dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluxes resulting from land-use change (mainly deforestation) In this section, we provide a new estimate of carbon emission from land-use changes in Africa. These are mainly deforestation, although we attempt to estimate also forest degradation and the role of agroforestry systems. Annual emissions of carbon from land use (e. g. timber harvest) and land-use change (e. g. conversion of forest to cropland) were determined as described in  Houghton and Hackler (2006)  except that the calculations were updated here with the most recent FAO data on changes in forest area  (FAO, 2010). Briefly, the analysis considered four sub-regions within sub-Saharan Africa: western, eastern, central, and southern Africa. Land uses included permanent croplands (cultivated lands), pastures, tree plantations, industrial wood harvest, and shifting cultivation (a rotational land use with alternating cropping and fallow, in which trees regrow until being cleared again for the cropping period).
These impacts have been assessed as minor disturbances by previous studies  (Deckker and De Graaf, 2003). We present here some results from a study on the impact of selective logging in African tropical forests on biomass and biodiversity losses, studied by co-authors of this paper (Cazzolla  Gatti et al. , 2014). The study sites selected were tropical forest areas located in West Africa along the border between Ghana and Ivory Coast (Bia National Park and surrounding areas) and Sierra Leone and Liberia (Gola National Park), and in central Africa, within the Congo River basin, on the border between Cameroon and Central African Republic (Lobèkè National Park and surrounding areas). African tropical forests biomass was estimated from dendrometric data collected in 150 plots of 500 m 2 each, subjected to different management (unmanaged/old growth; selectively logged; clear-cut).
Over larger scales, probably the most frequently used data set is the Global Fire Emissions Database (GFED), which combines burned area from  Giglio et al. (2010)  with the satellite-driven Carnegie-Ames-Stanford approach biogeochemical model that estimates fuel loads based on carbon input from net primary production (NPP), and losses through heterotrophic respiration, herbivory, fuelwood collection, and fires with a monthly timestep on a 0. 5 × 0. 5 grid  (van der Werf et al. , 2010). Total annual carbon emissions estimates for Africa according to GFED were 1. 03 ± 0. 22 Pg C yr -1 , averaged over the period 1997 to 2011 (Table  8 ) 13. About 90 % of these emissions originated from savanna and woodland burning, with a smaller role from forest and deforestation fires. Since there is relatively more woodland burning in Africa south of the equator, mean fuel consumption was somewhat higher here (∼ 450 g C m -2 burned) than in northern Africa (∼ 380 g C m -2 burned) and total emissions were thus also somewhat higher in the Southern Hemisphere of Africa.
These estimates are about a factor 3 lower than those from GFED, but when combined with burned area estimates, they yield a better correspondence with fuel consumption measurements in the field than those from GFED. At the same time, combining measurements of fire-emitted gases from several sources with inverse modeling techniques yielded better agreement with modeled fire emissions (AMMABB) in West Africa when the L3JRC burned area product  (Tansey et al. , 2008)  used in this database was boosted by a factor 2 and 1. 67 for the land cover classes open broadleaf deciduous forest and deciduous shrubs, respectively  (Liousse et al. , 2010). This emissions' estimate is about twice as high as GFED although part of the discrepancy originated from using different emission factors, so estimate carbon emissions are more similar.
Lateral fluxes through river flows Significant amounts of carbon are transported from terrestrial ecosystems to oceans and other places, mainly through river flow, but also through agricultural and wood product transport. Although locally lateral transport may not be a very large flux, the sum of individual contributions is significant in the calculation of regional carbon budgets  (Raymond et al. , 2008). To estimate the land to ocean carbon flux and transport to endorheic regions, we used the 21 ocean coastline (26 212 740 km 2 ) and 8 endorheic (3 706 207 km 2 ) segments with their corresponding river catchments for Africa as described by the COSCAT database  (Meybeck et al. , 2006). The lateral transport of carbon to the coast was estimated at the river basin scale using the Global Nutrient Export from WaterSheds (NEWS) model framework  (Mayorga et al.
