dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Clinical decision support systems (CDSSs) are software components that aim to support clinicians in their decision-making process. CDSSs have been used in many medical areas. They have proven to improve the quality of patient care by increasing the compliance of clinicians' decisions with clinical practice guidelines (CPGs)  [1]. DESIREE 2 is a recent European-funded project which aimed at developing a webbased platform to improve the management of primary breast cancer patients. Among other services, DESIREE includes a guideline-based decision support system (GL-DSS) that some authors of this article have developed  [2]. As part of the DESIREE project, we had to evaluate the GL-DSS. However, we were lacking a large sample of resolved clinical cases, including breast cancer patient data and their corresponding decisions.
CPGs used in the project, including the APHP CPGs considered as the update of CancerEst CPGs, are formalized as sets of Natural Rule Language (NRL) rules  [6]  matching the data model described in the BCKM. We have used the sample of clinical cases previously resolved with OncoDoc by the MTBs of the Tenon Hospital  [3]. Each case includes the description of the patient profile as a set of instantiated clinical parameters and the corresponding MTB decision. The two CDSSs use different representations of the breast cancer domain knowledge. For example, the tumor size is represented as a categorical variable ("less than 2 cm", "between 2 and 4 cm", "more than 4 cm") in OncoDoc, whereas it is represented in DESIREE as a quantitative variable in millimeters, partly related to the T of the TNM classification, another categorical variable.
Results We observed that implementation of Newton's method increased vastly the range of convergence of the fixed-point iterative algorithm. It extended convergence in two directions. Firstly, it allowed to describe more 1-zero 1-pole amplitudes, see figure  7. Secondly, it allowed to describe amplitudes with arbitrary number of CDD zeros. In the latter case, we observed a rich pattern of convergence to various solutions as a function of the starting point, with a fractal structure, see figure  11. Newton's method also converged much faster than the fixed-point iteration, due to the help of the gradient. Compare for instance figures 2 and 8. From Atkinson's program perspective, these results are truly new, as it had not been shown, even theoretically, that one could go beyond the convergence radius of the fixed-point iteration.
Acknowledgements We would like to thank  Amit Sever  for collaboration at an early stage of the project. We would like to thank  Miguel Correia ,  André Martin ,  Slava Rychkov , and  Amit Sever  for useful discussions on related topics. PT is grateful to CERN for hospitality during the final phase of the project. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement number  949077 ).
Axial Gap Diffusion Regimes observed for paired conduit wall permeabilities (relative to ISF) and locations within the conduit. All combinations apply to conduits of any length in the tested range of 12-16mm. Due to animal trial data indicating that for a xed nerve gap the length of the conduit employed has a signi cant effect on peripheral nerve regeneration, modeling was performed to explore the effect of conduit length on oxygen concentration and distribution. Five different conduit lengths were tested, ranging from 12mm to 16mm in 1mm intervals. The base CNF permeability of Table  2  was employed and the model was run until the oxygen concentration reached 0 mol/m 3. Figure  7  presents the resultant oxygen concentration vs time data as a function of conduit length for a nominal nerve to conduit diameter ratio of 0.
Methods A model of the severed peripheral nerve/conduit system was created in the software package COMSOL Multiphysics®. The model integrates material properties of the conduit with in-vivo conditions internal and external to the conduit in order to emulate the post-surgical environment. The model, presented in Fig. 9 , includes an outer hollow cylinder which represents the conduit (variable length, diameter, and permeability of the wall to oxygen), and two inner solid cylinders which represent the proximal (right side of Fig. 9 ) and distal (left side of Fig. 9 ) nerve stumps (3mm diameter). The boundaries of the model, that is the end of the nerves external to the conduit, were set to be impermeable to the in ow of oxygen to mimic the nerve extending for considerable distances within the body.
