dataesr/scientific-paragraphs-categorization huggingface dataset:
E4a ). Furthermore, the amplitude of the second harmonic is 19. 8 dB below the fundamental (20. 9 dB in the simulated case), meaning that if the fundamental energy is E, the second harmonic has energy E/95 (Fig. E4b ). Therefore, we can reasonably neglect the non-linear effects in the calculations of the thermal effects, as they account for ~1% of the energy involved. The same conclusions were drawn at 0. 5 MHz and 15 MHz. Linear wave propagation approximations considerably decreased the computing cost of the simulations. Linear propagation simulations were conducted with the Field II toolbox in Matlab  52, 53  , in monochromatic mode, with the same medium properties as kWave (water), to obtain the 3D maximum pressure fields. These maximum pressure fields were used to build a       ( -  ) +  in kWave, where   is the blood specific heat capacity (3630 J.
Acknowledgements The authors would like to thank  C. Joffrois ,  M. Valet ,  Q. Cesar ,  M. Desrosiers ,  S. Fouquet ,  P. Annic ,  M. Celik ,  Z. Raics  for technical help and scientific advice. This work was supported by the  European Research Council (ERC)   Synergy Grant Scheme  (holistic evaluation of light and multiwave applications to high-resolution imaging in ophthalmic translational research revisiting the Helmholtzian synergies,  ERC  Grant Agreement # 610110 ), by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 785219  ( Graphene Flagship Core 2 ) No. 881603  ( Graphene flagship Core3 ), by the  Foundation Fighting Blindness, La Fondation pour la Recherche Médicale  ( FRM EQUIPE EQU202106012159 ), l'UNIM, la  Fédération des Aveugles de France , Optic 2000, the city of  Paris, Région ile de France , the  Agence Nationale de la Recherche  ( ANR BrainOptoSight ), and  French  state funds managed by the  Agence Nationale de la Recherche (ANR)  within  Programme Investissements d'Avenir, Laboratoire d'Excellence (LABEX) LIFESENSES  ( ANR-10-LABX-0065 ) and  Institut Hospitalo-Universitaire FOReSIGHT  ( ANR-18-IAHU-0001 ), by  NIH CORE  Grant  P30 EY08098  to the  Department of Ophthalmology , the  Eye and Ear Foundation of Pittsburgh , and from an unrestricted grant from  Research to Prevent Blindness, New York, NY.
Data availability Data supporting the findings of this study are available within the paper and Supplementary information as well as on FigShare : https://figshare. com/projects/Ectopic_expression_of_a_mechanosensitive_channel_confers _spatiotemporal_resolution_to_ultrasound_stimulations_of_neuronal_circuits_for_visual_r estoration/154041. All other data are available from the corresponding author upon reasonable request.
19, 20 These data are well in line with a primary role for CLDN16 in allowing permeation to divalent cations and are consistent with the pattern of expression of CLDN16 along the TAL (as outlined below). As pathogenic variants of CLDN19 cause FHHNC with similar renal wasting, CLDN19 could play a similar functional role as CLDN16. At least two different cell types have been described within the TAL. 8, 21, 22 4] [25]  Both CLDN16 and CLDN19 have been shown to be expressed predominantly in the outer stripe of outer medulla (mTAL OSOM ) and the cTAL. 12, 24, 25 Here, CLDN16 localizes to the tight junction and basolateral membrane domains. Similarly, CLDN19 has been described to localize to the tight junction and basolateral membrane domains with some, but not all antibodies. 12, 24, 25 DN16 and CLDN19 colocalize to select tight junctions in a mosaic expression pattern.
Expression was observed in the cTAL of wild-type animals, where it was coexpressed in the same tubules as the furosemide-sensitive Na + -K + -2Cl -cotransporter (NKCC2), a TAL marker, on 7-μm frozen sections (Figure  1C ). A similar expression profile was seen in the mTAL OSOM (data not shown). In both segments, CLDN19 was found in basolateral membrane domains and the tight junction. In Cldn19-deleted animals, immunoreactivity was lower albeit detectable (Figure  1D ). Immunohistochemical staining revealed anti- we have previously reported. 24 Additional tubular staining outside the TAL segment was seen in both genotypes (Figure  1I-L ). The tubules with marked expression in the basolateral membrane domains that was retained in the Cldn19-deleted mice appeared positive for AQP2 expression (Figure  1M-P ).