, 2010) , including NEWS basin areas. The carbon species models are hybrid empirically and conceptually based models that include single and multiple linear regressions developed by the NEWS effort and  Hartmann et al. (2009) , and single-regression relationships assembled from the literature. Modeled dissolved and particulate organic carbon (DOC and POC) loads used here  (Mayorga et al. , 2010)  were generated largely using drivers corresponding to the year 2000, including observed hydro-climatological forcings, though some parameters and the observed loads are based on data spanning the previous two decades. Total suspended sediment (TSS) exports were also estimated by NEWS. Dissolved inorganic carbon (DIC) estimates correspond to weatheringderived bicarbonate exports and do not include CO 2 supersaturation; the statistical relationships developed by  Hartmann et al.
Acknowledgements. The research leading to these results has received funding from the  European Research Council (ERC)  under grant agreement no. 247349  " Africa-GHG ". We would like also to thank  Christian Roedenbeck  and  Wouter Peters  for having provided the results of two atmospheric inversions (JENA and CarbonTracker Europe systems) that were further processed and made available through the CarboScope website like the other inversions (http://www. carboscope. eu). We also thank the ecosystem modeling groups that participated in the Trendy inter comparison project, for having made available their simulations. Edited by:  J. Canadell.
Study design All subjects underwent in-laboratory hdEEG polysomnography (PSG) that utilized 256 channel hdEEG (Electrical Geodesics Inc. , Eugene, OR), as well as standard sleep monitoring leads, including electrooculogram, sub-mental electromyogram, electrocardiogram, bilateral tibial electromyogram, respiratory inductance plethysmography, pulse oximetry, and a position sensor. Participants arrived at the laboratory between 4:00 and 5:00 p. m. for set-up that took approximately two hours. Baseline EEG and an attention and a fear conditioning task were performed. After 9pm the participants were allowed to sleep undisturbed in the laboratory beginning within one hour of their usual bedtime. Additional measures were collected the next day. In this report we will focus only on the EEG data during sleep.
Sleep PSG assessment and hdEEG data analysis Sleep staging, which was based on six mastoid-referenced channels (F3, F4, C3, C4, O1, and O2), a sub-mental electromyogram and an electrooculogram, was performed by a registered polysomnographic technologist in 30-second epochs according to standard criteria  [26]  using AliceH Sleepware (Philips Respironics, Murrysville, PA) The sleep technician was blind to group assignment. PSG recordings were reviewed by a board certified sleep medicine physician, who was able to confirm the absence of sleep disorders in 26 out of 29 LTM. Two LTM met the clinical criteria for sleep-related movement disorders (periodic limb movement arousal index. 10/h), while one had sleep-disordered breathing (apnea-hypopnea index. 10/h). As a result, data from these three LTM (as well as from the age-and sex-matched meditation naives) were not further analyzed.
All-night sleep hdEEG recordings were collected with vertex-referencing, using a NetAmps 300 amplifier and NetStation software (Electrical Geodesics Inc. , Eugene, OR). EEG data were sampled at 500 Hz, and a first-order high-pass filter (Kaiser type, 0. 1 Hz) was applied to eliminate the DC shift. Data were then band-pass filtered (1-50 Hz), down-sampled to 128 Hz and average-referenced to the mean power in all channels. Spectral analysis was performed for each channel in six-second epochs (Welch's averaged modified periodogram with a Hamming window). For NREM sleep data, a semi-automatic artifact rejection was conducted to remove six-second sleep epochs which exceeded a threshold based on the mean power at either a low (1-4 Hz) or high  (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30)  frequency band  [27] ,  [28].
We also performed correlation analysis between duration of meditation practice in LTM and NREM sleep EEG gamma power, as well as TST and WASO. Statistical analyses were performed using MATLAB (The MathWorks Inc. , Natick, MA) and STATISTICA (StatSoft Inc. , Tulsa, OK). Non-parametric statistics were used to assess group differences in EMG, EOG, and global EEG gamma power (Wilcoxon rank sum test), as well as to correlate gamma power with variables of interest (Spearman rank correlation). An outlier subject was excluded from the correlation between EEG gamma power and hours of daily practice in LTM based on a suspected over inflation of daily practice (value above 1. 5 interquartile ranges from the 75th percentile). Specifically, this LTM had the least amount of retreat time compared with all the LTMs, but the highest amount of non-retreat hours.
