dataesr/scientific-paragraphs-categorization huggingface dataset:
Incomplete trials did not count as completed trials. Mice went through consecutive light training days until they reached the success criteria. During failed trials, the experimenter punished the mouse by holding it by the tail suspended in the air for ten seconds before placing it back into the refuge. Following light training, mice then underwent five consecutive days of dark training. The same protocol as the light training applied to dark training. Mice were required to achieve a minimum of eight successful trials out of ten total complete trials within a session in any of the five days. All mice reported in the dataset achieved success criteria. Four mice that did not pass the training criteria were excluded from the analysis. These mice included two young nTG mice, one young APP mouse, and one aged APP mouse.
Following dark training, mice then underwent five consecutive days of light and dark testing. On days 1, 3 and 5, five light trials were conducted, followed by five dark trials. Incomplete trials counted as trials. This was repeated until the mouse achieved ten complete trials in each of the light and dark conditions. On days 2 and 4, the same protocol was applied, but the mouse started with five dark trials, followed by five light trials. Analysis of path integration behavior. All path integration behavioral data were recorded at 30 frames per second. The positional coordinates of the mice for each trial were obtained using an open-source deep-learning tracker algorithm called DeepLabCut  57. DeepLabCut was only used to quantify positional data in the path integration task and not for electrophysiological recordings.
Custom Matlab scripts were used to analyze various behavioral parameters from the mice's positional data. Genotyping. Tail samples were collected at weaning for genotyping, and just prior to brain perfusion for additional confirmation. DNA samples were extracted and amplified using the REDExtract-N-Amp™ Tissue PCR Kit (MilliporeSigma, XNAT-100RXN) and the primer sequence and PCR protocol provided by The Jackson Laboratory (MMRRC, 34836-JAX). Genotyping results were visualized using a QIAxcel instrument (Qiagen). Immunofluorescence. Mice were anesthetized with Isoflurane (Baxter, FDG9623) and intracardially perfused with 0. 05% heparin (Sandoz, 10750) in ice-cold saline followed up cold and filtered 4% paraformaldehyde that was freshly made from powder (MilliporeSigma, 158127-500 g). Extracted brains were cryopreserved in 30% sucrose (MilliporeSigma, S0389-1Kg), flash frozen in 2-methylbutane (Fisher Scientific, 03551-4), and kept at -80 °C until sliced on a cryostat (Leica, CM3050-S).
25 µCi/ml final dilution) for 2 hr at 4°. Sections were then washed in PBS, rapidly rinsed in water, dried, and exposed to x-ray films (Biomax MR, Kodak) for 5 days. Standard radioactive microscales were exposed to each film to ensure that labeling densities were in the linear range. Densitometry measurements were performed with MCID analysis software 7. 1 (InterFocus, Ltd) on sections for each region per mouse (4 mice per experimental group for a total of 16 mice). VGLUT1, VGLUT3, VAChT and VGAT are vesicular transporters that mediate neurotransmission from the presynaptic side. VGLUT1 is necessary for the vesicular accumulation of glutamate and is a general marker for the glutamatergic drive in synapses. In the context of grid cells, the excitatory drive is a prerequisite for grid cell generation  28.
Acknowledgements We graciously thank  S. Kim ,  Z. Ante ,  K. Harandian ,  Q. He ,  A. Ismailova ,  D. Patel ,  A. Zhen , and  A. Milette-Gagnon  for their assistance in experiments. We also thank  J. Poirer ,  M. Hasselmo ,  J. Hinman ,  S. Villeneuve ,  S. Williams ,  R. Rozeske ,  J. Lee ,  J. Robinson , and  E. Vachon-Presseau  for comments on earlier versions of this manuscript and to all members of the  Brandon laboratory  for helpful discussions. This work was funded by  CIHR  Project Grants # 367017  and # 377074 , an  NSERC   Discovery Grant  # 74105 , a  Scottish Rite Charitable Foundation  Grant, a  Canada Fund for Innovation Grant , and a  Canada Research Chairs  award to M. P. B. J. Y. is supported by a  Doctoral Training Grant  from the  Fonds de recherche du Québec , and previously by a  Master's Training Grant  from the  Fonds de recherche du Québec  and a  CIHR Master's Training Fellowship.
