dataesr/scientific-paragraphs-categorization huggingface dataset:
Within each region, the calculated main frequency can vary a few Hz, but was grouped around a central frequency with a narrow bandwidth. Successive mask images of pixels with main frequency within increasing bandwidth of 5 Hz (5-10 Hz, then 10-15 Hz, and so on until 50 Hz) were created, and analyzed using MATLAB image processing functions (regionprops). We selected as cilia only regions of constant frequency with area larger than 200 pixels (at least 3 mm in diameter). The position of each region was then compared to the position of the central line to label the cilia as dorsal or ventral cilia. First, the central frequency within each region was calculated. Because we extracted the average frequency over a large spatial scale, this measurement was independent of the cilium phase, and potentially free from sampling artifacts.
Bead transport analysis To measure CSF transport, four groups of eight 30 hpf embryos (16 contracting and 16 paralyzed embryos) were injected with beads and TexasRed within a five minutes time frame. We recorded the exact injection minute for each embryo. Widefield fluorescence images of each embryo were acquired every 10 min for 60 min using an epifluorescence microscope (Nikon AZ100) equipped with a 2X air objective imaged the entire embryo in one image. The image creation time recorded by the computer was automatically extracted and subtracted from the injection time. The front propagation was obtained by applying a low-pass filter on all images and by manually drawing the contour from the rostral extremity of the CC to the propagation front with a MATLAB script. The contour was drawn by successive lines changing direction every time the canal changed direction.
Characterization of the ventral opening of the RV primitive lumen 30 hpf embryos were injected with Texas Red and alpha-bungarotoxin in the DV/MV and imaged with a confocal microscope (SP8, Leica). A Z-stack of 50 mm around the position of the ventral opening of the RV pL was obtained and displayed as a maximal projection to visualize its entire length as well as the beginning of central canal. A MATLAB script was used to perform measurement of this geometry. The contour of the ventral opening of the RV pL was manually drawn by drawing successive lines up to the most caudal part of the image for both the dorsal and ventral walls of this cavity. We could therefore measure the local diameter versus the curvilinear abscissa along the line. Its length was calculated between the initial point up to the point of maximal diameter derivative.
Computational Fluid Dynamics (CFD) and numerical methods for solving the equations We used MATLAB to obtain the numerical resolution of the system of three coupled equations detailed in the Results subsection 'A bidirectional flow generated by the asymmetric distribution of beating cilia'. Our simulations were performed by the finite element method solver COMSOL, using the Creeping Flow Module. The flow simulations were carried out by solving the Stokes equation in a rectangular domain composed of 120088 domain elements and 15046 boundary elements (corresponding to 630516 degrees of freedom). The simulations accounting for the transport of particles in the presence of a flow were performed by coupling the diffusion equation to the Stokes equation in the same rectangular domain, using the Transport of Diluted Species Module.
Contractions are also emphasized by dark asterisks and a gray background. (E) Quantification of the integral of the average volumetric flow during 2. 5 s after each of the 204 contractions from n = 22 WT embryos. These values are plotted versus the contraction strength, that is the integral of intensity derivative during the time of contraction. The dataset is separated into positive flows (red dots), in which flow after contraction is directed towards the tail, and into negative flows (blue dots), in which CSF flow is directed towards the head. Horizontal scale bar is 15 mm in (A, B, C). The vertical bar in (B, C) is 4 s.
Acknowledgements We thank  Prof. Rebecca Burdine  for the kurly (cfap tm304 allele) mutant,  Prof. Jarema Malicki  for the elipsa mutant (ift54/traf3ip) and  Prof. Brian Ciruna  for the Tg(beta-actin:Arl13b-GFP) line. We would like to acknowledge the kind and expert assistance of  Aymeric Millecamps ,  Tudor Manoliu , and  Basile Gurchenkov  from the  ICM. Quant imaging facility for instrument use and scientific assistance on optical imaging, as well as  Sophie Nunes-Figueiredo ,  Bogdan Buzurin , and  Monica Dicu  from  PHENOZFish platform  for fish care. We are grateful to  Martin Catala  for advising us on zebrafish histology. We greatly thank  Jenna Sternberg  for article proofreading. We thank the  Yaksi laboratory members  and the fish facility support team for scientific and technical assistance to NJK.