This work was supported by the  National Center for Complementary and Alternative Medicine (NCCAM)   P01AT004952  to TG, RJD and AL, grants from the  National Institute of Mental Health (NIMH)   R01-MH43454 ,  P50-MH084051  to RJD, grants from the  Fetzer Institute  and the  John Templeton Foundation  to RJD, a  core grant  to the  Waisman Center  from the  National Institute of Child Health and Human Development  [ P30 HD003352-449015 ], and a  International Reintegration Grants (IRG) ,  FP7-PEOPLE-2009-RG for AL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
While more liberal analyses testing a greater number of EEG channels and time samples could be implemented, correction for multiple comparisons would largely diminish either sensitivity or confidence in the presence of a given brain response. To overcome these common electrophysiological issues, we evaluate in the present research the potential of a single-trial multivariate pattern (MVP) analysis. We implemented, separately for each subject, a MVP classifier that aims at maximally extracting information from each trial by combining evidence from multiple EEG channels and multiple timesamples. After training on an independent dataset, the classifier estimates the probability that each trial contains a local or a global response to auditory novelty. This prediction can be compared to trials' effective classes.
Following the original design, in a given block, 80% of trials consisted in one type of sequence (e. g. aaaaB) and 20% of trials were global deviants (aaaaa in this example), pseudo randomly distributed at least one and at most six global-standard trials apart (Fig. 1 ). In all experiments, trials immediately following a global deviant were removed from the analyses. Each block started with a 30 s habituation phase during which the frequent sound sequences were repeatedly presented to establish the global regularity, before the first infrequent stimulus was heard. In Experiments 1, 3-5, sounds were presented via headphones with an intensity of 70 dB, using E-prime v1. 2 (Psychology Software Tools Inc. ). Trials from the habituation phase were not included in the analyses. In Experiment 2, sounds were directly presented from the computer's speakers because the intracranial recording apparatus was incompatible with headphones.
All signals were digitally low-pass filtered at 40 Hz, and referenced with a common average. Trials were then segmented from -800 ms to 736 ms after the onset of the fifth sound, and were baseline corrected over a 200 ms window before the onset of the first of the five sounds. All EEG processing stages were performed in the EGI Waveform Tools Package and with the Fieldtrip toolbox  (Oostenveld et al. , 2011)  and MATLAB 2009b.
Electrode locations are reported in Supplementary Fig. 2. The task was identical to Experiment 1s. After removing channels showing inter-ictal activity, analyses were performed by pooling over the 401 channels recorded across all subjects. All signals were digitally low-pass filtered at 40 Hz and down-sampled to 256 Hz. Trials were then segmented from -800 ms to 700 ms after the critical stimulus onset, and were corrected for baseline over a 200 ms window before the onset of the first of the five sounds. All EEG processing stages were performed with MATLAB 2009b and the Fieldtrip toolbox  (Oostenveld et al. , 2011).
Experiment 3: Attentive task (MEG) In the third experiment (partially reported in  Wacongne et al. , 2011) , ten healthy adults (Age: M = 25 years old, SD = 4. 7 years, 5 females) performed a modified version of the Local-Global paradigm (see below) while their brain activity was measured with MEG (Elekta Neuromag® MEG system, Helsinki, Finland, comprising 204 planar gradiometers and 102 magnetometers in a helmet-shaped array) and EEG (built-in 64 electrodes system). Scalp EEG electrodes were not analyzed in the present study. Data were sampled at 1 KHz with on-line analog low-pass filtering at 330 Hz, and on-line analog high-pass filtering at 0. 1 Hz. The head position with respect to the sensor array was determined by four head position indicator coils attached to the scalp. The locations of the coils and EEG electrode positions were digitized with respect to three anatomical landmarks (nasion and preauricular points) with a 3D digitizer (Polhemus Isotrak system®).
