dataesr/scientific-paragraphs-categorization huggingface dataset:
| Synthesis of results The following data were extracted from each study: participants' demographic data, RMSSD values (mean and standard deviation [SD]) of the various conditions, the method for inducing the diving response, and information about the moderators listed above. RMSSD means and SD were pooled following the guidelines in the Cochrane Handbook (see formulae in chapter 6. 5. 2. 10 Combining groups, Table  6. 5. a)  (Higgins et al. , 2019). The tables used are accessible via Open Science Framework (DOI 10. 17605/OSF. IO/9W2QY or https://osf. io/9w2qy/ ?view_only=61462 76c0a 13434 69a6d d5d65 b7bfd6c). In the within-subjects-designs, data were pooled into one mean and SD for each pre-and during and/or postexposure. Differences between the total studies' sample size and the sample size used in this analysis are due to additional conditions which were not relevant for this meta-analysis (e.
| Statistics Statistical analyses were conducted using R (version 4. 1. 0). The script can be accessed via Open Science Framework (see 2. 4). All outcome measures were standardized using Hedges' g for changes from pre-to exposure, and pre-to post-exposure conditions. The Hedges' g (SMD = MD/ SD pooled ) and its standard error were computed according to the formula for crossover trials from the Cochrane Handbook (chapter 23, section 23. 2. 7. 2). The standard error computation required the imputation of a correlation coefficient between the pre-and during exposure, and between the pre-and post-exposure RMSSD values. To do so, correlation coefficients were computed from the available (or provided by the authors) raw values in the included studies. Then, these coefficients were averaged per condition ((0.
, 2021; Kinoshita et al. , 2006; Kiviniemi et al. , 2012; Wieske et al. , 2013)  had to be excluded because no RMSSD data were provided. One article did not provide pre-exposure RMSSD values  (Lemaître et al. , 2008). A total of 17 articles were eligible for the systematic review, among which 13 were included in the meta-analysis (k = 14) investigating the effect on RMSSD pre-compared to during exposure. Six were included in the meta-analysis (k = 7) investigating the effect on RMSSD pre-compared to post-exposure. One article reported two independent effect sizes for each condition  (Schlader et al. , 2016).
PATIENTS AND METHODS This institutional retrospective study was carried out according to the principles outlined in the Declaration of Helsinki and was approved by an ethics committee (Comité de Protection des Personnes Sud-Est III), independent from our institution, as required by French law. The present study is an ancillary study on GA imaging registered in clinicaltrials. gov (NCT04128150 and NCT04129021). All participants gave informed consent to take part in this study. Standard procedures were used for multimodal imaging including color fundus photographs (Topcon TRC-501X) and infrared (IR) and short wavelength autofluorescence (swAF) scanning laser ophthalmoscopy (SLO), and optical coherence tomography (OCT) (Spectralis R , Heidelberg Engineering, Heidelberg, Germany). Near infrared autofluorescence (NIRAF) was captured using the Heidelberg Retina Angiograph (Heidelberg Engineering).
FUNDING This study was funded by the  Region Ile-de-France  ( EX047007 -SESAME 2019 ), the  LabEx LifeSenses  ( ANR-10-LABX-65 ), the  Institut Hospitalo-Universitaire ForeSIGHT  ( ANR-18-IAHU-01 ) and the  Edward N. & Della L. Thome Memorial Foundation. The funding organizations had no role in the design or conduct of this research.
The performance assessment will be too optimistic by tacitly assuming that the budget can be set properly without additional costs. On the other hand, runtimes may be overestimated (by less than a factor of r). A code example is provided in example_experiment_non_anytime. py. vastly different success rates via simulated restarts and bootstrapping. The performance assessment aspects discussed in this paper are rather generic and, for example, independent of the search domain. Most of them are implemented in the benchmarking platform COCO [20] for continuous and mixed-integer optimization and have stood the test of time in our benchmarking practice. Most elements presented here were touched upon already in [16] where a general benchmarking framework is presented. The purpose of this paper is to lay out specifics and caveats of the performance assessment aspect.
