dataesr/scientific-paragraphs-categorization huggingface dataset:
, , 2009)). Fourth, we did not explicitly investigate the influence of the sample size of individual studies on the outcome of a meta-analysis. However,  Tahmasebi et al. (2012)  used the same IMAGEN dataset (though with a different contrast) to measure the effect of the sample size on the variability of the locations of peak activity in group analyses (study level). Their results indicate that 30 participants or more are needed so that locations of peak activity stabilize around a reference point. For similar results, see  Thirion et al. (2007)  who recommend at least 20 participants in a group analysis to achieve acceptable classification agreement. This was defined as the concordance between group analyses containing different subjects performing the same experimental design on declaring which voxels are truly active.
combined with random effects meta-analyses which rely on both the coordinates and effect sizes of the local maxima. Our results indicated (1) a higher balance between the false and true positive rate when compared to a high-powered reference image and (2) a higher reliability if the meta-analysis contains at least 20 or 35 studies. The popular ALE method for coordinate-based metaanalysis provides a slightly lower but still comparable balance between false and true positives. However, it needs at least 35 studies to approach the higher levels of activation reliability associated with a random effects model for coordinate-based meta-analysis. The main advantage of our work consists of using a large database, while the main limitation is the restriction to only one dataset. We argue that this work provides substantial insight into the performance of coordinate based meta-analyses for fMRI.
RS and BM would like to acknowledge the Research Foundation Flanders (FWO) for financial support (Grant G. 0149. 14). Furthermore, this work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant "STRATIFY" (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), the FP7 projects IMAGEMEND(602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300-2), the Medical Research Council Grant "c-VEDA" (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Center at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1, SM 80/7-2, SFB 940/1).
Further support was provided by grants from: ANR (project AF12-NEUR0008-01 -WM2NA, and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Luttecontre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the National Institutes of Health, U. S. A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence.
ACKNOWLEDGMENTS We would like to thank  Jean-Baptiste Poline  for the many, fruitful comments and discussions on this study. Furthermore, we thank the  IMAGEN Consortium  for providing the data set. The computational resources ( Stevin Supercomputer Infrastructure ) and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by  Ghent University , the  Hercules Foundation , and the  Flemish Government department EWI.
FUNDING RS and BM would like to acknowledge the Research Foundation Flanders (FWO) for financial support (Grant G. 0149. 14). Furthermore, this work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant "STRATIFY" (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), the FP7 projects IMAGEMEND(602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300-2), the Medical Research Council Grant "c-VEDA" (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Center at South London and Maudsley NHS Foundation Trust and King's College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1, SM 80/7-2, SFB 940/1).
Alignment data and 3D protein modelling The sequences for pea eIF4E from resistant and susceptible lines were collected from  Gao et al. (2004)  (GenBank ID AY611423, AY423375 and AY611425). Protein alignments were carried out using MultiAlin (http://multalin. toulouse. inra. fr) and BoxShade (http://www. ch. embnet. org/software/BOX_form. html). The 3D modelization of AteIF4E1 and eIF4E1 R protein was constructed by homology prediction with YASARA software (http://www. yasara. org; Krieger and Vriend, 2014)  using the crystal structure of Pisum sativum eIF4E1 (PDB ID: 2WMC-C) as a template. Protein structure was visualized with PyMOL software  (The PyMOL Molecular Graphics System, version 1. 8, Schrodinger, 2015 ; https://www. pymol. org/; Schrodinger, 2015).
Acknowledgments We thank  Joan Estevan  for technical assistance and  S everine Lacombe  for help with the anti-eIF4E1 antibodies. We thank  Zo e Terret  for advices on 3D protein modelization and  Jean-Franc  ßois Lalibert e ( INRS, Canada ) for kindly providing the TuMV-GFP plasmid. We thank  Rebecca Stevens  ( INRA GAFL Avignon ) for editing the manuscript and  Benoît Moury  ( INRA PV Avignon ) for discussions. Funding was provided by  INRA BAP  ( AAP MARASME) as well as through ANR-KBBE COBRA  ( ANR-13-KBBE-0006 ) and  ANR - POTYMOVE  ( ANR-16-CE20-000803 ). The authors declare no conflict of interest.
