dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter approach, which we refer to as interdigitation, is quite common in DIY context (Figure  1, b, c, d, e ), and can be found in product briefs and white-papers  [15, 30]. It is, however, not clear if and how such designs effect resistive pressure matrices. We investigate effects of interpolation algorithm choice and electrode interdigitation on accuracy, precision, and consistency of touch-position sensing. In doing so we make three contributions:  (1)  We provide a data-set of 1122 swipe gestures performed on 17 sensors. This dataset is designed to Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
SENSORS Design We created a processing sketch that generates interdigitated patterns and exports them as. pdf, ready for laser cutting (Figure  3 ). For the patterns used in the dataset we used triangular digits. We varied the digits using two parameters: Digit Width: Low digit width allows many digits to fit (Figure  3 , top). High digit width means that only few digits fit on the strip (Figure  3 , bot). Digit Width is the dimension parallel to the sensing strip. Digit Length: Low digit length means that there is little overlap between electrodes (Figure  3 , left). High digit length means that there is significant overlap (Figure  3 , right). Digit Length is the dimension perpendicular to the sensing strip.
DATA COLLECTION We used a CNC machine  [40]  to ensure consistent touchbehavior in terms of pressure and location. We created modified Dremel bits that acted as 'fingers' that would press on the sensor. The 'fingers' were made of Sugru with a rubber disk (cut from a mouse-pad  [39] ) beneath them to approximate the softness of the human fingertip (Figure  5 ). Each strip was sampled by an analog input of an Arduino Uno. We wrote a JavaScript controller that coordinates data logging and communicates between the CNC and Arduino. The controller is open source and can be downloaded from the projects GitHub page. We used this controller to perform an approximation of a swipe gesture by measuring pressure at 70 consecutive locations on the sensor with 1mm spacing. For each sensor we repeated this swipe gesture 11 times, with 2.
DATA ANALYSIS Using the collected pressure values, we (1) compute the touch position for each measured position and each method. The resulting positions are in units of strip-width. We (2) scale each sensor*method combination to match the expected range in mm. We then (3) calibrate the values by subtracting the y intercept of the regression line and multiplying each value by one minus the slope of the regression line (Figure  6 ). The resulting positions are in the  correct range, with a regression line of y = x (blue). We then subtract the known position from the estimated position to find the error. This resulted in 36,960 error measures per sensor (770 positions by 8 methods, 3 touch sizes, 2 pressure levels) for a total of 628 320 error measures. We investigate sensor accuracy, precision and consistency over pressure levels, using measures derived from this error measure.
The CUBIC method appears as a reasonable choice in terms of accuracy, however it performed significantly worse than all others in terms of precision. A visualization comparing interpolation methods for the non-interdigitated sensor and the 35% digit width, 100% digit height condition can be seen in Figure  11. The supplementary material includes these for all sensors. While the mTOUCH method performed well on the clean data, anecdotally, we observed that the mTOUCH method led to periodic jumps when testing on the non-normalized dataset. In the normalized dataset this did not happen, presumably due to removing the noise-floor. The COM method did not display this problem. An Excel sheet for exploring all combinations is attached in the supplementary material as well as our GitHub repository.
While we encourage that such designs utilize interpolation, our work is aimed specifically at interdigitated sensors. Our work points towards periodic interactions between the ratio of touch size and strip width and that of digit lengths (Figures  7  and 8 ). Our data and the analysis by Donneaud et al. [6]  suggest that this is a topic that deserves further exploration, for example by disrupting the periodic interactions through varying strip-widths. We created and share a prototype tool for testing electrode patterns which could be used to explore this. Additional work is however required to validate this tool using the dataset we provide. Fisher and Naidu  [7]  suggest reducing error by modelling the periodic effects. This would also be relevant to eTextile pressure matrices. All peak estimation methods tested by us assumed an underlying behavior of the signal distribution.
