dataesr/scientific-paragraphs-categorization huggingface dataset:
EEG acquisition and analysis. EEG activity was continuously collected using a Biosemi ActiveTwo 10-20 system with 64 active channels at 1024 Hz sampling rates and the ActiView software. The electrode offset was kept below 20 mV. The offset values were the voltage difference between each electrode and the CMS-DRL reference channels. EEG analyses were performed with MNE-Python v. 0. 22. 0  64. The Autoreject algorithm  65  was used to detect and repair artifacts. The motive in using this algorithm was to maximize the signal/noise ratio in adapting automatically the artifact detection parameters for each participant. It implements topographic interpolations  66  to correct bad segments. One participant was removed from EEG analysis due to an excessive number of artifacts in the recording. The procedure rejected a mean average of 36 trials (SD = 8).
A surface Laplacian filter was applied (stiffness m = 4, λ = 10 -5 ) to the data resulting in reference-free current source densities (CSD) which increase the spatial resolution of the signal and reduce the signal deformation due to volume conduction  67. For the CNV analysis, the data were filtered with a. 1 Hz high pass filter and a 30 Hz low pass filter. Then, the segmentation of the trials included a time interval starting 1200 ms before the offset of the warning signal and ending 700 ms and 1300 ms after the offset of the warning signal for the short and long FP, respectively. We selected the electrodes presenting the peak of the CNV component (i. e. electrodes C1, C2, Cz, CP1, CP2, CPz, as in  30, 37  ) recorded over centro-parietal sites. CNV activities were then z-score normalized, using the mean average of the 200 ms interval before the offset of the warning signal.
These comodulograms reflected the influence of the phase of theta-band oscillations recorded over the medial fronto-central cortex (electrode FCz) on the amplitude of the beta-band and gamma-band (24-40 Hz) oscillations recorded over the left motor cortex (electrode C3, similar to  36  ). For each participant and each cueing condition, comodulograms were computed from the current source densities of long FP trials using the entire 0-1000 ms time-interval following the offset of the warning stimulus, providing a phase and amplitude frequency resolution of 0. 2 Hz and 1 Hz, respectively. RStudio (v. 0. 99. 489) and the rstatix (v. 0. 6. 0) package were used to perform two-sided repeated-measures analysis of variances (rANOVA) and planned comparisons analysis with Tukey's HSD tests corrected for multiple comparisons with the falsediscovery rate method  70.
METHOD DETAILS Experimental Protocol. The experiment used the Unity software (Unity technologies, v. 2019. 3. 9f1) to create the virtual environment. The HTC Vive Eye Pro (HTC Corp. ) headset and controllers were used to immerse the participants in VR. Participants wore both the EEG and VR headsets while sitting on a chair. The use of VR has several advantages. Firstly, VR allows a better trade-off between fully-controlled experimental settings and ecological experience (e. g. 3D visual percepts) in comparison with 2D screen setups. Secondly, it increases the engagement of the participant to the task at hand. Thirdly, the embedded eyetracking system to the VR headset allows researchers to easily track and record the gaze in the 3D space. Eye-tracking was used to trigger visual stimuli. Participants were instructed to fixate the warning signal without moving their eyes, and it was only after a time interval free of saccades and eye-blinks that the warning signal was switched off.
This procedure improves the data quality of EEG recordings. Each participant was immersed in a virtual room, facing four 3D robots, each with a light whose color and onset were manipulated (Figure  1A ). The experiment was composed of two intertwined tasks: a variable foreperiod task and an asynchrony detection task. The asynchrony detection task consisted of discriminating whether the lights of two robots appeared synchronously or asynchronously (using delays of 11 ms, 33 ms, or 66 ms). Participants performed four blocks of trials for each task. The participant switched tasks every block to reduce boredom. At the end of each experimental block, a break was proposed to the participant to remove the VR headset. Given the research questions investigated in this article, only data collected from the variable foreperiod task are presented.
Second, raw data (i. e. the 3D gaze position over time) of each trial were interpolated with a spline method to increase the temporal precision followed by the calculations of the derivations of the speed of vertical and horizontal movements. Saccades were detected using a modification of a published algorithm 60 based on gaze's velocities. A threshold criterion for saccades detection was determined in the 2D velocity space based on the horizontal and the vertical velocities of gaze movement. This 2D space represented a plane surface located at the stimulus area (i. e. the warning signal and target). This threshold was represented by a 2D ellipse. For each trial, we set the threshold to be six times the SD of the gaze velocity 37,39,61 using a median-based estimate of the SD. Small fixational saccades were defined when six or more consecutive velocity samples (i.