At the end of the recording, a list of questions was submitted to the subject to check that they had detected the various auditory regularities. Signal space separation (SSS,  Taulu et al. , 2004)  was applied to suppress unwanted magnetic interferences (e. g. outside disturbances, limb movements), to interpolate noisy MEG sensors and to realign MEG data into a subject-specific head position. This reference head position was determined from all head position measurements done at the beginning of each recording session. This data transformation helps the direct comparisons of MEG data between conditions and blocks. Except if explained otherwise, eye blinks and cardiac artifact were corrected separately for each type of channel (gradiometer and magnetometers) by decomposing the average artifacts into principal components, and regressing out those principal components from the continuous recording -a technique known as signal space projection (SSP).
Experiment 4: Distracting task (EEG) In the fourth experiment (partially reported in  Bekinschtein et al. , 2009) , 9 subjects (6 females aged between 21 and 33 years old) were actively distracted by a continuous speeded visual detection task simultaneous with the presentation of the Local-Global auditory stimuli, while EEG was continuously recorded at 250 Hz using a 256-channel EEG geodesic net (EGI) referenced to the vertex. Participants were instructed to detect a visual target in a rapid stream of successive letters presented at the fovea and were explicitly asked to neglect the unrelated auditory stimuli. The visual stimuli were twelve 1° × 1° colored upper or lower case letters, and were maximally presented for 1000 ms, using E-prime v1. 1 (Psychology Software Tools Inc. ). To check that subjects did not consciously perceived the global structure of the auditory stimuli, they were asked at the end of the experiment whether they had perceived any global regularity or novelty.
Recording high-density scalp ERPs from non-communicating patients in the intensive care unit or a similar environment is very challenging for technical reasons. First, the electromagnetic environment is noisy, and patients were not recorded in a shielded room but at bedside. Second, many patients presented physiological artifacts such as EMG, eyemovements and blinks, or other involuntary movements. Therefore, it is particularly important to systematically evaluate the technical quality of data before statistical analysis. Recordings including at least one block with more than 50% of rejected trials were discarded from further analyses in order to avoid possible biases across experimental conditions. EEG preprocessing was identical to Experiment 1. across blocks and are therefore less likely to be contaminated by block-design pitfalls  (Goldfine et al.
Analyses The decoding steps are summarized in Supplementary Fig. S6. A ten-fold stratified crossvalidation was implemented for each within-subject analysis. Stratified cross-validation balances the proportion of each class across K folds (K = 10) in order to maximize the classifiers' ability to generalize to unknown data. Stratified cross-validation is thus particularly relevant for problem with unbalanced class frequencies. Cross-validation consists in repeatedly applying a series of computations fitted to a fraction of the dataset (1 -1/K percent of the trials, called the training set), and then evaluate the results on the remaining fraction (1/K percent of the trials, called the test set). Within each fold, we estimated the mean (μ train ) and the standard deviation (σ train ) of each sample of each sensor across all training trials.
S5 ). After fitting the SVC on the training set, classification scores were estimated on the independent test set. All preprocessing steps posterior to trials segmentation (feature selection, normalization, parameter selection etc) were fitted, within each cross-validation loop, on the training set only, and are thus immune to circular analysis issues  (Lemm et al. , 2011). Crossvalidation was constructed from chronologically shuffled data which minimizes effects of non-stationarity that could have been observed between the beginning and the end of the recording session for instance  (Lemm et al. , 2011). To further minimize the possibility of a trial ordering confound, the cross-validation was applied four times for each patient. Each of these cross-validations was based on a different chronologically shuffled dataset thus implying different folds too.