This work has been carried out within the FHU EPINEXT, with the support of the A * MIDEX project (ANR-11-IDEX-0001-02), funded by the "Investissements d'Avenir" French Government program managed by the French National Research Agency (ANR). Cyberonics and Livanova provided some financial and material support to Dr Carron for participation in scientific meetings. Dr Carron has received honoraria from Livanova for sharing his expertise in VNS surgery. The other authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
Altogether, our data show that the absence of AMPK in Caco-2 cells induces a delay in the reassembly of tight junctions during the calcium switch assay. Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 5 of 16 (Figure  3A ). However, in AMPK dKO cells, the development of TEER was delayed upon readdition of calcium (Figure  3A ), suggesting a role of AMPK in the process of tight junctions reassembly. To further investigate the role of AMPK in the establishment of intestinal barrier integrity, we measured paracellular permeability in WT and AMPK dKO Caco-2 cell monolayers after the calcium switch assay. We observed that AMPK dKO cells displayed a 4-fold increased paracellular flux to 0. 4 kDa FITC-conjugated sulfonic acid compared to WT cells, suggesting major defaults in intestinal barrier integrity associated with the delay in TEER development (Figure  3B ).
Pharmacological AMPK Activation Enhances the Reassembly of Tight Junctions We next investigated the effect of AMPK activation on the reassembly of tight junctions by treating Caco-2 cells with the pan-AMPK activator 991 upon readdition of calcium during the calcium switch assay. To confirm the effect of 991 on AMPK activation, we monitored the phosphorylation of AMPK and ACC during the experiment. Western blot data showed increased levels of AMPK and ACC phosphorylation in WT cells in response to 991 treatment in the absence of calcium (Supplementary Figure  S3A ) and during the calcium switch experiment at 1 h and 30 min, 3 h and 6 h; the effect of 991 was lost in AMPK dKO cells (Figure  5A ). In the presence of 991, the establishment of TEER was significantly increased at 3 h and 6 h in WT cells whereas no effect was observed in AMPK dKO cells (Figure  5B ).
Thus, pharmacological activation could ensure a better recovery of intestinal integrity after disruption of barrier function. calcium switch assay. To confirm the effect of 991 on AMPK activation, we monitored the phosphorylation of AMPK and ACC during the experiment. Western blot data showed increased levels of AMPK and ACC phosphorylation in WT cells in response to 991 treatment in the absence of calcium (Supplementary Figure  S3A ) and during the calcium switch experiment at 1 h and 30 min, 3 h and 6 h; the effect of 991 was lost in AMPK dKO cells (Figure  5A ). In the presence of 991, the establishment of TEER was significantly increased at 3 h and 6 h in WT cells whereas no effect was observed in AMPK dKO cells (Figure  5B ). In accordance with the values of TEER, the paracellular permeability measured with the 0.
Electron Microscopy Cells on filters were fixed with 3% glutaraldehyde in phosphate buffer 0. 2 M (pH 7. 2) and were then embedded in epoxy resin. Samples were examined with a transmission electron microscope (JEOL 1011, JEOL Europe SAS, Croissy-sur-Seine, France) at the Cochin Institute electron microscopy facility using a GATAN eslangshen camera and the Digital Micrograph software (Gatan Microscopy Suite, Gatan Inc. , Pleasanton, CA, USA).
Statistical Analysis Results are expressed as means ± SD. Groups were compared in unpaired two-tailed Student's t-tests, one-way ANOVA with Dunnett's post hoc test for longitudinal and one single time point data comparisons or two-way ANOVA with Bonferroni's post hoc test for multiple comparisons as appropriate with GraphPad Prism 5. 0 (GraphPad Software Inc. , San Diego, CA, USA). Differences between groups were considered statistically significant if p < 0. 05. Supplementary Materials: Supplementary materials can be found at http://www. mdpi. com/1422-0067/20/20/ 5171/s1.
Contributions: B. V. and S. O. designed and performed experiments, interpreted data, and wrote the manuscript. A. G. and J. T. designed and cloned sgRNA to produce recombinant lentivirus. B. V. , S. O. and J. L. generated and characterized AMPK-deficient Caco-2 cells. M. F. assisted the generation and characterization of AMPK-deficient Caco-2 cells. B. V. , S. O. , M. F. and J. T edited the manuscript. Funding: This research was funded by INSERM, CNRS, Université Paris Descartes and SFD (Société Francophone du Diabète, allocation de recherche SFD-Industrie 2016 -Pierre Fabre Médicament). Séverine Olivier holds a doctoral fellowship from the Région Ile-de-France (CORDDIM).