ACKNOWLEDGMENTS The authors kindly thank  Alan Yu  ( University of Kansas Medical Center, Kansas City, USA ) for providing antibodies against  CLDN19 ,  Mikio Furuse  ( National Institute for Physiological Sciences, Okazaki, Japan ) for providing antibodies and Cldn19-deleted animals, and  Olivier Bonny  ( University of Lausanne, Lausanne, Switzerland ) for providing the antibody against NCX1. The authors thank  Tilman Breiderhoff  ( Charité-Universitätsmedizin Berlin, Berlin, Germany ) for fruitful dis-cussions. The authors would like to thank  Inger Nissen  at the  University of Southern Denmark for expert technical assistance and Centre d'Histologie, d'Imagerie et de Cytométrie team  (a core facility of  Centre de Recherche des Cordeliers ) for their technical assistance. The authors thank  Jean-Marc Verbavatz  and  Remi Le Borgne  ( Université Paris Cité, CNRS, Institut Jacques Monod, Paris, France ) for their technical support and acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the  France BioImaging infrastructure  ( ANR-10-INBS-04 ) and  GIS-IBiSA.
C. P. -B. has been supported by the  Fondation pour la Recherche Médicale  ( FRM FDT201904007918 ). This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-17-CE14-0032  awarded to P. H. ), Filières Maladies Rares OSCAR and ORKID (awarded to C. P. -B. ), and the  Independent Research Fund Denmark  and  Novo Nordisk Foundation  (awarded to H. D. ).
Animal experiments were performed in accordance with the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes and approved by the local Animal Care and Use Committee of Bordeaux University. The appearance, weight, clinical signs and the behavior of the mice were monitored every day in order to prevent animal pain and distress. Both males and females were used. The number of animals per group necessary to have a 90% chance to find a significant difference between 2 groups was calculated using G power 3. 1 software. The calculation was made for the measurement of cardiac function (notably LVEF). Mice were either sacrificed by cervical dislocation or exsanguination under deep anesthesia (ketamine 100 mg/kg and xylazine 20 mg/kg, IP).
The system software uses a formula based on a cylindrical-hemiellipsoid model (volume=8. area²/3π/ length)  22. The LVEF was derived from the following formula: (EDV-ESV)/EDV*100. The cardiac wall thickness, left ventricular posterior wall (LVPW), inter-ventricular septum (IVS) and left ventricular internal diameter (LVID) were calculated by tracing wall limits in both the long and short axis views. LV pressure /systolic blood pressure measurement LV diastolic pressure measurement was assessed via an invasive catheterization technique. Mice were anesthetized with Isoflurane. A Scisense pressure catheter (Transonic) was inserted into the LV through the common carotid artery. Pressure was recorded using LabChart software. End diastolic pressure, dP/dt minimum and maximum, Tau and heart rate were automatically calculated by a curve fit through end-systolic and end-diastolic points on the pressure plot.
Cardiomyocyte mean surface area was measured using ImageJ software after membrane staining with Wheat Germ Agglutinin (WGA), Alexa Fluor™ 488 Conjugate (Invitrogen). Fibrosis was assessed after sirius red staining of heart sections by quantifying the percentage of red stained area. Quantifications were done on images were acquired by using an Axiozoom V16 or axioscope A1 (Zeiss) under 200x magnification. They were conducted on 10 randomly selected images were performed using ImageJ/Fiji v2. 0. 0-rc-59 software (National Institute of Health, USA) by an investigator blinded to genotype. More precisely, each sample received a unique number. At the end of the experiment, the genotype/treatment for each sample was revealed to enable data analysis. For immunohistochemical analyses, primary antibodies were sequentially coupled with biotinconjugated secondary antibodies and streptavidin-horseradish peroxidase (HRP) complex (Amersham).
Sources of funding This study was supported by grants from the  Fondation pour la Recherche Médicale (équipe FRM) , and the  Agence Nationale pour la Recherche (Appel à Projet Générique). Additionally, this study was co-funded by the " Institut National de la Santé et de la Recherche Médicale" (Inserm) , and by the  University of Bordeaux. Lauriane Cornuault  received a fellowship from the  Fondation pour la Recherche Médicale. François-Xavier Hérion  received a fellowship from  Bordeaux university hopital.
Recordings were digitized on-line and filtered at 10 kHz (Digidata 1550B, Molecular Devices). All experiments were designed to gather data within a stable period (i. e. , at least 1-2 min after establishing whole-cell access). ii. For the whole spinal cord preparation, motor outputs were recorded from L2 lumbar ventral roots by means of glass suction electrodes connected to an AC-coupled amplifier. Signals from the electrodes placed in contact with the ventral root recordings were amplified (Â2000), high-pass filtered at 70 Hz, low-pass filtered at 3 kHz, and sampled at 10 kHz. Custom-built amplifiers enabled simultaneous online rectification and integration (100 ms time constant) of raw signals. Locomotor-like activity was induced by (i) rising [K + ] e to 6 mM and decreasing [Ca 2+ ] e to 0.