The volume in between the nerve segments (3mm in length), and the volume between the nerve stumps and the inner conduit wall (variable), were modeled to comprise interstitial uid (ISF). As may be seen in Fig. 9  (a), a 1mm section at the end of the proximal stump is delineated, and in accordance with the ndings of Lim et al. , is modeled to be regenerating with a resultant oxygen consumption twice that of the remainder of the nerve. The package "Transport of Diluted Species" was implemented within COMSOL Multiphysics® in order to appropriately represent oxygen diffusion coe cients for the various media of the model, and to generate concentration pro les even at comparatively low numerical values. Since most of the quantitative research regarding oxygen concentration during nerve regeneration has been performed employing murine models, the model was built using data reported for mice.
The base parameters used for modelling are summarized in Table  2  and are discussed below. Values for the nerve diameter, nerve gap length, conduit length and conduit diameter were chosen based on the experimental murine studies performed by the authors and their collaborators. The remaining parameters were determined from literature. Speci cally, the base oxygen concentration in the ISF was taken to be that of the average partial pressure of oxygen in arterioles (P O2 = 26. 4mmHg)  (21)  and was assumed to be constant since the vasculature continuously replenishes it. The oxygen diffusion coe cient for ISF was estimated to be equivalent to that for water and was obtained by extrapolating data of oxygen diffusion coe cient vs. temperature reported by  Han et al. (1996)  to physiologic temperature.
To collect representative data three positions within the conduit were selected; at the center of the distal stump (position 1), the center of the gap between the nerve stumps (position 2), and at the center of the proximal stump at the intersection of the regenerating and baseline sections (position 3), see Fig. 11. Figure  11  The three locations where oxygen concentration vs. time data were monitored. Note the ends of the nerves are not shown in this gure. Location 1 was chosen as a representation of a region with normal, baseline oxygen consumption by a nerve. Location 2 is in the center of the nerve gap, and in the center of the conduit. In location 2 there is no direct oxygen consumption by the nerves (due to their absence), however oxygen could potentially be depleted from this region due to consumption by the two neighboring nerve stumps.
In this case, the parameters of the liquid (density and viscosity) become a function of coordinates and time (cases in Figure  2e, f ). In  [107] , a model was proposed for calculating the response of a quartz resonator under the action of a multilayer viscoelastic load, when the viscosity and density of quartz coatings change from one layer to another. However, even in this simplified representation, the behavior of a quartz sensor under an anhomogeneous load can only be determined by numerical calculations. The presence of various types of protein molecules (cases in Figure  2g-j ) requires the solution of a system of equations for each particle, taking into account their electrochemical interaction. In the present work, this was done using numerical simulations in COMSOL Multiphysics Software.
1 M NaOH for 5 min and resuspending in Laemmli buffer), followed by SDS-PAGE, western blotting, and incubation with anti-myc (dilution 1:10000, 9B11 #2276, CST) or anti-RPS9 serum (dilution 1:10000, polyclonal rabbit antibodies were a generous gift of Prof. S. Rospert). Protein expression and purification. V. vulnificus ExoY is expressed as a module of MARTX toxins, which are post-translationally cleaved inside the host cell. As in the article, we discuss exclusively the ExoY effector, we simplified the amino acid numbering by designating amino acid 3229 of the MARTX from Vibrio vulnificus strain BAA87 (GenBank: KJ131555. 1) as the residue 1. Vibrio nigripulchritudo ExoY, or fusion proteins of P. aeruginosa ExoY, V. nigripulchritudo ExoY, V. vulnificus ExoY or their chimeras, and maltose-binding protein (MBP) were purified from E.
Fifteen minutes before plunging, F-actin was diluted to 2 µM, mixed with 4 µM of PaExoY, 2 mM 3′-deoxyguanosine-5′-triphosphate (3′-dGTP, Jena Bioscience) and 4 mM MgCl 2. Shortly before plunging, Tween-20 was added to the sample to a final concentration of 0. 02% (w/v) to improve the ice quality. Plunging was performed using the Vitrobot Mark IV system (Thermo Fisher Scientific) at 13 °C and 100% humidity. 3 µl of sample were applied onto a freshly glow-discharged copper R2/1 300 mesh grid (Quantifoil), blotted for 8 s on both sides with blotting force -20 and plungefrozen in liquid ethane. The dataset was collected using a Krios Titan transmission electron microscope (Thermo Fisher Scientific) equipped with an XFEG at 300 kV using the automated data-collection software EPU, version 2. 7 (Thermo Fisher Scientific).