These comodulograms reflected the influence of the phase of theta-band oscillations recorded over the medial fronto-central cortex (electrode FCz) on the amplitude of the beta-band and gamma-band (24-40 Hz) oscillations recorded over the left motor cortex (electrode C3, similar to  36  ). For each participant and each cueing condition, comodulograms were computed from the current source densities of long FP trials using the entire 0-1000 ms time-interval following the offset of the warning stimulus, providing a phase and amplitude frequency resolution of 0. 2 Hz and 1 Hz, respectively. Significance testing R (v. 3. 6. 1) sofware and the rstatix (v. 0. 6. 0) package were used to perform two-sided repeated-measures analysis of variances (rANOVA) and planned comparisons analysis with Tukey's HSD tests corrected for multiple comparisons with the falsediscovery rate method  70.
For panels b, e, h: maximum Z projection stacks (schematized in the bottom right corner with squares including multiple lines); f: single optical sections (schematized in the bottom right corner with squares including a single line). PP, prepontine; P, pontine; RP, retropontine; Med, medulla. Extended Data Fig. 2  | Brainstem nomenclature applied in this study according to  58. The anatomical landmarks were defined based on the projecting neurons labeled by spinal backfills, the expression patterns of Tg(chata:GAL4) and Tg(vmat2:GFP) transgenic lines. All images were taken from the web-interface of mapzebrain (https://fishatlas. neuro. mpg. de/, download: 12 Apr. 2020). The prepontine region contains the isthmus and rhombomere 1 (r1). The trochlear nucleus (4N) lies in the rostral border of the isthmus and, amongst other structures, r1 contains the locus coeruleus (LC).
MLR electrical stimulation Homemade glass-coated tungsten microelectrodes (0. 7-3. 1 MΩ, 10-35 µm exposed tip) were used to perform monopolar electrical stimulations. The electrodes positioned with a 35°angle penetrated the skin at different sites in the prepontine region using a motorized micromanipulator (MP-285A, Sutter Instruments, California, USA). In order to guide the electrode position to the MLR, we used the locus coeruleus as a landmark by labeling it with GFP in the Tg(vmat2:GFP) zf710Tg transgenic larvae expressing GFP under the vmat2 promoter  106. Stimulation was delivered using an isolated stimulation unit (2100, AM systems, USA) controlled with a Digidata series 1440A Digitizer (Axon Instruments, Molecular Devices, USA) to synchronize the electrical stimulation and behavioral recording.
The timing of the electrical stimulations was triggered and recorded using Clampex 10. 3 software (Axon Instruments, Molecular Devices, USA). When searching for the MLR, the behavioral response to stimulations in the locus of the MLR was typically recorded for multiple loci by applying continuous stimulation at 10 Hz and 1 µA not exceeding 30 s. If after 5 loci tested, no clear behavioral response was observed, the larva was discarded. To ensure success, a rest period of at least 3 min was allowed between successive trials. Once the proper site for the MLR was found, we implemented experimental trials lasting each about one minute. The stimulation protocol consisted of negative pulses of 2 ms duration, 5-20 Hz frequency at 0. 1-3 µA intensity for 1-4s. To allow recovery and avoid synaptic habituation, the resting period between trials was at least 2 minutes.
Tail tracking and automated segmentation of bouts We used the open-source software ZebraZoom (https://github. com/oliviermirat/ZebraZoom) to track the position of the tail for each frame and extract bouts defined as discrete events when the tail was continuously moving  91, 109, 110. The tail angle was defined as the angle between the body axis of the fish and the tip of the tail  91. The quality of the tail tracking was carefully assessed by visual inspection. Electrically-induced bouts were defined as all bouts starting during the electrical train stimulation. Swim bouts starting just before the stimulation were excluded from analysis. "Spontaneous" bouts were defined as bouts starting and ending before the first electrical stimulation of the trial. Note that bouts occurring in the 1 min long time window after a stimulation train were excluded from the analysis as their properties could have been impacted by the prior electrical stimulation.
