dataesr/scientific-paragraphs-categorization huggingface dataset:
5 3. 30EϪ05 is-cDNA 33 Helicase conserved C-terminal domain, DEAD/DEAH box PF00271. 26, PF00270. 24 4. 6EϪ7, 1EϪ4 helicase Present in PNW VGIIc is-cDNA 34 Fungus-specific transcription factor domain PF04082. 13 2. 50EϪ11 is-cDNA 35 Lyase, adenylosuccinate lyase C terminus PF00206. 15, PF10397. 4 1. 5EϪ47, 2. 6EϪ18 is-cDNA 36 Sugar (and other) transporter PF00083. 19 5. 40EϪ80 is-cDNA 37 No significant matches is-cDNA 38 No significant matches is-cDNA 39 Protein of unknown function PF11175. 3 1. 40EϪ80 is-cDNA 40 Sugar (and other) transporter PF00083. 19 8. 30EϪ63 is-cDNA 41 Protein of unknown function PF11175. 3 6. 70EϪ83 (Continued on following page).
In silico tagging and assessment protocol A primary goal of this study was to assess the potential of using 3D tagged MRI in parameter estimation applications. Even though 3D tagged MRI offers a rich dataset for parametrization, the process may be compromised by low-resolution or noisy data and error introduced during the motion-tracking Fig. 1  Workflow followed for the study of practical identifiability using 3D tags. The in silico testing protocol is presented in blue, while in red is the pipeline followed in the in vivo case procedure. In order to investigate this issue, we have created synthetic 3D tagged images directly from simulation results. Within this controlled environment, the actual parameters of the heart model are known, allowing for an assessment of the error between actual and estimated parameters.
3D tagged MRI/in silico tagged motion extraction Myocardial motion was extracted from 3D tagged and in-silico tagged images (step "MOTION TRACKING" in Fig. 1 ) using the Image Registration Toolkit. 1 This software uses a nonrigid registration technique proposed by  Rueckert et al. (1999) ;  Schnabel et al. (2001)  and subsequently extended to tracking of cardiac motion  (Chandrashekara et al. 2004; Shi et al. 2012). The registration algorithm which is based on freeform deformations and optimizing the similarity between two subsequent images allows tracking any point within the myocardium throughout the cardiac cycle and provides the deformation field with respect to the beginning of systole. The obtained deformation fields were then applied on an initial mesh and propagated through time, resulting in a set of deformed meshes, which were used as the "observations" within our parameter estimation process.
In vivo J characterization The study was then extended to an in vivo case of a healthy volunteer, to allow for the assessment of practical identifiability and model fidelity in a real-world setting. The data used in this study were acquired from a healthy 28-year old male volunteer with a normal heart function. The geometry and cardiac motion were characterized using cine MRI images in retrospective ECG gating (short-axis view, acquired spatial resolution 2 × 2 × 8 mm, temporal resolution ∼20 ms), and 3D tagged MRI images in prospective ECG triggering (acquired in 3 breath-holds and reconstructed to spatial resolution of 1 × 1 × 1 mm, temporal resolution ∼30 ms). The myocardial motion was extracted from the 3D tagged MRI images using the algorithm described in Sect. 2. 1. 3. The cubic Lagrange hexahedral mesh used for the simulations was based on the end-systolic frame of the data  (Wang et al.
Comparison of practical identifiability using 3D tags For each of the considered constitutive laws, the behavior of J over the parameter space is examined and the error between actual and estimated parameters is quantified. For each law we select a ground truth set of parameters which gives physiologically reasonable end-diastolic volume and pressure and generate synthetic 3D tags from an LV inflation simulation. The extracted myocardial motion applied on meshes is then used as our data and compared with simulations with varying parameter combinations to provide the landscape of the objective function and assess the error in the parameter estimates. Through this process we are able to characterize the practical identifiability of each law and assess its potential use in patient-specific applications.
All tests under consideration were implemented in CHeart-a multi-physics software tool based on (Nordsletten 2009;  Nordsletten et al. 2010 ) and expanded by the CHeart team at KCL. The problems were solved on a Dell OPTIPLEX 990, quad-core (Intel Core TM i7-2600 CPU @ 3. 40 GHz), on a quad-core (Intel 4th Generation Core TM i7-4790 CPU @ 3. 60 GHz) and on an 2. 1 GHz AMD Opteron TM Interlagos 32 processor.
