dataesr/scientific-paragraphs-categorization huggingface dataset:
Speci city Accuracy Only IAS 100% 95% 96. 4% All patients 85% 94% 91. 5% Figure  4  presents the prediction delay of the proposed approach using the Siena scalp and the local database. The blue and orange points re ect the prediction delay for the Siena and the local databases, respectively. As it can be seen in these Fig. 4 , the prediction delay vary from a few seconds before the seizure up to more than 15-20 min before the seizure depending on patients. In Addition, for almost all patients with more than one seizure acquisition, the prediction time of the different seizures were close to each other. Moreover, green points re ect the mean prediction delay of the Siena database acquisitions, which re ects our high prediction delay achieved. Table  8  presents the percentage of the predicted seizures by the time of prediction, either between 0 to 5 min, 5 to 10 min or predicted before the seizure by more than 10 min.
As it can be seen, in the table, our approach was able to predict 58. 7% and 57. 2% of the seizure before more than 10 min for both post-ictal and local database, respectively. For the Siena database, our approach was able to predict only 40% of the seizures before 10 min from the onset. Siena database 60% 0% 40% Figure  5  shows an example of the STD operator calculation on the ECG signal taken from the epileptic patient 9 taken from the Siena scalp EEG database. In this example, the red discontinuous line presents the moment of the seizure. In fact, our approach was able to quantify and predict epileptic seizures before 33. 6 seconds. In this work, we also proposed an automatic threshold technique for onset prediction. We believe that for normal patients, the variation of both features (ApEn and NRRi), especially the NRRi, which presents the heart rate changes, varies around the value of the mean.
Then, the pre-ictal periods will be used to select the proper β value that will return the best performance (maximum onset prediction and the less false alerts results) to be selected. The value will be selected for each patient separately. Table  9  presents the results of testing the performance of the automatic threshold prediction approach using the same three databases used with the manual threshold technique. In fact, acquisitions of less than 10 min in length will not be taken into consideration in this T hresh = M ean + β (9) M ean β M ean β M ean β M ean β β β part of the work. Table  9  The performance of the automatic threshold approach using local database.
Speci city Accuracy The local Database 85% 81% 82% Siena Scalp EEG Database 75% 85% 82% Post-ictal Database 90% 83% 85% From the results presented in the tables above, the prediction approach using the manual threshold value selection gave a better result. In fact, the proposed automatic threshold technique is based on the mean of the two features computed. In fact, even for the inter-ictal period, if the changes (peak) in both curves' features is instantly, even if these changes are small compared to pre-ictal period, this kind of change is considered as a prediction by our automatic proposed approach (false alert). Another weak point in our proposed automatic threshold technique is the gradual increase of the value. If the values of both curves increase gradually in time, the threshold value also will increase gradually with the curves which will lead to unpredicted seizure.
For those reasons, a manual threshold selection technique gave of course better results. In the inverse, the manual threshold selection is not sensible of this kind of those problems. Figure  6  presents an example of application of the automatic threshold approach proposed on the second acquisition of patient number 6 from the Siena Scalp EEG database. For almost all the acquisitions studied, signi cant changes for both features NRRi and ApEn were seen in the pre-ictal period of the input ECG signal. In fact, a signi cant decrease was seen in the NRRi features, especially right before the seizure. This increase can be explained by the fact that about 82% of epileptic seizures are associated with ictal tachycardia which proceeds the epileptic seizures  [19]. In addition, the features ApEn showed signi cant change in the pre-ictal period, and a clear decrease was seen for almost all the patients right before the seizure which can be associated with the increase in the sympathetic activity  [19].
Only acquisitions taken from the Siena scalp EEG database are studied, where no information about localization, lateralization, or even the seizure types was given for the acquisitions taken from the post-Ictal database. Besides, the local database contains mixed acquisitions for adults and children, and we still don't know if the HRV will react in the same way for both categories. A signi cant change in the sample and fuzzy entropy curves was seen starting approximately 30 min before the seizure for patients PN06, PN10 (bilateral seizure), and PN14 (starting from the interrupted green line, where the red line presents the start of the seizure). Whereas for patient PN09, these changes occurred starting before the seizure by 15 min. In the same way, for both fuzzy and sample entropy curves, a clearly signi cant decrease is seen either at the moments of the seizure or just after the seizure for all the acquisitions studied.