2% triton X-100. Slides were then washed 3 Â 5 min in PBS and incubated for 2 h with an Alexa Fluor ® Plus 555-conjugated secondary antibody (Invitrogen #A32794, 1/400), an Cy5-conjugated secondary antibody (Jackson ImmunoResearch, #715-175-151, 1/400) or Alexa Fluor ® Plus 555conjugated secondary antibody (Invitrogen #A32816, 1/400) diluted in the blocking solution. After three washes of 5 min in PBS, they were mounted with a gelatinous aqueous medium containing Hoechst 33342 (ThermoFisher, #62249, 1/10000, initial concentration: 20 mM). Images were acquired using a confocal microscope (LSM700, Zeiss) equipped with either a 20Â air objective, a 40Â oil objective or a 63Â oil objective and processed with the Zen software (Zeiss).
| Two-photon Ca 2+ imaging analyses ΔF=F ¼ F 1 À F 0 ð Þ =F 0 , ð1Þ where F is the fluorescence at any given point in time, and F 0 is the mean fluorescence value for the 5-10 s range, preceding the bath perfusion of the oscillatory aCSF or the neuromodulator cocktail. Following this, the signal-to-noise ratio (SNR) is: SNR ¼ ΔF=F ð Þ peak =σF, where σF is the standard deviation of the baseline period. We took those events as a Ca 2+ response that exceeded the threshold of two times the standard deviation of the baseline period. The peak amplitude, duration and frequency of the Ca 2+ transients were analyzed by using a Matlab script. In each FOV where dorsal is up and ventral is down, all astrocytic Ca 2+ transients above the central canal were considered as dorsal astrocytic activity and distinguished from the ventral astrocytic Ca 2+ transients.
| In vivo behavioral analyses For walking, the CatWalk XT software (Noldus Information Technology, Netherlands) was used to measure a broad number of spatial and temporal gait parameters in several categories. These include (i) dynamic parameters related to individual paw prints, such as duration of the step cycle with the respective duration of the swing and stance phases; (ii) parameters related to the position of paw prints with respect to each other, for example the stride length (distance between two consecutive placement of the same paw) and the base of support (the width between the paw pairs); (iii) parameters related to time-based relationships between paw pairs, as well as step patterns. These parameters were calculated for each run and for each paw. In the rotarod test the average latency of the subject to fall was recorded (in seconds).
(g) Average effect of oscillatory aCSF and intracellular perfusion of BAPTA on the peak amplitude of the astrocytic Kir4. 1 current at À140 mV compared to standard aCSF (n = 8 and 7 astrocytes from 3 and 4 mice for control and BAPTAi, respectively). Ns, no significance, *p < 0. 05 (Kruskal-Wallis one-way ANOVA test with Dunn's post hoc test for (g)). F I G U R E 4 Mean ± SEM. For detailed p values, see Source data file. performed a set of behavioral locomotor tests on freely moving mice (Figure  7a ). We first used footprint analysis to evaluate the locomotor pattern (Figure  7b ). Mice transduced with Kir4. 1-shRNA displayed an increase in both swing and stand phases compared to Ctrl-ShRNA support (Figure  7d ). We next tested whether Kir4. 1 channels also modulate locomotor behaviors in more challenging tasks.
ACKNOWLEDGMENTS We thank  I. Vanzetta ,  P. Weber  and  S. Roux  for their technical advices in imaging sessions,  A. Duhoux  for animal care,  C. Brocard  for genotyping and  J. Verneuil  for providing the Matlab script. We also gratefully acknowledge  E. Gascon ,  F. Jaouen ,  C. Lepolard  and  A. Borges-Correia  from  Neuro-Vir  platform of the  Neuro-Bio-Tools facility (Institut de Neurosciences de la Timone, UMR 7289, Marseille, France)  for their advices, support and assistance in the design and production of vectors used in this work. We also thank  G. Rougon  for her critical insights on the manuscript.