J characterization of the Neo-Hookean law We begin by investigating the practical identifiability of the Neo-Hookean law, by examining the behavior of J over a range of stiffness values. Due to the simple structure of the law and its linear parameter dependence, we expect that given some deformation, we should be able to get good identifiability characteristics. Specifically, we expect J to have a unique and distinct minimum, and we anticipate that the incorporation of unbiased noise should not affect the behavior of J and the estimated parameter, but only cause a shift toward larger J values. Indeed, as illustrated in Fig. 4a , the objective function J has a unique and distinct minimum at the initial stiffness value (μ = 10 kPa). Further, the actual stiffness value is retrieved even in the case of noisy data (5 and 20 % Gaussian noise), and the overall behavior of J remains the same, with just a small shift toward bigger values.
If non-discerning, proving that unique parameter identification is achievable becomes challenging as it then must rely on the observations not exposing non-uniqueness of the objective function. Nevertheless, the chosen J uses an L 2 norm on the displacements, which is generally considered a robust criterion and should be able to provide adequate information and accurately describe the identifiability characteristics of each law. Ten diastolic frames were used as observations in the in silico test. This choice was based on the number of diastolic frames in the available 3D tagged MRI data (e. g. , where the cavity volume is increasing). However, the number of diastolic image frames used in other studies is variable with authors considering all or part of diastole. This variability is due to assumptions on residual active tension, the presence of which is confirmed by decreasing cavity pressures even after the opening of the mitral valve  (Pasipoularides et al.
Acknowledgments Authors would like to acknowledge funding from the BHF New Horizons program (NH/11/5/29058) and partially by Engineering and Physical Sciences Research Council (EP/H046410/1). The Image Registration Toolkit (IRTK) was used under Licence from Ixico Ltd. In addition, the authors are thankful to P. Moireau and D. Chapelle (Inria, France) for providing HeartLab software library used for creating the synthetic MR images. We also acknowledge the support from the Wellcome Trust-EPSRC Centre of Excellence in Medical Engineering (WT 088641/Z/09/Z) and the NIHR Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust and KCL. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the DoH.
Slices were stained with DAPI (Sigma-Aldrich; dilution 1:10,000) to identify the nuclei. After washing, slices were air dried and mounted with Mowiol (polyvinyl alcohol; Sigma-Aldrich) and visualized on an Axiophot 2 microscope (Zeiss). Mutagenesis. Site-directed mutagenesis was performed to engineer the mutation into the human Na V 1. 1 channel (GenBank sequence NM_006920. 4 subcloned into the pCDM8 vector;  Cestele et al. , 2008)  using QuikChange (Agilent Technologies; primers are available upon request). The mutant cDNA was fully resequenced before use in experiments to confirm the introduced mutation and exclude any additional sequence alterations. The human Na ϩ channel subunits h␤ 1 and h␤ 2 in the pCLH vector were provided by GlaxoSmithKline. We exchanged the Hygromycin coding region in the vector with the sequence coding for either enhanced GFP (EGFP) or CD8 marker genes to obtain pCLH-h␤1-EGFP and pCLH-h␤2-CD8  (Liao et al.
After allowing the neurons to settle down for 5 h in a 37°C incubator with 5% CO 2 supply, the solution was replaced with Neurobasal culture medium (Invitrogen) supplemented with B27 (Invitrogen), glutamine, and penicillin/streptomycin. Since homozygous animals with EGFP-labeled interneurons were not viable, we could use only Scn1a RH/ϩ animals with EGFP-labeled interneurons for our Na ϩ current analysis. Electrophysiological recordings. Whole-cell patch-clamp recordings of inhibitory and/or excitatory neurons within thalamus, cortex, or hippocampus were performed at 34°C using Axopatch 200B, Multiclamp 700B (Molecular Devices), or BVC 700A (Dagan) amplifiers; a DigiData 1420 digitizer (Molecular Devices); and pClamp 10. 3 software (Molecular Devices). Slices were positioned in a submerged-type recording chamber (Luigs & Neumann or Warner Instruments), continuously superfused with ACSF, and visualized with an Axioskop 2FS (Zeiss) or an Eclipse FN1 (Nikon) microscope.
2% biocytin, or Lucifer yellow. Membrane voltage was clamped to Ϫ70 mV, and IPSCs were recorded over 5 min epochs. For current and voltage-clamp experiments, the sampling rate was 100 kHz and the data were low-pass filtered at 30 kHz. Series resistance (Ͻ20 M⍀) was monitored during the experiment. Cells showing unstable series resistance or resting membrane potential were discarded. Nucleated patch recordings were performed using an Axopatch 200B amplifier (in primary cortical cultures) and a multiclamp 700B amplifier (in brain slices), a DigiData 1420 digitizer, and pClamp 10 data acquisition software (Molecular Devices), as has been described previously  (Liao et al. , 2010; Lauxmann et al. , 2013). For whole-cell patch-clamp recordings of tsA201 cells, an Axopatch 200B amplifier, a DigiData 1320A digitizer, and pCLAMP 8 data acquisition software were used (Molecular Devices), and cells were visualized with an inverted microscope (Axio Vert.
Electrical stimulations were applied with bipolar electrodes (FHC Inc. ), a SIU91A isolator (Cygnus Technologies), pCLAMP software, and a DigiData 1420 digitizer, as in  Liautard et al. (2013). Sampling rate was 10 kHz and data were low-pass filtered at 4 kHz. Recording electrodes were positioned in the S1 cortex [layer IV (LIV) and layer V (LV)] and in the thalamus [nRt and ventrobasal part of the thalamus (VB)]. Paired-pulse stimulations of the VB, at the intensity eliciting the maximal LIV response, consisted of the application of two pulse stimuli with increasing interpulse interval (IPI); analysis was performed on the monosynaptic response  (Agmon and Connors, 1991). For coastline analysis, the coastline index  (Korn et al. , 1987)  was the length of the line describing the monosynaptic responses, which was calculated by applying Pythagoras' theorem to each pair of data points, computed with Origin version 8.
5 software (Origin-Lab) as follows: Coastline index ϭ i ͱ͑ x i Ϫ x iϩ1 ͒ 2 ϩ ͑ y i Ϫ y iϩ1 ͒ 2 with x i and y i being the time (in milliseconds) and amplitude (in millivolts), respectively, of each data point ( i ). The paired-pulse ratio was calculated by dividing the amplitude, or the coastline index, of the second monosynaptic response in the pair by the amplitude, or the coastline index, of the first one. For displaying highfrequency oscillation (HFO) traces, data were bandpass filtered off-line at 40 -200 and 200 -600 Hz with pClamp Bessel eight-pole filters. For analysis of HFOs, we also subtracted the contribution of the noise to the filtered traces: for both 40 -200 and 200 -600 Hz filters, we computed the absolute area of the oscillations with Origin version 8. 5 software and subtracted the absolute area of an eventless period of the same duration immediately preceding the activity under analysis.
Electrodes had an input impedance of 30 -50 k⍀ according to the specifications of the manufacturer (Multi Channel Systems). Signals from all 60 electrodes were simultaneously sampled at 25 kHz, visualized, and stored using the standard software MC_Rack provided by Multi Channel Systems. Spike detection was performed off-line by the SPANNER software suite (RESULT Medical; see also  Illes et al. , 2009). Synchronous network activity was analyzed by population burst (PB) detection using custom-built Matlab software. Spikes from all electrodes were aggregated in nonoverlapping 5 ms bins and smoothed by a Gaussian kernel with a 100 ms SD to obtain the population firing rate (PFR;  Schock et al. , 2012). PB detection was performed in a three-step procedure. First, PB candidate intervals were identified whenever the PFR exceeded the slowly varying 1 s firing rate average.
5ϫ tube lens (InfiniTube FM-100, Infinity Photo-Optical Company). Images were recorded using a 1 megapixel electron multiplying charge-coupled device camera (iXon EMϩ, Andor). ROIs were detected using a Gauss-filtering-and contour-filteringbased cell detection algorithm written in MatLab  (Allene et al. , 2008). Ca 2ϩ traces were recorded as changes in mean pixel intensity in each ROI over 20 min. Ca 2ϩ events were detected using a custom-made detection algorithm written in MatLab. Background was subtracted and photo bleaching was removed by dividing a double exponential function. Traces were high-pass filtered by subtracting the low-pass-filtered signal using a Butterworth low-pass filter. The signal over threshold was depicted as a possible event and confirmed if the unfiltered event showed an exponential decay with a time constant 500 Ϯ 200 ms  (Grewe et al.
, 2010). The maximum of the second derivative of the Ca 2ϩ event was chosen as the time point for the underlying cell activity  (Henze et al. , 2000). Readout data were processed using Origin (version 6. 1, OriginLab). Data and statistical analysis. Traces were displayed off-line with the Clampfit software pClamp version 10. 0 (Molecular Devices). Graphics were generated using Excel (Microsoft) and Origin (version 6. 1 or 8. 5), statistics were performed using SigmaStat 3. 1 (STATCON). All data were tested for normal distribution. For statistical evaluation, the following tests were used as indicated in the respective figure legends or in text: (1) Mann-Whitney rank-sum test for unpaired datasets of WT and Scn1a RH/ϩ animals; (2) Wilcoxon signed-rank nonparametric test for paired datasets (e.
g. , before and after the application of drugs); (3) ANOVA on ranks (Kruskal-Wallis test) with Dunn's post hoc test for comparing more than two groups; or (4) t test or one-way ANOVA when two datasets of unpaired groups were normally distributed. All data are shown as the means Ϯ SEM. Box-and-whisker plots show medians (lines), means (square), lower and upper quartiles, minimums, and maximums. Data are shown as the mean Ϯ SEM; n gives the number of cells or activities. For all statistical tests, significance with respect to the control is indicated on the figures using the following symbols: *p Ͻ 0. 05, **p Ͻ 0. 01, and ***p Ͻ 0. 001.
This work was supported by the  Deutsche Forschungsgemeinschaft  (Grants  Le1030/10-1/2  to H. L. and  SFB 1089  to H. B. ), the  Bundesministerium fu ¨r Bildung und Forschung (National Genome Research Network/Epilepsy  and  Migraine  integrated network Grants  01GS08123  and  01GS08122  to H. L. and H. B. ;  EuroTransBio/ESSENCE  Grant  FKZ0315641A  to M. D. , S. T. , and J. S. ), the  European Commission  (Grant EPICURE,  LSHM-CT-2006-037315  to H. L. , M. M. , and H. B. ),  Laboratory of Excellence Ion Channel Science and Therapeutics  (to M. M. ), and the  Fondation Recherche Medicale  (to M. M. ). Research for this publication was supported by the  National Institute of Neurological Disorders and Stroke of the National Institutes of Health  under Grant  R01-NS072221  (to A. E. ). We thank  Drs.
Disease resistance gene mapping, conservation and evolutionary patterns To identify the largest set of plant R-genes, three complementary methods (see Methods section) were combined (illustrated as Additional file 1: Figure  S1 ) consisting in  (1)  the detection of PFAM  [64]  domains, (2) the exploitation of public genome annotations, and (3) the use of the Plant Resistance Gene Database, PRGdb (http://prgdb. cbm. fvg. it/index. php, [65] ). The integration of the three previous approaches allowed to construct a non-redundant set of putative R-genes in the plant species considered in this study (Table  1 , Additional file 1: Figure  S2  and Additional file 2: Dataset S1). Based on the genome annotation approach, 2697 R-genes were identified in monocots, and 3021 in eudicots, corresponding to a total set of 5718 plant R-genes (Table  1 ).
Regarding the PFAM domain identification procedure, 8013 sequences in monocots and 14996 in eudicot species were identified, corresponding to 23009 putative R-genes sequences in total (Table  1 ). Finally, the PRGdb aligned on the 13 genome sequences investigated unraveled 1874 sequences in monocots and 2001 sequences in eudicot species. These three complementary methods lead us to deliver the most complete and non-redundant list in angiosperms consisting in 23641 R-genes sequences, 7883 for the monocots and 15758 for the eudicots (cf Table  1  and Additional file 2: Dataset S1). The identified R-genes families deriving from the annotation, PFAM and PRGdb approaches are detailed in Table  1  and Additional file 1: Figure  S2 , showing that the total number of non-redundant R-genes in plants is close to the dataset obtained with the PFAM detection approach, suggesting that such method is the most appropriate in delivering the largest and most complete set of R-genes in any genomic sequence of interest.
The conservation of R-genes between genomes was then investigated using rice and grape as reference genomes respectively for monocots and eudicots as they represent the most closely related modern genome structures of the reconstructed plant ancestral karyotypes  [66]. The 2637 rice R-gene sequences were aligned against the three other monocot species available, i. e. sorghum, Brachypodium and maize. Similarly, the 1078 grape R-genes sequences were aligned against the eight eudicot species investigated (Table  1 , 'Evolutionary Data' column). 413 orthologous R-genes were identified in sorghum corresponding to 24. 1% of the reference dataset (i. e. rice). Similarly, 319 orthologous R-genes were found in maize (17. 1% of the reference) and 495 in Brachypodium (29. 8% of the reference). This result suggests that R-genes may appear as more conserved than the total protein-coding genes, with respectively 24.