This work was funded by  Human Frontier Science Program (HFSP)   Research Grant  (grant  n˚RGP063-2018 ), and the  New York Stem Cell Foundation (NYSCF)  (grant  n˚NYSCF-R-NI39 ) and an  ICM  postdoctoral fellowship kindly attributed to OT. The research leading to these results has also received funding from the  program 'Investissements d'avenir'   ANR-10-IAIHU-06  ( Big Brain Theory ICM Program ),  ANR-11-INBS-0011  ( NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences ), and a  Helse Midt-Norge Samarbeisorganet  grant to NJY and the  Kavli Institute for Systems Neuroscience at NTNU.
Data availability The data enabling to plot all graphs for figures and supplemental videos have been deposited to Dryad Digital Repository doi:10. 5061/dryad. 4mj3pv1. The full MATLAB script can be found on Github https://github. com/wyartlab/eLife_2019_OriginAndRole (copy archived at https://github. com/elifesciences-publications/eLife_2019_OriginAndRole). The following dataset was generated:.
ACKNOWLEDGMENTS JPKD and MMCT gratefully acknowledge  É. Grelet  and  Z. Dogic  for helpful discussions. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie Grant  Agreement No. 641839. The authors would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC)  facility in carrying out this work (http://dx. doi. org/10. 5281/zenodo. 22558). MMCT made use of time on the  ARCHER UK National Supercomputing Service  (http://www. archer. ac. uk) granted via the  UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim  (http://www. hecbiosim. ac. uk). MMCT is grateful to the  UK Materials  and  Molecular Modelling Hub  for computational resources, which is partially funded by  EPSRC  ( EP/P020194/1 ).
GM is grateful for financial support from the  Department of Science and Technology India  in the form of an  INSPIRE faculty grant  ( DST/INSPIRE/04/2014/002085 ), and acknowledges the computational facilities provided by  Calcul Québec  (www. calculquebec. ca) and  Compute Canada  (www. computecanada. ca). DP is supported by the  EPRSC Centre for Doctoral Training in Theory and Modelling in the Chemical Sciences  ( EP/L015722/1 ).
Seizure dynamics vary in human epilepsy While analyzing this dataset, we noted that one patient had two consecutive seizures belonging to different types: one supH/supH and one supH/SH, raising the possibility that an individual may express different types of seizures. This finding was surprising, as many clinicians assume that a person's seizure should be 'stereotyped', that is consistent over time. In fact, multiple medical devices have been designed under the presumption that a patient's seizures would have similar appearance over time  (RNS System in Epilepsy Study Group and Morrell, 2011; Cook et al. , 2013). To test whether individuals display different types of seizures over time, we used a unique dataset from Melbourne in which patients had intracranial EEG recorded continuously for many months  (Cook et al.
Simulated seizures Validation of the classification scheme required a gold standard dataset, which we generated by simulating 60 random iterations of each type of onset and offset bifurcation. The simulation used the model and methods to begin/end at specific bifurcations described in Saggio et al. , 2017 and a function to generate a trajectory across each bifurcation. The trajectory was an arc of great circle as described in section II for the model, but the crossing point on the bifurcation curve and the inclination of the path with regards to the bifurcation curve changed randomly at each iteration to produce adequate variability within each bifurcation type. These conditions were implemented to create a robust data set that would mimic biologic variability. We simulated an equal number of seizures per bifurcation.
Two of the onsets (SubH and SupH) required a small amount of noise to be added to the simulations in order for the system to be able to leave the unstable equilibrium. The velocity of the slow variable, c, was choosen for each type to be small enough to ensure time scale separation so that the prescribed scaling law would be visible. As these simulations had arbitrary timescale and different levels of background noise, we then post-processed the data to blind the reviewers. We first added normal random noise (MATLAB's 'normrnd', mu = 0, sigma = 0. 01) to all samples of every signal based upon the existing noise. We then rescaled all seizures so that the final interspike intervals would be ~0. 2 s, then downsampled ('decimate' function in Matlab) so the sampling rate would be 200 Hz. All codes are available for download at https://doi.
I. 2 Spike analysis As stated in the main body, we define a 'spike' in dynamical terms: any prominent sharp transient associated with the dynamical process, which for these human EEGs means any fast transient <250 ms long and amplitude discernable from the background. Inter-spike interval (ISI) is defined as the time between sequential spikes. Amplitude for a given spike is defined as the absolute maximum peak-to-peak difference in a window of time ranging from the halfway point of the first spike and the halfway point of the latter spike (see Figure  2A  in main body). Beginning/ending spikes utilize only the halfway point of the nearest spike. All analyzed data were decimated to ~200 Hz for efficient analysis. In order to remove slow transients and identify the local spike amplitudes, raw EEG data were first high-pass filtered (MATLAB's 'highpass', 1 Hz).
Peaks were found in Matlab using the 'findpeaks' function, after manually optimizing amplitude, time, and prominence for each patient. This process required iterating the features until visually confirming that spikes were correctly detected, using plots in Matlab with the spike detections superimposed upon the EEG signal (see Appendix 1-figures 1-9). Due to the wide variability between patients, we were unable to develop a reliable automated method to determine the spike locations in every patient. Sentinel spikes. Some seizures began with a single high amplitude 'sentinel spike,' which typically was present in many electrodes, often far more than were involved in the subsequent seizure. We used this spike to indicate the initial seizure onset time based upon the decision of our clinical experts.
For an onset baseline, this segment was taken as the start of the waveform up until seizure onset. For an offset baseline, this segment was taken as the point after seizure offset to the end of the waveform. DC vs. Non-DC: Data acquired at Kyoto (Nihon Kohden EEG 1100 amplifier) or Michigan (Natus Quantum amplifier), both of which record down to 0. 016 Hz, were included in the DC cohort, and all other data was considered 'non-DC'. Non-DC data were first filtered with 1 Hz highpass filter (Matlab 'highpass'), then all features extracted. On the DC data, features were computed on the raw, unprocessed EEG. Preprocessing steps: 1) Onset/offset times, as well as the relative bifurcation window lengths, were determined by a trained epilepsy specialist. 2) Spikes were identified using findpeaks. m (Matlab) to locate upper (maxima) and lower (minima) spikes.
3) Seizure polarity was determined by determining whether the median of the upper or lower spikes had a greater absolute difference from the baseline median. The value with larger difference was chosen as the true 'spikes' and were used for all further analysis. All amplitudes were taken as absolute values. This step accounts for the fact that spikes can be either positive or negative in intracranial electrodes. Feature 1 -ISI trend: The ISI of all spikes were computed and plotted consecutively. A simple line was fit to the data with a least square algorithm and the slope of the line fit was extracted as the overall ISI trend. For onset (offset), the first (last) 5-15 spikes were used, using as many as possible until a clear inflection point in the line. The order was reversed for offset. Feature 2 -Amplitude trend: This feature was computed exactly as in ISI trend, except the peak-to-peak spike amplitudes were used in place of the ISI.
More importantly, even under ideal conditions any such analysis would be limited due to the lack of a gold standard. We therefore decided on an alternate and more rigorous approach, appropriate for the data quality of clinical seizures. In order to validate our classification system, we used a multi-step approach. The key to this validation was the generation of a gold-standard with simulated data, in which we know which bifurcation is present. Using the gold standard, we first used machine learning tools to assess how accurately our chosen features are capable of identifying the differences between the bifurcations. We then tested how accurately human reviewers could identify each bifurcation using visual review. After proving that the human reviewers were accurate and reliable in the gold standard, we tested their reliability in the true clinical dataset.
These steps are detailed below. Step 1: Feature validation and bootstrapping: OBJECTIVE: Determine whether the chosen features capable of discerning between the four bifurcations in the gold standard. METHODS: Using the simulated seizures (240 onsets, 240 offsets), supervised learning was chosen to quantify how well the simple features in I. 4 captured the differences between the different dynamotypes. This was performed by fitting labeled features to a statistical model to generate a goodness-of-fit measurement. Specifically, we used a multinomial logistic regression model  ('mnrfit', Matlab) , which outputs a model parameter 'deviance' that estimates the Goodness-of-Fit. The GoF was compared with the gold standard in the simulation data. Note that this method is not used for classification nor to determine the 'best' potential fits.
