dataesr/scientific-paragraphs-categorization huggingface dataset:
The current behavioral task was used to help participants pay attention to the display. EEG epochs from these target trials were not included in subsequent analyses. Peak Analysis of the N170 ERP N170 amplitudes and latencies were extracted from each of the two temporo-occipital scalp electrode clusters for each participant and condition for subsequent statistical testing. N170 was maximal over the temporo-occipital scalp, as demonstrated by the topographic voltage maps (Figure  2 ) plotted at the time point at which the N170 was maximal in amplitude. N170 was elicited in all stimulus conditions (Figure  3 ). N170 latency and amplitude data for each condition and hemisphere are shown in Table  1. A 4-way repeatedmeasures ANOVA for N170 peak amplitude differences revealed a significant main effect for hemisphere (F (1,15) = 11.
Finally, if N170 modulation was driven by general low-level effects of stimulus luminance and contrast, then we might expect to observe a significant main effect of Stimulus type  [Real, Line]. Interestingly, our analysis generated effects that were more complex than predicted for N170 amplitude. We observed a significant main effect of Motion [Away, Direct] × Stim [Real, Line]-which was not what we had predicted. The nature of these differences was clarified with paired t-tests, which indicated that N170 was larger for averted vs. direct gaze only for real faces-consistent with our previous study  (Puce et al. , 2000). Also consistent with our previous work  (Rossi et al. , 2014)  there was no effect of gaze aversion on N170 in our line-drawn face stimuli (or control stimuli). Having said that, there were other striking differences in the current dataset that resulted from our initial predictions not being upheld.
Why would ERPs elicited to impoverished mouth movements behave so differently to those observed to gaze transitions? Bassili (1978 Bassili ( , 1979) )  originally reported behavioral data to viewed emotional expressions on point-light face stimuli. Success in recognizing different emotions was driven by the subject focusing on either the upper or lower regions of the impoverished face (i. e. , eyes/brows vs. mouth, respectively)  (Bassili, 1979). The line-drawn stimuli in this study can also be regarded as biological motion stimuli (see  Oram and Perrett, 1994, 1996). Most typically, these impoverished forms of stimuli are used to represent very effectively the articulated motion of the joints of the body-where information related to the type of activity being observed can be readily identified from seeing these minimalist displays  (Blake and Shiffrar, 2007).
However, some interesting differences in behavior and neurophysiology were observed for the impoverished stimulus categories. Subjects detected target stimuli that consisted of image negatives for all presented stimulus types. Participants were slower at detecting impoverished face targets relative to real face targets, and were the slowest for impoverished faces relative to impoverished controls. For real stimuli, face targets were identified faster relative to controls. We cannot directly relate our behavioral data to our ERP findings: the ERPs were recorded to trials where no behavioral response was recorded, so we can only speculate about the potential nature of our ERP findings to the impoverished stimuli. One consideration might be that the impoverished faces in the current study might not be treated as faces by the brain.
At this point in time much more data are needed to make sense of these changes in oscillatory activity to facial motion stimuli and their relationship to ERP activity-it is not yet customary to perform both types of analysis in studies. Caruana et al. (2014)  performed an intracranial EEG study where ERPs and oscillatory activity were examined side-by-side. They presented epilepsy surgery patients with faces showing dynamic gaze changes while intracranial EEG was recorded from electrodes sited in the posterolateral temporal cortex. Intracranial N200 ERP activity (the analog of the scalp N170) and transient broadband high frequency gamma band activity (out to 500 Hz) occurring at around the same time as the N200 was significantly larger when patients viewed averted gaze relative to either direct gaze or lateral switching movements  (Caruana et al.
Transmembrane migration assay Human Mo of each subset (4×10 5 cells in 75 μl) were placed in the upper chamber of a Transwell insert (5 μm pore size, 96well plate; Corning Costar), and 230 μl of medium (RPMI 1640 with 0. 5 % FBS), supernatants from M. tuberculosis-stimulated pulmonary epithelial cells (A549) and PMNs (polymorphonuclear cells) or cell-free pleural effusions from TB patients were A u t h o r C o p y placed in the lower chamber. After 3 h, cells that had migrated to the lower chamber were removed and analysed. The relative number of cells migrating was determined on a flow cytometer using Calibrite beads (BD Biosciences), where a fixed number of beads was included in each sample and the number of cells per 1000 beads was evaluated. Data were normalized to the number of initial cells.