Indeed, 176067 segments without PaExoY density were removed and the remaining 1. 54 million were introduced into the final round of 3D refinement with a tight mask covering actin and PaExoY subunits. The final reconstruction map was postprocessed using 3D local filter based on the local resolution of the map in Sphire. Maps presented on Fig. 1a  and Supplementary Movie 2 were postprocessed using DeepEMhancer version 1. 0  42. The overall processing, FSC curves and local resolution maps are available in Supplementary Fig. 1. To build a model of the PaExoY-F-actin complex, we performed flexible fitting of the PaExoY partial crystal structure (5XNW  22  ) into the EM density using iMODFIT Chimera plugin  43  , version 1. 2. Then, we build the remaining 106 amino acids using Rosetta software version 3  44, 45  and added 3′-dGTP, which was modeled with eLBOW  46.
The data collection was performed on a Krios Titan transmission electron microscope (Thermo Fisher Scientific) equipped with an XFEG at 300 kV and CScorrector using the automated data-collection software EPU, version 2. 7 (Thermo Fisher Scientific). Four images per hole with a defocus range of -1 to -2. 5 µm were collected with a Gatan K2 camera operated in counting mode. Image stacks with 64 frames were collected with a total exposure time of 8 s and a total dose of 80 e -/Å 2. 10,659 images were acquired and 9329 of them were used for processing. Motion correction and CTF estimation have been similarly performed in CTFFIND  35  , version 4. 1. 13, and MotionCorr2 36 , version 1. 2. 6, during image acquisition using TranSPHIRE 37 , version 1. 4. 28. Particle picking using crYOLO  38  , version 1.
Then, the mixture was applied onto a freshly glow-discharged copper R2/1 300 mesh grid (Quantifoil), blotted for 3 s on both sides with a blotting force -5 and plunge-frozen in liquid ethane using the Vitrobot Mark IV system (Thermo Fisher Scientific) at 13 °C and 100% humidity. The data collection was performed on a Krios Titan transmission electron microscope (Thermo Fisher Scientific) equipped with an XFEG at 300 kV and CScorrector using the automated data-collection software EPU, version 2. 8 (Thermo Fisher Scientific). Three images per hole with a defocus range of -1. 2 to -2. 5 µm were collected with a Gatan K3 camera operated in superresolution mode. Image stacks with 60 frames were collected with a total exposure time of 2 s and a total dose of 60 e -/Å 2. Following acquirement of 3773 movies without stage tilt, 3106 micrographs were imaged in an equal number using 10-, 20-or 30-degrees stage tilt.
7. To build a model of the VvExoY-G-actin-profilin complex, we first modeled VvExoY using tFold server (https://drug. ai. tencent. com/) and fit it into the EM density using iMODFIT Chimera plugin, version 1. 2  43. Amino acids 1-38, 236-273, 399-432 that did not fit the experimental density were removed and remodeled using Rosetta software, version 3  44, 45. The VvExoY model was then merged with profilin-G-actin complex from PDB 6NBW  52  and further refined using ISOLDE, version 1. 0B4  47  and Phenix, version 1. 17  48. Homology modeling. Homology models of the chimeric ExoY proteins were built using MODELLER  53. For this, we used PaExoY and VvExoY as templates, but deleting the areas of the protein that should not be taken into account (e. g. , PaExoY's C-terminus in Chimera A). Molecular dynamics simulations.
Acknowledgements We thank  O. Hofnagel  and  D. Prumbaum  for assistance with data collection and maintaining the EM facility,  S. Bergbrede  for the excellent technical assistance,  W. Linke  and  A. Unger  ( Ruhr-Universität Bochum, Germany ) for providing us with muscle acetone powder,  S. Rospert  ( University of Freiburg, Germany ) for providing us anti-RPS9 serum,  J. Funk  and  P. Bieling  for providing us with profilin-1, and  S. Pospich ,  Y. Belyi , and  D. Ladant  for fruitful discussions. This work has been funded by the  Max Planck Society  (S. R. ). U. M. was supported by  ANR  ( Agence nationale de la recherche)  grant  ANR-18-CE44-0004. A. B. was supported by a fellowship of the Humboldt foundation and an  EMBO  long-term fellowship.