Analysis of kinematics From the tail angle, the ZebraZoom algorithm extracted the timing and amplitude of each tail bend that we then used to calculate instantaneous tail beat frequency and instantaneous tail bend amplitude  91, 109, 110. For each episode (Fig. 1c , 2), we estimated the corresponding duration, number of oscillations, and maximal tail bending amplitude as previously done for bouts in freely-swimming animals  91, 109, 110  , together with median bend amplitude and median instantaneous tail beat frequency (https://github. com/mathildelpx/Carbo-Tano_Lapoix_2022/).
Behavioral forward index In order to estimate the efficiency of a stimulation site to elicit forward swimming, we computed for each stimulation trial a forward index defined as: Forward index_stim = (number of forward swim episodes elicited -number of struggles episodes elicited) / (number of struggle episodes elicited +number of forward swim episodes elicited) As we describe in the results section and in Extended Data Fig. 1 , electrical stimulation at frequencies lower or above 10 Hz, or with intensities above 2 µA elicited mainly struggle behaviors. For this reason, we computed the median forward index of each stimulation site (Fig. 1e ) only using the stimulation trials with optimal stimulation parameters, i. e. trains of pulses at 10Hz with current amplitudes of 1-2uA.
Registration of electrical stimulation sites in the brain atlas To assess the position of the stimulation electrode, Z projection confocal stacks were acquired combining the GFP signal and the differential interference contrast (DIC) channel using a 20X/1. 0 DIC D = 0. 17 M27 75mm (Zeiss, no:421452-9880-000) objective. The z-stacks were acquired with a step of 1 µm depth using Slidebook software 6. 0 (3i Intelligent Imaging Innovations, Inc. , Denver, CO, USA). From each image volume, we annotated manually in Fiji 111 (https://fiji. sc/Fiji) the coordinates of the electrode position from the DIC channel and three anatomical reference coordinates from the GFP pattern of expression of the Tg(vmat2:GFP) transgenic line (medial boundary of the locus coeruleus on each side, anterior limit of the superior raphe and anterior limit of the inferior raphe).
After larvae were again anesthetized with 0. 02% MS-222 and mounted in 1. 5% low melting point agarose. The retrogradely-labeled neurons were then imaged in a confocal microscope as described above. From each image volume, the coordinates of the backfilled neurons in the region of the MLR were annotated using Fiji. The positive backfilled neurons selected for this analysis were taken from the MLR area defined by electrical stimulations (with a diameter of approximately ~30 µm). For the experiment in Fig. 3a-d , the injections were done in WT fish. Since the dye injection forms a background labeling in the entire nervous system, we chose four anatomical landmarks also easily distinguishable in the mapzebrain DAPI fish space. For the experiments in Fig. The script used and the registration steps applied were the same as described in the precedent section.
The larvae were next imaged using a confocal microscope using a spinning disk head (CSU-W1, Yokogawa) on an upright microscope (Examiner Z1, Zeiss), using the 488 nm and 561 nm laser light sources (LaserStack, 3i Intelligent Imaging Innovations, Denver, USA). Images were acquired using a 20X N. A. = 1. 0 DIC D=0. 17 M27 75mm (Zeiss, no:421452-9880-000), using Slidebook software 6. 0 (3i, Intelligent Imaging Innovations).
Calcium imaging movie pre-processing For all analysis of the fluorescence movies, we used the software suite2p  119  (https://github. com/MouseLand/suite2p, v. 0. 8. 0). The pipeline first corrected the movies for motion artifacts in 2D using rigid and nonrigid registration. Most regions of interest (ROIs) were automatically identified by the pipeline, and we manually added ROIs on cells that were not detected, including the soma of neurons that did and did not show activity during the recording. Then, using a custom-built Python script, for each ROI: 1) we corrected the raw fluorescence trace by subtracting the neuropil signal (i. e. , sum of surrounding pixels' fluorescence, with a factor n = 0. 7); 2) we calculated the DF/F(t) = (F(t) -F0) / F0, where F(t) is the fluorescence at time t and F0 is the cell baseline fluorescence (median fluorescence value selected on a 3 s-long period of inactivity); 3) we estimated the noise, as the standard deviation of the signal during the same 3-s long period of inactivity, which was further used to set the recruitment threshold of the cell; 4) the DF/F was filtered using an average running filter with 3 time steps.