Flow cytometry Cells were incubated with 20 μl of Fc-receptor-blocking agent (Octagam, Octapharma) for 20 min. For the identification of human cells in mice tissue, cells were incubated with anti-human HLA-DR PE (phycoerythrin)-conjugated antibodies (clone G46-6; BD Biosciences) for 30 min at 4 • C. This monoclonal antibody does not cross-react with murine cells  [17]. Data were acquired in a Guava Easy flow cytometer (Millipore) and analysed using FCS Express Software (De Novo Software). For the phenotypification of murine cells, cells were incubated with the following antibodies: anti-Ly6C [clone AL-21, APC-Cy7 (allophycocyanin/indotricarbocyanineconjugated); BD Biosciences], anti-Ly6G (clone 1A8, PE-conjugated; BD Biosciences), anti-CD11b (clone M1/70, FITC-conjugated; eBioscience), and anti-CD11c (clone CHL3, APC-conjugated; BD Pharmingen) for 30 min at 4 • C.
ACKNOWLEDGEMENTS We thank  Juan Carlos León-Contreras ,  Pablo Vallecorsa  and  Dr Roberto Meiss  for excellent technical support. We also thank the staff of the  Hemotherapy Service of the Garrahan Hospital (Buenos Aires) , the  Etablissement Francais du Sang (Toulouse)  and the Hospital C. M. N. La Raza  ( IMSS, Mexico City ) for their contribution with donor recruitment.
FUNDING This work was supported by the  National Agency for Science and Technology Promotion, Ministry of Science of Argentina  [grant numbers  PICT 2011-0572  (to M. d. C. S. ) and  PICT 2012-0221  (to L. B. )], the  Mexican National Council of Science and Technology  [grant numbers  179388  (to C. S. -T. ) and  84456  (to R. H. -P. )], the  French National Research Agency  [grant numbers  ANR-2010-01301   Migre-Flame  (to I. M. -P. ) and  ANR-12-BSV3-0002   ANR -funded grant B-TB (to O. N. )] and the  Fondation pour la Recherche Médicale  [grant number FRM, DEQ 20110421312 (to I. M. -P. )]. E. G. -D. is the recipient of a  Mexican National Council of Science and Technology pre-doctoral scholarship  [grant number  302832 ]. L. B. was awarded a fellowship by the  Ministry of Foreign Affairs of Mexico.
Splitting the Data into Training and Test Sets To evaluate the performance of the detection procedure, we divided the data base into a training set (used to derive optimal thresholds via ROC-like curves) and a test set used to compute the performance of the method. Thirty S2 segments and 15 non-S2 segments were randomly selected from the data of each of the 14 individuals (i. e. , 630 sleep EEG data segments in total: 420 S2 segments and 210 non-S2 segments). This ensured a balanced representation of data from across all subjects. Note, that our emphasis on S2 stems from the fact that it is the sleep stage of primary interest for the detection of spindles and K-complexes. (Carskadon and Dement, 2011) , this proportion was representative of using random sampling of sleep segments. In addition, for practical purposes, we also explored the effect of reducing the size of the training set to evaluate minimal training requirements (scenario 3).
Detection of Local Maxima and Thresholding To detect the occurrence of sleep spindles from the timefrequency (T-F) map of the oscillatory component, we first search  for all local maxima by identifying T-F values that exceed those of all eight surrounding neighbors of any given value in the 2D time-frequency space (using a sliding window across the T-F twodimensional space). Next, we apply a detection threshold to the obtained maxima in the T-F map. Selecting an optimal threshold is a critical step. We chose a procedure that determines the best threshold as the one that maximizes the difference between sensitivity (Sen) and FDR of spindle detection (Note that other options are of course possible and can easily be included in our framework). A practical way to achieve this goal is by using an ROC-like approach on a training data set.