The model simulates the global 3D solar wind structure from the solar corona to 1 AU driven by the output from the coronal WSA model (the WSA-Enlil is available for runs on request at the website of the Community Coordinated Modeling Center, https://ccmc. gsfc. nasa. gov/). Despite some quantitative differences, the WSA-Enlil results are consistent with the SW latitudinal structure seen in the SWAN data at the solar maxima. However, physical reasons for formation of the two maxima in middle latitudes remain unknown. Koutroumpa et al. (2019)  used the same dataset of Lyman-α observations from SOHO/SWAN during 1996-2018 to calculate heliolatitudinal profiles of the SW velocity. To derive the velocity profiles, they assumed that either the dynamic pressure or the energy flux (both calculated using the OMNI data) is constant over heliolatitude.
Both methods showed similar results (up to at least ±60 • ). The profiles are consistent with annual averages of the solar wind velocity profiles from interplanetary scintillation observations provided by  Tokumaru (2013) , and also with in situ measurements by  Ulysses (McComas et al. 2013). Therefore, the SW latitudinal structure derived from SWAN is in agreement with the assumptions of the dynamic pressure and energy flux invariance. However, during solar maxima, the differences are more pronounced. A possible explanation is that SWAN data indicate global heliospheric conditions, while OMNI and Ulysses data provide in situ observations of the local SW conditions. Strumik et al. (2020)  used a database of International Ultraviolet Explorer (IUE) spectral ultraviolet (UV) observations of astrophysical point sources and SWAN maps to estimate the distribution of non-Lyman-α extraheliospheric emission in the sky.
The results of simulations in the frame of the state-of-art 3D timedependent kinetic model of the H distribution, which is based on the global heliospheric model by  Izmodenov and Alexashov (2015) , were compared with the SWAN data of 1996. Katushkina et al. (2015a)  concluded that deflection of the interstellar hydrogen flow in the heliosphere is potentially a very powerful tool to estimate the IsMF, but in doing this the considered effects need to be taken into account. Koutroumpa et al. (2017)  suggested a method to determine the longitude of the interstellar H flow, which is model-independent and based on the parallax effect provided by the SWAN motion around the Sun. Even without the use of the H cell data,  Koutroumpa et al. (2017)  have shown that for the 20 years covered by the SWAN dataset, the longitude of the interstellar hydrogen flow vector varies insignificantly from its average value of 252.
The issues and details on calibration and inter-calibration were reported by  Quémerais et al. (2013b). There are ways to improve and verify existing calibration using numerical models. For instance, a comparison of long-term observations with results of model calculations of Lyman-α intensities allows estimation of the sensitivity degradation of a particular instrument. This approach was applied to the SOHO/SWAN 1996-2018 dataset. A preliminary result shows that the sensitivity of the SWAN sensor units degrades at a rate of ∼ 2% per year. This should be taken into account in the future data analysis. The second factor is connected with the fact that in most studies, a hot model and its modifications are applied to obtain the reference number density. These models assume that parameters of interstellar hydrogen are uniform at large heliocentric distances (∼ 70-80 AU) and velocity distribution is Maxwellian or bi-Maxwellian.
(2000)  and  Quémerais and Bertaux (2002) , Lyman-α intensity sky maps can be utilized to forecast the solar activity up to two weeks in advance (a half the solar rotation period). Additionally, by applying an inversion technique to the maps, location and strength of the active regions on the far side of the Sun can be estimated. Future developments and automation of inversion algorithms will provide an important contribution to a wide range of tools used for space weather prediction. The following lists investigations with spectrometric Lyman-α observations (line profiles): 1. A measurement of the deflection of the interstellar hydrogen flow in the heliosphere is potentially a powerful tool to constrain a direction of the magnetic field. Lallement et al. (2005 Lallement et al. ( , 2010) )  performed analysis of the SOHO/SWAN data with an H cell using a hot model of interstellar hydrogen with a single Maxwellian boundary condition.
Also, tensorbased techniques do not require orthogonality or mutual inde- pendence between the sources, enjoying essential uniqueness under mild constraints, and can perform BSS from rather short signal records, on a beat-to-beat basis. The BTD tensor model is flexible, as it can accommodate sources with different characteristics, i. e. , with diferent number of poles (or degrees, in the case of rational functions). Two commonly used methods for computing this decomposition are the nonlinear least squares (NLS) method implemented in the Tensorlab MATLAB Toolbox  [12]  and the alternating least squares method with enhanced line search (ALS-ELS)  [13]. However, their performance depends considerably on their initialization and model structure, i. e. , the rank of the matrix factors and the number of blocks of the tensor.
