dataesr/scientific-paragraphs-categorization huggingface dataset:
A seminal study by Vallerand shows a hierarchical model of extrinsic and intrinsic motivation not only generates a framework to organize the literature on the subject, but also new and testable hypotheses  (Vallerand, 1997 )  (Holman & Walker, 2018). The 'bayesvl' R package is coded up to support the Bayesian hierarchical multilevel analysis in this paper. They bayesvl package contains approximately 3,000 lines of code, which combine the ability of R to generate eye-catching graphics and the ability to simulate data of the Monte Carlo Markov Chain (MCMC) method (CITE Github). bayesvl was created based on some of the seminal works in Bayesian statistics such as  (Kruschke, 2015; McElreath, 2016; Muth, Oravecz, & Gabry, 2018; Scutari, 2010; Thao & Vuong, 2015). One can find a complete user guide at  (La & Vuong, 2019).
Checking conditional posteriors: The bayesvl package enables prediction of the outcome value of the model after regression. To execute the prediction, we need to add the test parameters when creating the nodes for the model. As can be seen, when creating node Int1_or_Int2, the bayesvl code has the following form: model <-bvl_addNode(model, "Int1_or_Int2", "trans", fun = "({0}> 0 ? 1 : 0)", out_type = "int", lower = 0, test = c(0, 1)) The paramter test = c(0, 1) allows bayesvl to add new codes to estimate "fixed predicted outcome" when Int1_or_Int2 = 0 and Int1_or_Int2=1. This command will run a simulation for the model, the software will compute the sets of outcome value yrep_Int1_or_Int2_1 and yrep_Int1_or_Int2_2 after each regression iteration. Consequently, we will have n new value sets for the outcome.
Autocorrelation of each coefficient The MCMC algorithm produces the autocorrelated samples, not the independent samples. Therefore, the slow mixing due to too high acceptance rate or too low might lead to the process not ensure the Markov property. This check is to ensure after certain finite steps; autocorrelation will be eliminated (to 0). The plot function of the bayesvl R package for generating the graphic representation of the autocorrelation function (ACF) follows: > bvl_plotAcfs(model, NULL, 4, 3) The function helps to produce ACF graphs presented in Fig. 6. Figure  6  shows that the effective sample sizes (ESS) for all coefficients are well above 1000. Besides, the coefficients most quickly converge before lag 3; the fact that lends support to computing efficiency and the Markov property of the chains.
Comparing situations with and without intervention: The code for tests on variable Outcome ("O") when there are and are not external elements intervening in the story yield two distribution graphs y_rep as follows. > bvl_plotTest(model, "O", "Int1_or_Int2_1") > bvl_plotTest(model, "O", "Int1_or_Int2_2") The following pair of graphs in Figure  19  corresponds to the values of Int1_or_Int2 in the above commands. The two graphs appear rather similar, meaning that the ending of a story does not depend significantly on the factor of external intervention. In addition, the respective distributions of coefficients a_Int1_or_Int2  [1]  and a_Int1_or_Int2[2.
Data deposition: The dataset is available from the Github (https://github. com/sshpa/bayesvl/tree/master/data), and CRAN 'bayesvl' R package (https://cran. r-project. org/package=bayesvl). The executable computer code for this paper is provided in the available on https://github. com/sshpa/bayesvl/tree/master/examples, file: simulation_example. R.
D where M describes the biadjacency matrix of the relevant vertices, and M is the matrix produced by starting with M and then adding row 1 to row 2, taking sums modulo 2. Furthermore, if the diagram on the LHS has a focused gFlow, then so does the RHS. We now describe our extraction procedure at a high level. To aid in understanding this procedure, we provide a fully worked-out example in Appendix A and a pseudocode description in Appendix C. To start, let the frontier F consist of all the output vertices. If a vertex of the frontier has a non-zero phase, unfuse this phase in the direction of the output. Whenever two vertices of the frontier are connected, unfuse this connection into a CZ gate. If the diagram started out having a gFlow, then the resulting diagram still has a gFlow since we only affected output vertices.
