dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated Stereotactic-EEG (SEEG) data Invasive methods such as SEEG implantation are used in clinical situations for patients with drug-resistant epilepsy to determine the focal location of epileptic seizures. The implanted SEEG electrodes record the local field potential generated by the neuronal tissue in its neighborhood. The gain matrix (also known as lead-field matrix or projection matrix) maps the source activity to the measurable sensor signals, as a function of the distance between the sources and sensors. Each sensor collects the source signals in its proximity, weighed by the distance from the sources. A post-SEEG-implantation CT scan is used in this study to find the exact locations of the SEEG electrodes and to calculate the gain matrix. To model the SEEG signals, we assume a linear relation between the source activities and the measurable signals at the sensors: S i (t) = Nn j=1 G ij X j (t), (3) where S i (t) is the SEEG signal at sensor i ∈ {1, 2,.
MAF generates each data conditioned on the past dimensions, and density estimation only needs a single forward pass through the flow using architecture like MADE  (Germain et al. , 2015). For the purpose of fitting the VEP model to the brain epileptic patterns, we trained NFs by maximizing the log-likelihood of the observed data under our model (Eqs (  2 ) and (  3 )), equivalently, minimizing the Kullback-Leibler divergence or discrepancy between the true posterior distribution p(θ | y) and the variational approximation of Eq. (  11 ) denoted by a family of densities q φ , through learning the variational parameters φ. In practice, this is performed by adjusting the weights ψ of a neural network F so that q F (y,ψ) (θ) p φ (θ | y). Assuming that our dataset has N samples {(θ i , y i )} N i=1 with parameters drawn from the prior p(θ) and observations generated from the forward model p(y | θ), the posterior p(θ | y) is obtained by minimizing the loss function L(ψ) = - N i=1 log q F (yi,ψ) (θ i ) (12) over network parameters ψ.
, 2021). Several SNPE methods have been developed recently based on different choices of the loss function for later rounds, namely SNPE-A  (Papamakarios and Murray, 2016) , SNPE-B  (Lueckmann et al. , 2017) , and SNPE-C  (Greenberg et al. , 2019). In this paper, we used SNPE-C (or automatic posterior transform) as it avoids a post-hoc analytical correction and importance weighted loss, which can have high variance during training, leading to inaccurate inference  (Lueckmann et al. , 2021). By dynamically reparameterizing the proposals and the formulation of the loss function for training, it recovers the true posterior directly  (Greenberg et al. , 2019). The PyTorch-based SBI package  (Tejero-Cantero et al. , 2020)  implements state-of-the-art algorithms for neural network-based density estimation based on sequential sampling methods (SNPE/SNLE/SNRE).
The SBI package works with any simulator as long as it can be wrapped in a Python callable, with a flexible choice of network architectures  (Tejero-Cantero et al. , 2020; Gonçalves et al. , 2020). The simulation step can be easily run on multiple CPU/GPU cores to benefit from effective computational parallelization, and the likelihood function is encapsulated as a feed-forward ANN allowing for parallel evaluation by design, referred to as likelihood approximation networks (LANs; Fengler et al. (  2021 )). To perform SBI using SNPE, three inputs are needed to be specified  (Gonçalves et al. , 2020) : (i) a prior distribution describing the possible range of parameters, and we can draw samples from it easily, (ii) a mechanistic model as a simulator that takes parameters as the input and generates simulated data as the output, (iii) a set of observed data (or lowdimensional data feature) as the target of fitting.
Taking prior distribution p(θ) over the parameters of interest θ, a limited number of simulations (N ) are carried out to generate a dataset {(θ i , y i )} N i=1 , where θ i ∼ p(θ) and y i is simulated data given model parameters θ i. In other words, the simulated data set is a set of N independent and identically distributed samples from the generative model p(θ, y) = p(θ)p(y | θ). Then, we estimate the posterior q φ (θ | y) by training the NFs on the generated data set {(θ i , y i )} N i=1. Once the distribution is learned, for new observed data y obs , we can readily approximate the true posterior p(θ | y obs ). Active learning can be used in this approach to adaptively reduce the number of simulations (over multiple simulation rounds by adding their loss terms together  (Lueckmann et al.