Acknowledgments: The authors thank the cellular imaging, electron microscopy and cytometry and immunobiology facilities at  Institut Cochin  for their support. We thank  François Jouret ,  Pascal Rowart  and  Laurence Poma  ( University of Liège, Liège, Belgium ) for their help in ZO-1 quantification and  Grahame Hardie  ( University of Dundee, Dundee, UK ) for generously providing AMPKα1 and AMPKα2 antibodies. We also thank  Kei Sakamoto  and  Robin Willows  ( Nestlé Research, Lausanne, Switzerland ) for tentative analysis of cingulin phosphorylation.
Author Contributions: B. V. and S. O. designed and performed experiments, interpreted data, and wrote the manuscript. A. G. and J. T. designed and cloned sgRNA to produce recombinant lentivirus. B. V. , S. O. and J. L. generated and characterized AMPK-deficient Caco-2 cells. M. F. assisted the generation and characterization of AMPK-deficient Caco-2 cells. B. V. , S. O. , M. F. and J. T edited the manuscript. Funding: This research was funded by INSERM, CNRS, Université Paris Descartes and SFD (Société Francophone du Diabète, allocation de recherche SFD-Industrie 2016 -Pierre Fabre Médicament). Séverine Olivier holds a doctoral fellowship from the Région Ile-de-France (CORDDIM).
The intracellular solution contained in mM: K-Gluconate 150, NaCl 10, K-Hepes 10, Mg-ATP 3, Na-GTP 0. 3, EGTA 0. 05 (305 mOsm, pH 7. 3). A liquid junction potential of +13 mV was corrected for. All electrophysiological measurements were performed with a HEKA EPC10 amplifier (HEKA Elektronik, Lambrecht/Pfalz, Germany) under control of the Patchmaster software. All measurements were performed at 34-37°C.
Current clamp recordings in GCs Action potentials were evoked in current-clamp mode by current pulses (amplitude 20-400 pA, duration 300 ms). To determine the input resistance, subthreshold current pulses were applied from -20 to + 20 pA in 2 pA steps. The resistance of the solution-filled patch-pipettes was between 6-12 MW and the mean series resistance was not compensated during the measurement. Data were sampled at 200 kHz.
Outside-out recordings in GCs To reliably clamp potassium currents from the soma of GCs (Figure  2 ), potassium currents were measured in outside-out patches pulled from the soma of inner and outer GCs by applying 10 ms voltage steps from -90 to +60 mV with 10 mV increments at an intersweep interval of 1 s. The intersweep holding potential was -90 mV. Data were sampled at 100 kHz.
Dynamic Clamp of MF conductance in GCs In order to analyze the response of GCs on in-vivo like MF inputs, we used a dynamic clamp implemented with the microcontroller Teensy 3. 6 (https://www. pjrc. com) as described by Desai et al. (Desai et al. , 2017). The Teensy was programmed using the Arduino integrated development environment with the code provided by  Desai et al (2017)  and modified for our need as described in the following. The time course of MF conductance was  "#$%  =  ()*  -. /( -   Jost et al. , 2014). Facilitation was implemented as an increase in the release probability according to p r = p r + 0. 2*(1-p r ) and decaying back to p r0 with a time constant of 12 ms  (Saviane and Silver, 2006). Depression was implemented according to a recovery process with a time constant of 25 ms, which approximates a biexponential recovery process of 12 ms and 2 s  (Hallermann et al.
Data analysis Data were analyzed using custom-written procedures in Igor Pro software (WaveMetrics, Oregon, USA). Intrinsic properties of GCs were determined from the injected currents that elicited the largest number of action potentials. The action potential threshold was defined as the membrane voltage at which the first derivative exceeded 100 V s -1 , the minimal action potential peak was set as -20 mV and the minimal amplitude to 20 mV. All action potentials with a half-width smaller than 50 µs and larger than 500 µs were excluded. Action potential frequency and half-width were calculated from the first three action potentials. Membrane capacitance, resting membrane potential and series resistance were read from the amplifier software (HEKA) after achieving the whole-cell configuration.