Expert Identification and Inter-annotator Agreement Metrics Two annotators visually identified all K-complexes and spindle events in our database. Unless otherwise stated all automatic detection results are evaluated against the annotation of Expert 1 and 2, independently. When evaluating the minimal number of training segments needed for our method (Section Impact of the Amount of Available Training Data on the Performance) and when exploring the results on a subject by subject basis (Section Performance of the Method in Individual Subjects) we restricted the analysis to the segments where Expert 1 and Expert 2 fully agreed (consensus). Inter-annotator agreement was assessed using two metrics: (i) percent agreement (portion of events on which raters compared to total number of events) and (ii) Cohen's kappa coefficient κ, a statistical measure of inter-annotator agreement that takes into account the agreement occurring by chance  (Cohen, 1960).
Stability of the Proposed Method with Regards to Sleep Stages The results presented above were obtained with EEG segments that were recorded during S2, the sleep stage where K-complex and spindles are most frequent. However, as indicated above, our method does not require sleep staging as a preliminary pre-processing step. The method is in theory equally valid for EEG segments from all sleep stages. We therefore also examined the performance of the detection algorithm by using 420 EEG segments including data from all sleep stages. Half of the segments were S2 (i. e. , 210 segments) and the other half were non-S2 sleep (i. e. , 210 segments). The 210 non-S2 segments were composed of: 126 REM segments, 42 SWS segments and 42 S1 segments. Note that these proportions were chosen to be close to the natural distribution (frequency of occurrence) of the various sleep stages across a typical night's sleep  (Carskadon and Dement, 2011).
Performance of the Method in Individual Subjects The results presented so far were obtained by combining EEG sleep segments extracted from multiple subjects (n = 14). But how robust is the proposed method for the detection of K-complexes and spindles in each individual subject? And, in particular, how good are the performances in single subjects when only a handful of events have been visually marked and thus available for training? To address this question we launched the entire detection pipeline in each subject individually using only 15 segments for training (On average these 15 30-s segments contained 18 ± 4. 3 spindles and 11. 9 ± 2. 3 K-complex events). In addition, as in the previous analysis (Section Impact of the Amount of Available Training Data on the Performance) we restricted the analysis to all segments where the annotations of both experts were in agreement (consensus).
Performance Evaluation on a Publicly Available Database To investigate the performance of our method on sleep EEG data other than our own recordings, we detected spindles and K-complexes by applying our method to the DREAMS data set, a publicly available database of annotated sleep EEG. EEG recordings from two specific databases were used: The Sleep Spindle database and the K-complexes database, which have both been made available by University of MONS -TCTS Laboratory and Université Libre de Bruxelles-CHU de Charleroi Sleep Laboratory. The spindles data can be accessed online at: http://www. tcts. fpms. ac. be/~devuyst/ Databases/DatabaseSpindles/ while the K-complex data can be found at: http://www. tcts. fpms. ac. be/~devuyst/Databases/ DatabaseKcomplexes/. The spindles and K-complexes databases consist respectively of 8 and 10 excerpts of 30 min of annotated central EEG channel extracted from whole-night PSG recordings.
Here, we used recordings from the subjects that were recorded with identical sampling rate (200 Hz) and for which the visual annotation was complete. This meant that for spindle detection we used 6 participants out of 8 and for the K-complex detection we used the data from all 10 participants. We used the annotation by Expert 1 as benchmark since the annotations of Expert 2 are not available for all subjects. The straight-forward application of our method to these data, without any specific parameter adaptations, yielded a sensitivity of 71. 77% and FDR of 30. 54% for spindle detection, and a sensitivity of 83. 31% and FDR of 36. 31% for K-complex detection.
The subsequent filtering of the oscillatory component enhances the spindles with regards to baseline, and thereby improves their detectability with standard spindle detectors. Using this technique with a readily available EEG spindle database provided a mean increase of 13. 3% in the by-sample F1 score and 13. 9% in the bysample Matthews Correlation Coefficient score. A recent study by the same group also provides compelling evidence for the added value of using sparse optimization to detect spindles and K-complexes  (Parekh et al. , 2015). A direct comparison between these approaches and the methodology proposed here is not straightforward given the use of by-sample metrics in the  Parekh et al. (2014 Parekh et al. ( , 2015) )  studies. Most importantly, the current method and those proposed by  Parekh et al.