Data features For many dynamical models, the simulation output is high-dimensional, and the summary statistics are used as a dimension reduction technique for faster training  (Sisson et al. , 2018; Wood, 2010; Wrede et al. , 2021). Importantly, reducing the high-dimensional data to low-dimensional summary statistics makes inference possible, where the likelihood is intractable. In particular, the choice of informative summary statistics is critical for efficient parameter inference as it determines the similarity/discrepancy between simulated proposals and the observed data. Although SNPE can operate inference without summary statistics, the observations at the whole-brain level (such as SEEG) are high-dimensional. In this study, different summary statistics as data features were extracted, comprising seizure onset/offset, power envelope based on signal energy, and statistical moments calculated independently per channel for each patient.
The SBI-VEP workflow The aim of using SBI is to obtain an efficient and accurate approximation to the true posterior of a parameterized stochastic simulator such as the VEP, i. e. , dynamical systems from which we can generate samples, but we cannot exactly evaluate the likelihood function. In particular, Normalizing Flows (NFs) embedded in SBI approach such as Sequential Neural Posterior Estimation (SNPE) enable us to approximate the full posterior distribution of parameters conditioned on (low-dimensional summary statistics of) observed data, with the aid of only forward simulations, while also potentially capturing degeneracy or multi-modalities. To perform the SBI, three inputs are needed to be provided: (i) a prior distribution describing the possible range of parameters from which we can easily draw samples, (ii) a simulator in computer code such as the VEP that takes parameters as input and generates data from drawn parameters as output, (iii) a set of low-dimensional data features (sufficient informative statistical summary) for training a neural density estimator.
Collecting a simulated dataset by repeatedly drawing samples from the prior distribution specified over parameters and performing model simulations with the randomly chosen parameters, the SNPE trains an ANN such as Masked Autoregressive Flow (MAF) to learn an invertible transformation between data features of simulated dataset and parameters of a parameterized approximation of posterior distribution. After training, SNPE is able to rapidly approximate the full posterior of parameters for new observations or empirical data. By training the deep neural density estimators on a large number of simulations given low-dimensional sufficient statistics, this approach allows for efficient and accurate inference of full posterior, with no demand for further simulations at the inference step due to amortization (i.
To do this, we used a bandpass Butterworth filter of order 3, with cut-off frequencies between 10 Hz and 100 Hz to remove the baseline shift at seizure onset of observations. Following the Hilbert transformation, a Savitzky-Golay filter with polyorder of 3 was also applied on the analytic signal for the purpose of smoothing in the calculation of the seizure onset. Here we carried out 10 4 model simulations from the reduced VEP model (see Eq. (  2 )) to prepare the training data set, while only the statistical moments (m = 4) of time-series and the seizure onsets were used as the data features. The parameters are drawn from a uniform prior in the ranges: η i ∈ U(-5, -1) and K ∈ U(0, 2), and SNPE was run using a single round. Finally, we used Masked Autoregressive Flow (MAF) to learn the invertible transformation between the feature datasets generated by samples drawn from the prior distribution, and the parameters of approximated posterior distribution.
However, this approach is efficient by offering the ability to run model simulations in parallel processors (each VEP run to simulate 100 sec of whole-brain epileptic patterns takes only 0. 003 sec using JIT compiler). For SBI, the model simulations can be easily run independently to create the training dataset (the more simulations, the more informative posterior), whereas HMC is amenable to embarrassingly parallel execution of only independent chains as each HMC chain itself is a simulation of Hamiltonian dynamics. Using Masked Autoregressive Flow (MAF) as the deep neural density estimator in SNPE, the posterior estimation took around 1000 sec (for simulation and training as generating 200 samples from posterior took less than 1 sec), whereas generating 200 samples using HMC (with 200 warm-up iterations, the expected acceptance probability of 0.
SNLE family utilizes deep neural networks to learn an approximation to the intractable likelihood followed by MCMC sampling to obtain the posterior. SNRE approaches use classifiers to learn likelihood ratios between two choices of parameters, and MCMC is used to obtain samples from the posterior. Rather, SNPE approaches use deep neural networks to directly target the posterior, bypassing the need for MCMC. Additionally, we use the current state-of-the-art implementations of Normalizing Flows (NFs) for training on simulations known as Mixture Density Network (MDN), Masked Autoregressive Flows (MAF), and Neural Spline Flows (NSFs). We ran these algorithms using SBI package  (Tejero-Cantero et al. , 2020) , with the same model simulations in training data set, and observation for fitting. The metrics used here to rank the algorithmic performance are the accuracy in the estimation of EZ/PZ/HZ across wholebrain regions, the reliability of estimation (measured by posterior z-scores versus posterior shrinkages), and the computational cost for convergence.