The vertex After all vertices have been extracted by the above procedure, we will have some permutation of the frontier vertices to the inputs. The extraction is finished by realising this permutation as a series of SWAP gates. Remark 7. 2. An interesting thing to note is we do not actually need a pre-calculated focused gFlow for this algorithm: knowing that one exists is enough. In step 3, it suffices to apply primitive row operations until a row with a single 1 appears in the biadjacency matrix. If this is possible by some set of row operations (i. e. if there exists a focused gFlow), such a row will always appear in the reduced echelon form, so we can simply do Gauss-Jordan elimination. This is how we describe this step in the pseudocode presented in Appendix C. Remark 7. 3. The cost of the implementation of the extraction algorithm is dominated by the Gauss-Jordan elimination steps.
As mentioned in the introduction, this example is available as a Jupyter notebook 'demos/example-gtsimp. ipynb' in the PyZX  [28]  repository on GitHub. In Figure  3 , we give an empirical comparison between the full optimise-and-extract technique and the naïve approach of optimising all of the Clifford sub-circuits of a general Clifford+T circuit. This data was produced by generating random 8 qubit Clifford+T circuits with 800 gates where the probability that each gate is a CNOT gate is 0. 3 and we vary the probability p t of T gates from 0 to 0. 15. For each value we generate 20 circuits and we report the average total and 2-qubit gate counts for the original circuit (labelled original in Figure  3 ) and 3 different reduced versions. The first reduced version (original+) is the original circuit after some basic postprocessing.
Acknowledgements. SP acknowledges support from the projects  ANR-17-CE25-0009   SoftQPro ,  ANR-17-CE24-0035  VanQuTe,  PIA-GDN/Quantex , and  LUE / UOQ. AK and JvdW are supported in part by  AFOSR  grant  FA2386-18-1-4028. We would like to thank  Quanlong Wang  and  Niel de Beaudrap  for useful conversations about circuit optimisation and extraction with the ZX-calculus.
nbviewer. jupyter. org/github/Quantomatic/pyzx/blob/906f6db3/demos/example-gtsimp. ipynb Note this link is read-only. To run the notebook interactively, download the file and open it in Jupyter (with PyZX installed). The simplest way to do this is to clone or download the PyZX repostory from: github. com/quantomatic/pyzx and find the notebook in demos.
3E ). The data obtained in mdx mice revealed the presence of a metabolic endo-phenotype in these mice. The observed changes were consistent for the whole study duration supporting a role of these metabolites in disease progression. To further test whether the 31 metabolites identified in the comparison between mdx and WT mice were associated with disease trajectories and disease severity, we studied mdx mice carrying 0, 1 or 2 functional copies of the dystrophin paralog utrophin, as utrophin gene dosage has been linked to disease progression in mdx mice  (26). Plasma samples were obtained at 6, 12, 18, 24 and 30 weeks of age for mice carrying either 1 or 2 functional Utrn alleles, while for double knock-out mice samples were only obtained at 6 weeks of age given the severe phenotype of these mice.
Sample preparation and data acquisition Plasma samples were kept on ice and centrifuged at 18 000g for 5 min at 5 • C. After centrifugation, the supernatant was aliquoted and frozen at -80 • C pending use. The sample order of the sample preparation and the analytical batch was randomized to avoid bias. The procedure for data acquisition has been previously described for the analysis of human plasma  (21). Briefly, plasma samples were introduced into a Transcend 1250 LC (Thermo Fisher Scientific) fitted with a Sequant ZICpHILIC 5 μm, 2. 1 × 150 mm column (Merck). This was then coupled to a Q-Exactive mass spectrometer (Thermo Scientific) in both positive and negative ionization modes, alternatively. Data analysis (peak picking and features annotation) was performed using TraceFinder 3. 1 (Thermo Fisher Scientific).