The VA estimated by the Löwner-BTD is then subtracted from the ECG, resulting in a signal that contains mainly AA. Another advantage of the proposed method is that no technique for atrial source selection is needed, since the resulting signal is already the desired signal. The main contributions of the paper can be summarized as follows: • We put forward a new tensor-based method for noninvasive AA estimation in the challenging scenarios of low amplitude f-waves or short R-R intervals, typical of persistent AF episodes. This work is a significant extension of the preliminary results reported in the conference paper  [16]. Indeed, while the NLS method is used to compute the Löwner-BTD and tested in a reduced database in  [16] , we provide herein an improved method for Löwner-BTD computation and statistically more significant results by doubling the size of the AF patient database.
The rest of this paper is organized as follows. Section II presents the notation and some basic definitions of tensor algebra. Section III recalls BTD as a tensor approach to solve BSS problems and how to build a tensor from an ECG data matrix. Section IV describes AGL for computing BTD, while Section V formulates its variant with Löwner constraints and derives the linear projection onto the Löwner subspace. A numerical evaluation of the proposed approach is shown in Section VI with synthetic signals. Section VII presents the database setup and results of experiments with real AF data. Section VIII discusses the advantages and limitations of the present work and, finally, Section IX concludes the paper and provides some prospects of future research.
VII. EXPERIMENTAL RESULTS IN REAL AF ECGS In order to validate the proposed tensor approach, experimental results using real AF ECG recordings, comparing LCAGL with the Löwner-BTD computed using the NLS method, and with CAGL imposing Hankel constraints, are reported and discussed in this section. First, the indices used to measure AA quality and the observed database are summarized. Then, the experimental results from the two challenging scenarios of this arrhythmia, which were previously described in Section I, are shown and discussed.
B. AF Database and Experimental Setup Real ECG data from 20 patients suffering from persistent AF are used in this study. All the recordings belong to a database provided by the Cardiology Department of Princess Grace Hospital Center, Monaco. The recordings are acquired The 10 segments with disorganized and/or weak AA, from patients P1 to P10 are composed of one heartbeat, i. e. , the QRS complex followed by the T wave and the visible f waves, and have between 0. 8 and 1. 7 seconds. The other 10 segments with short R-R intervals from patients P11 to P20 have 1. 5 seconds of duration with at least 2 QRS complexes. All the 20 segments are downsampled by a factor of 10, since this significantly reduces the computational complexity without any noticeable loss of quality, as the signals' energy is concentrated in lower frequencies and no spectral aliasing is generated  [14].
The NLS method used for comparison in the experiments is available in Tensorlab MATLAB Toolbox and is set up as in  [16]. As this method is often very sensitive to initialization, 10 Monte Carlo runs are performed for each segment and the best one is chosen. For CAGL and LCAGL, no Monte Carlo runs are needed and the value of γ between 8×10 -4 and 10 -2 In Figure  6 , we can see the observed recording, the estimated VA and AA of the segment of Patient P2 processed by LCAGL. The segments are shown now on lead V1 for a better clarity of the estimated AA, in the time domain on the left and in the frequency domain on the right, with a DF equal to 6. 67 Hz, typical of AA. It can be seen that the SC provided by LCAGL is 58. 73% and P (r) value equal to 2. 02 × 10 -4 mV 2 , while the ones provided using the Löwner-BTD computed by the NLS method, choosing the best match (R, L r ), are 5.
ACKNOWLEDGMENT  P. M. R. de Oliveira  was funded by a PhD scholarship from the  IT Doctoral School (ED STIC)  of  Université Côte d'Azur. J. H. de M. Goulart 's work was partially supported by the  MIAI chair "LargeData" of Université Grenoble Alpes. V. Zarzoso holds the 3IA chair "IAblation" of  Université Côte d'Azur.
Here we show that the Shaker channel gene AtKC1 (Arabidopsis thaliana K + channel 1) (accession number AT4G32650) contributes to these mechanisms. Shaker channels, which dominate the plasma membrane conductance to K + in most cell types, are encoded by a family of nine members in Arabidopsis  (Véry et al. , 2014). These channels are sensitive to voltage and activated by either membrane depolarization for K + efflux (outwardly rectifying channels), or membrane hyperpolarization allowing K + influx (inwardly rectifying channels). They are tetrameric proteins, and the four subunits that assemble to form a functional protein can be encoded by the same Shaker gene (giving rise to a homotetrameric channel) or by different Shaker genes (heterotetrameric channel)  (Daram et al. , 1997; Urbach et al.