11 The formula is crafted to give 100%, 40%, 11%, and 2% from 1, 10, 100, and 1000 values, respectively, and 1061 values of 10 20. This appears to be both, large enough to make exploitation very difficult and small enough to represent a reasonably recent algorithm state and be easily manageable. The 1%-ile worst is taken (instead of the worst) to yield a measure that is independent of data size. For constrained optimization, the quality indicator can be the value of the last feasible solution. This is however not  9  The ordering between indicator values is independent of the chosen percentile if and only if for every pair of f -value distributions either one stochastically dominates the other or they are the same, as it is implemented in the bbob-noisy test suite of COCO. 10  As discussed at https://github.
Runtimes to the right of the crosses at 10 6 × n have at least one unsuccessful run and their further increase beyond this budget is induced by simulated restarts. This can be concluded, because pure random search exploited in all runs the same maximum budget. The small dot beyond 10 7 × n evaluations depicts the fraction of target precisions that were reached at least once. Empirical runtime distributions of single functions, in particular comparing different algorithms, are in our experience the most important data to scrutinize when investigating performance results. The COCO framework defines subgroups of functions sharing similar properties (for instance separability, unimodality, etc. ). The five functions plotted in Figure  3  belong to the separable function group. The right plot shows the same runtimes as in the middle but in a single graph (where the y-steps are respectively normalized).
Finally, most of the presented methodology is implemented in the open source platform COCO that also comes with hundreds of data sets of already benchmarked algorithms [16]  which can be directly used in the cocopp module for comparison. Importantly, the implemented methodology allows for comparison also across publications. Additionally, new benchmarking data can be provided in online repositories. 29 ow, researchers can spend more time on exploring new ideas advancing science instead of reimplementing baseline algorithms or known benchmarking methodology for comparison. Additionally, the easiest way to adopt good assessment standards is by using already existing tools with these standards.
Hansen, Anne Auger and Dimo Brockhoff are with Inria and Ecole Polytechnique, Institut Polytechnique de Paris, France. Tea Tušar is with the Jožef Stefan Institute, Ljubljana, Slovenia. Manuscript received 18 September 2021; revised 16 February 2022 and 16 June 2022; accepted 14 September 2022. This work was supported in part by the French National Research Agency (NumBBO) under Grant ANR-12-MONU-0009, and in part by the Slovenian Research Agency under the Grants P2-0209 and N2-0254.
ACKNOWLEDGMENTS The authors would like to thank  Steffen Finck ,  Tobias Glasmachers ,  Oswin Krause ,  Ilya Loshchilov ,  Olaf Mersmann ,  Petr Pošík ,  Raymond Ros ,  Marc Schoenauer ,  Thanh-Do Tran , and  Dejan Tušar  for their many invaluable contributions to the COCO platform.
Stimuli Grayscale luminance-defined sinusoidal Gabor grating stimuli were generated using MATLAB (MathWorks, Natick, Massachusetts, United States of America, RRID:SCR_001622) and the Psychophysics Toolbox (Brainard, 1997). During the behavioural session for the fMRI study, the stimuli were presented on a PC (1920 × 1200 screen resolution, 60-Hz refresh rate). In the fMRI scanning session, stimuli were projected onto a rear projection screen using an Epson EB-L1100U Laser projector (1920 × 1200 screen resolution, 60-Hz refresh rate) and viewed via a mirror (viewing distance 91 cm). On grating-present trials (50%), auditory cues were followed by a grating (0. 5-cpd spatial frequency, 33-ms duration, and separated by a 750-ms blank screen), displayed in an annulus (outer diameter: 10° of visual angle, inner diameter: 1°, contrast decreasing linearly to 0 over 0.
The online study was created and hosted using Gorilla Experiment Builder (www. gorilla. sc)  78. Participants were recruited through Prolific (www. prolific. co). Prior to the start of the instruction blocks participants were asked to keep a 50cm distance from the screen. They were required to adjust the size of a rectangle on their screen to match a bank card, to ensure that the visual angle was equal across participants. Subsequently, they were asked to adjust the volume of their headphones to a high but not unpleasant volume. The instruction phase of the online study was the same as the instruction phase of the fMRI study. The timings for the trials were identical as well, except that the stimuli appeared on the screen for 50ms instead of 33ms, due to software constraints. After completing the 7 instruction blocks, participants were required to complete 4 blocks with different grating contrast levels (7.