Information Sharing Statement The patient data sets cannot be made publicly available due to the data protection concerns regarding potentially identifying and sensitive patient information. Interested researchers may access the data sets by contacting Clinical Data Manager Aurélie Ponz (aurelie. ponz@univ-amu. fr) at the Institut de Neurosciences des Systèmes, Aix-Marseille Université. The main source codes needed to reproduce the presented results are available on GitHub (https://github. com/ins-amu/BVEP).
Acknowledgements This work was funded by the  French National Research Agency  (ANR; https://anr. fr) as part of the second " Investissements d'Avenir" program ,  ANR-17-RHUS-0004 ,  EPINOV , to VJ and FB, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the Specific Grant Agreement No. 785907  and  945539 ,  Human Brain Project SGA2  and  SGA3  (https://ec. europa. eu/programmes/ horizon2020 ) to VJ, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the Specific Grant Agreement and No. 826421 , VirtualBrainCloud (https://ec. europa. eu/programmes/horizon2020) to VJ,  PHRC-I 2013 EPISODIUM  (grant number  2014-27 ), the  Fondation pour la Recherche Médicale  ( DIC20161236442 ), and by  SATT Sud-Est ,  827-SA-16-UAM  (https://www.
Search Strategy This systematic review was conducted following the PRISMA guidelines  (38). Two independent investigators performed the review (FG, TB). A literature search was performed on Pubmed, Scopus, and the Cochrane library, from inception until 31 November 2020. The following terms were used on the three databases: ("brain stimulation" OR "TMS" OR "RTMS" OR "Transcranial Magnetic Stimulation" OR "Theta burst" OR "transcranial direct current stimulation" OR "TDCS" OR "DBS" OR "neuromodulation") AND ("Anxiety disorders" OR "agoraphobia" OR "social, Anxiety" OR "phobic disorders" OR "Panic disorder" OR "Phobia" OR "stress disorder, posttraumatic" OR "PTSD" OR "anxious symptoms" OR "HAMA" OR "Hamilton anxiety" OR "Anxiety scale" OR "Beck Anxiety Inventory"). Unpublished studies were sought with Google Scholar.
Data Extraction Included articles were reviewed to extract relevant data : article indexing information (authors, year of publication), study design (control, randomization, blinding), sample characteristics (diagnosis, number of patients in each arm of treatment, mean age, gender ratio), brain stimulation protocols (technique, target brain region, target identification method, stimulation and sham systems descriptions, stimulation settings, duration and number of sessions), concomitant therapeutic interventions, symptom measurement tools, dropout rates, and pre-and posttreatment data, including follow-up data (attrition rates, mean score and standard deviation of scale ratings, response and remission definitions and rates, adverse events). Study authors were contacted directly to retrieve data unavailable in original publications (twice in the absence of response to the first request).
Strategy for Data Synthesis (Meta-Analysis) Analysis was performed using R software (R R 4. 1. 2). For the treatment and control groups of each study, we extracted (1) baseline/post-treatment mean differences in general anxiety rating scales and/or specific disorder rating scales, and (2) response and remission rates. The change-from-baseline SD was calculated following guidelines of the Cochrane Handbook for Systematic Reviews of Interventions  (39). SD change = SD pre 2 + SD post 2 -2 × Corr × SD pre × SD post Where Corr represent the correlation between pre-and posttreatment variance, and was set to 0. 5 as suggested by Follman et al. (40). We calculated between-group standardized mean difference (SMD) and their variance for continuous variables, and Hedges' g to control for small sample bias, as suggested by Harrer et al.
Literature Search Results and Characteristics of Included Studies A total of 1,586 records were identified through database search (Medline: 588, Scopus: 559, Cochrane: 439). Among those, 234 were duplicate references. Eighty-two articles were retrieved for inclusion assessment. After full-text reading and adding articles found through cross-referencing, 27 studies were included. All were randomized controlled trials (RCTs) (Figure  1 ). From these, 19 studies fulfilled all eligibility criteria for the primary outcome (42-60) (Tables  1, 2 ), for a total of 589 participants. The mean age of participants was 40. Twenty-one RCTs were included for the secondary analysis, which was based on disorder-specific scales. Those 21 studies comprised 13 RCTs included in the primary outcome analysis [exclusion of one trial without specific scale measure  (48)  and five MDD trials  (43, 45, 50, 57, 59) ], and eight additional RCTs, in which only disorder-specific scores were reported (61-68) (Tables  1, 2 ).