Finally, the last approach consisted in averaging brain signals within a given time windows (Fig. S3 ). The method, rationale and results are presented in the supplementary materials. Support Vector Machine (SVM)-Each trial was transformed into a p-dimensional vector x, in which each coordinate ("attribute") corresponds to a single data sample at a given sensor (p = n sensors • n samples ). The entire dataset can hence be represented as a matrix X in which each row i corresponds to one trial x i , and each column corresponds to an attribute. Moreover, trial classes (standard or deviant) can also be represented as a binary vector y, in which standard trials are labeled as 1, and deviant trials as -1. As for all linear classification analyses, the aim is to find a hyperplane (materialized by a p-dimensional vector of weights w) that discriminates the two classes (y standard and y deviant ).
Finally, sample weights were applied in proportion of the trial classes (LSGS, LSGD, LDGS, LDGD) so that each of the category would equally contribute to the definition of w, and this independently of the contrast of interest (local or global). This step is known to contribute to the minimization of imbalanced dataset artifacts and thus maximizes the number of trials that can be considered in the training process. In practice this step did not lead to a strong improvement of classification performance. All multivariate analyses were performed with the Scikit-learn toolbox  (Pedregosa et al. , 2011). As a control, in supplementary analyses, we applied the same approach after randomly shuffling the label (y) of each trial and subsequently training the classifier with this incorrect class labeling.
Ideally, this method would be applied 10,000 times per subject to allow the estimation of the null distribution of each subject. However, this approach would be too computationally expensive with the present dataset, because the classification of a single EEG recording session takes approximately 30 min of computer time. We thus applied shuffling only once per EEG recording, and thus only report across-subjects statistics. Statistics-ROC analyses and AUCs are methods to estimate the size of a given effect, and in our case, the classifiers' ability to discriminate two types of trials. To test for statistical significance within subjects, we performed Mann-Whitney U tests across trials. Because the classifier attributes a continuous estimate to each trial (its predicted probability of belonging to a given class C), we can indeed efficiently compare those predicted probabilities across the two levels of the trials' true classes.
It should be noted that our method, although different in technical details, follows a similar approach to that taken in a recent study  (Tzovara et al. , 2013). Tzovara and collaborators decoded, from EEG recordings of coma patients, the difference between regular and irregular trials, that is, the equivalent of the local effect in the present study. To classify each trial, the authors used a different method which modeled the ERPs with a mixture of Gaussians. Advantages and disadvantages can be identified in each of these classification methods. Our approach does not make any Gaussian assumptions, can make use of imbalanced training dataset and can extract a large number of different types of topographies by searching across all channels -which therefore are not restricted to EEG. In contrast, Tzovara and collaborators first transform the ERPs into components.
This computational step reduces the dimensionality of the data, and thus likely improves the efficiency of the ensuing classification process. However, their classifier is unable to optimally use imbalanced datasets, which are necessarily encountered in this type of novelty paradigm. Taken together, these differences may explain why our approach provided slightly better classification scores (AUC = 77. 8%) than theirs (AUC = 71%) in similar control subjects recorded with EEG. Future efforts should capitalize on a combination of these methodological technicalities. Crucially, our MVP classifiers replicated and extended previous observations on the key electrophysiological properties of local and global effects  (Bekinschtein et al. , 2009). As expected, local novelty mainly affected the early part of the neural signal (< 300 ms) and remained unaffected by visual distraction.
Acknowledgments We thank  Gael Varoquaux  for useful discussions, and the  Neurospin , the  ICM  and the hospital infrastructure for their generous help and administrative support. This work was supported by a  Direction Générale de l'Armement (DGA)  grant to JRK, by an 'Equipe FRM 2010' grant of  Fondation pour la Recherche Médicale (FRM)  to LN, by  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Commissariat à l'Energie Atomique (CEA) , and a  European Research Council (ERC)  senior grant " NeuroConsc " to SD, by  Journées de Neurologie de Langue Française and FRM (Fondation pour la Recherche Médicale ) to FF, by  INSERM  to BR and by  AXA Research Fund  to IEK. The Neurospin MEG facility was sponsored by grants from  INSERM, CEA, FRM , the  Bettencourt-Schueller Foundation , and the  Région île-de-France.