dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Ashida et al. , 2010; Vinck et al. , 2010 Vinck et al. , , 2012;; Jiang et al. , 2015; Li et al. , 2016; Zarei et al. , 2018)  which are suboptimal for modern datasets. Indeed, state-of-the-art multichannel electrophysiology systems  (Dickey et al. , 2009; Jun et al. , 2017; Juavinett et al. , 2019)  allow simultaneous recording of hundreds or even thousands of sites  (Pesaran et al. , 2018; Jun et al. , 2017; Buzsáki, 2004; Fukushima et al. , 2015). This growth in dimensionality and complexity requires the parallel development of appropriate methodologies and models  (Stevenson and Kording, 2011; O'Leary et al. , 2015; Gao and Ganguli, 2015; Williamson et al. , 2019). In particular, recent technological advances offer an unprecedented opportunity to study the relationship of large scale collective organization binding the activity of individual units (e.
g. as spiking activity) with spatio-temporal field potential dynamics (e. g. wave patterns  (Ermentrout and Pinto, 2007; Ermentrout and Kleinfeld, 2001) ). Alongside this experimental progress, there is a growing need for conceptual and methodological frameworks to investigate this relationship. In particular, computing an interpretable summary of the coupling between neurophysiological quantities is of paramount importance in this high dimensional setting. We develop a "Generalized Phase Locking Analysis" (GPLA) to address this growing need. GPLA characterizes and assesses statistically at once the coupling between the spiking activity of large populations of units and large-scale spatio-temporal patterns of LFP. After demonstrating GPLA's interpretability on toy simulated datasets and statistical benefits with respect to pairwise coupling approaches, we use neural field models to illustrate how this method characterizes key aspects of the underlying neural circuits.
We ran 5000 simulations with only 3 units and compared the coupling assessment based on PLV, pPLV, and gPLV. Figure  3D  represents the estimated PLVs, with averages matching the couplings obtain with a larger number of spikes Figure  3C. Performance of each measure is assessed based on its detection rate, which is defined as the percentage of simulations for which significant coupling is detected, as assessed using spike-jittered surrogate data (see STAR Methods section Significance assessment of gPLV) and with a significance threshold of 5%. As it is demonstrated in Figure  3E , gPLV detection outperforms the competing approaches (PLV and pPLV). Beyond better detection of significant coupling, GPLA-based estimation of pairwise couplings based on the approximation of Equation 3 may be more accurate than individual estimates when the data is very noisy and multivariate, benefiting from the SVD procedure to disentangle noise from the ground truth coupling.
Analysis of Utah array data in the prefrontal cortex After testing the capabilities of GPLA on simulations, we investigated its performance on a largescale electrophysiological dataset. We applied the method on Utah array (10×10 electrodes, interelectrode distance 400 ) recordings from the ventrolateral prefrontal cortex of one anaesthetized rhesus monkey (see Figure  8A ). LFP signals were preprocessed as described in STAR Methods, section Animal preparation and intracortical recordings, and multi-unit activity with a minimum of 5 Hz firing rate was used. Recorded signals are exemplified in Figure  8B-F. Exemplary LFP traces are illustrated in Figure  8B. Each trace is recorded from the location specified in Figure  8C. Spike trains are also displayed in Figure  8D  (for the same epoch used in Figure  8B ).
As the analysis is performed in band-limited frequency ranges we also exemplified band-passed LFP signals (together with spikes) in Figure  8E  and F. The dataset consisted of 200 trials of visual stimulation (10 sec) and intertrials (10 sec) each 20 sec. Computing GPLA in different frequency bands revealed that the strongest coupling was in the alpha range (5-15Hz) (Figure  8G ). Furthermore, we assessed the significance of coupling with both surrogate and analytical tests (see STAR Methods, section Significance assessment of gPLV). GPLA above 50 or 60 Hz should be considered with caution, as in high frequencies the spike-LFP relationships can be largely effected by the local coupling between spikes and LFP recorded from the same electrode. Furthemore, contamination of high frequency LFP bands by spike waveforms  (Zanos et al.