05; V1: mean=6208, SD=1799 voxels; V2: mean=9370, SD=3256 over participants). Subsequently, the orientation preference of each voxel was estimated by contrasting the 2 orientation regressors. The 500 voxels that most strongly favoured the 45° and 135° gratings, respectively, constituted the two orientationspecific ROIs within V1 and V2. Finally, each voxel's time course was normalised (z-scored), and multiplied by the absolute T-value of the orientation contrast (45° versus 135°), to weight the data by the most robust orientation preference. Note that all reported effects in the zscored data were also present without z-scoring. These ROI definitions were identical to those used in previous studies that successfully resolved orientation-specific BOLD signals with layer specificity  [38] [39] [40].
Acknowledgements The authors would like to thank  Fraser Aitken  for help with early piloting of the experimental paradigm. This work was supported by a  Wellcome/Royal Society Sir Henry Dale Fellowship  [ 218535/Z/19/Z ] and a  European Research Council (ERC)   Starting Grant  [ 948548 ] to P. K. The  Wellcome Centre for Human Neuroimaging  is supported by core funding from the  Wellcome Trust  [ 203147/Z/16/Z ].
8 ms; TE, 3 ms; flip angle, 15°; 256 × 256 matrix; voxel size, 1 mm 3 ). Participants were scanned sitting in an upright position in the MEG with their eyes open and fixated on a cross on a screen in front of them inside a dark room. Instructions were to stay still, relax, avoid structured thinking and to let their mind wander. The position of the participant's head was monitored continuously through 5 head position indicator coils. We used the spatiotemporal signal space separation algorithm  67  , implemented in the MaxFilter program of the MEG system, for artifact removal and head movement correction. This analysis depends on several toolboxes and software packages (freely available). The preprocessing and source-space parcellation analyses are performed using the Fieldtrip, SPM, FSL(v5.
0), and some part of the OHBA Software Library (OSL; https://ohba-analysis. github. io/osl-docs/), using the most adapted tool for each step of the preprocessing and analysis. MEG resting-state data were preprocessed using previous published methods  14, 15. We used the Fieldtrip toolbox (http://www. fieldtriptoolbox. org/) run on MATLAB software and data were downsampled to 300 Hz, bandpass filtered at 1 to 150 Hz, a notch filter was applied at 60 and 120 Hz. The first and last 10 seconds of the recorded data were removed, leaving 280 s of resting-state data for each participant. Independent component analysis was used (runica function) to remove artifacts associated with cardiac artifacts, eye blinks, breathing and muscle activity, as identified by visual inspection. For each individual, fiducial points (i.
It infers a multivariate Gaussian distribution describing a delay-embedding of the source time-courses and is appropriate for application to large-scale brain networks inferred from parcellated source-space MEG data. HMM is a probabilistic model assuming that brain time series is composed of a sequence of states such that, at each time point, only one state is active. Importantly, the probability of a state being active at time point t is modeled to be dependent on which state was active at time point t-1 (i. e. , it is order-one Markovian). The HMM is inferred using the HMM-MAR toolbox https://github. com/ OHBA-analysis/HMM-MAR, through the hmmmar. m function (details about the options can be found here https://github. com/OHBA-analysis/HMM-MAR/wiki/ User-Guide#-hmm-marmodel-estimation) Data processing.
Here we used a window of 50 ms and 36 × 2 PCs to be better able to capture higherfrequency differences. Stochastic inference. The HMM analyses can be very computationally intensive, thus we adapted the stochastic inference batch settings and used Linux workstations with an Intel Xeon E5 CPU clocked at 1. 90 GHz running in parallelization and 16 Gb of RAM. Run-to-run variability. To test the stability of the HMM results across several runs of the inference, the HMM is run multiple times and the result of each iteration is compared using the free-energy value. The model with the lowest free-energy can be taken as the one which best explains the data without becoming too complex. We repeated the HMM inference ten times and the analysis proceeded with the iteration with the lowest free energy.
Extracting spectral information. Once the HMM model is trained and the state time courses obtained, we estimated the spectral content of the model, i. e. , powerspectral density and coherence. To do that, we used a nonparametric estimation, using a state-wise multitaper approach, which provided power and spectral coherence for each frequency bin between 1 and 45 Hz (introduced in ref. 34  ). The spectral information computed contains (states by frequency bins by channels by channels) a lot of values. This is an overwhelming amount of information for ease of interpretation. For this reason, power and spectral coherence information for each state were factorized into different frequency bands. We performed this frequency decomposition in a data-driven way as in Vidaurre et al. 32  , we applied the factorization to the spectral estimation data of each subject.