CONCLUSION: The chosen features are capable of discerning between the bifurcations. Step 2: Human visual analysis of simulated data: OBJECTIVE: Validate whether visual analysis by human reviewers accurately identifies the different bifurcations in the gold standard. METHODS: Using the simulated seizures (240 onsets, 240 offsets), three independent reviewers labeled all bifurcations using an in-house Matlab viewing program that kept labels blinded while also randomizing the order in which the seizures were viewed. DC-coupled data were shown with two waveforms: raw data and 1 Hz highpass filtered data ('highpass  ', Matlab) , in order to highlight DC components while also allowing viewing of spike dynamics. Reviewers had to choose exactly one of the four potential bifurcations for each example.
I. 6 Challenges with classification There were several conditions in the data that required rules for disambiguation between the different bifurcations: 1. Amplitude scaling. Amplitude was often arbitrary in our clinical data, showing considerable variability from spike to spike. This variability sometimes made it difficult to determine if there was scaling to zero (square-root). In those cases, we observed the spikes closest to T = 0: if the first 5-10 spikes appeared to arise from the baseline noise and consistently grow in later spikes, it was classified as square-root scaling (SupH). If the first 5-10 spikes were clearly >3 times higher than baseline noise, the seizure was classified as constant (i. e. arbitrary) amplitude even if later spikes increased in amplitude. 2. Late amplitude increase.
In many seizures, after ~10 s it was common for the spiking activity to increase in amplitude as the seizure spread to neighboring electrodes. This late increase was not part of the onset bifurcation, and only the initial trend during the first 10 s was used for classification, which was typically not SupH. 3. 'Arbitrary' scaling usually constant. Although the 'non-zero' scaling listed in Figure  1  theoretically includes many arbitrary patterns, we found that the vast majority of examples manifested as a constant value, similar to the analysis in  Jirsa et al. , 2014. For instance, nearly all SN onsets had constant amplitude and ISI, and SH offsets had constant amplitude. 4. Mixed dynamics. In cases where features from more than one bifurcation were present in the data (e. g. DC shift and amplitude scaling), we used the following prioritization order: DC shift ! amplitude scaling ! frequency scaling.
The highest priority was used as the final bifurcation, for example if DC shift and amplitude scaling were both present, it was classified as SN (onset) or SH (offset). Appendix 1-table 1 presents the bifurcations in this order. 5. Unusual dynamics. There were rare examples in which the dynamical behavior was highly unusual, all of which we present in this Appendix (Appendix 1-figures 6, 10, 17-19). Each of these patterns was classified as 'arbitrary'. See section I. 8 for further details. Despite these rules, there were examples in which the human reviewers were either incorrect in the simulated data or did not agree with each other in the clinical data. While these errors were uncommon, we evaluated each case to determine the conditions responsible. The following are a list of the conditions that can make classification difficult.
By our definition, this bifurcation is defined as a SN; however, some of these examples were produced by parameters designed to generate SupH bifurcations. This is an issue of labeling of the simulation parameters but is not a failing of model or TSD; as seen in Figure  5  and explained in the text, TSD predicts this very effect, and we saw several examples of it in our clinical data (SN and SupH coexisting in the same human epileptic network). The following phenomena are not described by the canonical features of the bifurcations and were difficult for the reviewers to classify. The majority of disagreements between reviewers contained at least one of the following: 1. Polyspikes or spike waves at seizure onset, especially prior to low voltage fast activity 2. Preictal spikes 3. Noisy data, especially 'extraneous' spikes that disrupt scaling laws and 'Unusual dynamics' (#5 in preceding section) 4.