The normalized coordinates were subsequently used for mapping neurons position in 3D. on the python package AgglomerativeClustering from scipy. cluster library. We manually selected an arbitrary number of clusters (3 to 6) such that at least one cluster was specifically recruited during forward swimming only. To quantify that a group of neurons was indeed more active during forward swims, we computed for each neuron a forward activity index as : forward activity index = (mean(max DF/F during forward swims) -mean(max DF/F during struggles) ) / (mean(max DF/F during forward swims)+ mean(max DF/F during struggles)) We verified for each trial that the cluster of neurons assigned as "forward" had the highest forward activity index.
Acknowledgements We thank  Sophie Nunes Figueiredo ,  Monica Dicu  and  Antoine Arneau  from  Animalliance  for fish care. We thank  Tod Thiele  and  Minoru Koyama  for their comments on the results. We are grateful to  Harold Burgess  and  Ashwin Bhandiwad  for providing critical inputs to the manuscript. We thank  Faustine Ginoux  for the morphological registration pipeline and  Gautam Sridhar  for his inputs on the modeling approach. We thank  Danielle Veilleux  for making the monopolar micro-electrodes and her help with the histology.
Data visualization and statistical analysis Analysis was done either in R 120 (v4. 0. 4) using the tidyverse package  121  (v1. 3. 0) or in Python 3. 7. Initial figures were done either in R (v4. 0. 4) using the tidyverse package (v1. 3. 0) or using the matplotlib 122 (v3. 3. 2) and seaborn 123 (v0. 11. 0) Python libraries. Figures were finally assembled in Inkscape (version 1. 1, https://inkscape. org/). All statistical tests applied are specified in the legend of the corresponding figure. Statistical tests were performed in R (v4. 04), except for the multiple linear model used in Fig. 5, for which we used the OLS function from the library stats. models developed for Python (https://github. com/statsmodels/statsmodels).
Leading-order (LO) results were readily obtained  [1, 2, 5, [16] [17] [18] [19] , followed shortly thereafter by the calculation of the nextto-leading order (NLO) corrections  [20] [21] [22] [23] [24] , which were recently confirmed by an independent study  [25]  and even extended to three loops (NNLO) in the non-singlet case  [26]. On the phenomenological side, early efforts were devoted to developing GPD evolution codes. Vinnikov  [27]  developed the first open-source code in momentum space able to evolve GPDs at LO accuracy. However, the code webpage no longer exists and, as far as we can tell, the only public version of this code is its implementation in the PARTONS framework  [28]. A few years earlier, Freund and McDermott  [29]  developed a code able to evolve GPDs at NLO tailored to the computation of deeply virtual Compton scattering.
However, to the best of our knowledge, this code was never made fully open-source, and as of today it is difficult to find a clean copy. In parallel, a strong effort was put into obtaining an evolution procedure at NLO in conformal space (see e. g. Ref. [30] ), yielding the only public NLO evolution code available today  [31, 32]. We point out that all the codes mentioned above are rigidly associated with specific GPD models or families of parameterisations, and can hardly be used out of the box to evolve different input GPDs. Moreover, to the best of our knowledge, none of them allows for the treatment of heavy flavours, while a significant amount of current experimental data lies above the charm threshold. In the last decade, these codes have not taken the front stage mainly because the latest and most precise experimental data related to GPDs were obtained in relatively small ranges and at relatively small values of the hard scale Q 2.
The necessity of using evolution equations for a consistent theoretical analysis of experimental data was jeopardised by the poor accuracy of LO perturbative QCD at low scales. This has made evolution of GPDs less critical for phenomenological purposes. However, with the forthcoming EIC and EicC, the situation is expected to change drastically, as exclusive processes will be measured in a larger kinematic range, making the need for evolution pressing. In this paper, we revisit the LO evolution equations of GPDs in momentum space computing the one-loop unpolarised anomalous dimensions renormalised in the MS scheme in the light-cone gauge. In order to make the paper self-contained, we provide a pedagogical description of the computation targeting newcomers unfamiliar with the most technical aspects of the field, a community which is expected to grow in view of the timeline of the EIC and EicC projects.