However, long-term efficacy data were insufficient to conduct a meta-analytic study, being scarce and heterogeneous. Nevertheless, follow-up data were qualitatively in favor of a prolonged favorable effect of neurostimulation. The meta-analysis purposely gathered results from heterogeneous conditions and protocols, which were investigated in subgroup analyses. Regarding stimulation techniques, both TMS and tDCS showed a tendency toward an improvement of anxiety symptom intensity and resulted in greater response and/or remission rates than control conditions immediately after treatment. Mild adverse events typical of the stimulation techniques were reported. DBS showed a tendency toward an improvement of anxiety symptom intensity immediately after treatment. However, the meta-analysis was limited to two studies on MDD, which included patients with moderate baseline anxiety levels.
LIMITATIONS First despite performing an extensive search of literature databases and applying PRISMA recommendations, a limited number of studies were included, with relatively small sample sizes [except for two studies measuring disorder-specific symptoms;  (63, 66) ]. Second, the primary outcome of the meta-analysis (generic anxiety scales results) was the explicit primary outcome in only 5/19 of the studies included. Third, therapeutic efficacy was evaluated at the end of brain modulation trials, while long-lasting reduction of symptoms intensity is warranted. Fourth, as pharmacological treatments were often administered concomitantly with brain stimulation, the relative contribution of the two strategies and their interactions remain to be clarified. Fifth, although only RCTs were analyzed, the risk of bias was high in a majority of studies, including the three studies showing the strongest positive effects  (42, 48, 55).
ACKNOWLEDGMENTS Professor Marie Tournier  is acknowledged for her inputs at the early stage of this study. Scientists who provided data from their studies are deeply thanked for their contributions, in particular  Professor Maria J. Portella  for re-analyzing data from Puigdemont et al. (57). The authors thank  Ekaterina Escoffier  for her efforts in retrieving missing full text articles. TB is the recipient of a  CCA-Inserm Bettencourt clinician-scientist fellowship  from  Fondation Bettencourt-Schueller.
Data collection The DCE was conducted online with participants from France, Germany, Italy, Spain and the United Kingdom. The survey was translated to the respective languages of the countries. In previous experiments in pediatric, behavioral health conditions, sample sizes ranging from 38 to 247 were reported. (21) (22) (23) (24)  Given the untypically large number of attributes, the two experiments with the CARS2-ST and CARST2-HF versions, as well as two different participant groups, i. e. , caregivers and clinicians, we aimed for a sample size of 1,000 caregivers and 1,000 healthcare professionals, evenly distributed across countries. This sample size allows for main analyses pooled across countries, while looking separately at the respondent groups per instrument. Data was collected between 26 th March 2021 and 17 th September 2021.
Movies were computationally projected using MetaMorph, and data processing (including movie registration, neuron segmentation and tracking over time) was conducted with a custom-developed Matlab program detailed below and available at https://github. com/gcharvin/ viewworm (a user guide is provided in Supplementary Information). To image light response in I2 and PHA neurons, L4 worms were mounted on a slide covered with 3% agarose pads in M9 supplemented with 1mM levamisole. Video-recordings were performed on the spinning-disc microscope using the 40X oil objective. Animals were exposed to blue light (485nm) while their neuronal response was simultaneously recorded in stream mode (10 frames/sec, single Z, 100ms exposure, laser 100%) for 30sec.
Next to the segmentation procedure, we tracked the identified neurons using distance minimization, and we quantified the mean fluorescence signal in each neuron over time. Last, fluorescence data corresponding to individual animals were pooled after synchronization from the time of exposure to H 2 O 2 and signal normalization. This image analysis pipeline is available at https://github. com/gcharvin/viewworm and a tutorial for use is included in Supplementary Information. As some movies could not be quantified due to uncontrolled animal movements, a visual classification of neuronal responses (high, moderate, absent) was made by comparison to successfully tracked movies. For light response analyses in I2 and PHA neurons, the same image processing pipeline as in  [8]  was used, except that ROI were manually drawn in Fiji.