(2014 Parekh et al. ( , 2015) )  provide converging evidence of improved spindle detection via time-frequency sparsity, and they collectively suggest that this framework is a promising path for enhanced performance of event detection in sleep EEG. Overall, the results reported here (either by combining data across participants or by performing the detection algorithm separately for each individual) are comparable with the results of existing methods. However, we performed further analyses in order to gain additional insights into (a) how the performance of the pipeline proposed here compares to existing methodology (Section Comparison with a Standard Detection Method) and (b) how well it performs on other available data sets (Section Performance Evaluation on a Publicly Available Database).
The results suggest that our method provides better detection than the RMS-based method and that the performance of the latter can be improved if we first apply the TQWT-MCA processing to the data before computing the RMS (Table  5 ). Furthermore, application of our method to the  Devuyst et al. (2010 Devuyst et al. ( , 2011) )  online database, yielded a sensitivity of 71. 77% and FDR of 30. 54% for spindle detection, and a sensitivity of 83. 31% and FDR of 36. 31% for K-complex detection. The original papers associated with these databases do not directly report sensitivity and FDR, but these metrics can be inferred from the confusion matrices they provided for each expert. Using Expert 1 as ground truth (as we did here), they detected spindles with sensitivity of 68. 40% and FDR of 62. 04% (computed from confusion matrix in  Devuyst et al.
, 2014). Such benchmark data carry the potential to significantly advance the field of automatic spindle and K-complex detection, as well as sleep staging. This was performed in a recent report by O'Reilly and  Nielsen (2015)  where the authors compared four automatic spindle detection algorithms: Teager detector  (Ahmed et al. , 2009) , Sigma index  (Huupponen et al. , 2007) , RSP  (Devuyst et al. , 2011) , RMS  (Mölle et al. , 2002). To this end, four data bases were used, two of which are open access: the DREAMS database  (Devuyst et al. , 2010 (Devuyst et al. , , 2011) )  and the Montreal Archive of Sleep Studies (MASS)  (O'Reilly et al. , 2014). The results obtained and conclusions drawn from this important comparison highlight limitations and shortcomings of classical detection performance evaluations frameworks.
Instead, we evaluated the performance of our method by using either only S2 segments, or by pooling segments from all stages (S2 and non-S2 segments). Future studies with larger annotated sleep EEG databases will be needed to assess and compare the robustness of our method in each single sleep stage. One way to increase the performance of our method could be to fine-tune parameters of the TQWT and of the MCA procedures on a subject by subject basis, so as to account for interindividual differences in spindle and K-complex properties. To what extent the performance can be improved by modifying the tuning Q-factor (globally or for each individual) is not clear and could be the focus of further investigation. Future explorations may also benefit from exploring the use of alternative wavelets, such as the Morse wavelet  (Lilly and Olhede, 2012)  which has successfully been used in recent studies  (Zerouali et al.
Conclusion The current study demonstrates the feasibility of identifying spindles and K-complex events in sleep EEG using a single methodological framework by literally tuning into the oscillatory characteristics of the target events via the TQWT. Because of the now well acknowledged challenges that face performance evaluation of automatic and semi-automatic procedures  (O'Reilly et al. , 2014) , the next step would be to validate our method on a larger open-access benchmarking sleep database. This would allow us to perform fair and informative comparisons with other existing methods, and possibly to fine-tune the parameter selection for our method. From a broader perspective, the flexibility with which the TQWT and MCA decomposition  (Selesnick and Bayram, 2009; Selesnick, 2011a,b,c ) can be tuned to specific oscillatory or transient phenomena in the signal suggests that it could be a promising tool for the detection of other structures in sleep EEG signals beyond those included in this study, such as vertex wave, slow waves and apnea.
Acknowledgments Tarek Lajnef was supported in part by travel funds from  EDST doctoral program  and the  LETI Laboratory, Sfax, Tunisia. Jean-Baptiste Eichenlaub  is supported by the  Fyssen Foundation. This study was partly performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program  ANR-11-IDEX-0007. This research was undertaken, in part, thanks to funding from the  Canada Research Chairs program.
Posture Data in the Baseline Condition The baseline condition (the cross image) data for the two groups of subjects for each body sway variable were compared. Table  1  reports the mean values and standard deviations for both groups as well as Mann-Whitney U test result and the p-value for each variable. As shown in this table, no differential effect was found for any body sway variable. Therefore, the postural performances  of the two groups were similar in the baseline condition. This is important as the next sections present posture data for the two groups seeing the paintings with different instructions (fixating vs. exploring). This design was used to avoid any learning effect due to multiple presentations of a given painting.