Moreover, the epileptic distribution is more concentrated around the value 0. 8 compared to the healthy distribution where the concentration of the values is more spread outin the range of [0. 6-0. 9]. To more prove the difference between both data. The Mann-Whitney algorithm was used. In fact, the statistical P value given by the Mann-Whitney algorithm for both features taken from both kind of subjects (epileptic and healthy) is less than null hypothesis 0. 05 which mean that both distributions are not from the same category, especially for the NRRI features distribution. In fact, further research needs to address the limitations of the proposed work, which includes only a small number of subjects in the study. Moreover, all patients from the Siena scalp database suffer from IAS seizures with only one patient with WIAS seizure and only one patient with bilateral seizure.
For that reason, no investigations can be made on the effects of the seizure's type on the ECG signal in the pre-ictal period. In addition, it would be more important to study pre-ictal and inter-ictal periods of ECG acquisitions taken under the same conditions to have more accurate results and contributions not from three separated databases. In addition, having the clinical details about patients' medical history, more information about the patient's condition, and video monitoring can lead to better explications about the changes in the ECG signal and be 100% con dent of the contributions made by the proposed methodology. In addition, with more ECG acquisitions, another study can be made to compare the effect of the epileptic seizures on the pre-ictal state of acquisitions taken from both children and adults.
Conclusion In this study, we proposed two new approaches to ensure the quanti cation of the signi cant changes in the pre-ictal period based on the use of the threshold technique on the STD curves. Moreover, the STD curves were computed using the time-domain analysis feature NRRi and the non-linear feature ApEn to re ect the high increase and the irregularity of the ECG signal before the epileptic seizures. Using the manual threshold approach on the IAS seizures taken from the Siena scalp EEG database, we achieved a sensitivity of 100%, speci city of 95%, and an accuracy of 96. 4% whereas using the acquisition from the Post-Ictal database we achieved a sensitivity of 100%, speci city of 91% and an accuracy of 94%. and using the acquisitions from 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, 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 the local database respectively, whereas using the 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%. In future work, additional ECG acquisitions will be collected and used to improve the performance of the proposed approaches and to give more credibility to the results obtained.
A recent study showed that the ROS produced after injury induces the Toll pathway in hemocytes  (41). Thus, we speculate that the low levels of Gst in gcm 26 /+;Toll 10b /+ hemocytes (Figure  2D ) may increase their level of ROS, hence leading to a stronger response to Toll activation. Unexpectedly, the GO term enrichment analyses carried out on each dataset highlighted GO terms related to synapse activity enriched in the two comparisons (Supplementary Figures  S2C, E ). These genes included several neurotransmitter receptors, suggesting an involvement of neurotransmitter-mediated signaling in Toll activation and gcm 26 sensitization. A targeted analysis of neurotransmitter receptor expression in our dataset revealed the expression of 26 receptors in hemocytes and significant upregulations of the nicotinic acetylcholine receptor alpha 6 (nAchRalpha6), the serotonin receptor 5hydroxytryptamine receptor 1B (5-HT1B), the dopamine receptor Dopamine 1-like receptor 1 (Dop1R1), and the short neuropeptide F receptor (sNPF-R) by Toll 10b and/or by gcm 26 (Figure  2H , Supplementary Table  S1 ).
Altogether, these data demonstrate that nAchRalpha6 regulates the number of hemocytes in the larva. To assess the cause(s) of the different hemocyte numbers in nAchRalpha6 KD and GOF, we quantified hemocyte proliferation and apoptosis using antibodies against phosphorylated Ser10 of histone 3 (pH3)  (53)  and cleaved caspase Dcp-1 (54), respectively. No difference was found in the rate of apoptosis (Supplementary Figures  S4B-D ). A significant reduction of the proliferation rate was observed in nAchRalpha6-KD hemocytes in feeding as well as in wandering third instar larvae (FL3, WL3, respectively) and a significant increase in proliferation in nAchRalpha6 CRISPR GOF hemocytes compared to control hemocytes (Figures  3H, I ; Supplementary Figure  S4A ). To assess if the impact of nAchRalpha6 on proliferation is cell autonomous, we quantified the number of proliferative hemocytes that express nAchRalpha6 in nAchRalpha6-T2A-Gal4/+;UAS-GFP/srp(hemo) 3xmcherry larvae.