FWER-corrected p-values are calculated for each set of interconnected regions of interest using permutation testing. Moreover, NBS was used at the nodal level because of the expected nonnormal distribution of differences in brain microstates measures. Statistical analyses were performed using MATLAB software (R2019b, Math-Works, Naticks), the estimation statistics  72  package under Python (https://github. com/ACCLAB/DABEST-python; for more details also see https://www. estimationstats. com) and GraphPad Prism (version 7. 03; www. graphpad. com).
Acknowledgements The authors thank  Drs. Luis Garcia Dominguez  and  Richard Wennberg  for technical assistance in MEG acquisition and  Keith Ta  and  Brian Li  for expert technical assistance in MRI acquisition. The authors also thank  Renise Ayearst  for clinical data retrieval and  Dr. Lee. B. Kisler ,  Kara Jamal ,  Daeria Lawson , and  Ammepa Anton  for their help in patient recruitment. This work was supported by the  Canadian Institute of Health Research  ( PJT 156409  and  SCA-145102 Strategy for Patient-Oriented Research (SPOR)  funding of the  Canadian Chronic Pain Network) , the  Multiple Sclerosis Society of Canada , and  The Mayday Fund. Camille Fauchon  was a recipient of a  CIHR Post-Doctoral Research Award  ( MFE-171251 ) and the foundation pour la recherche médicale (ARF202110014013).
Code availability All analyses were done using freely available tools in MATLAB. The code used to conduct the analysis in this paper can be found at: https://github. com/OHBA-analysis/ HMM-MAR Information can also be found here: OHBA Software Library (OSL; https:// ohba-analysis. github. io/osl-docs/). Furthermore, we used the scripts previously published by Vidaurre et al. 32  using the OHBA toolbox, which is freely available. The script containing the entire pipeline is available from the original work of Vidaurre et al. 32  (https://github. com/OHBA-analysis/HMM-MAR/blob/master/examples/ NatComms2018_fullpipeline. m) which can be used to reproduce the methodological approach.
Bioinformatic and Statistical Analyses Bioinformatic process and statistical analyses were operated in the R 4. 0. 2 software (R Core Team, 2019) environment. The DADA2 pipeline (v. 1. 16)  [54], an open-source program implemented in R package (https://benjjneb. github. io/dada2/tutorial. html accessed on 1 October 2022), was used to process the sequencing data. Briefly, sequence reads were filtered and trimmed using optimized parameter settings as recommended. Sequence reads were dereplicated, denoised, and merged using DADA2 default parameters. Then, sequences were aligned and categorized to infer amplicon sequence variants (ASVs) grouping amplicon sequencing data by using 100% of sequence identity  [55]. Sequences presenting only once (singletons) in the whole data set were eliminated. Validity of sequencing depth was controlled from rarefaction curves, computed using the "rarecurve" function from the Vegan package in R.
The taxonomic assignment of ASVs was performed following a previously established two-step approach  [13, 53]. In the first step, the Silva v132 database formatted for DADA2 was used to assign ASVs from kingdom to genus (minimum bootstrap 80) with the assignTaxonomy () command and the blast parameters as constitutively formatted for DADA2  [56]. From the ASVs assigned as AMF with Silva database, affiliation of each ASV was assessed at the genus level by performing a BLAST analysis against the NCBI database and MARJAAM, a web-based database containing referenced Glomeromycota DNA sequence data. The ASVs identified as non-Glomeromycota at the phylum level at the end of these two stages of assignment were excluded from further analyses. In a second step, aiming at a refined taxonomic identification of each ASV, a phylogenetic tree was subsequently constructed as proposed by  Stefani et al.
, 2020 [53] : multiple alignment including ASVs identified as Glomeromycota and multiple consensus sequences was computed using the web portal Kalign  [57]  (http://msa. sbc. su. se/ accessed on 1 October 2022). Then, a maximum-likelihood tree was obtained using RAxML v8. 2. 10  [58] , through the CIPRES web portal  [59]. Visualization of the output was finally obtained with the FigTree v1. 4. 4 program. With the aim to identify the influence of former RP inputs on AMF communities, we studied AMF communities across the six sampling sites as well as inside root or rhizospheric soil using proxy richness, alpha diversity, distribution, and abundance of taxa. Then, data relative to P sites were compared to those of nP sites, inside each location, or after pooling data from the 3 P sites on the one hand and the 3 nP sites on the other hand.