Behavioral Testing Sensorimotor function was assessed with the pole test, as previously described. 11 Briefly, preoperative training was performed for 3 days with four trials per day. Animals were tested before and at 7 and 14 days after surgery. For each trial, mice were placed facing upward on the top of a 60cm pole and the time required for the animals to both turn to face downward and descend to the home cage were recorded. Data from the four daily trials were pooled. A standard place task in the Morris water maze was used to assess spatial learning and reference memory. A circular maze (120 cm diameter) was filled with opaque water to cover a submerged escape platform (fixed location). Three daily trials were performed for each animal over 7 consecutive days. A trial consisted of lowering the animal into the pool from different starting points and allowing them 90 seconds to find the platform, or be collected.
Trials were spaced 10 minutes apart and the animals were allowed to rest under a heat lamp in between trials. Escape latency (seconds), total distance travelled (cm), and swim speed (cm/s) were measured by a computerized tracking system (VideoMot, TSE Systems, Bad Homburg, Germany). Data from the three daily trials were pooled. At 8 days, a probe trial was performed in which the platform was removed and the mice were allowed to swim for 90 seconds. The total time spent in the former target quadrant (s) was assessed.
Image Analysis During the first experiment, T 2 and the different quantitative DTI parameter maps were calculated using the Paravision software (Bruker BioSpin, Ettlingen, Germany) and exported to ImageJ freeware (National Institutes of Health). Several volumes of interest (VOIs) were chosen: the entire cortex, striatum, corpus callosum, and internal capsule. Each of these structures was manually delineated on a slice-by-slice basis on the T 2weighted images; this created several individual regions of interest (ROIs) for each structure. Subsequently, the ROIs were transferred to the FA maps and distortions in the EPI images were accounted for by manually adjusting the individual ROIs wherever necessary. Finally, integrated density was used to extract the different quantitative parameters across each VOI, thus providing one quantitative parameter value per structure.
A custom-written ImageJ Plugin was used to fit T 1 in the selective and nonselective FAIR experiments, and subsequently calculate CBF. In the case of the single slice CBF map, resolution only permitted identification of two ROIs: the striatum and the cortex. During the second experiment, quantitative T 2 maps were calculated again using the Paravision software and data were rescaled by a factor of 10 and exported into FSL software (Analysis Group, FMRIB, Oxford, UK). For the T 2 images, the BET (brain extraction tool) was used to segment the brain, and the process was completed manually. The FLIRT (FMRIB's linear image registration tool) was used to co-register the segmented images to a mouse brain template (average of 10 C57/BL6 mouse brains). Subsequently, the FAST (FMRIB's automated segmentation tool) was used to segment the ventricles.
All resulting images were exported into ImageJ. A binary mask was created for VOIs that corresponded to the striatum and the corpus callosum. The mask was used to extract T 2 across both VOIs in each animal. Overall ventricle size and brain volumes were measured, and the determinant from the coregistration process was used to extrapolate the original volume. Angiography images were also exported to FSL and the FLIRT was used to coregister them to the first acquired data set. Subsequently, the images were exported back into ImageJ and the same volume was selected to build a maximum intensity projection of the Circle of Willis using a custom-written Plugin. A threshold that corresponded to roughly half of the signal in all the images (14,000) was chosen to exclude most of the brain tissue and convert the maximum intensity projection to a binary image.
4G2), antibodies against CD3 (145-2C11), CD4 (GK1. 5), CD8a (53-6. 7), CD19 (1D3), CD11b (M1/70), and Ter119 (TER-119) were applied. All antibodies were purchased from eBiosciences (San Diego, CA, USA), Miltenyi Biotec (Bergisch Gladbach, Germany), or produced in-house (DRFZ, Berlin). A total of 2 × 10 6 cells were stained in 100 μL for 30 minutes on ice in the dark. Data were acquired on LSRII (BD Biosciences, Heidelberg, Germany) and analyzed by the FlowJo software (Tree Star Inc. , Ashland, OR, USA).