The theoretical statistical test makes the method applicable to high dimensional electrophysiology data with low run-time complexity, which is important for modern probes such as Neuropixel with 960 recording sites  (Jun et al. , 2017). Notably, the conventional statistical testing procedures based on the generation of surrogate data are computationally very expensive as their computation time is scaled by the number of recorded neurons and number of surrogate datasets generated for construction of a null distribution. In summary, GPLA is a powerful tool to quantify, statistically assess and interpret the interactions between spiking activity and large-scale dynamics of current recording techniques.
Given spike times { ,( ) } =1. ( ) , ( ) LFP (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. phase, and ( ) the analytical LFP, we can define the coupling matrix , based on PLV (Equation  11 ) as follows, ( ) , = 1 ∑ =1 ( ) ∑ =1 exp ( ( ) ) , ( ) , (14) or based on (Equation  13 ), ( ) , = 1 √ ∑ =1 ( ) ∑ =1 ( ( ) ) , ( ) , (15) where , and respectively indicate the index of spiking unit, index of spike time and index of LFP channel and refers to number of spikes recorded in spiking unit 1. Let and be the number of LFP channels and number of spiking units, respectively, is thus a × complex-valued matrix (or × if whitening is applied). As (or ) and are not necessarily equal in electrophysiological datasets, the coupling matrix is not square in general.
Optional normalization for gPLV As gPLV is a singular value of a matrix, it grows with the dimensions of the coupling matrix. This makes the comparison of gPLV resulting from different datasets difficult. For instance, assume the hypothetical situation of having two datasets recorded from two homogeneous populations of neurons, if the strength of coupling is the same in two populations, the populations with a larger amount of recorded neurons (therefore larger dimension of the coupling matrix) will have larger gPLV. Certainly, this can be misleading for investigating the spike-LFP coupling with GPLA when datasets with variable number of spiking units and/or LFP channels. To overcome this issue we suggest normalizing the gPLV to become independent of the size of the neural population (dimension of the coupling matrix) and the number of channels.
Surrogate-based test In contrast to uni-variate methods for which the distribution under a null hypothesis is more likely to be (possibly approximately) derived based on theoretical analysis (e. g. Rayleigh test for PLV  (Fisher, 1995, Chapter 4 )), such approaches are usually unavailable in multi-variate settings (nevertheless, we have developed one for gPLV, see section Analytical test). Following a common alternative approach, we build the null distribution by generating many surrogate datasets  Grün (2009). The resulting gPLVs values forms an empirical 0 distribution that can be used to compute the p-value for statistical assessment of the significance gPLV in the data. Importantly, the choice of the appropriate surrogate data according to characteristics of neural data is critical. For instance, generating surrogate data by shuffling inter-spike-intervals (ISI) is not an appropriate method when we have non-stationarity in firing rates  (Grün, 2009).
In this work, we used an interval-jittering rather than a spike-centered-jittering 3  , as the former was reported to be more reliable for detecting temporal structures in spike data  (Platkiewicz et al. , 2017). We devised the two following spike-jittering-based methods for GPLA. We also verify the appropriateness of our jittering approaches with various simulations (see the Results). Simple interval jitter: Each surrogate dataset is generated by jittering all the spikes (from all neurons) with a particular jittering window (or dither width). In the interval jittering, per each spike, a new spike time is drawn within the jittering window around the spike. The timing of jittered spikes should be drawn from a uniform distribution. The size of the jittering window can be specified by the frequency wherein the spike-LFP coupling is being investigated.
The smallest jittering window (or dither width) that can be used in order to destroy the temporal structure potentially exists in the range of frequency-of-interest. In the phase-locking analysis of electrophysiological data we usually extract the analytic signal or instantaneous phase of LFP by applying Hilbert transform on band-limited LFP signals  (Chavez et al. , 2006). The central frequency of the band-limited filter can be used for specifying the jittering window (or dither width), i. e. jittering window is the inverse of this central frequency. Group preserved jitter: Similar to "simple interval jitter" we generate each surrogate dataset by relocating all the spikes within a window. For each surrogate data, we first divide the spike trains into equally-sized windows. Then we circularly shift the spike sequence within each window for all neurons together using a uniformly distributed time shift.