Data availability The coordinates for the cryo-EM structures of PaExoY-F-actin, VvExoY-G-actinprofilin, and VnExoY-G-actin have been deposited in the Electron Microscopy Data Bank under accession numbers EMD-13158, EMD-13159, EMD-13160. The corresponding molecular models for the PaExoY-F-actin, VvExoY-G-actin-profilin complexes have been deposited at the wwPDB with accession codes PDB 7P1G, 7P1H. The raw data generated during the current study are available from the corresponding author on reasonable request. Individual data points from the graphs at Fig. 4b-d , Supplementary Figs. 2e, g, 3b, d, g , and 5 are available in Source data. Uncropped gels and western blots from Supplementary Figs. 2d, f, 3e, f , and 8c can be found in Supplementary Fig. 10. Source data are provided with this paper.
e. , q and w; the correlation between these two parameters: r (37) = 0. 258, p=0. 113). We next constructed the neural RDMs by calculating the correlation dissimilarity of the parameter estimates of subjective-level contrasts during bribery-related decision-making extracted from the bilateral dlPFC (i. e. , the contrast of Bribe vs. Control). We then measured the Figure  6. Illustration of the inter-subject representational similarity analysis. We created a parameter representational dissimilarity matrix (RDM), which measures the dissimilarity across participants in the bribery-specific preference that is calculated by the Euclidean distance between each pair of participants in q and w driven from the winning model. Both parameters together characterize the individual differences in bribery-specific preference (see the scatter plot: each dot represents the data of a single participant; the density curve indicates the distribution of each parameter, respectively).
We also created a neural RDM, which measures the dissimilarity across participants in the neural activities within bilateral dorsolateral prefrontal cortex (dlPFC) during bribery-related decision-making (i. e. , Bribe vs. Control) that is measured by the correlation distance between the multivoxel patterns of each pair of participants (i. e. , those heat maps; only for illustration). The region of dlPFC was defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (http://neurovault. org/images/39711/). We then calculated the Spearman rank-order correlation between these two RDMs and implemented a permutation test to confirm the statistical significance. similarity between the neural RDMs and the preference RDM (see Figure  6 ).
Cover story Participants were assigned the role of the power-holder who decides to accept or reject financial offers (see Figure  1A ). They were informed that they would be presented with a series of choices from an independent group, whose data were previously collected by the experimenter. This independent group was actually fake, and the choices made by this group were controlled by the experimenters (see Stimuli for details). Specifically, participants were led to believe that this independent group of online attendants participated in a 'Game of Chance' and had been randomly assigned to one of two roles, that is, a proposer and the third party. The proposer played either alone (i. e. , the Solo scenario) or with a randomly matched third party (i. e. , the Dyad scenario). In the Solo scenario, each proposer was presented with two options that would earn them different payoffs with the total of the two payoffs fixed at CNY ¥100.
e. , the real participant) at their own cost. The offer proportion was defined as the proportion of the amount the proposer decided to share with the power-holder from the payoff the proposer would have earned in the reported option, ranging from 10% to 90%, which in turn increases the variance of participants' responses and further benefited the computational modeling analyses. As a result, 36 different offers were adopted for the present study (see Supplementary file 1j for details). Each unique offer appeared once in each of the four experimental conditions (i. e. , SC, SB, DC, and DB). An event-related design was adopted and consisted of two functional runs with each containing 72 trials and lasting about 12-15 min. For each participant, we randomly divided the 36 offers into two equal datasets, then randomly associated two conditions with the first subset and two other conditions with the second subset, and assigned these stimuli to the first run (e.