Annotation was based on the exact m/z ratio of the pseudo-molecular [M + H] + or [M -H]ions in positive and negative mode, respectively (±5 ppm mass tolerance); retention time and isotopic pattern were also used to align to an in-house database of authentic standard compounds. The obtained data set was cleaned based on several parameters as described by Dunn et al. (63). The coefficients of variation of the areas of chromatographic peaks of features in QC samples (pool of each sample analyzed every five samples) should be below 30%, the coefficient of correlation between QC dilution factors (series of dilution of the QC sample) and areas of chromatographic peaks should be above 0. 7 and the ratio of chromatographic peak areas of biological to blank samples above a value of 3. After LC-MS analysis of samples and annotation of features, QC samples were re-injected for higher energy collisional dissociation MS/MS experiments in positive and negative ion modes on the same instrument set in targeted mode using inclusion lists.
Pathway information from WikiPathways was mined using an internal workflow that interacts with the application programming interface services of WikiPathways  (70). Workflows created with the open source software Taverna Workbench (71) can be found at http://www. myexperiment. org/packs/689. All pathways and corresponding metabolites were downloaded. P-values from the global test were adjusted using the max T test correction  (72). Cluster analysis of the metabolites was performed using Morpheus (73). The statistical analyses described in this paper were performed using R. We used the R package nlme  (74)  to estimate the linear mixed models and the R package globaltest  (68)  to compute the global test. Network visualization was performed using the MetScape App  (75)  in Cytoscape  (76).
Tissue equivalent proportional counters (TEPC) A TEPC has the ability to provide values of the dose equivalent in tissue-equivalent material from most radiation components reasonably well. It is therefore particularly useful in comparisons of cosmic radiation measurements onboard aircraft  (EURADOS 1996). Several different TEPCs were used to measure the dose equivalent during the REFLECT. for IRSN and 0. 5 keV•μm -1 for SL) are not recorded. An extrapolation function based on 60 Co gamma-rays LET spectrum was chosen for the SL Hawk. For the IRSN Hawk data analysis, a simple coefficient (the average of correction factor determined for 60 Co and 137 Cs gamma-rays) was applied  (Farah et al. 2017). No compensation of the counting loss due to dead time is included in the analysis software.
Calculations Ambient dose equivalent rates for different particles can be calculated using various models; the overview of codes assessing radiation exposure of aircraft crew is given in  (Bottollier-Depois et al. 2012). All these codes provide calculations for the GCR induced radiation field in aircraft flight altitudes agreeing within 20 % with reference measurements  (Bottollier-Depois et al. 2012). In this publication, the EPCARD. Net code  (Mares et al. 2009 ) was used for comparison.
The EPCARD. Net parameter database includes energy-averaged dose conversion coefficients, calculated by folding each single-particle fluence spectrum with the appropriate dose conversion function  (Mares et al. 2004; Mares and Leuthold 2007) , which depends on barometric altitude, cut-off rigidity, and solar activity, since the shape of the particle energy spectra also depends on these parameters. Ambient dose equivalent, H*(  10 ), and effective dose, E, are calculated separately for each particle, i. e. the dose contributions from neutrons, protons, photons, electrons, muons, and pions are assessed individually. More general information about EPCARD is available on the web site (EPCARD 2020), where a simplified on-line version of the EPCARD calculator for public use can also be found.
Acknowledgements The research flight has been part of the research activities of the  CRREAT (Research Center of Cosmic Rays and Radiation Events in the Atmosphere)  project funded by the  European Structural and Investment Funds  under the  Operational Program Research, Development and Education  ( CZ. 02. 1. 01/0. 0/0. 0/15_003/0000481 ). This work was carried out within the European Radiation Dosimetry Group (EURADOS, WG11 High energy radiation fields). We would like to thank ABSJets pilots and technicians for their assistance during the flight and its preparation.
While triaging the clinical vignettes, nurses in the control group will not be exposed to distractors. Nurses in experimental groups will be exposed to noise (group A), task interruptions (group B), or both noise and task interruptions (group C). Nurses will be block-randomized across the four groups by a computer program. The groups will be of equal size or as similar as possible in this regard. The study design comprises repeated measures. We will collect sociodemographic and personal data from participants before they begin evaluating the clinical vignettes. Then, during the evaluation of each clinical vignette, the following data will be gathered systematically: (1) emergency level assigned, (2) level of perceived confidence in emergency level assignment, and (3) duration of each clinical vignette evaluation.