41E-04, 2. 70E-05, 9. 47E-07 in comparison to rice Brachypodium, sorghum, and maize; illustrated as blue star in Figure  1C bottom ). According to the reconstructed five protochromosomes, dating back to ~50-70 mya before the speciation of the four modern species investigated (containing 1622, 732, 845, and 832 R-genes excluding Pkinase domains in rice, Brachypodium, sorghum, and maize respectively), we were able to reconstruct a minimal founder (conserved) pool of 465 ancestral R-domains consisting in 361 LRR, 54 NBS, 6 TIR, 11 LySM, 42 WRKY, and 52 RG (Figure  1C  top, Additional file 3: Dataset S2). Based on the same strategy, the evolutionary scenario of the eudicots has been used to unravel a minimal founder pool of 150 R-genes (Additional file 1: Figure  S4 ). In order to understand in more details the evolution of the major PTI/ETI families, we have reconstructed their ancestral pools.
e. SSD) from these gene pairs (detailed in Methods section). In rice, Brachypodium, sorghum, and maize, we found 13. 04% (153 out of 1173), 9. 08% (74 out of 815), 12. 35% (115 out of 931), and 35. 63% (404 out of 1134) R-genes loci (R-genes located in the same clusters were considered as a single locus) involved in single-gene duplications (Additional file 1: Table  S8  and Additional file 4: Dataset S3), which is higher than the 5% to 7% of single-gene duplication frequency reported for the total annotated protein-coding genes in grasses  [76]. Among grasses, the single-gene duplication frequency in maize is significantly higher than in rice, Brachypodium, and sorghum respectively (P-value = 6. 32E-24, 2. 28E-29, and 1. 71E-22 in Fisher's Exact Test respectively, cf Additional file 1: Table  S9 ).
PFAM profiles were identified within genomes using the hmmsearch algorithm (e-value cut: 1e-10) from HMMER3 (http://hmmer. janelia. org/; [91] ). PRGdb -R-genes sequences from the Plant Resistance Gene database were downloaded (http://prgdb. cbm. fvg. it/index. php, [65] ). Annotation, PFAM and PRGdb -based R-genes were aligned against Soybean (46194 protein sequences), Cacao (27814 protein sequences), Strawberry (34809 protein sequences), Lotus (15470 protein sequences), Papaya (19205 protein sequences), Poplar (30260 protein sequences), Apple (58979 protein sequences), Brachypodium, (32255 protein sequences) Sorghum (36338 protein sequences), and Maize (53764 protein sequences) genome data using BLASTP (PFAM and Annotation R-gene sequences) and BLASTX (PRGdb sequences). BLAST results were parsed using CIP (Cumulative Identity Percentage) and CALP (Cumulative Alignment Length Percentage) parameters (70% as minimum threshold) delivering a non-redundant list of R-genes for each species  [44].
Orthologs/Paralogs identification and synteny relationships Orthologous and paralogous R-genes were identified aligning Rice RefBank against Brachypodium, Sorghum and Maize using BLASTALL. BLASTP results are parsed with CIP and CALP parameters set to 60% and 70% as minimum threshold for ortholog identification and 60% and 70% as minimum threshold for paralog identification as described in  [44]. Ancestral relationship between monocot species were represented as concentric circles with the visualization tool Circos  [92]. Relationship between rosids species were investigated with the same protocol. Grape RefBank was aligned against Soybean, Cacao, Strawberry, Lotus, Papaya, Poplar and Apple genome data. BLAST results were parsed with the same parameters described previously. The synteny relations at the chromosome levels were considered using public synteny data available for both Monocotyledones and Eudicotyledones  [66].
Because fluorescence can be artificially reduced on optical slices as the result of incomplete antibody penetration, we used ImageJ software (http://imagej. nih. gov/ij/) to identify the five images within each stack with the strongest fluorescence intensities. A maximal intensity z-projection was made with these five selected optical slices and we calculated the mean fluorescence values of the PC layer as percentage of fluorescence normalized to PN20. For colocalization studies, the fluorescence intensity profiles were obtained from single optical sections of a PC after biocytin-filling (green) and synaptophysin labeling (red). For the localization of PC boutons in the different PC layers, biocytin-loaded PC were reconstructed from an area of 350 × 350 μm and consisting of 80 z-sections (0.
8 μm each). To visualize recurrent collateral boutons on PC somata, z-stacks containing 30 images (thickness of 0. 4 μm per section) and covering a region of 14 × 14 μm were selected. We applied a median filter to each z-projection from the stacks to reduce noise and to perform morphology measurements (e. g. , bouton volume) with ImageJ tools. To assess the distribution pattern of boutons from several PC, all PC somata were superimposed and axonal arbors were oriented in such a way as to respect their position within cerebellar layers and to position the main axon path toward the white matter. We then determined x and y positions for each bouton. These coordinates were used to build a 2D-matrix in MatLab environment permitting to quantify the number of boutons within 20-μm side length squares.