Input resistance (R in ) was analyzed from alternating subthreshold current injections from -20 to 20 pA (2 pA steps). The voltage was plotted against injected current and a spline interpolation was performed to obtain the slope at the holding membrane potential (0 pA current injection). Peak-current from outside-out patches was determined from voltage steps (-90 to +60 mV) with Fitmaster software (HEKA). Steady-state inactivation was determined from the last 2 ms of the respective sweep. Cells were only included if 50 pA < I max < 1 nA to exclude potential whole-cell measurements and membrane-vesicles. EPSP measurements from PCs and EPSC measurements from GCs were analyzed with the Fitmaster software (HEKA). For PC EPSPs, 20-80% rise time and time to peak were determined from the average of 30 individual single EPSPs.
Neuronal networking modeling The neuronal network consisted of varying numbers of MF inputs, GCs and one PC and was implemented in Matlab (The MathWorks, Inc, Natick, Massachusetts, R2017a). For each simulation, a random set of MF inputs was generated. This input was then fed to a layer of integrate-and-fire GCs. A modeled PC then used the output of these GCs for trial-to-trial learning. The PFto-PC synaptic weights were optimized with the aim to make the PC spiking sequence similar to the target sequence. In the following, each component of the model is explained in detail.
Target sequence and van Rossum measure Based on in vivo firing patterns  (Witter and De Zeeuw, 2015a) , an arbitrary target firing sequence of 80, 40, and 120 Hz for 300, 100, and 100 ms, respectively, was chosen. The distance between the PC and the target spiking sequence was quantified with the  van Rossum error (van Rossum, 2001). Both spiking sequences were convolved with an exponential kernel with a decay time constant of 30 ms (or values ranging from 2 to 300 ms in (Figure  8 -figure  supplement  1A,B). The van Rossum error was defined as the integral of the square of the difference between these two convolved traces. We also tested another algorithm to calculate the van Rossum error  (Houghton and Kreuz, 2012) , which C++ code was taken from http://pymuvr. readthedocs. io/ and incorporated into Matlab via the MEX function and results were comparable.
Learning and minimization algorithm For each random set of MF inputs, the GC to PC synaptic weights were changed according to the following algorithm with the aim to minimize the van Rossum error between the PC spiking sequence and the target sequence. The initial values of the synaptic weights were 1, and values were allowed to change between 0 and 100. First, an algorithm was used that was based on supervised learning  (Raymond and Medina, 2018)  to punish the GCs that have spikes that precede unwanted PC spikes. Subsequently, an unbiased optimization of the GC to PC synaptic weight was performed using the patternsearch() algorithm of Matlab to minimize the van Rossum error. To increase the chance that a global (and not local) minimum was found, the minimization of the routine was repeated several times with random starting values.
Other optimization routines such as a simplex [(fminsearch() of Matlab) or a genetic algorithm (ga() of Matlab)] revealed similar results. To exclude the possibility that the differences in the minimal van Rossum error between models with and without gradients were due to a bias in our learning algorithm, we performed a set of simulations with networks consisting of less than 100 GCs, in which we skipped the learning algorithm and only used unbiased minimization algorithm. This resulted in similar difference in the minimal van Rossum error between models with and without gradients, indicating that the learning algorithm was not biased towards one type of model. For networks with consisting of more than 100 GCs the pre-learning was required to facilitate the finding of the global minimum.
Live imaging and data analysis Live imaging was performed with an Olympus IX81 microscope equipped with a spinning disk (CSU-X1 5000 rpm, Yokogawa) and Okolab environmental chamber maintained at 37 C. Image were acquired with a 20X objective by EMCCD camera (iXon3 DU-885, Andor technology). Usually for spinal cord culture, 15-30 planes spaced of 0,5-3mm were imaged for each spinal cord at 30-minute interval for 10 hours approximatively. To reduce exposure time and laser intensity, acquisitions were done using binning 2x2. Images were acquired using IQ3 software using multi-position and Z stack protocols. Z stack projections of the movies were analyzed in ImageJ software. The analysis of pHLuo-flashes was performed from time-lapse acquisitions in vivo. For cartography representation, the lengths of PRE-crossing and POST-crossing compartment were normalized on FP length.
STED imaging and data analysis The staining was observed with a STED microscope (TCS SP8, Leica). STED illumination of ATTO 647N was performed using a 633-nm pulsed laser providing excitation, and a pulsed bi-photon laser (Mai Tai; Spectra-Physics) turned to 765 nm and going through a 100-m optical fiber to enlarge pulse width (100ps) used for depletion. A doughnut-shaped laser beam was achieved through two lambda plates. Fluorescence light between 650 and 740 nm was collected using a photomultiplier, using a HCX PL-APO CS 100/1. 40 NA oil objective and a pinhole open to one time the Airy disk (60mm). Images were acquired with using Leica microsystem software and a Z stack protocol. Usually 10-20 planes spaced of 0,5mm where imaged for each growth cone. The growth cone perimeter was outlined basing on the mb-tomato signal.
