared the prediction latency of the proposed approach with the other approaches seen in the state of arts as it can be seen in Table  4.
In fact, in some acquisitions, we can see that there's no very huge different between the prediction delay of our approach compared with  [17]  (both threshold and SVM results) such as for patient 1, patient 5. Otherwise, our approach predicted the epileptic seizure before 43. 08 min, 22. 32 min and before 46. 5 min, 35. 84 min for both seizures of patient 2 and patient 6 respectively. Similarly, we tested the performance of the proposed approach on the local database as can be seen in Table  5. The proposed approach achieved a sensitivity of 100%, a speci city of 97% and an accuracy of 97. 5%. The local database consists of acquisitions taken from 14 patients with different seizures type which prove the feasibility of the proposed approach to predict the epileptic seizures using ECG of different seizures type.
Speci city Accuracy Local data base 100% 97% 97. 5% Table  6  presents the delay prediction of the proposed approach using the local database data. All patients in this database are presented with only one seizure expect patient 13. Our proposed approach was able to achieve a prediction delay starting from few seconds before the seizure up to more than 15 min before the seizure. We also measured the performance of the proposed approach using the epileptic database Siena scalp EEG database as can be seen in Table  7. The Siena scalp database is composed of acquisitions with IAS, focal onset without impaired awareness (WIAS), and bilateral seizures, where the majority of the acquisitions are IAS. For that reason, the performance of the proposed approach was measured rstly using only IAS acquisitions and then with all the acquisitions extracted from the database.
As it can be seen In Table  7 , using only the acquisitions with IAS seizure, our approach was able to achieve a sensitivity of 100%, speci city of 95%, and an accuracy of 96. 4%. However, using all the acquisitions (different seizures type) we achieved a sensitivity of 85%, a speci city of 94%, and an accuracy of 91. 5%. Unfortunately, we cannot compare this performance with other approaches because to the best of our knowledge, we are the rst work that studied the effect of epileptic seizures on the ECG signal using the new public database Siena scalp. Using only data taken from patients with the IAS seizures, from the 61 inter-ictal segments, our approach sent a false alert only on 3 inter-ictal segments where they showed unexpected changes in both curves. In fact, expect all patients with temporal epileptic seizures in the Siena scalp EEG database, patient PN14 is the only one who had WIAS where we were able to study only two seizures (the rest contain a lot of noise), and our approach predicted only one of the two seizures studied.
Subject PN10 presents only patients in the database who suffer from Bilateral epileptic seizures. Our approach was able to predict only 5 seizures from 8 seizures studied. No more investigations could be made to assume why no seizure alert was triggered for those acquisitions taken from both PN10 and PN14 patients, especially due to a lack of patient data with the same type of seizures. Table  7  The performance of the proposed approach using the Siena scalp EEG database.