dataesr/scientific-paragraphs-categorization huggingface dataset:
Elevated Plus Maze The elevated plus maze consists of four arms (two enclosed arms and two open arms) elevated 100 cm above the floor. Anxietyrelated behavior was defined as time spent in the open arms (perceived unsafe arms) of the maze, compared to the time in the closed arms (perceived safe arm) of the maze over the 5 min test session. Each mouse was placed in the center of the maze and the amount of time spent in each arm was recorded automatically by EthoVision XT 8. 0 video tracking software (Noldus Information Technology).
0 video tracking software. Time spent was operationally defined as occurring when an animal directed its nose to the object at a distance of less than 2. 0 cm and/or by the animal touching the object with its nose or mouth. Data are presented as the D2 discrimination index. The D2 index is a common measure of discrimination between novel and familiar objects. It is considered one of the most reliable measures of discrimination because it corrects for total exploratory activity of each animal  [15]. The D2 index is calculated for an A/B session by examining the difference in time spent exploring the novel and familiar objects divided by the total exploration time for both objects (D2 = (novel -familiar) / (novel + familiar)). In this way, the D2 index corrects for total exploratory behavior of each mouse.
The activation was performed by using the Genesis Freedom 200 ™ robotic unit (Tecan), in fresh medium supplemented with 2 % of synthetic serum, free of lipids (Ultroser ™ , Biosepra) supplemented with the tested compounds (compound of interest or reference molecules) or vehicle (DMSO 0. 1 %). Luciferase activity was measured using the Steady-Glo Luciferase Assay System (Promega, Madison, WI, USA). All transactivation experiments were performed at least two times. Activation curves were realized using SigmaPlot (version 7. 0 from SPSS, IBM) software. SigmaPlot was also used to fit the standard curves and then determine the specific EC 50 values, maximum effect versus reference molecules and Hill slope. The E max effect of GFT1803 was calculated relative to the maximal induction (plateau) obtained with the corresponding reference compound.
For methoxy-XO4  [28]  staining, sections were rinsed in PBS, incubated in 10 μM methoxy-XO4 in 40 % ethanol/ 60 % H 2 O adjusted to pH 10 with 0. 1 N NaOH for 10 min at RT, washed three times with water, incubated for 2 min in 0. 2 % NaOH in 80 % ethanol, and washed three times with water. Sections were analyzed using a BX61 microscope equipped with a disk scanning unit to achieve confocality (Olympus, Hamburg, Germany). Image stacks were deconvoluted using Cell^P (Olympus, Hamburg, Germany). Quantitative assessment of plaque areas was done using the MBF-ImageJ 1. 43m software bundle (NIH, Bethesda, MA, USA).
Open Field Behavior The open field consisted of a 61×61×61 cm Perspex box with dark walls and a white floor and was dimly illuminated. The open field was virtually divided into corridors along the walls (10 cm wide), corners (10×10 cm), and a center (40×40 cm). Mice were put in the middle of the open field. The mice were tracked for 5 min on three subsequent days using Ethovision software (Noldus, Wageningen, The Netherlands).
Acknowledgments The E7 antibody developed by  M. Klymkowsky  was obtained from the  Developmental Studies Hybridoma Bank. This study was supported by the  Deutsche Forschungsgemeinschaft  ( KFO177 ,  TP4 ) to MTH and by grants of the  INMiND  project of the  European Union. MTH is member of the  DFG Cluster of Excellence ImmunoSensation.
PATCH CLAMP RECORDINGS AND NEUROBIOTIN LABELING OF RECORDED CELLS Cells were visualized with infrared-differential interference contrast optics (Axioskop2; Zeiss). We performed patch-clamp recordings in the whole cell configuration at 35-37 • C using the Digidata 1344A interface, the Multiclamp 700A amplifier and PClamp8 software (Axon Instruments, Foster City, CA, USA). For current-clamp recordings, patch electrodes (6-10 M ) contained (in mM): 120 K-gluconate, 20 KCl, 10 HEPES, 2 MgATP and 0. 5 NaGTP, pH 7. 2-7. 4 (285-295 mOsm). Biocytin (Sigma-Aldrich; 5 mg/ml) was added in the pipette solution and osmolarity corrected when necessary. To reveal the biocytin injected during whole-cell recordings of pyramidal neurons or interneurons, the recorded sections were fixed in paraformaldehyde (Antigenfix, 4%) for 48 h.
DATA ANALYSIS Fluorescent traces were analyzed with custom developed routines in Matlab and calcium events identified using asymmetric least squares baseline and Schmitt trigger threshold. We took into account only fields where more than 15% of fura2-loaded cells were active to ensure sufficient numbers of active cells to detect network-wide synchronization if present. We used 5% of baseline noise as high threshold and 2% as low threshold. Onset and offset identification allowed the generation of raster plots and the estimation of the extent of cortical network correlation. We applied two main tests: pairwise and group tests. Throughout we define "co-active" as meaning those onset transients occurring simultaneously within ± 1 frame time window (411 ms). Pairwise correlation test detected repeated co-activation between two cells (cell pairs continuously correlated).