Molecular characterization of vagal mechanosensory neurons To perform scRNA-seq, we isolated vagal sensory neurons using flow cytometry after genetically labeling all vagal neurons with a nuclear GFP (nGFP) reporter (ganglia from 15 VGlut2 Cre ;R26 nGFP mice at postnatal day [P] 4, Figure  S1A ). We used Cell Expression by Linear amplification and Sequencing-2 (CEL-Seq2), a low-throughput method with high gene detection sensitivity, and sequenced 1,536 cells. 31, 32 After removing cells with more than 250,000 or less than 17,000 unique molecular identifiers (UMIs), 1,392 cells remained for which a median of 9,323 genes and 70,667 UMIs were identified. Bioinformatic analysis defined 22 distinct neuronal subtypes (Figure  S1B ). We integrated our data with two scRNA-seq datasets of vagal neurons published during this study to perform a meta-analysis on 4,442 vagal neurons, i.
e. , 1,392 neurons isolated at P4 (this study), 395 and 956 neurons isolated at P42 and P56-84, respectively, 10 and 1,707 neurons isolated between P32 and 35. 11 Uniform manifold approximation and projection (UMAP) analysis of the combined datasets improved the resolution of vagal neuron types and segregated the roughly 10,000 vagal neurons per mouse into 27 transcriptomically distinct subtypes (Figure  S1C ). Cells from all datasets contributed to all 27 subtypes, indicating that shortly after birth all subtypes are specified (Figure  S1D ). The metaanalysis can be explored at https://shiny. mdc-berlin. de/VGIE/. A semi-supervised method was applied to group the 27 vagal neuron subtypes into ''superclusters'' (Figure  1A ). The superclusters distinguish the two parts of the vagal ganglion, the nodose and jugular ganglion, which derive from the epibranchial placode and cranial neural crest, respectively.
Neuronal control of esophageal motility Abnormal esophageal motility encompasses a heterogeneous class of disorders, and hereditary, autoimmune, and infectious factors, as well as nervous system degeneration, were suggested to cause or contribute to the pathology. 75, 76 In humans, VFSS is used to diagnose and monitor swallowing dysfunction  77  and was recently adapted for use in rodents. 28, 78, 79 We used VFSS to show that the ablation of Prox2/Runx3 neurons results in marked esophageal dysmotility. The histological, retrograde, and anterograde tracing studies assign this function to the Prox2/Runx3 neuronal subtypes that form IGLEs in the esophagus. The scRNA-seq datasets provide information on genes expressed in these neurons and can be mined for the identification of potential drug targets.
1, 0. 2, 0. 3 ml) to distend the entire stomach, allowing pressure equilibration by removing the added liquid and waiting 5 minutes between each distension. Stomach distention was continuously recorded via a force measurement device connected to a 1 mm probe placed on the ventral wall of the stomach. Raw data were recorded using an analog output from a Neurolog amplifier, filtered (100-5 kHz) and digitized (10 kHz) using a Powerlab 4/30 system and Labchart 8 software with the spike-histogram extension (ADInstruments Ltd. , Dunedin, New Zealand). Single nerve fiber recordings were further analyzed to identify single units using Spike2 software (ced. co, Spikes2 version 10). Individual spike units were identified based on the following parameters: new template width as % of amplitude: 32, minimum % of points in template: 60, minimum occurrence of events: 1/50, spike duration considered for the sorting -1.
ACKNOWLEDGMENTS We thank the following persons at the  MDC Berlin :  Bettina Brandt  and  Sven Buchert  for technical assistance,  Petra Stallerow  and Claudia P€ aseler for animal husbandry, Thomas M€ uller for help with the submission and for critical reading of the manuscript,  Fritz Rathjen  for critically reading the manuscript,  Hans-Peter Rahn  ( Flow Cytometry facility ),  Sandra Raimundo  ( Advanced Light Microscopy facility ), and the staff of the  Next Generation Sequencing facility  for their help. In addition, we thank the  University of Missouri veterinary staff and pathology laboratory  for the monitoring of mice and the authors of Bai et al. 10 for providing scRNA-seq data. We also thank  Mohammed Khallaf  and  Julia Ojeda-Alonso  for help in establishing the ex vivo esophagus/stomach vagal nerve preparation.