The probability of accepting the offer was determined by the softmax function (Equation  8 ): p SV accept À Á ¼ e t SVaccept e t SVaccept þe t SVreject ( 8 ) where t is the inverse softmax temperature parameter (0 <= t <= 10) denoting the sensitivity of an individual's choice to the difference in SV between the choice of accepting the offer and that of rejecting the offer. We fitted all the above-mentioned candidate models using the HBA approach via the 'hBayesDM' package  (Ahn et al. , 2017). The 'hBayesDM' package is developed based on the Stan language (https://mc-stan. org/), which adopts a MCMC sampling scheme to perform full Bayesian inference and obtain the actual posterior distribution. We used HBA rather than maximum likelihood estimation (MLE) method because HBA provides much more stable and accurate estimates than MLE does  (Ahn et al.
g. , Akaike Information Criterion), the LOOIC score offers the estimate of out-of-sample predictive accuracy in a fully Bayesian way. Conventionally, the lower LOOIC score indicates better out-of-sample prediction accuracy of the candidate model. A difference score of 10 on the information criterion scale is regarded as decisive  (Burnham and Anderson, 2004). We selected the model with the lowest LOOIC as the winning model for subsequent analysis. We also perform a parameter recovery analysis to ensure that our model was robustly identifiable. We first generated a simulated dataset (choices) for each participant using the individual-level posterior mean of these parameters (i. e. , the true value) corresponding to that specific participant based on the winning model. Next, we fitted our winning model to the simulated dataset with the same methods (see Materials and methods for details) and obtained the individual-level posterior mean of these parameters (i.
e. , the recovered value). We quantified the performance of the parameter recovery by calculating the bivariate correlation, with Pearson correlation test, between the true value and the recovered value for each parameter, respectively. To further examine the absolute performance of the winning model (i. e. , whether the prediction of the winning model could characterize the features of real choices), we also performed a PPC  (Zhang et al. , 2020). Specifically, we generated new choice datasets, given each individual's joint posterior MCMC samples (i. e. , 4000 times), in accordance with the actual trial-wise stimuli sequences presented to each participant, resulting in 4000 choices per trial per participant. Thus, for each participant, we obtained the model prediction by calculating the average offers given these new datasets in the four experimental conditions, respectively.
We also performed an out-of-sample PPC to avoid the overfitting concern. Given that the stimuli (e. g. , proposer's offers) in each condition were randomly but evenly distributed into two fMRI runs for each participant (see Materials and methods for details), we used the choice data from Run 1 for model estimation based on the winning model. Then, we used the mean of the posterior distribution of the individual-level parameters to simulate an independent dataset based on the stimuli used in Run 2 and calculated the acceptance rate for each participant in the different conditions (i. e. , predicted acceptance rate), as we did for the real choice data in Run 2 (i. e. , actual acceptance rate). For both PPC, we examined to what degree the individual difference in model prediction was correlated with that of the actual acceptance proportion using the Pearson correlation test.
fMRI data analyses Functional imaging data were analyzed using SPM12 (Wellcome Trust Centre for Neuroimaging, University College London, London, UK). The preprocessing procedure followed the pipeline recommended by SPM12. Functional images were first realigned to the first volume to correct motion artifacts, unwarped, and corrected for slice timing. Next, the structural T 1 image was segmented into white matter, gray matter, and cerebrospinal fluid with the skull removed, and co-registered to the mean functional images. Then all functional images were normalized to the MNI space, resampled with a 2 Â 2 Â 2 mm 3 resolution, based on parameters generated in the previous step. The normalized images were smoothed using an 8 mm isotropic full width half maximum Gaussian kernel. High-pass temporal filtering was performed with a default cut-off value of 128 s to remove low-frequency drifts.
001 as the cluster-defining threshold  (Eklund et al. , 2016). Moreover, a small volume correction (SVC) was conducted within these hypothetical regions of interest (ROI). All masks were defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (https://neurovault. org/collections/2099/). Compared with an ROI definition based on specific peak coordinates, this approach generates masks that are much less biased by a specific scientific research (e. g. , an empirical study or a meta-analysis) and are larger in volume (vs. a sphere with a radius of 6-8 mm). This ensures the validity and the reliability of the SVC. Regions were labeled according to the AAL template via the xjView toolbox (http://www. alivelearn. net/xjview8/). To illustrate the parametric effect of potential loss for the third party (GLM1b, GLM1b-s), and relative SV (GLM2a), we adopted the rfxplot toolbox (Gla ¨scher, 2009) (http://rfxplot.