Patch-clamp recordings were carried out in parallel experiments (alternating measurements on WT and mutant plants grown simultaneously) to compare the electrical properties of pavement cell protoplasts from WT and atkc1-2 plants. Among 28 protoplasts from WT pavement cells, 10 (ca. 36%) belonged to the first category, i. e. , displaying a Shaker-type time-dependent activation of inward currents (as shown in Figure  3F ). In agreement with the data shown by Figure  3B  and E , the I-V curve obtained from these 10 protoplasts displays a low level of rectification (Figure  3H , black symbols). In the second category of protoplasts, i. e. characterized by the absence of an inward current component displaying a time-dependent activation (18 protoplasts out of the 28 ones), at least three patterns of current traces could be identified (Figure  S3A-C ).
5 with KOH (final K + concentration assayed by flame spectrophotometry: ca. 140 mM). Under these conditions, the pipette resistance was about 18 MΩ. Seals with resistance > 1 GΩ were used for electrophysiological analyses. The bath solution contained, except when otherwise mentioned, 100 mM potassium glutamate, 0. 1 mM CaCl 2 , 10 mM HEPES, the osmolarity being adjusted to 520 mOsm with D-mannitol and the pH to 7. 5 with KOH (final K + concentration: 105 mM, assayed by flame spectrophotometry). Whole-cell recordings were obtained using an Axon Instruments Axopatch 200B amplifier. pCLAMP 8. 2 software (Axon Instruments, Foster City, CA) was used for voltage pulse stimulation, online data acquisition, and data analysis. The voltage protocol consisted of stepping the membrane potential from -40 mV (holding potential) to +80 mV or -205 mV, or from + 25 mV (holding potential) to either +130 mV or -140 mV, in 15 mV steps.
ACKNOWLEDGEMENTS We thank  Isabell Kufner  for providing ProOCT3:GUS lines. This work was supported by the  Alfonso Martin Escudero Foundation  (to M. N. -C. ), the  Marie Curie Programme  (grant no. 272390 FP7-IEF  to M. N. -C. ), and the  European Research Council  (PhyMorph # 307387  to A. B. ).
The energy calibration for the XES detector was conducted by fitting the Kβ 1,3 fluorescence line including the valence-to-core signal from a Fe foil to a reference Fe emission line  [41]. The dispersion of the setup on the detector plane is 300 meV per pixel with a FWHM of 5 eV for the Kβ main peak of the measured Fe foil. XRD measurements were performed using a second ePix100 detector downstream of the DAC, providing a scattering angle (2θ ) of 11. 8 • to 30 • or a momentum transfer (Q) of 1. 4 Å -1 to 3. 4 Å -1. To prevent saturation during exposure with several trains, the detector sensor was covered with a Cu foil of 20 μm thickness. The angular range of the detector was calibrated using a CeO 2 standard and the data was analyzed using DIOPTAS  [42]. Each run lasted for roughly 3000 trains during which the XES and XRD detectors collected at 10 Hz repetition rate.
XRD imaging Spatial XRD mapping was performed at beamline P02. 2 at PETRA III to identify phases in the quenched sample still contained in the DAC at 51 GPa. The XRD map had a step size of 2 × 2 μm. The incident beam with a photon energy of 42. 7 keV was focused to 2 × 2 μm (VxH, FWHM) using Kirkpatrick Baez mirrors  [43]. The grid size was 31 × 31 steps. Each step had an exposure time of 30 seconds while scanning over an ±5 • rotation around the vertical axis perpendicular to the x-ray propagation. The signal was collected with a 2D Perkin Elmer detector, which was calibrated using a CeO 2 standard. The maps were analyzed with the X-ray Diffractive Imaging software  [44].
C. Temperature simulation Using finite-element analysis provided by the simulation program COMSOL MULTIPHYSICS  [11] , we calculated the sample's temperature response to the absorption of the x-ray pulses during a single x-ray pulse-train at 2. 2 MHz and 455 kHz, assuming the x-ray beam hits the sample in its center. We suppose a pulse duration of 30 fs and a Gaussianshaped beam with a radius of 30 μm FWHM. The pulse energy was set to 15 μJ for 2. 2 MHz and 5 μJ for 455 kHz to reach realistic temperatures at the time of probing by the subsequent x-ray pulse. Thermodynamic properties of the compressed FeCO 3 and the diamond anvils assumed in the model are given in SM Table  II. [9]  and a combination of 44% HS spectrum and 56% LS spectrum, resembling the run at highest used pulse energy. The difference between each spectrum and the LS spectrum is shown below.
C. Discussion on the reaction path Our results imply very complex phase relations in the FeCO 3 system during multiple heating cycles up melting temperatures followed by quenching. Since only pulse train averaged information from the in-situ XRD and XES measurements is available, a full understanding of all in-situ reactions is difficult. Ex situ and postmortem analyses have helped us to understand the processes retrospectively and have shone light on the highly complex nature of the FeCO 3 system at extreme conditions. Our results, both from in situ and ex situ analysis, suggest that, at pulse energies of 51. 8 ± 1. 9 μJ at 2. 2 MHz, the following reactions occur during heating (T+) and quenching (T-): HS -FeCO 3 -→ T+ Fe 4 C 3 O 12 -→ T-Fe 4 C 3 O 12. Our data support a HS state of the hot and temperature quenched Fe 4 C 3 O 12 as reported by Albers et al.
ACKNOWLEDGMENTS We acknowledge European XFEL in Schenefeld, Germany, for provision of XFEL beamtime and would like to thank the staff for their assistance. We also acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities. Parts of this research were carried out at PETRA III and we would like to thank  Hans-Peter Liermann ,  Rachel Husband , and  Konstantin Glazyrin  for their assistance in using P02. 2  ECB  and  Hlynur Gretarsson  and  Martin Sundermann  for their support at the P01 beamline. We are grateful to the  EuXFEL DAC Community  and the  HIBEF User consortium  for the fruitful discussions. The use of equipment in the Potsdam Imaging and  Spectral Analysis Facility (PISA)  is gratefully acknowledged. The authors acknowledge financial support by the  Federal Ministry of Education and Research of Germany  (Project No.
| Electrophysiology Whole-cell patch-clamp recordings were performed at room temperature (20-22°C). Pipettes were made from borosilicate glass (Clark Electromedical Instruments, Reading, England) and pulled on a P-87 puller (Sutter Instrument Company, Novato, CA, U. S. A. ). Patch electrodes had a resistance of 2. 5-3. 5 MΩ when filled with internal physiological solution (see below). Membrane currents were recorded using an Axopatch200B patch-clamp amplifier (Axon Instruments, Union City, CA, U. S. A. ), and they were filtered through an integrated 8-pole lowpass Bessel filter at 2 kHz. The filtered signals were digitized by a 12-bit A/D converter (Digidata 1200B, Axon Instruments. ) and were stored using pCLAMP 8 software (Axon Instruments). Data were acquired at a sample rate of 10 kHz.
Recordings were analyzed using Origin 7 software (OriginLab Corp. , Northampton, MA, U. S. A. ), taking advantage of the built-in regression functions. Routines for manipulating data were developed by G. O. using the integrated "Labtalk" programming language. The standard external and internal solutions had the following compositions (in mM): 140 NaCl, 3 KCl, 2 CaCl 2 , 1 MgCl 2 , 10 HEPES, pH = 7. 4 (external solution) and 110 KCl, 10 NaCl, 2 MgCl 2 , 2 EGTA, 10 HEPES, pH = 7. 2 (internal solution). The internal solution was filtered through 0. 2 µm Millex filters (Millipore, Saint-Quentin en Yvelines, France). The osmotic pressures of the internal and external solutions were 280-290 mOsm and 300-305 mOsm, respectively, as measured with a freezing-point osmometer (Knauer, Berlin, Germany).
For both Cs + and Ba 2+ , and in contrast with fractional outward currents, fractional inward currents were dependent on the K + concentrations. For each K + gradient, the fractional currents expressed as a function of the blocker concentration (see one example in Figure  4c  for Cs + and Figure  5c  for Ba 2+ ) were extracted from the mean fractional currents plotted against the potential in Figures  4b  and 5b. The dissociation constant K D was then determined as a function of the membrane potential, by fitting these data points with Equation (1). Best fits were obtained using a Hill coefficient of 1 for Cs + and 1. 3 for Ba 2+ , and gave for each K + concentration gradient (distinguished by grey scale) the apparent K D values plotted with circle symbols in Figure  4e  for Cs + and Figure  5e  for Ba 2+.
, 5, Downloaded from https://physoc. onlinelibrary. wiley. com/doi/10. 14814/phy2. 15200 by Portail BibCNRS INSB, Wiley Online Library on [22/11/2022]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons Licensecurrents extracted from the averaged data shown in Figure2c,d have been plotted against the blocker concentration (Figure2e,f, respectively). The dissociation constant K D was then determined as a function of the membrane potential, by fitting these data points with Equation (1) (Langmuir-Hill equation, see Methods), derived from the mass action law:.