Cholinergic signaling regulates hemocyte homeostasis We evaluated the impact of other neurotransmitter receptors and downregulated 5-HT1B, Dop1R1, nAchRbeta3 or the glutamate receptor Eye-enriched kainate receptor (Ekar) (Figure  3B ). In all cases, the KD animals are completely viable (data not shown). In terms of hemocyte number, 5-HT1B-KD and Dop1R1-KD show no difference compared to control WL3. Ekar-KD displays less hemocytes (Figure  3B ), suggesting that glutamate signaling may also be involved in the regulation of hemocyte homeostasis. nAchRbeta3-KD shows a strong increase in the number of hemocytes (Figure  3C ), which further highlights the importance of cholinergic signaling in hemocyte homeostasis. At last, to assess the impact of cholinergic transmission on hemocytes, we monitored the number of larval hemocytes after inhibiting the expression of the choline-acetyltransferase ChAT in neurons.
Acknowledgments We thank  K. Bruckner ,  D. Siekhaus ,  D. Hultmark ,  H. Tanimoto ,  S. Kondo ,  S. Russell ,  V. Honti ,  I. Ando  and  P. Soba  for providing fly stocks and antibodies. In addition, stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and antibodies obtained from the  Developmental Studies Hybridoma Bank  created by the  NICHD  of the  NIH  and maintained at The  University of Iowa (Department of Biology, Iowa City , IA 52242) were used in this study. We thank  F. Stenger ,  M. Heinis ,  M. Moog ,  A. Kucan ,  A. Bhan ,  E. Naegelen ,  G. Zhang  for technical assistance. We thank the  Imaging Center of the IGBMC  for technical assistance. We thank  A. Maglott-Roth  from the  IGBMC screening facility  for the hemocyte analysis. The sequencing was performed by the GenomEast platform, a member of the " France Geńomique " consortium ( ANR-10-INBS-0009 ).
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by  INSERM ,  CNRS ,  UDS ,  Ligue Reǵionale contre le Cancer, Hopital de Strasbourg ,  ARC ,  CEFIPRA ,  USIAS ,  FRM  and  ANR  grants. PC was funded by the  ANR  and by the  ARSEP , WB by the  USIAS  and by the FRM ( FDT20160435111 ). SM was funded from  CEFIPRA  and  ANR  fellowships. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program.
1 CAG repeat number in the ATN1 gene was analyzed by a polymerase chain reaction with fluorescence-labeled primers as described elsewhere. 4 The number of repeats was determined by capillary electrophoresis using an ABI 3130X automated DNA sequencer and the GeneMapper version 4. 0 software (Applied Biosystems, Foster City, CA, USA). To assess the number of repeats, Genescan 500 ROX size standard was used (Applied Biosystems) and several samples with different DRPLA CAG allele lengths were sequenced as positive controls.
Genetic testing We utilized Twist custom panel, which allows screening of 34 genes, 27 risk variants, and 8 candidate genes, which are mostly associated with Parkinsonism (panel details are available in the Table  S1 ). The custom Twist EF Library Prep (Twist) was used to capture all exons, intron-exon boundaries, 5 0 -and 3 0 -UTR sequences and 10-bp flanking sequences of target genes (RefSeq database, hg38 assembly). Specific probes for NGS target enrichment were designed using Twist software, and amplicon length varied between 250 and 500 bp. Runs were performed on Illumina MiSeq sequencer. The assay was performed according to the manufacturer's recommended protocol. Variants were prioritized based on the following criteria: frequencies <0. 01% in public databases (ExAC/ GnomAD) and our in-house database of 500 exomes, nucleotide and amino-acid conservation (based on alignments), relation of the gene to disease (per family), and inheritance pattern conservation (based on alignments).