We formalise our results in a way that allows us to study their properties and that facilitates the numerical implementation. The solution of the evolution equations is implemented in the open-source code APFEL++  [33, 34]  that is interfaced to the PARTONS framework. In Sect. 2, we derive the GPD evolution equations and present our calculation of the kernels. These equations are presented in a form that resembles the DGLAP equations, thus allowing us to exploit the capabilities of existing evolution codes such as APFEL++ for their solution. In Sect. 3 , we present a thorough study of the analytical properties of the ensuing evolution kernels. In Sect. 4, we discuss the numerical implementation and provide quantitative evidence that the evolution fulfils fundamental requirements such as correct DGLAP and ERBL limits, continuity, polynomiality, and equivalence with the conformal-space approach.
Numerical results In Sect. 2, we recast the GPD evolution kernel in a form suitable for a straightforward implementation in the numerical code APFEL++  [33, 34] , allowing for robust evaluations and handling of heavy-flavour thresholds. In Sect. 3, we detailed the theoretical properties of this particular form. In this section, we present a series of numerical checks aimed at establishing the validity of our implementation to high numerical accuracy. To the best of our knowledge, this provides the most extensive set of tests of an implementation of GPD evolution equations ever presented in the literature, at least with respect to publicly released codes. Although here we are not concerned with performance and computing speed, our implementation guarantees a fast evaluation of GPD evolution suitable for phenomenological extractions.
( 102 ) This simple ansatz allows us to benchmark our x-space evolution code using a realistic behaviour of the non-singlet GPDs with respect to conformal evolution. In Fig. 6 , we compare the second (n = 4) conformal moment, computed by means of Eq. (  75 ), of the RDDAbased model evolved to μ = 2, 5, 10, 100 GeV obtained by numerically solving Eq. (  28 ) to the solution of Eq. (  78 ) with the evolution kernel given in Eq. (  86 ). The upper panel of the plot displays the evolved conformal moment as a function of ξ for the different values of μ computed by solving Eq. (  28 ), while the lower panel shows the ratio to the solution of Eq. (  78 ). It is clear that the agreement between the two evolution methods is excellent over the entire range in ξ considered, validating our implementation in the light of Sect.
3. 5. Before proceeding to comparing our evolution code with another implementation, we emphasise that all the numerical tests performed in Sects. 4. 1-4. 4 turned out to be very successful. Namely, we found that all the fundamental properties of GPD evolution, including DGLAP and ERBL limits, polynomiality conservation, and equivalence with the conformalmoment approach, are fulfilled to the sub-per-mil level or better. We regard this as a very strong consistency check of our code.
Comparison with Vinnikov's code In this section, we compare the evolution obtained with APFEL++ to that presented in Ref. [27] , which in the following will be referred to as "Vinnikov's code" after its author. Specifically, we use an implementation of Vinnikov's code available in the PARTONS framework  [28]. A limitation of Vinnikov's code is that it does not implement the variableflavour-number scheme; i. e. it does not allow one to cross heavy-quark thresholds along the evolution. Therefore, for the comparison, we have used the n f = 3 fixed-flavournumber scheme in which three quark flavours (up, down, and strange) are active at all scales. Like APFEL++, Vinnikov's code can perform GPD evolution only at LO. As initial-scale distributions we have used the model presented in Refs. [61] [62] [63]  which also depends on the momentum transfer t that we set to t = -0.
1 GeV 2. In Fig. 7  we present the comparison for the evolution between μ 0 = 2 GeV and μ = 10 GeV for the GPDs H - u , H + u , and H g. The upper panels display the absolute distributions for four different values of the skewness parameter ξ = 10 -4 , 0. 05, 0. 5, 1 with the solid lines showing the results obtained with Vinnikov's code and the dashed lines those obtained with APFEL++. The lower panels show the same curves normalised to APFEL++. We observe general very good agreement between the two codes for almost all values of ξ and across the full range in x considered. The only exception is ξ = 1, for which a disagreement at the percent level for H - u (non-singlet sector) and as large as 20% for H + u and H g (singlet sector) is observed. We could not identify the origin of this disagreement, but in view of the reported numerical instabilities of Vinnikov's code in the large-ξ region  [64] , we suspect that the results of this code at ξ = 1 might be affected by numerical inaccuracies.