I. 9 Patient metadata Full clinical metadata were available for 88 of the patients (Appendix 1-table  4 ). We stratified all patients by their onset and offset bifurcations and evaluated the prevalence of gender, age, pathology, and seizure localization in each case. Due to the vast heterogeneity of electrode locations, all electrodes were stratified into the four main lobes  (frontal, occipital, parietal, temporal). Note that not all patients had onset bifurcations available, and some of the rows do not reconcile because some patients had portions of the metadata missing, most commonly the pathology. For simplification and to include all data, all SN and SH were treated as if there were no DC coupling, and so were grouped with the SubH and SNIC bifurcations as shown. There was a correlation between patient age and onset bifurcation, with a higher proportion of SupH in ages 41-60 (p=0.
This implies that, if noise-induced transitions rather than bifurcations are causing seizure onset in any seizure in our dataset, these seizures would be classified under the 'SN or SubH' label. The presence of such events in our dataset would not undermine our classification since we already know that 'SN or SubH' onset is overestimated. However, it is interesting that in these cases, even though the onset is not caused by a bifurcation, the label in our classification would describe the bifurcation that creates the bistability necessary for the noise-induced transition to occur.
The two paths lie very close on the map. We then added normally distributed noise, which allowed the system to deflect off its normal trajectory. This noise then allowed to get close to different bifurcations within a single seizure. Transitions of the type observed in data (which is a subcase of type SN / SH) were observed in 10 of the 100 simulations we ran with the same settings. The other simulations produced mostly SN / SH seizures (43/100) and some SN / SupH (8/100), other forms of transitions within seizures and 12 examples of status epilepticus in which the system entered a long seizing state that did not resolve by the end of the simulation. When more than one seizure occurred in the same simulation, we classified only the first unless otherwise stated. Seizures were classified by determining the onset and offset bifurcations crossed by the path, rather than by analyzing the frequency/amplitude profile of the time series.
Identification of the Complete Set of O. Europaea L. and F. Excelsior Aquaporins The "Build your query" function including "Major Intrinsic Protein" and "Aquaporin" in Phytozome (V12) were used to identify the complete non-redundant AQP family of the wild olive O. europaea var. sylvestris. To investigate whether potential OeuAQP genes were lacking or insufficiently annotated in the databases, the genomes of the cultivar olive O. europaea L. cv. Picual (OliveTreeDB website) and the Oleaceae related species F. excelsior (hardwoodgenomics website) were BLAST-searched using several AQP members belonging to each subfamily from A. thaliana, L. esculentum and P. trichocarpa as queries. Identified AQP sequences from each Oleaceae species were thereafter iteratively used as queries. Subsequently, every output putative AQP gene was manually inspected.
This approach demonstrated that for the database-annotated AQP genes, output coding sequences and exon/intron selections sometimes resulted in a panel of non-satisfactory AQP gene models. With the exception of OeuPIP2;11 and OeuNIP5;2 sequences for which a manual annotation procedure generated very long AQP sequences, thirty-six non-satisfactory AQP gene models encode partial sequences due to an incomplete or miss-annotation procedure alongside the chromosomes or the scaffolds. These situations are linked to deletions or mutations that result in a shift in the reading frame leading to the synthesis of premature stop codons or non-functional proteins (e. g. , OeuNIP4;2). Wherever possible, each erroneous AQP sequence was manually corrected using orthologs from F. excelsior and L. esculentum, with the addition of AQPs from P.
trichocarpa for the examination of the XIP subfamily. This last approach helped identify twenty new candidates encoding complete AQP sequences meeting the typical AQP features. Similarly, the alignment of the incomplete putative OeuAQP sequences due to their scaffold edge location with orthologous counterparts gave three complete OeuAQP sequences (OeuPIP2;9, OeuPIP2;10 and OeuTIP4;1). Two OeuAQP genes were not detected and annotated in Phytozome databases (OeuSIP2;2 on chromosome 11 and the incomplete sequence OeuNIP4;2-related on chromosome 8). Lastly, two raw OeuPIP2-related gene sequences initially emerged as two distinct 3 and 5 truncated AQP candidates (Oeu017254. 1 and Oeu017256. 1, respectively). By homology with orthologous PIP2s, these two sequences were gathered in one sequence to generate the putative OeuPIP2;11 in its full-length version.