Analytical test Challenges in generation of surrogate data  (Grün, 2009)  and considerable increase in the dimensionality of datasets  (Pesaran et al. , 2018; Jun et al. , 2017; Buzsáki, 2004; Fukushima et al. , 2015) , suggest that deriving mathematically (asymptotic) properties of GPLA under the null hypotheses, as is done for univariate testing (e. g. Rayleigh test for PLV  (Fisher, 1995, Chapter 4) ) is an interesting alternative. In a companion work  (Safavi et al. , 2020) , by using martingale theory  (Aalen et al. , 2008)  we derive an asymptotic distribution for the entries of the coupling matrix in fairly general settings. Furthermore, by exploiting RMT  (Anderson et al. , 2010)  we can find a good approximation of the distribution of eigenvalues (or singular values) of the coupling matrix in absence of coupling between spikes and LFPs.
Acknowledgments We thank  Britni Crocker  for help with preprocessing of the data and spike sorting;  Joachim Werner  and  Michael Schnabel  for their excellent IT support. Andreas Tolias  for help with the initial implantation's of the Utah arrays; scidraw. io for providing a free repository of high-quality scientific drawings (in particular  Macauley Smith Breault  for providing her brain drawing in this repository). This work was supported by the  Max Planck Society.
Patient Demographic Characteristics Of 5859 participants who reported a vitiligo diagnosis and were directed to the survey, 3919 (66. 9%) completed the survey, 378 (6. 5%) were excluded for data quality issues (eTable 2 in Supple-ment 1), and 3541 (60. 4%) were included in the analysis. The median age was 38 years (range, 18-95 years); 1933 (54. 6%) were male, 1603 (45. 3%) were female, and 5 were nonbinary (0. 1%). Race and ethnicity were not solicited in France (n = 250) or Germany (n = 250). A total of 929 of the 3041 patients with available data were Asian (30. 5%), 283 were Black (9. 3%), 144 were Central or South American (4. 7%), 135 were Middle Eastern or North African (4. 4%), and 1555 were White (51. 1%). A total of 75 patients (2. 5%) reported race and ethnicity as "other," as categorized within the survey, with no further breakdown available.
Background Translation is a critical biological process that entails the conversion of genetic information encoded in messenger RNA (mRNA) into functional proteins. Traditionally, eukaryotic mRNA was thought to be inherently monocistronic, denoting that each mRNA molecule codes for a single protein sequence. Recent advances in ribosome sequencing and proteomics research have challenged the traditional view of protein translation, revealing a more complex translational landscape  [1] [2] [3] [4]. It is notable that assumptions regarding protein translation are deeply ingrained in proteomics research, as protein databases are built upon these assumptions and do not encompass the entirety of the human proteome. We recently reviewed microproteins and dual-coding properties of mRNA in neurobiology and their potential involvement in neurodegenerative disorders  [5].
State-dependent TMS mapping procedures To explore whether STN-DBS modifies dynamic changes in corticospinal excitability during movement initiation, all patients performed a task-TMS paradigm that measured shifts in MEP size during the motor preparation phase and their reaction times (RT; pushing a joystick) following self-selected action preparation (endogenous cues) versus exogenously presented cues. The experimental task was coded and run on Matlab using the Psychophysics Toolbox  (Kleiner et al. , 2007; ref). Patients remained seated in the chair of the robotic TMS while staring at the presentation screen centered at eye level and placed at a comfortable viewing distance between 60 and 100 cm. After the presentation of a 1,000 ms fixation cross, they were asked to move the joystick as fast and accurately as possible towards the green arrow displayed during the 500 ms cue.
EMG EMG data were recorded using a Cambridge Electronic device system (CED, Cambridge, GB), sampled at 5 kHz, and processed using Signal (CED, Cambridge, GB). Electrodes were placed on the hand presenting the least number of tremors in a tendon-belly montage for the first dorsal interosseus with the ground electrode placed on the ulna. More relevant to the behavioural task, EMG electrodes were also placed over two other extrinsinc hand muscles, the agonist flexor (FCR) and antagonist extensor (ECR) carpi radialis.
EEG EEG data was recorded using a 128-channels active cap and TMS compatible system (BrainAmp DC amplifiers, and ActiCap, Brain products, GmbH, Germany). At the start of Session #2, the cap was placed according to the 10-20 standard system. Impedance levels were adjusted and kept under 5 kOhms using conduction gel. Impedance was checked between each block and adjusted if necessary. EEG signal was recorded with the amplifier in DC mode with an anti-aliasing filter and digitized at 5 kHz sampling frequency. The reference and ground electrodes were Fz and AFz, respectively. To reduce the impact of the TMS click, patients were equipped with noise cancelling earbuds (Bose QC-25). A small layer of plastic was placed on the coil's surface to reduce any sensory impact. At the end of session two, electrodes positions were recorded using the neuronavigation software.
EMG processing The EMG signals were processed using CortexTool  (Harquel et al. , 2016b) , a Matlab toolbox developed in the lab and freely available online. EMG data were band-pass filtered (50-600 Hz), any trials presenting muscle activity in the baseline were removed. MEPs were visually inspected and automatically detected. The peak-to-peak amplitude of MEPs was extracted using Signal software in the time window between 10 and 40 ms after the TMS stimulus (Cambridge Electronic Design, Cambridge, UK). For the first dorsal interossei (FDI) muscle, we constructed mediolateral corticomotor excitability profiles based on the mean MEP amplitudes for each TMS target site along the central sulcus forming the hand knob. We derived three complementary measures from the MEP amplitude profiles to study in more detail dynamic changes in the muscle-specific representations in M1 with or without DBS.
EEG preprocessing EEG signal was semi-automatically processed using Fieldtrip  (Oostenveld et al. , 2011)  with home-made script written in Matlab (The MathWorks Inc. , United States), using  (Rogasch et al. , 2014)  two-rounds independent component analysis (ICA) method. First, a visual inspection of each trial and each channel was performed to remove channels with electrical noise (flat signal or amplitude >100 μV). Then, the signal was epoched from -1,000 to +1,000 ms around the TMS pulse. The signal was then cut from -5 to +15 ms to remove the TMS stimulation artifact. For the ON condition, an additional signal processing step was applied to accurately cut the signal in consideration with the DBS induced artifacts (see Supplementary Figure  S1A  ). The method was based on the derivative of the absolute raw signal.
Statistics Descriptive statistics were performed on the behavioral and EMG data using GraphPad Prism software version 8. 0 for Windows (GraphPad Software, San Diego, CA, USA). Additionally, a Wilcoxon signed-rank test was performed on the EMG derived measures and a Friedman test was applied on reaction times with factors DBS state (ON vs. OFF) and Movement type (Endogenous vs. Exogenous) as it accounts for non-gaussian distribution and for relatively low-sample sizes. The variability in the muscle representation between ON and OFF states was assessed by calculating the standard deviation of the weighted mean positions along central sulcus at the group level. The difference between ON and OFF states was then statistically assessed using a permutation test, where the observed "true" difference between the two standard deviations was compared with those obtained from the 32 possible permutations, the final p-value being the proportion of permutations resulting to a stronger difference than the observed one.
Funding This work was funded by the  Agence Nationale pour la Recherche  grant " ANR-15-CE37-0015-1 " and by  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). Data were acquired on a platform of  France Life Imaging Network partly funded by the grant  " ANR-11-INBS-0006. ".
The mice were then returned to the home cage for a 30 min intertrial interval and then placed back in the starting position but now with free access to all three arms for 5 min (test phase). The arm that was closed during the familiarization phase was considered the "novel" arm, and the arm visited during the familiarization phase was considered the "familiar" arm. Data were expressed as DI ((Time spent on the novel arm -Time spent on the familiar arm) / Total exploration time)). Anxiety was measured through an elevated plus maze (EPM) (Ugo Basile, Germonio (VA), Italy). The EPM is an elevated nonreflective metal with two closed arms (35 × 15 × 60 cm). As described previously  [49] , all mice were placed in the center of the maze and allowed to run freely around the maze for 10 min. Data were expressed as the percentage of time spent exploring the open arms compared with the total time in the EPM.
Social interaction was evaluated in a three-chamber social interaction paradigm. The animals were placed in the central chamber and allowed to freely explore all chambers for 10 min (habituation phase). Immediately after the habituation phase, we performed the test phase (10 min). The mouse was allowed to explore either an object or a con-specific mice protected by a fence placed in the lateral chambers. The chamber in which the conspecific object was placed was balanced. In addition, we used four different con-specifics (two males and two females) to balance them. Data were expressed as the percentage of time spent exploring the con-specific mice compared with the total time spent exploring. Climbing or running around it was not considered exploration. Pain sensitivity by mechanical stimulus (Von Frey test).
Finally, the sections were mounted on slides and covered using Fluoromount-G mounting medium (Invitrogen, Waltham, MA, USA). Imaging and analysis. Fluorescence images were taken with a confocal scan unit with a module TCS SP8 equipped with argon and helio-neon laser beams attached to a Leica DMi8 inverted microscope (Leica Microsystems). Excitation and emission wavelengths for Cy3 were 433 and 560-618 nm, respectively; Alexa488 labeled excitation wavelength was 488 nm, and its emission was 510-570 nm. For quantification of Tyrosine Hydroxylase labeling, we used a 10× lens. Image J software was used to count Tyrosine Hydroxylase labeling. Data were expressed as the percentage of Tyrosine Hydroxylase labeling with respect to the sham group. For the quantification of Iba1 labeling, we used 20× lens.
Image J software combined with a FracLac plugin  [52]  was used to analyze the microglia morphology in sections from the sham and 6-OHDA groups of both females and males, as previously described  [46]. The microglia morphological parameters that were analyzed were (1) cell area, meaning the total number of pixels corresponding to the area occupied by the cell, soma, and branches, and (2) cell perimeter, which was based on the single outline cell shape. For the quantification of Ref1/Ape1 labeling, we used a 20x lens. Data were expressed as the mean of gray values per area (insula and ACC).
Statistical Analysis The analysis was carried out with Graph Pad software (GraphPad Prism V8 software, GraphPad, La Jolla, CA, USA). Data were subjected to the Shapiro-Wilk test for Gaussian distribution. If normality was confirmed, data were reported as the mean ± SEM and the "n" the number of independent subjects. For differences in either, a two-way ANOVA was carried out to evaluate the interaction between lesion and ABA treatment, which was followed by a Tukey's multiple comparison test. A two-tailed unpaired Student's t-test with the probability set at α < 0. 05 was used in specific cases where one variable (either lesion or ABA) was compared in two groups. This is indicated by a § symbol in the graphs.
Calcium imaging analyses Calcium data analyses were performed using the Matlab-based 'Caltracer3beta' software (Columbia University), which enables the tracing of regions of interest (ROIs) matching the identified GCaMP6sexpressing neuronal somata within the dentate granule cell layer. We kept only the ROI of neurons that were detectable in both successive movies performed on the same slice (e. g. , in baseline then vehicle or UBP310 condition). Thus, the same ROI map was used for the two movies performed with the same slice. If there were signs of degradation (e. g. , very bright neurons with fluorescence in the nucleus), or slice movements, the movies were completely excluded from analysis. Then, the averaged fluorescence signal from each cell-based ROI as a function of time was automatically calculated.
To distinguish the actual neuronal calcium activity from background activity (i. e. , fibers or nearby cells), the fluorescence within a halo of the pixels surrounding the ROI was subtracted from the fluorescence signal recorded in the ROI. From the resulting signal, the onsets and offsets of the calcium events were identified. Onsets were automatically detected when fluorescence exceeded a threshold set for every slice (based on the background noise) during a minimum time of 1 s (based on the kinetics of GCaMP6s), and offsets were defined as the event peak time. Onsets and offsets were manually corrected after automatic detection and used for analysis. Using custom-made MATLAB-or Python-based scripts, calcium activity was investigated using the 5 following methods: 1) Quantification of the level of activity: we calculated the number of active neurons, as well as the frequency, amplitude, and duration of single calcium transients.