Procedure for participant recruitment and SG delivery First, the aims of the research will be presented at the annual meeting of ED SETS® users. Second, all the care facilities with an emergency unit in the five cantons of the French-speaking part of Switzerland (Geneva, Vaud, Fribourg, Jura, Neuchâtel) will be contacted to validate their interest in participating in the study. Third, nurses interested in participating in the study will receive an information and consent form. The definitive list of participants will be drawn up after consent forms are signed. Nurses will be block-randomized across the four groups just before starting the SG by order of arrival for the test. Each participant will then be assigned an identification number when they start the SG. Fourth, the study data will be collected directly in each ED by the research team.
To establish a controlled set for the SG, participants will be isolated in a meeting room previously prepared by the head nurses in each participating emergency unit. Members of the research team will be on site to provide technical support, if needed, and to document any technical problem that might occur during the SG. Fourth, once the 2 h has elapsed, each participant will stop the game session even if not all 20 vignettes have been evaluated. All the data collected during the SG session will be automatically recorded, and they will be saved on a secured server located in Switzerland. After their 2-h SG session, the participants will return to their workplace.
Data analysis plan The nurse participants (expected N = 80) will be the analysis units for the descriptive analyses, and the assigned triage scale scores will be the analysis units for the correlational analyses and some descriptive analyses (number of nurses multiplied by number of vignettes-expected N: 80 × 20 = 1600). The following data analysis plan will be carried out to answer the research questions: First, the collected data will be verified (compliance with inclusion criteria, identification of missing data and outliers). Second, the data on the nurses (sociodemographic and professional) will be analyzed via descriptive statistics, both univariate (mean, median, standard deviation, interquartile range, and absolute and relative frequency) and bivariate (contingency table and marginal frequency).
In addition, recordings of the animal's speed were performed to track locomotion (Figure  5A-B ) and analyze hippocampal theta oscillations during theta-run episodes (speed > 1 cm/s; Figure  5E , left) and SWRs during quiet states (speed < 1 cm/s; Figures  5D, bottom  and 5E , right)  [42, 46]. To estimate the firing onset in IS3 cells during different phases of network oscillations, we extracted the spike onset times from Ca 2+ transients (CaTs) using three different algorithms (Figure  5F ). These included the Matlab toolboxes MLSpike  [47]  and UFARSA  [48] , as well as a custom Matlab code estimating spike times using the speed of the Ca 2+ signal (DF/Dt)  [49]. The algorithms were first fine-tuned for IS3 cell-specific somatic CaTs using in vitro patch-clamp recordings of somatic CaTs in response to APs evoked by current pulse injection in anatomically confirmed IS3 cells (Figure  5F, 5G ).
Model details and code The models used in this study (SDprox1 and SDprox2) were previously custom-built to simulate electrophysiological characteristics of IS3 cells (ModelDB accession #: 223031)  [38]. Models were simulated in the NEURON environment and results were plotted using customized Python and Matlab scripts. Note that all code and scripts can be accessed online (synaptic optimization code: https://github. com/FKSkinnerLab/IS3-Cell-Model/tree/master/LayerSpecificInputTests; rhythmic input simulations code: https://github. com/FKSkinnerLab/IS3-Cell-Model/tree/master/RhythmTests)  [79].
Stationary epochs were defined as contiguous periods when the instantaneous speed was smaller than 1 cm/s for a minimal time period of 3 s (556 epochs of mean duration 9. 31s ± 0. 31 s). The image analysis was performed off-line using Leica LAS, Igor Pro (Wavemetrics, Lake Oswego, USA) and Matlab. For extraction of somatic Ca 2+ -transients, a region of interest was drawn around individual somata to generate the relative fluorescence change (F) versus time trace. For the purposes of automating the analysis, the baseline fluorescence level (F 0 ) was taken to be the median of Ca 2+ signal. Somatic Ca 2+transients were expressed as ΔF/F = (F -F 0 )/F 0 or ΔF/F = (F -F 0 )/F 0 x 100%. This analysis was performed for 18 different pIS3 cells, of which 14 were recorded across two independent imaging sessions (5 min each) separated by 1 min interval, and 4 cells -during one session.