Average density of pHLuo-receptors in the growth cones was calculated from Z stack projections with MATLAB software. Particle numbers and surfaces were calculated from Z stack projections with ImageJ. Fluorescence Recovery After Photobleaching FRAP experiments were performed on spinal cord open-books electroporated with either pHLuo-Robo1-ires-mb-tomato or pHLuo-PlxnA1 and mb-tomato using a Leica DMI6000 (Leica Microsystems, Wetzlar, Germany) equipped with a confocal Scanner Unit CSU-X1 (Yokogawa Electric Corporation, Tokyo, Japan) and a scanner FRAP system, ILAS (Roper Scientific, Evry, France). Images were acquired in both green and red channels using a 63X objective and an Evolve EMCCD camera (Photometrics, Tucson, USA). Growth cones located in the FP were first monitored for 9 s each 3 s and then bleached using a 488nm diode laser at full power.
ACKNOWLEDGMENTS We thank  A. Chedotal  for sharing the Robo mouse model,  E. Derrington  for input on the manuscript, and  O. Raineteau  for scientific discussions. STED and FRAP microscopy were performed at the  Bordeaux Imaging Center (BIC) ,  CNRS-INSERM-Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We thank  C. Poujol  and  M. Mondin  ( BIC ) for advice,  M. Sainlos  for sharing the anti-GFP nanobody, and  B. Tessier  and  S. Benquet  for technical assistance. This work was conducted within the frame of the  LabEx CORTEX and DEVWECAN of Universitede Lyon , within the  program ''Investissements d'Avenir''  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The study was supported by an  ANR  funding to V.
1 Neurobasal Thermo Fisher Scientific 21103049 B27 Thermo Fisher Scientific 17504-044 PLL Sigma-Aldrich P1399 Laminin Sigma-Aldrich L2020 Recombinant Mouse Netrin-1 R&D 1109-N1-025 Methyl cellulose Sigma-Aldrich M7140 Critical Commercial Assays JetPrime reagent PolyPlus -Ozyme Pol114-15 Deposited Data Raw and analyzed data This paper https://data. mendeley. com/datasets/ fwcr3csbff/draft?a=93767d36-5553-48a3- a3ef-c5ff63637b29 Experimental Models: Cell Lines COS-7 cells ATCC ATCC Cat# CRL-1651, RRID:CVCL_0224 Experimental Models: Organisms/Strains Embryonated eggs, naked neck strain E ´levage avicole du Grand Buisson, N/A Saint Maurice sur Dargoire, France Robo and Robo2 double KO, C57 Black 6 mice Alain Chedotal lab. Long et al. , N/A Delloye-Bourgeois et al. , 2015 PlxnA1 KO Mice, OF1 mice Yoshida et al.
We also performed selective PS deprivation on a rat for 6 hours by activating the vibrating motor fixed on the device when PS was automatically detected via a custom online sleep scoring algorithm  (Libourel et al. , 2015). We compared the effect of the deprivation with published data obtained with gentle handling and automated deprivation methods. Finally, to demonstrate the feasibility of recording in the wild, we recorded a pigeon in a large aviary with other birds.
ONEIROS was plugged into the animal's implant and baseline recordings started after 2 days' habituation to the device and the new environment. Signals were collected using DaqReverse, a custom Matlab (Mathworks, matlab r2016b) program, and were sampled at 256 Hz except for the temperature and accelerometer which were sampled at 64 Hz. Vigilance states were scored using SlipAnalysis a custom Matlab program with a 5-s sliding time frame window according to the following criteria: Active wake (AW) was characterized by desynchronized and irregular lowvoltage and high-frequency (5-9 Hz) EEG activity, sustained EMG neck muscle tone, and movement detected by the accelerometer. EEG activity was similar during AW and Quiet wake (QW), but QW was differentiated from AW by the absence of movement. Slow-wave sleep (SWS) was characterized by high-voltage slow-waves  (1.