Indicator species analysis was performed using the multipatt function from indicspecies package  [63]  to compute the indicator value index (IndVal), i. e. , a measure of specificity (based on abundance values) and fidelity (computed from presence data) of each ASV to a targeted clustering group of samples related to a targeted ecological condition  [60]. This index, therefore, led to identifying ASVs (hereafter referred to as "indicator ASVs") that can be considered closely related to the ecological condition of their group  [64]. The significance of IndVal of each species was assessed by a random permutation procedure from 9999 permutations with a significance at the α = 0. 05 level (IndVal's p_values with the p. adjust () function).
Analysis of AMF Diversity After Illumina Miseq sequencing, the global dataset resulted in a total of 4,795,981 raw MiSeq reads (yielded across 24 soil samples and 24 root samples, Table  S2 ). After quality filtering and chimera removal, a total of 3,527,278 sequences were retained as the AMF 18S rDNA dataset (Table  S3 ). The rarefaction curves obtained after quality filtering reached a plateau, indicating we had a good representation of the microbial community, as most of the abundant species were represented and the sequencing depth effort was adequate to progress to further AMF community analysis (Figure  S2 ). From the AMF 18S rDNA dataset and using the Silva v132 database formatted for DADA2, 392 ASVs were inferred and identified as Glomeromycota. Among them, 74 ASVs were not assigned at the phylum level after inferring the phylogenetic tree (Figure  S3 ).
Richness and Alpha-Diversity The diversity (Shannon and Simpson indexes) and richness (Chao1 estimator) were used to compare AMF communities across P versus nP sites as well as between root or rhizosphere biotopes. A significant difference was observed between the two habitats, with higher diversity in roots compared to rhizosphere soil biotopes. In contrast, no significant difference was recorded comparing the P and nP sites either for the entire mining area (Figure  2 ) or within each localization (data not shown). No impact of P versus nP status was noticed in either the root or rhizosphere biotopes (data not shown). Microorganisms 2022, 10, x FOR PEER REVIEW 7 of 21.
Analysis of AMF Diversity After Illumina Miseq sequencing, the global dataset resulted in a total of 4. 795. 981 raw MiSeq reads (yielded across 24 soil samples and 24 root samples, Table  S2 ). After quality filtering and chimera removal, a total of 3,527,278 sequences were retained as the AMF 18S rDNA dataset (Table  S3 ). The rarefaction curves obtained after quality filtering reached a plateau, indicating we had a good representation of the microbial community, as most of the abundant species were represented and the sequencing depth effort was adequate to progress to further AMF community analysis (Figure  S2 ). From the AMF 18S rDNA dataset and using the Silva v132 database formatted for DADA2, 392 ASVs were inferred and identified as Glomeromycota. Among them, 74 ASVs were not assigned at the phylum level after inferring the phylogenetic tree (Figure  S3 ).
Richness and Alpha-Diversity The diversity (Shannon and Simpson indexes) and richness (Chao1 estimator) were used to compare AMF communities across P versus nP sites as well as between root or rhizosphere biotopes. A significant difference was observed between the two habitats, with higher diversity in roots compared to rhizosphere soil biotopes. In contrast, no significant difference was recorded comparing the P and nP sites either for the entire mining area (Figure  2 ) or within each localization (data not shown). No impact of P versus nP status was noticed in either the root or rhizosphere biotopes (data not shown).
Taxonomic Assignment of ASVs The 318 ASVs identified as Glomeromycota were assigned to Glomerales (81%), Diversisporales (12%), Paraglomerales (3%), and Archaeosporales (2%) (Figure  3A ) while no taxonomic assignment at order level was obtained for 2% of them. Among them, five families were identified: Glomeraceae, Claroideoglomeraceae, Diversisporaceae, Paraglomeraceae, and Archeosporaceae. About 20. 7% of the ASVs could be affiliated to a genus, mainly identified as Rhizophagus, Funneliformis, and Glomus (18, 15, and 14 ASVs, respectively), but also, to a lesser extent, as Archaeospora, Paraglomus, Septoglomus, and Claroideoglomus (Table  S4 ). With assignment up to the species rank, 39 ASVs (12. 2%) could be named in line with referenced strains in the published databases. They were representative of eight species and six genera, namely Archaeospora trappei, Claroideoglomus lamellosum, Funneliformis mosseae, Glomus indicum, G.