Data were collected using two different experiment paradigms that differed in the sounds that were repeated within a scanning session. The results were similar between the two paradigms (S3 Fig), and so we describe them together (see Materials and methods for details; subjects S1, S2, S3, S7, and S8 were scanned in Paradigm I; subjects S4, S5, and S6 were scanned in Paradigm II. Group results are based on data from Paradigm I). In Paradigm I, only responses to natural sounds were repeated, while in Paradigm II, both natural and model-matched sounds were repeated. Only voxels with a reliable response are plotted (test-retest NSE < 0. 4; see "Evaluating the noise-corrected NSE with simulated data" in Materials and methods for a justification of this criterion; reliability was calculated using natural sounds for Paradigm I and both natural and modelmatched sounds for Paradigm II).
Most computational models of auditory processing beyond the periphery are based on tuning for modulation  [20]. Such models have been used to explain responses throughout the auditory pathway in non-human animals  [2, 11, [26] [27] [28] [29] [30] [31] 34]. In humans, modulation-based models have been shown to have relatively good predictive accuracy throughout both primary and nonprimary regions  [7, 12, 45] , which has led to the hypothesis that sounds are represented in a distributed manner  [69]. This view contrasts with the notion of hierarchical organization and, in its most extreme form, suggests that responses to seemingly complex attributes of sound in nonprimary regions (e. g. , speech and music selectivity) could reflect the same types of mechanisms used to code sound in PAC. Our study helps to reconcile the literatures on modulation-based auditory models and hierarchical organization.
Moreover, although responses to the model-matched sounds were substantially weaker than responses to natural sounds, the model-matched sounds still drove responses to natural sounds above baseline and were correlated with responses to natural sounds. Thus, one natural hypothesis is that nonprimary regions transform a lower-level acoustic representation, such as the spectrotemporal representation considered here, into a representation that makes behaviorally relevant variables more explicit (e. g. , easier to decode). This hypothesis could be tested with hierarchical models that transform the output of modulation filters with additional stages of nonlinear and linear operations  [70]. In principle, such models could be fit to existing neural data sets and then evaluated with model-matched stimuli.
This finding suggests that a wide range of spectrotemporal filter models can be ruled out as models of nonprimary auditory cortex. Our stimuli and fMRI data are available, so that alternative models can be evaluated using this approach: https://osf. io/73pfv/. In other situations, matching with one model may entail matching with another, but not vice versa. This was the case for the four models we compared in Fig  4 -the full spectrotemporal model is inclusive of the other models. The higher NSE values observed with the other models provides evidence for the necessity of the spectrotemporal model features. As with any method for model evaluation, the interpretation of our results is constrained by the resolution of the brain measurements used for evaluation. Because fMRI is primarily sensitive to brain responses that are spatially clustered, our results bear most directly on aspects of cortical tuning that are organized at the scale of voxels.
Participants The experiment comprised 41 scanning sessions, each approximately 2 hours. Fifteen subjects participated in the experiment (ages 19-36; five male; all right-handed; one subject, S1, was author SNH). Two different experiment paradigms were tested (hereafter referred to as Paradigm I and Paradigm II). We have chosen to describe these two paradigms as a part of the same experiment because the stimuli and analyses were very similar. In Paradigm I, eight subjects completed a single scanning session, three subjects completed five sessions, and one subject completed three sessions (this subject chose not to return for the fourth and fifth sessions). We chose this approach because it allowed us to compute reliable group maps by averaging across the 12 subjects, as well as reliable individual subject maps using a larger amount of data from the subjects with multiple scan sessions.
Data acquisition parameters and preprocessing Data for Paradigm I were collected on a 3T Siemens Trio scanner with a 32-channel head coil (at the Athinoula A. Martinos Imaging Center of the McGovern Institute for Brain Research at MIT). The functional volumes were designed to provide good spatial resolution in auditory cortex. Each functional volume (i. e. , a single 3D image for one participant) included 15 slices oriented parallel to the superior temporal plane and covering the portion of the temporal lobe superior to and including the superior temporal sulcus (3. 4 second TR, 30 millisecond TE, 90-degree flip angle; five discarded initial acquisitions). Each slice was 4 mm thick and had an in-plane resolution of 2. 1 × 2. 1 mm (96 × 96 matrix, 0. 4-mm slice gap). iPAT was used to minimize acquisition time (1 second/volume).
T1-weighted anatomical images were also collected for each subject (1 mm isotropic voxels). Data for Paradigm II were collected more recently using a 3T Prisma scanner (also at the McGovern Institute). We used a multiband acquisition sequence (3× acceleration) to reduce slice thickness, while maintaining coverage (36 slices with 2-mm thickness and no gap) and thus reducing voxel size (2 mm isotropic). iPAT was not used. Other acquisition parameters were similar (3. 45 second TR, 1. 05-second acquisition time, 34 millisecond TE, 90-degree flip angle; three discarded initial acquisitions). Functional volumes were preprocessed using FSL software and custom MATLAB scripts. Volumes were motion corrected, slice-time corrected, skull stripped, linearly detrended, and aligned to the anatomical volumes (using FLIRT  [84]  and BBRegister  [85] ).