Bioinformatics analysis of gene panel data Human reference genome UCSC hg19 was used for sequence alignment and variant calling with the Burrows-Wheeler Aligner (BWA)  18  and the Genome Analysis Toolkit (GATK). 19 PCR duplicates were removed prior to variant calling using Picard software. The mean coverage was 9939 (range 594-12419), and the mean percent coverage at 309 was 98. 7% (range 96. 5-99. 6%) for all individuals tested. Variants were annotated with ANNOVAR software. 20 Variants were prioritized based on the following criteria: frequencies <0. 01% in public databases (ExAC/gnomAD) and our inhouse database of 500 exomes, nucleotide and amino-acid conservation (based on alignments), relation of the gene to disease (per family), and inheritance pattern. We visualized the molecular effect of the missense variant on the structure of iPLA2-b protein using UCSFC ChimeraX.
Sanger sequencing Filtered variants were PCR-amplified and validated (Sanger sequencing) to confirm the genotypes in affected individuals. The variants were Sanger-sequenced in three unaffected relatives (the mother, a non-identical twin to patient 1 and, a brother) to study the segregation. Forward and reverse primers were designed using Primer3 online tool. 22 Multiple sequence alignment was performed using Bioedit software v7. 2. 5 23 using default parameters.
Funding Information Program " Investissements d'Avenir "  ANR-10-IAIHU-06 , and from a grant from  France Parkinson : to J-C C, SL, and CT. UKB:  PSP-Element A-397. 0003mit  der Kostenstelle 993000: for UW and YB. Princess Nourah bint Abdulrahman University Researchers supporting project number  PNURSP2022R172 ,  Princess Nourah bint Abdulrahman University, Riyadh ,  Saudi Arabia  to LEOE.
Primary cilia counting and length measurements Threshold was applied to the images using Image J software to define the primary cilium structure. When length was not measured, the number of primary cilia per field was determined using the particle analyzer. The number of Dapi-positive nuclei in the same field was determined in the same way. The percentage of Dapi-positive and ciliapositive cells was then calculated. To determine the cilia length, 3-D reconstructions were obtained from stacks acquired with a Leica SP8 confocal microscope equipped with an oil-immersed 63× objective, with a 2048 × 2048 resolution, and 0. 5 μm between focus points. Each individual primary cilium was traced using the "Simple neurite tracer" plug-in from the Image J software.
Acknowledgements For microscopy experiments, we acknowledge the imaging facility MRI, member of the national infrastructure  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»). The authors thank the iExplore Facility, RAM, BioCampus. We thank  Dr. Michael Kyba  ( University of Minnesota ) for A2Lox. Cre mES cells and the p2Lox-GFP plasmid. The generation of Htr6-EGFP KI mice was supported by  Foundation for Medical Research (FRM, France)  and  ANR  contract  Sero6Cognet  ( ANR-11-BSV4-008 ). Generation of Htr6 KO mice was supported by the  Phenomin consortium. Funding: This study was supported by  ANR  grant  Sero6Dev  ( ANR-17-CE16-0010-01 ), the  University of Montpellier  ( ANR-16-IDEX-0006 ),  CNRS  and  INSERM.
Neural entrainment, defined as unidirectional synchronization of neural oscillations to an external rhythmic stimulus, is a topic of major interest in the field of neuroscience. Despite broad scientific consensus on its existence, on its pivotal role in sensory and motor processes, and on its fundamental definition, empirical research struggles in quantifying it with non-invasive electrophysiology. To this date, broadly adopted state-of-the-art methods still fail to capture the dynamic underlying the phenomenon. Here, we present event-related frequency adjustment (ERFA) as a methodological framework to induce and to measure neural entrainment in human participants, optimized for multivariate EEG datasets. By applying dynamic phase and tempo perturbations to isochronous auditory metronomes during a finger-tapping task, we analyzed adaptive changes in instantaneous frequency of entrained oscillatory components during error correction.
Participants Twenty ( N = 20) right-handed healthy participants took part in the experiment (11 females, 9 males; mean age = 32. 8 years, std = 6. 2 years). All participants had normal hearing and normal or correctedto-normal vision; none reported any history of major medical, psychiatric or neurological conditions; none reported to be a professional mu-Fig. 1. ERFA paradigm workflow. Orange boxes represent the context of the experimental tasks, blue boxes represent data processing. The approach started with an attunement phase, where participants were exposed to a perturbation-free auditory metronome for 60 seconds. The purpose was to induce a stable oscillation attuned to the stimulation frequency, facilitating its separation from the broadband multivariate EEG signal via generalized eigendecomposition (GED)  ( Cohen and Gulbinaite, 2017 ; Cohen, 2022 ).
Participants underwent this period of steady stimulation under distinct conditions of active listening and finger-tapping: using these two datasets, we designed for every participant two different spatial filters to extract a purely perceptual (without movement) and a sensorimotor (with movement) entrained component, respectively. After 60 seconds of finger-tapping, metronomes started to be perturbed by introducing unpredictable tempo changes or phase shifts during 405 seconds, depending on the experimental condition. During these perturbation tasks , participants were instructed to synchronize their finger-taps to the metronome, therefore following the stimulus dynamics. The spatial filters designed based on the attunement phase were applied to the EEG signal recorded during the tasks, allowing us to track the dynamic changes in the frequency of both perceptual and sensorimotor oscillatory components.
Experimental tasks The experiment consisted of one listening task without perturbations and two finger-tapping tasks with perturbations, performed in a fully-randomized order. Two different perturbation types were used as separate experimental conditions, these were tempo changes and phase shifts (see Fig. 1  ). The listening task lasted 60 seconds, while both fingertapping tasks lasted 465 seconds, of which the first 60 seconds were free of perturbations and the following 405 seconds contained perturbations. The listening task was used to collect EEG data in the absence of movement and design a spatial filter, to extract the perceptual component maximally attuned to the auditory stimulus. Stimulation consisted of a metronome set at 100 Bpm (1. 67 Hz), which was chosen as optimal rate for sensorimotor synchronization  ( Kliger Amrani and Zion Golumbic, 2022 ; London, 2012 ; Zalta et al.
The importance of minimizing body and head movements during the task was stressed, as well as the importance of staring at a black spot (i. e. a fixation point) painted on the tapping pad. Both measures were taken to minimize the contamination of EEG recordings. Immediately before the start of the experiment, participants were given a simple demonstration by the experimenter and familiarized themselves with the finger-tapping to the non-perturbed metronome until they felt confident with the procedure. Each finger-tapping task lasted a total of 465 seconds, the first 60 of which were free from perturbations. This steady period was used to design a spatial filter to extract the sensorimotor component maximally attuned to the metronome, based on EEG data collected in condition of overt movement.
Experimental apparatus Participants were sitting on a comfortable armrest chair, and a circular pad was placed on the table in front of them for recording fingertapping responses. Stimuli were presented via DefenderShield® airtube earbuds. Volume was adjusted before the beginning of the experiment, to make sure that stimuli were clearly audible without creating any discomfort. A circular tapping pad, containing a strain gauge pressure sensor, was used to detect finger-tapping onsets with a 1 ms resolution. A folded towel was placed underneath the pad, so that no auditory feedback from the finger-taps was perceivable. The pressure sensor was connected to a Teensy 3. 2 microcontroller, which worked as serial/MIDI hub for data logging and communication between the stimulation computer and the EEG recording computer.
Upon occurrence of events of interest (i. e. , perturbations), a TTL trigger was sent from the Teensy microcontroller to the EEG amplifier via BNC connection, granting the alignment between behavioral and neural timeseries. The EEG signals were recorded with an ANT-Neuro eego TM mylab system at a sampling rate of 1 kHz. Each participant was equipped with an EEG headset (64-channel waveguard TM original with Ag/AgCl electrodes). Recordings were performed with a referential montage, with 'CPz' being the reference for all electrodes. The stimuli sequence was played back by specifically designed software developed in Max MSP 8 (Cycling '74, USA), running on the stimulation computer (Windows 10, Intel core i7 8th gen, Focusrite Rednet PCIExpress ASIO low-latency soundcard). Prior to each trial, a randomized balanced list of perturbations was generated and inspected by the experimenter.