Statistical analyses For pairwise comparisons of data sets with a normal distribution (or N>30), p-values were calculated using an unpaired two-tailed Student test, and the Welch correction was applied when samples variance was not homogeneous. When distributions were not normal (Anderson-Darling and Shapiro Wilk tests not satisfied), a Mann Whitney test was used. Multiple comparisons were analyzed using a one-way ANOVA with Bonferroni's correction (for normal distributions), or a non-parametrical Kruskal-Wallis test followed by a Dunn's multiple comparisons test. Statistical analyses were conducted with the GraphPad Prism9 software. Mean are represented and error bars indicate standard deviation (SD) in all figures. The data presented here come from at least three independent experiments.
Acknowledgments We are grateful to all the staff members of the Imaging Center of the IGBMC, especially  Elvire Guiot ,  Marine Silvin ,  Erwan Grandgirard  and  Bertrand Vernay  for assistance in confocal microscopy. We thank  Christelle Gally ,  Basile Jacquel  and  Eric Marois  for helpful discussions and critical reading of the manuscript,  Sandra Bour  for assistance with figure design,  Doulaye Dembele  for help with statistic analyses. We are indebted to the  Reymann, Vermot and Jarriault labs  for sharing their equipments and reagents, and for scientific input. We thank the  Horvitz lab , especially  Na An , for providing the MT GCaMP strains and the  Pujol lab  for providing the sek-1 mutant. We thank  WormBase  for providing resources and the  Caenorhabditis Genetics Center  (funded by  NIH Office of Research Infrastructure Programs   P40 OD010440 ,  University of Minnesota ) for providing strains.
Funding: 'This work was funded by the  Agence Nationale de la Recherche  (grants  ANR-10-LABX0030-INRT  to  Gilles Charvin  and  ANR-10-IDEX-0002 ;  ANR 20-SFRI-0012 ;  ANR-17-EURE0023  to the  Interdisciplinary Thematic Institute IMCBio of the University of Strasbourg ,  CNRS  and  Inserm , part of the  2021-2028 Investments for the Future Program.
Single-cell RNA-seq Single-cell RNA-seq analysis was performed on publicly available datasets of Emx1-Cre;R26-LSL-EYFP C57BL/6J mice  (Borrett et al. , 2020 ; GEO: GSE152281). High-throughput transcriptomic profiles were obtained using Illumina HiSeq 2500 on a 10X genomics platform. Two objects, P2-SVZ (GSM4610595) and P20-SVZ (GSM4610600), were created using the Seurat v. 3. 0 package in R-Studio 4. 1. 6 (Stuart et al. , 2019). Data were log normalized and variable features selected with FindVariableFeatures() (including variance stabilizing transformation, nfeatures = 2,000). Anchors were identified with FindIntegrationAnchors() prior to integrating datasets with IntegrateData(). Principal-component analysis scaled and reduced data. A non-linear uniform manifold approximation and projection (UMAP) dimensional reduction (dim = 1,20) allowed us to cluster cell populations with FindClusters() (resolution = 0.
e. , biological replicates), and technical replicate numbers are indicated in the text. Culture experiments were conducted three times (n = 3), and 10 pictures were taken per well. p < 0. 05 was considered significant. Data are shown as scatterplots and bars depicting mean ± SD, as a heatmap for qPCR, and as violin or cluster plots for single-cell RNA-seq data. Asterisks/letters indicate statistical significance.
ACKNOWLEDGMENTS We thank the  ImagoSeine platform of the Institut Jacques Monod (UniversiteReneDiderot). We also thank  Fabien Uridat  and Stephane Sosinsky for excellent animal care. P. V. was supported by the  European Thyroid Association (basic science grant ) and the  Fondation pour la Recherche Medicale  ( SPF201909009111 ). This work was additionally supported by the  Centre National de la Recherche Scientifique  and the  Museum National d'Histoire Naturelle, NeurATRIS  ( IONESCO grant ), and the  EU H2020  contract  ATHENA (666869 ).
ACKNOWLEDGMENTS We are particularly grateful to  Lee G. Fradkin  for helpful discussion and comments on the manuscript. This work was supported by the  Centre National de la Recherche Scientifique , the  Association pour la Recherche sur le Cancer  ( PJA 20151203422 ) and the  Fondation pour la Recherche Médicale  ( Programme "EQUIPES FRM2016 " project  DEQ20160334870 ). All the figures were created with BioRender. com.