Library generation and sequencing Single cell RNA sequencing was done following the CEL-Seq2 protocol. 31 cDNA Libraries were prepared for 16 96-well plates, pooled together, and sequenced on a NextSeq 500 (Ilumina) in two separate runs by the next generation sequencing core facility of the Max-Delbr€ uck Center for Molecular Medicine. Analysis of the P4 vagal ganglia CEL-Seq2 data Data processing and gene quantification was performed using dropseq-tools v2. 0 and picard-tools v2. 18. 17. We used the standard pipeline for Drop-seq data, with the necessary adaptations to analyze the CEL-Seq2 data. We removed the bead barcode correction steps, and in the DigitalExpression quantification we inputted the list of 96 CEL-Seq2 barcodes. Alignment was performed with STAR v2. 5. 3a. We used the GRCm38 genome and the annotation from the GRCm38.
p4 assembly. Two thresholds were set to filter out wells without cells or wells with multiple cells. We set a lower threshold of 17,000 UMIs (unique molecular identifier) per cell, and an upper threshold of 250,000 UMIs per cell. These UMI thresholds filtered out 144 cells, leaving 1392 out of 1536 cells (from 16 96-well plates), e. g. 9. 4% of the total cell count were removed from further analysis. Downstream analysis was performed with Seurat v3. 0. Seurat was run with the default parameters with the following exceptions. 2000 genes were selected with the FindVariableFeatures function of Seurat. The first 30 principal components were selected after PCA, excluding PC3 and PC11 as these PCs represented glial contamination in our dataset. The neighbor graph was constructed with FindNeighbors with a k parameter of 11.
Integration analysis For the integration analysis the raw count tables were used from the Ernfors and Knight laboratories. 10, 11 The first was downloaded from a public repository and the second was kindly provided by the authors. The Bai et al. data consisted of two datasets, the targeted and the untargeted cells, which were sequenced with different protocols. 10 Seurat v3. 0 canonical correlation was used to integrate the four datasets (including ours), using 30 canonical components. Initially, PCA was performed for 30 principal components. The neighbor graph was constructed with those 30 components, with a k parameter of 20, and clustering was performed with a resolution of 1. After this initial analysis we identified clusters of glial and epithelial cells and sympathetic neurons. We then removed these cell clusters and repeated the integration analysis on the remaining cells.
The path signature is a series of iterated integrals computed from a multidimensional path. It is invariant under translation and time reparametrization, which makes it a robust feature for multichannel EEG time series. The performance can be further boosted by combining the path signature with the gold standard Riemannian classifier in the BCI field exploiting the geometric structure of symmetric positive definite (SPD) matrices. The results obtained on publicly available datasets show that the signature method is more robust to inter-user variability than classical ones, especially on noisy and low-quality data. Hence, this study paves the way towards the use of mathematical tools that until now have been neglected, in order to tackle the EEG-based BCI variability issue. It also sheds light on the lead-lag relationship captured by path signature which seems relevant to assess the underlying neural mechanisms.
Datasets In accordance with the principles of open science, we have exclusively worked with publicly available datasets. We have further chosen the most used datasets among open EEG-based BCI datasets for MI BCI applications: the BCI competition IV 2a dataset  26  and the Physionet motor imagery dataset  27. The BCI competition IV 2a dataset is an indispensable classical MI BCI dataset which consists of data collected from 9 subjects on 2 sessions on different days. Each session contains a total of 288 trials of 4 tasks: imagination of movement of left hand, right hand, feet and tongue. Each trial lasts for 6 s with 2 s of resting state at the beginning. After the cue appears on the screen at t = 2 s, participants have to perform the motor imagery task. EEG signals were recorded using 22 Ag/AgCl electrodes placed according to the international 10-20 system with the left mastoid as reference and right mastoid as ground electrode.
Signals were down-sampled to 250 Hz and band-pass filtered into the 0. 5-100 Hz band. A 50 Hz notch filter was applied. There were also 3 EOG channels, but they were not used in our experiments. Trials were checked by human experts and those containing artifacts were marked by the dataset providers. The trials marked with artifacts were excluded in our study. The Physionet MI dataset contains data recorded from 109 subjects performing motor movement and imagery, but only motor imagery data were used in our experiments. There is only 1 session consisting of 14 runs, each including 2 trials of 1 min resting state (eyes open/closed), and 3 runs of 2 min with the following 4 tasks: open and close left or right fist, imagine opening and closing left or right fist, open and close both fists or both feet, imagine opening and closing both fists or both feet.
First study In the first study, the use of the path signature as a feature map was explored. More precisely, we considered the EEG signal X t as an n-dimensional path X : [0, T] � → R n and computed the truncated signature up to level k. Then a classical classifier was applied on this feature vector. The focus here was exploring the performance with different truncation levels and classifiers, so only the most classical dataset, the BCI competition IV 2a dataset, was used. Cross-validation (Ten-fold for intra-subject and leave-one-out for inter-subject) was performed on the data with the left versus right motor-imagery paradigm. The results are summarized in Table  1  and Fig. 5. The full name of the classifiers associated with the acronyms used in the table are: support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), and multilayer perceptron (MLP).