Analyses between the two sessions were pooled together for each cell. Cross-correlations were computed using Matlab's xcorr(____,'coeff') function. To extract estimated spike times from Ca 2+ events, we applied three different algorithms: DF/dt, MLSpike and UFARSA. In all cases, we tuned the spike extraction algorithm parameters according to in vitro calibration experiments (Figure  5F , 5G). The first method was custom written using Matlab, whereas the other two are previously developed algorithms available in Matlab (see Supplementary Data 1 for details). For the first method, we took the gradient of the Ca 2+ signal (DF/dt)  [49]  and chose times where the DF/dt trace had crossed a threshold value of 0. 75 times the standard deviation of the DF/dt trace for at least two time-steps. In addition, the Ca 2+ signal itself needed to be greater its own median plus standard deviation during these same time steps.
e. poor time resolution, variability in noise level and Ca 2+ event shapes), all available methods for spike extraction from Ca 2+ imaging data cannot be considered as precise, which is why we decided to use three different algorithms to compare the data obtained. Ultimately, these methods were used to provide an estimate of spike phases relative to We labeled periods where the time-varying theta power was greater than its mean as periods of "high-theta activity". For obtaining spike phases from the estimated spike times, we took the Hilbert transform (i. e. Matlab's hilbert(____) function) of the thetafiltered LFP signal and interpolated the spike phases from this trace. In this case, we set 0° to the rising phase of the theta oscillation. Ripple events were detected automatically using custom written Matlab code.
Specifically, we computed the envelope of the ripple-filtered trace by taking the absolute (abs(____)) of its Hilbert transform (hilbert(____)). We then set a threshold criterion to identify time periods where the envelope was greater than its mean plus 5 times its ttest(x, y, 'Alpha', ____) function and paired-sample t-tests. All circular statistical and descriptive measurements were computed using the CircStat toolbox in Matlab. Significance of the non-uniformity of circular spike estimate distributions relative to theta phase were tested using Rayleigh's method (circ_rtest(____)), and directional circular statistics were calculated using V-Tests (circ_vtest(____)). Mean theta-phase modulation of spike estimates were computed using the circular mean (circ_mean(____)) and measures of dispersion included the vector length (circ_r(____)) and the angular deviation (circ_std(____)).
Acknowledgments This work was supported by the  Canadian Institutes of Health Research (CIHR)  and the  Natural Sciences and Engineering Research Council of Canada (NSERC). Vincent Villette  was supported by the  PDF  fellowship from  Savoy Foundation. Alexandre Guet-McCreight  was supported by an  NSERC   CGS-D2 award. We thank  Sarah Côté  for technical assistance,  Dimitry Topolnik  for equipment calibration and maintenance, and  Armen Saghatelyan  for providing Calb2-IRES-Cre mice.
Motivation: Cryogenic Electron-Microscopy offers the unique potential to capture conformational heterogeneity, by solving multiple 3D classes that co-exist within a single cryo-EM image dataset. To investigate the extent and implications of such heterogeneity, we propose to use an optimal-transport based metric to interpolate barycenters between EM maps and produce morphing trajectories. While standard linear interpolation mostly fails to produce realistic transitions, our method yields continuous trajectories that displace densities to morph one map into the other, instead of blending them. Implementation: Our method is implemented as a plug-in for ChimeraX called MorphOT, which allows the use of both CPU or GPU resources. The code is publicly available on GitHub (https://github. com/kdd-ubc/MorphOT.
7, with non-ChimeraX pure Python source code also available on GitHub. The plug-in can run both on CPU or GPU, allowing large high resolution maps to be treated as well. Although a Python library for optimal transport already exists  [7] , it does not include convolutional Wasserstein distance computation, and is limited to 2D distributions. In particular, there has so far not been any implementation of the method proposed by Solomon et al. that is suitable to the typical format and size of EM maps. To our knowledge, our tool is thus the first implementation in Python able to deal with 3-dimensional grids of arbitrary size. We illustrate in Figure  1    More generally, MorphOT takes two ChimeraX volumes (. mrc,. map,. ) as main inputs, and allows to compute and display either the movie of the interpolating trajectory, or simply one barycenter.
Background: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington's disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice. Results: Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels.