dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study is supported by a  Swedish Science Foundation  grant to K. B. ( Dnr2018-04695 ) and a  Swiss National Science Foundation Ambizione  grant to M. J. (No. PZ00P2_174101 ). We acknowledge  ICOS Sweden  and  SITES  for the provisioning of facilities, experimental support, and data. ICOS Sweden  and SITES are funded by the  Swedish Research Council  as a national research infrastructure. We are grateful to  Richard Bindler  for his support with field equipment. We would like to thank  Pernilla Löfvenius ,  Jacob Smeds ,  Xiangwen Zhang ,  Per Marklund ,  Rowan Messmer ,  Johan Westin ,  Paul Smith ,  Jutta Holst ,  Lamia Atouil ,  Matéo Wodiczko ,  Myriam Bupto , and  Matthias Peichl  for assistance in the field and/or data collection. A special thanks to  Julie Brochet  and  Eloi Mathis  for their great help with > 2000L peat water collection during the summer of 2021.
X-Ray Observations and Data Reduction We report the log of observations used in this work in Table  A1. We performed the data reduction using the HEASOFT 21  package (v. 6. 29c; Nasa High Energy Astrophysics Science Archive Research Center 2014). All uncertainties in the text are reported at 1σ confidence level, unless otherwise specified. Throughout this work, we adopt a distance of 4. 8 kpc (Karuppusamy et al. 2020).
2, 9. 5, 16. 4, and 19. 6 ks for the four observations ordered chronologically. No pileup was detected. We selected the source photon counts from a circle of 30″ radius, while the background level was estimated from a circle of 100″ radius on the same CCD away from the source. For the diffuse emission, we extracted the spectrum by selecting source photon counts from an annulus of radii 50″-110″ centered on the source and used the same background region as adopted for the point-like source (more details in sub-Section 3. 1). We focused this study on the EPIC-pn data but checked that MOS data gave consistent results.
Swift Swift/XRT  (Burrows et al. 2005 ) extensively monitored Swift J1818 since the outburst onset until the end of 2021 October. The observations were carried out either in photon counting (PC; time resolution of 2. 51 s) mode or in windowed timing (WT; time resolution of 1. 779 ms) mode. Data were reprocessed using standard prescriptions and software packages such as XRTPIPLINE. 23 Source counts were accumulated within a circular region of a radius of 20 pixels (1 XRT pixel corresponds to 2 36), while the background photons were extracted from an annulus with radii of 100-150 pixels and from a 20 pixel radius circle for PC-mode and WT-mode observations, respectively.
The ancillary response files for the diffuse emission spectra were generated using the ARFGEN tool with the extendedsource parameter set to yes, while the redistribution matrix files were created via the RMFGEN script. To study the spectral behavior of the diffuse component, we performed a simultaneous fit of the source and diffuse emission spectra obtained from the four EPIC-pn observations. The former were described by an absorbed two-blackbody model, while for the latter we adopted a single absorbed blackbody. The hydrogen column density N H was quantified with the TBABS model with the abundance of the interstellar medium taken from  Wilms et al. (2000)  and the photoionization crosssectional model from  Verner et al. (1996). In the fits, the N H was forced to be the same among all the data sets.
Spectral Analysis We used the XMM-Newton and NuSTAR data to study the X-ray emission of Swift J1818 from soft to hard X-rays. For the XMM-Newton/EPIC-pn observations, we grouped the X-ray spectra using the SPECGROUP tool to have a minimum bin size of 100 counts per bin. For the NuSTAR/FPMA observations, the spectra were grouped with GRPPHA to have a minimum bin size of 50 counts per bin. The spectral fitting was performed using XSPEC (v12. 11. 1; Arnaud 1996). To model the spectra, we selected the 3-13 keV energy range for all NuSTAR spectra, except for the first spectrum (Obs. ID 80402308002), which was modeled in the 3-20 keV energy range. We restricted the energy range of the EPIC-pn spectra to 1-10 keV due to the domination of the background below 1 keV for this highly absorbed source.
24 ± 0. 02) × 10 23 cm -2. We found that the model fits the data with c n 2 = 1. 41 for 607 dof (see Figure  3 ). We note that the power-law component is required only for the spectrum at the outburst peak, i. e. , the 2020 March 15 epoch; thus we did not include this component in the model for the remaining spectra. We obtained a power-law photon index of Γ = 1. 0 ± 0. 6, which is consistent with the result reported by Esposito et al. (2020), Γ = 0. 0 ± 1. 3. In Table  1 , we list the best-fit parameters for the blackbody temperatures kT BB and radii R BB , as well as the observed F X. obs and unabsorbed F X. unabs fluxes estimated in the 0. 3-10 keV energy interval. To supplement the XMM-Newton and NuSTAR observations, we initiated a Swift/XRT monitoring campaign of Swift J1818. These pointings were used to sample the flux and spectral evolution of the magnetar over a longer time span.
Burst Search The sky region of Swift J1818. 0-1607 has been extensively observed every year by the INTEGRAL satellite, starting from 2003 March. We have carried out a search for bursts from Swift J1818 using the data of IBIS/ISGRI, a coded mask imaging instrument with angular resolution of ∼12′ and field of view of 29°× 29°  (Ubertini et al. 2003). We selected all the public data with Swift J1818 in the field of view obtained until 2021 April. After the removal of time intervals with high and variable backgrounds, this amounted to an exposure time of about 43 Ms. Most of the considered data (41 Ms) were obtained before the discovery outburst. The burst search was done with the procedure described by  Mereghetti et al. (2021). Briefly, this consists of a first screening of the light curves binned at eight logarithmically spaced timescales from 0.
01 s to 1. 28 s to select excesses with respect to the locally measured background count rate. The search is carried out in the nominal 20-100 keV, 30-100 keV, and 30-200 keV energy ranges. In this first step, a threshold corresponding to ∼0. 001 false positives per science window and timescale is adopted (INTEGRAL data are divided into science windows of a few ks duration). All these excesses are then examined through an imaging analysis in order to reject the events caused by instrumental background or by bright sources located outside the field of view. This procedure led to the (re)discovery of several bursts from other sources in the field of view, most of which originated from the magnetar SGR 1806-20  (Götz et al. 2006 ), located at about 5°from Swift J1818. However, no significant bursts were found from Swift J1818, with a 3σ upper limit on the 20-200 keV fluence of about 10 -8 erg cm -2.
We also performed a burst search on XMM-Newton and NuSTAR data, using the method described by Borghese et al. (2020; see also, e. g. ,  Gavriil et al. 2004 ). We built the sourcebarycentered light curves with time resolutions of 1/16, 1/32, and 1/64 s. We tagged as bursts the bins with a probability <10 -4 (NN trials ) -1 , where N is the total number of time bins in a given light curve and N trials corresponds to the number of timing resolutions used in the search. No bursts were detected in the XMM-Newton/EPIC-pn light curves, while we list the epochs of the bursts found in the NuSTAR data sets in Table  B1. Due to the low photon statistics, we were unable to model the corresponding spectra.
Timing Analysis For the timing analysis of Swift J1818, we used the XMM-Newton and NuSTAR data sets as shown in Table  A1. For NuSTAR, we applied the clock corrections with up-to-date clock files and combined the FPMA and FPMB events files for each observation. For both XMM-Newton and NuSTAR data sets, we referred the photon arrival times to the solar system barycenter, adopting the source coordinates by Esposito et al. (2020), i. e. , R. A. = 18 h 18 m 00 16, decl. = - ¢  16 07 53. 2 (J2000. 0), and the JPL planetary ephemeris DE200. In an attempt to derive a phase-coherent timing solution, we first used a provisional ephemeris to assign the rotational phase of each photon in the data sets through an unbinned maximum likelihood method  (Livingstone et al. 2009; Ray et al. 2011 ). This was done with the PHOTONPHASE tool of the PINT 24  pulsar timing package  (Luo et al.
2021 ). To account for the different energy bandpasses, we created two separate template profiles for the XMM-Newton and NuSTAR observations by folding the strongest detection in their respective data set. Twocomponent Gaussian models were fitted to the binned profiles to construct smoothed standard templates. Times of arrival (TOAs) and associated errors were then computed for a number of subintegrations from the predicted photon-phase information, using the smoothed profile templates to define the fiducial point in the pulse phase. Using the TEMPO timing software (Nice et al. 2015), we then tried to obtain a coherent timing solution that simultaneously fits the XMM-Newton and NuSTAR TOAs. Similar to the postoutburst spin evolution seen in other magnetars (see the review by Kaspi & Beloborodov 2017), we also observed significant variability in the spin-down behavior of Swift J1818, particularly a large jump in spin frequency between MJDs 58972 and 59030.
273 9 ( ) × 10 -11 Hz 2. However, this simple model  poorly fits the data (c n 2 of 25. 7), and because of the large time gaps between our observations, we cannot determine whether the large residuals are caused by an unmodeled timing anomaly (glitch) or timing noise. We also note that we attempted to fold the XMM-Newton and NuSTAR data using the timing ephemerides provided by Considering our limited and sparse data set, as well as the poorly understood impact of magnetospheric processes on spin behavior associated with magnetar outbursts, we do not attempt to further model, quantify, and/or interpret the timing properties of Swift J1818 in the XMM-Newton and NuSTAR data. To study possible changes in the shape and amplitude of the X-ray pulse profile with photon energy, we extracted energyresolved pulse profiles from the EPIC-pn data sets in three energy bands: 0.
Radio Observations We observed Swift J1818 with the VLA under project code 21A-111 with the aim to detect the radio counterpart of Swift J1818, as well as the presence of any diffuse radio emission around the source. The VLA observation was performed on 2021 March 22 (MJD 59295) with the telescope on source between 14:58 and 15:38 UT. The data were carried out in the S band at the central frequency of 3 GHz and a total bandwidth of 2 GHz (comprised of sixteen 128 MHz sub-bands made up of sixty-four 2 MHz channels). 3C 286 was used for the bandpass and flux calibration, while the nearby (6°. 5 away) source J1822-0938 was used for phase calibration. Raw data were flagged for radio frequency interference, calibrated, and imaged following standard procedures with the Common Astronomy Software Application CASA (v.
Timing Analysis We attempted to derive a phase-connected timing solution from the XMM-Newton and NuSTAR data set, which covered a 7 month period from 2020 March to October. However, the sparsity of the observations led to phase ambiguity and prevented us from identifying the correct timing model for the source. During the monitoring campaign, the spin-down rate  n of the source fluctuates but is overall increasing with time. Despite being poorly constrained, the spin evolution we observe is consistent with the timing results obtained by  Rajwade et al. (2022)  from the radio monitoring of the source after 2020 July although we note a more rapid slowdown over.
, a pulsar wind nebulae component). Recent preliminary indication of a proper motion detected by VLBA  (Ding et al. 2022 ) hints to a motion of the source in the northwest direction, which would be toward the higher end of the observed radio-ringlike structure. Notes. a The instrumental setup is indicated in parentheses: PC = photon counting, WT = windowed timing, LW = large window, and FF = full frame. b The count rate is in the 0. 3-10 keV energy range, except for NuSTAR (3-10 keV). c Observations with the same superscripts were merged for the spectral analysis. d Data collected with FPMB were not included in the analysis as they are heavily affected by stray light contamination.
Quantification of in situ RNA transcripts. The number of individual RNA signals was quantified using the automated particle analysis plug-in for ImageJ Fiji software, version 2. 1 (NIH). For human KCNQ3 quantification, 11 MS samples were screened for demyelination using MOG IHC. Seven NAGM and 20 DMGM upper cortical layer (L1-3) and 20 NAGM and 14 DMGM deep cortical layer (L4-6) areas were selected. For 6 control samples, 19 upper layer and 18 deep layer areas were selected. Three to 4 regions of interest (ROIs) were quantified for each area, and the mean value per condition and layer was assessed to determine the average KCNQ3 expression per sample. Human and mouse snRNA-Seq data acquisition for bioinformatics analyses. Expression matrices and associated metadata were obtained from the UCSC Cell Browser (https://cells.
ucsc. edu/?ds=ms) for the Schirmer et al. data set (12) (PRJNA726991) and the NCBI's Gene Expression Omnibus (GEO) database for the Tasic et al. data  (42)  (GEO GSE115746). For analysis of the Schirmer et al. data set  (12) , the expression matrix was normalized using Seurat (version 3. 2. 3) SCTransform (70) with default parameters. Principal component analysis (PCA) was done using Seurat RunPCA() calculating top 50 principal components (PCs). Fifteen PCs were taken as input for Seurat RunUMAP(), FindNeighbors(), and Find-Clusters(). Finally, a subset of neurons and samples originating from the London Brain Bank (Supplemental Table  5 ) were renormalized using the same procedure and included 14 PCs for clustering. The Tasic et al. data set  (42)  was reanalyzed in the same way. Since barcode annotation discrepancies were found between matrix and metadata provided by the authors, only the barcodes present in both the matrix and metadata were included (Supplemental Table  10 ).
FitMaster (HEKA Elektronik) and PEAK Software were used for the analysis (Meuth IT Consulting). Voltage-clamp recordings. Variations in current amplitude were measured in a bath solution (see above) containing 2 μM mibefradil, 1 μM nifedipine, 30 μM ZD7288, and 0. 5 μM tetrodotoxin. Membrane outward currents were elicited at a holding potential of -65 mV, followed by a depolarizing step to -45 mV. Thereafter, neurons were repolarized to -60 mV. The duration of each pulse was 4 seconds. Current amplitudes were analyzed at the furthermost 500 ms of the depolarizing voltage step of -45 mV under control conditions and following RTG application (30 μM) using FitMaster software (HEKA Elektronik). Stereotactic surgeries. For craniotomy surgery and electrode implantation, anesthesia was administered and maintained  (8).
Recordings were performed at 24°C ± 1°C. In all experiments, 4-AP (MilliporeSigma; 100 μM) and RTG (Alomone; 0. 3 μM, 1 μM, 3 μM) were bath-applied. A CMOS camera (Thorlabs) was used to image in green channel (100 ms pulses, LED 488 nm) every 2 seconds. In the patch-clamp experiments, cells were approached and patched under DIC with 3 MΩ pipettes using a Narishige PC-10 puller. Neurons were maintained at -70 mV holding potentials using an Axoclamp 700B amplifier controlled by Clampex11 software (Molecular Devices). Series resistance varied between 8 and 10 MΩ. iENs in the recording chamber were continuously perfused with oxygenated (95% O 2 / 5% CO 2 ) bath solution containing 125 mM NaCl, 2. 5 mM KCl, 1 mM MgCl 2 , 2 mM CaCl 2 , 25 mM glucose, 1. 25 mM NaH-2 PO 4 , 0. 4 mM ascorbic acid, 3 mM myoinositol, 2 mM Na-pyruvate, and 25 mM NaHCO 3 (pH 7.
4, 315 mOsm). Synaptic currents were recorded with an internal solution that contained 140 mM Cs-methanesulfonate, 0. 5 mM EGTA, 1 mM MgCl 2 , 10 mM HEPES, 2 mM ATP-magnesium, 0. 4 mM GTP-sodium, and 10 mM Na-phospho-creatine, (pH 7. 2, 310 mOsm). Intrinsic and action potential properties were recorded using a pipette solution containing 125 mM KMeSO 3 , 20 mM KCl, 2 mM MgCl 2 , 0. 5 mM EGTA, 4 mM MgATP, 0. 3 mM NaGTP, 10 mM Na-phosphocreatine, and 10 mM HEPES-KOH (pH adjusted to 7. 3, 315 mOsm). Current-clamping membrane potentials were kept at approximately -70 mV using small (<30 pA) holding currents, and step currents were injected to elicit APs. Analysis and plotting of imaging data were performed using FluoroSNNAP (https://www. seas. upenn. edu/~molneuro/FluoroS-NNAP/user_guide. pdf) and custom R macros (https://github.
Single-unit analysis. Firing rates and z scores of sorted neurons were analyzed relative to baseline activity in 1 second segments (1 bin), 60 seconds before and during the presentation of an auditory stimulus, with a custom MATLAB interface (MathWorks). Firing rates of individual units were z scored to their mean baseline activity. Neurons were considered responsive if 1 or more bins showed a z score of 1. 96 or higher (P = 0. 05) in response to stimulus presentation  (9, 46). EAE induction and clinical scoring. EAE was induced in female C57BL/6J mice (The Jackson Laboratory) at 8-14 weeks of age as previously described  (74). Mice were immunized s. c. with MOG 35-55 peptide (100 μg, Anaspec, UCSF; 200 μg, Peptides & Elephants, UKE; 200 μg, Charité, UKM) in complete Freund's adjuvant (CFA) containing nonviable Mycobacterium tuberculosis (1 mg/mL, UKE; 2 mg/mL, UCSF; 5 mg/ mL, UKM; Difco).
Control mice were injected i. p. daily with the same volume of 0. 9% SAL. Data and material availability. Human snRNA-Seq data were obtained from the UCSC Cell Browser (https://cells. ucsc. edu/?ds=ms; accession number PRJNA726991). Mouse snRNA-Seq data were obtained from the NCBI's Gene Expression Omnibus (GEO) database (GEO GSE115746). Statistics. All results are presented as the mean ± SEM. Statistical analysis was performed using parametric (unpaired t test comparing 2 groups; 1-way or 2-way ANOVA comparing 3 or more groups) or nonparametric (Kruskal-Wallis test comparing 3 groups) tests. Tests were performed using 2-tailed analysis unless stated otherwise. The significance cutoff was set at a P value of less than 0. 05. Statistical analyses were performed using GraphPad Prism 9. 0 (GraphPad Software).
Acknowledgments We thank the  UK MS Society Tissue Bank  and the  Netherlands Brain Bank  for providing human postmortem retina, ON, and cortex tissue samples from patients with MS and control donors. We thank  Heidrun Potschka  ( Munich University ) and  Holger Lerche  and  Stephan Lauxmann  (both from  Tübingen University ) for advice and comments on the study. We thank  Matthew Smith  ( Johns Hopkins University ) for technical assistance. This work was supported by the  Swiss National Science Foundation (Eccellenza Professorship , to AKP); the  Propatient Foundation  (to AKP); the  Goldschmidt-Jacobson Foundation  (to AKP); intramural funding from the  University of Basel  (to AKP); the  Medical Faculty Mannheim of Heidelberg University  (to LS and ME); research grants from the  Hertie Foundation  (medMS MyLab,  P1180016 , to LS; medMS fellowship, to HK); the  UCSF Program for Breakthrough Biomedical Research  (to ACH); the  Adelson Medical Research Foundation  (to DHR and KAN); the  Cambridge Biomedical Research Center  (to DHR); the  National Institute of Neurological Disorders and Stroke (NINDS) ,  NIH  ( NS040511 , to DHR); the  Wellcome Trust  (to DHR); the  European Research Council (ERC)  ( ERC AdG  " AxoGLIA " and " MyeliNANO ," to KAN;  ERC AdG "Myel-IN-Crisis ", to DHR;  ERC StG  " DecOmPress ," to LS); the  National Multiple Sclerosis Society  ( FG-20102-A-1 , to ACH;  FG-1708-28871 , to AKP;  FG-1902-33617  and  RFA-2203-39300 , to LS); the  Italian Multiple Sclerosis Society  ( 2013/B/4 , to CC);  Fonds de recherche du Québec -Santé  ( 270746 , to MG); the  German Multiple Sclerosis Society.
11 (Wavemetrics) (https://github. com/AcunaLabUHD/Physiology-Macros/blob/main/ synaptic%20transmission_v4. 3. ipf; commit ID: 8f13ac5) or Clampfit11 software (Molecular Devices). Data derived from different batches were normalized to the control condition. Tissue preparation for electrophysiological experiments. Naive and MOG 35-55 -immunized C57BL/6J and Kcnq3-KO female mice (8-12 weeks old) were anesthetized and decapitated. Brains were removed and dis- sected in ice-cold slicing solution containing 2. 5 mM KCl, 1. 25 mM NaH- 2 PO 4.
The bulk has an identical morphology, as do the other samples. It should be noted that these extreme compositions showed some porosity, which is no longer visible in the intermediate samples. Figure  4c  presents the micrograph of the 75GIG_BTO sample, showing a clear phase separation, with the mixture of the two types of grains distinct both in shape and size. The smaller ones are associated with the barium-containing phase and the larger grains with the gadolinium-containing phase. ImageJ software was used to analyze the scanning electron microscopy data.
Cardiac dynamics have been a focus of image analysis, and their statistical models are used in a wide range of applications: generating synthetic datasets, derivation of specific biomarkers of pathologies, or atlas-based motion estimation. Current representations of dynamics, mainly based on displacements, often overlook the physiological basis of cardiac contraction. We propose to use local strain as a more accurate representation, and demonstrate this on 3D echocardiography surface meshes of the right ventricle. Our methodology, based on a differential geometry algorithm, deforms the surface mesh according to a pre-imposed strain field. This approach allows for a clearer disentanglement between cardiac geometry and dynamics, better differentiating deformation changes than those due to changes in cardiac morphology.
Data To illustrate the methodology, we used patient-specific triangular surface meshes of the RV obtained from a commercial software (4D RV-Function 2. 0, TomTec Imaging Systems GmbH, Germany) for segmentation and tracking along 3D echocardiographic sequences. The meshes of different individuals are in approximate point-to-point correspondence thanks to the model-based segmentation. We tested the methodology on the data from two male controls. They were selected from a small control cohort for being the subjects with the largest global longitudinal and circumferential strain, respectively. We focused on the meshes at end-systole (ES) and ED.
Acknowledgements The authors acknowledge the support from the  European Union -NextGenerationEU, Ministry of Universities and Recovery ,  Transformation and Resilience Plan , through a call from  Pompeu Fabra University (Barcelona) , and the  French ANR  ( LABEX PRIMES   of Univ. Lyon [ ANR-11-LABX-0063 ] and the  JCJC  project " MIC-MAC " [ ANR-19-CE45-0005 ]). They are also grateful to  E. Saloux  and  A. Hodzic  ( CHU Caen, France ) for providing the imaging data related to the studied population.
Many datasets are collected automatically, and are thus easily contaminated by outliers. In order to overcome this issue there was recently a regain of interest in robust estimation. However, most robust estimation methods are designed for specific models. In regression, methods have been notably developed for estimating the regression coefficients in generalized linear models, while some other approaches have been proposed e. g. for robust inference in beta regression or in sample selection models. In this paper, we propose Maximum Mean Discrepancy optimization as a universal framework for robust regression. We prove non-asymptotic error bounds, showing that our estimators are robust to Huber-type contamination. We also provide a (stochastic) gradient algorithm for computing these estimators, whose implementation requires only to be able to sample from the model and to compute the gradient of its log-likelihood function.