Model representation We synthesized sounds based on four different model representations. The simplest model was just based on the output of filters designed to mimic cochlear responses (i. e. , a cochleagram). The other three models were based on filters tuned to modulations in this cochleagram representation. Two models were tuned to either temporal modulation or spectral modulation alone, and one was jointly tuned to both temporal and spectral modulation. MATLAB code for measuring and synthesizing sounds from the models described in this paper is available here: https://github. com/snormanhaignere/spectrotemporal-synthesis-v2. We refer to specific scripts in this repository to clarify our descriptions and ensure that others can replicate our work. The cochlear representation was computed by convolving the audio waveform of each sound with 120 bandpass filters, spaced equally on an ERB N -scale between 20 Hz and 10 kHz, with bandwidths chosen to match those measured psychophysically in humans (individual filters had frequency responses that were a half-cycle of the cosine function, in order to exactly tile the frequency spectrum; adjacent filters overlapped by 87.
By bootstrapping the noise-corrected NSE, one can estimate a distribution over the true NSE values between natural and model-matched sounds, which can be used to perform statistics (see "Statistics" below). In practice, we observed similar trends with and without correction because voxel responses in both primary and nonprimary regions were similarly reliable (S4 Fig). MATLAB code implementing the noise-correction procedures described below can be downloaded here: https://github. com/snormanhaignere/general-analysis-code. see noise_corrected_similarity. m. Most noise-correction methods assume that the noise-corrupted response reflects the sum of a noise-free stimulus-driven signal plus noise that is statistically independent of the stimulus-driven signal: x ¼ s x þ n x ð11Þ y ¼ s y þ n y ð12Þ where in the context of this experiment, x and y are the measured response of a voxel to two sets of sounds (i.
For models with both cochlear and modulation features, we separately rescaled the two feature sets so that they would have the same norm and thus contribute similarly to the analysis (otherwise, the modulation features would dominate because there were many more features in the modulation representation). We used cross-validation across sounds to avoid statistical bias in fitting the weights, as well as to select the optimal regularization parameter. First, we split the response of each voxel to the 36 natural sounds into test and train data. The training data were used to fit the weights and select the regularization parameter (details below), and the test data were used to evaluate the predictions of the model. We used 4-fold cross-validation, splitting the data into four equally sized sets of nine sounds.
We then selected the regularization parameter that led to the best generalization accuracy averaged across the four splits (again using the noise-corrected NSE). Finally, given the selected regularization parameter, we fit the weights using all of the training set. We used the same procedure for the cross-prediction analyses, but instead of training and testing on natural sounds, we learned the voxel weights on the natural sound responses and tested them on model-matched sounds (and vice versa). MATLAB code implementing these regression analyses can be downloaded here: https://github. com/snormanhaignere/generalanalysis-code, see: regress_predictions_from_3way_crossval_noisecorr. m.
Coverage of the Body Space by the Acquired Knowledge We performed 20 different simulations lasting 8,000 trials each. At the beginning of each trial the units in the Goal Selector layer were recruited as representations of a different desired goal, while the units in the Goal Generator layer were triggered by the online encoding of the sensory inputs. The Goal Generator/Goal Selector matching pairs became related to touch events centered in different points in the body space during learning. We now focus on the analysis of the data referring to one simulation representative of the average performance of the system. Figure  3A  shows which sensors are activated when the different goals are pursued. The figure shows how after learning different goals produce touch events that cover the whole body space.
Goal Generator The Goal Generator performs the unsupervised formation of the abstract representations of the touch-sensor activation patterns that the system can select as goals. The activation of the touch sensors is filtered so that only the positive changes in the somatosensory activations are considered. The change pattern is transformed into a two-dimensional map of units where the horizontal dimension encodes the different sensors and the vertical dimension spatially encodes the activation intensity of each sensor change: this is done by determining the height of a Gaussian function used to activate the column units related to a certain sensor. Figure  13  shows this process with an example. The Goal Generator is implemented as a self-organising map (SOM,  Kohonen, 1998). SOMs are a particular kind of neural network that is able to categorise all the patterns of a given dataset in a unsupervised manner  (Kohonen, 1982).
Homology modeling To generate homology models of the B protease, the sequence was first aligned to that of mjS2P using the HHPred server to generate an initial alignment for homology modeling  [60]. This was further adjusted manually to correct a register error in the last transmembrane segment revealed by evolutionary co-variation analysis. The resulting modified alignment was used to construct a homology model in MODELLER  [61]  using the structures of mjS2P as a template (PDB ID: 3B4R). Both conformations were modeled, using 3B4R chain A as the template for the open conformation, and chain B as the template for the closed conformation.
Generation of evolutionary couplings Multiple sequence alignments (MSA) were generated for both the B protease (SP4FB_BACSU, residues 1-210) and mjS2P (Y392_METJA solved in PDB 3B4R, residues 1-224). The MSAs were built using jackhmmer  [62] , an iterative hidden Markov model-based sequence search tool, with 5 iterations querying against the April 2017 Uniref100 database  [63]. For SP4FB_BACSU, the alignment contained 5,390 sequences (2,005 effective sequences after downweighting sequences with more than 80% identity), with 92. 4% of the input residues covered with less than 30% gaps. Y392_METJA was aligned with 5,290 sequences (1927 effective sequences) with 92% coverage at 30% gap allowance. Evolutionary couplings were then determined as previously described  [39, 64, 65]. The full EVFold package and documentation can be found at https://github.
Acknowledgments We thank all members of the Bernhardt-Rudner super-group past and present for helpful advice, discussions, and encouragement;  Paula Montero Llopis  and the  MicRoN core  for advice on microscopy;  Hoong Lim  for advice on fluorescence quantification;  Seth Goldman  for help with the 35 cm polyacrylamide gel; and  Ross Tomaino  from the  Taplin Mass Spectrometry Facility.
Study sites and data collection We studied 115 dryland ecosystems from 13 countries (Fig. 1 ), which are a subset of the 236 sites from  Ochoa-Hueso et al. (2018). Annual mean temperature and rainfall ranged from 2. 6 to 25. 7°C and from 67 to 801 mm, respectively. Elevation, latitude and longitude varied from 76 to 4524 m a. s. l. , from -41° to 40°, and from -115° to 142°, respectively. The database includes three habitat types (grasslands, shrublands and open woodlands/savanna); however, we did not use open woodlands/savanna because there were very few sites suitable for our analyses (111 sites remained). Grassland and shrubland sites covered a wide range in species richness (2 to 39 perennial species) and total plant cover (from 4. 5 to 82. 8%). At each site, we established a 30 × 30 m plot for our field measurements.
Meassuring the size distribution of plant patches To measure the size distribution of plant patches, we focused on two metrics that describe the grain size and the shape of the size distribution. For each site, we extracted all the patches and their sizes resulting from the image classification analyses of the three 50 × 50 m subplots. The data from all the subplots were pooled to perform the next steps. The grain size of the patches was determined by extracting the median patch size across the three subplots. Median patch size informs about the most common patch size of each image and is an important descriptor of the overall scale of the system. It is important to note that, in systems where no particular scale is predominant (e. g. those whose PSD better fit to power laws), the characteristic median of patch sizes should be small (as those would be the most dominant patch sizes).
2005). To further investigate the effective precipitation due to seasonality, we estimated the percentage of rainfall in the warmest quarter of the year using bioclimatic variables extracted from the Worldclim database  (Hijmans et al. 2005). Total perennial cover ranged from 4 to 83% in our study sites. The height of the dominant species was obtained from previous studies, local floras and global databases  (Kattge et al. 2011 ; see  Soliveres et al. 2014a , b, Le Bagousse-Pinguet et al. 2017 , for more details on trait data acquisition). We introduced habitat type (grassland and shrubland) to control for differences in the growth form of the dominant species; these habitat types were identified depending on the dominant plant form inhabiting the sites. We measured two facets of facilitation in this study.
We evaluated all possible interactions of biotic attributes with aridity to assess the interplay between abiotic and biotic predictors of plant spatial patterns. We did not consider other abiotic x biotic interactions as this largely increased the number of predictors to be tested and because these interactions are more likely to be ecologically relevant to help predicting the future structure of dryland ecosystems with ongoing climate change. To know whether different habitats differ on the relative importance of other predictors of shape metrics, we repeated the analysis for grasslands and shrublands separately. We performed all these analyses with those sites for which we had all the information (n = 71), but analyses with the sites that did not include facilitation data (n = 111) were consistent with those shown here (Supplementary material Appendix 4 Fig.