To estimate the likelihood that the co-activations might be explained by chance we created random datasets for each active cell using inter-event interval reshuffling  (Mao et al. , 2001; Feldt et al. , 2013). We generated 1000 random data sets for each recording session and computed the pairwise Hamming similarity for all pairs in those data-sets, giving a distribution of 1000 randomized similarity scores for each pair of recorded neurons. The threshold for a significantly correlated pair of recorded neurons was set to the 95th percentile of that distribution. Separately we characterized the existence of neural ensembles, i. e. , groups of neurons with consistently correlated activity. To do so, each recording was analyzed using the method in Humphries (  2011 ), which we briefly describe here.
For each recording we generated 20 randomized versions by shuffling each neuron's inter-event intervals. We clustered each randomized version using the algorithm above, giving us 20 values of Q control. These allow us to estimate whether or not the actual recording's Q data value exceeded the 95% confidence level: thus a recording was deemed significant at this level if Q data > max (Q control ). The group event test identified incidences where a significant fraction of the network's cells were active together. The minimal size of these groups for each recording session was defined by the corresponding simulated random sets: for each of the 1000 random datasets we counted the number of co-active cells within each time-window; this defined a distribution of expected counts of co-active cells due to random activity.
ACKNOWLEDGMENTS The study was financed by  Inserm ,  Association France-Parkinson  ( Constance Hammond ),  ERA-Net Neuron  ( Constance Hammond ),  MRC   Senior non-Clinical Fellowship  ( Mark D. Humphries  and  Abhinav Singh ) and  Medtronic. We thank the  Gispert/Auburger team at the University Frankfurt am Main  for providing the PINK1-KO mice.
Data analysis Behavioral data. Nicotine self-administration data were subjected to ANOVAs, with nicotine exposure (NIC-1 d, NIC-8 d, NIC-60 d, yoked, saline) or saccharin exposure as the between-subject factor and selfadministration session as the within-subject factor. For reinstatement data, between-subjects factors were the treatment with the CB1 antagonist (vehicle or AM251) and the stimulation of the ILCx (sham or stim). Whenever main factor effects were found, post hoc comparisons were performed using Fisher's protected least significant difference test. The discrimination rate ([active nose-pokes/total nose-pokes] ϫ 100) was compared with chance (50%) with Student's t test. Statistical significance was set at p Ͻ 0. 05. Electrophysiological recordings. During electrical stimulation of the ILCx, cumulative peristimulus time histograms (5 ms bin width) of evoked-spike activity were generated for each neuron recorded in the BNST.
The fluorescence, after passing through an emission monochromator, was registered at 90°w ith respect to the incident beam using a room-temperature R928P detector (Hamamatsu). Corrected fluorescence spectra obtained by excitation at 300 nm were recorded between 320 and 550 nm. Ultra-high performance liquid chromatography (UPLC)mass spectrometry analysis. The LC/MS system was equipped with an UPLC chromatograph (ACQUITY UPLC from Waters) coupled to a quadrupole time-of-flight mass spectrometer (Xevo G2-QTof from Waters). UPLC analyses were performed using the same column used in HPLC analysis. The mass spectrometer was operated in both positive and negative ion modes. Mass chromatograms, i. e. representations of mass spectrometry data as chromatograms (the x-axis representing time and the y-axis signal intensity), were registered using different scan ranges.
Acknowledgements The present work was partially supported by  Consejo Nacional de Investigaciones Cientificas y Técnicas  ( CONICET-Grant PIP 0425 ) and the  Universidad Nacional de La Plata  ( UNLP-Grant   X586 ). C. C. thanks the  CONICET  for graduate research fellowships. A. H. T. thanks the  Royal Society of Chemistry  for a Journals Grant for International Authors. The authors thank the  Centre National de la Recherche Scientifique (CNRS)  and  CONICET  for supporting their collaboration through a  Programme de Coopération Scientifique  ( CONICET-CNRS/PICS N o 05920 ). A. H. T. and C. L. are research members of  CONICET.
Probabilistic diffusion tractography maps in Figure  2  were created using in-house created software in conjunction with the FMRIB Software Library (FSL). FSL was used to determine the parameters for a two-fiber diffusion model at each voxel. This fiber model was then used with the FSL software to perform probabilistic tractography. This type of tractography allowed for a representation of the relative cross-sectional probability of a streamline passing through a voxel. A high contrast MRI scan, FGATIR  (57) , was used to help delineate the anatomic region around the PPN. A large region of interest around the region of the PPN that fully encompasses the PPN was used as a seed for tractography. If no mask is used to limit the tractography results, probabilistic tractography results may not be specific enough to be useful for surgical targeting (as shown in the top row of Figure  2 ).
ACKNOWLEDGMENTS This work was inspired by the  Functional Neurosurgery Working Group of the Parkinson Study Group (PSG). We would also like to acknowledge the support of the  National Parkinson Foundation Center of Excellence , the  Bachmann-Strauss Foundation , and the  UF Foundation. We thank  Ludvic Zrinzo MD, Ph. D. ,  FRCS  for permission to use a figure which has been adapted for use in Figure 1 top row right.
To enforce a widespread use of efficient and easy to use brain-computer interfaces (BCIs), the inter-subject robustness should be increased and the number of electrodes should be reduced. These two key issues are addressed in this contribution, proposing a novel method to identify subject-specific time-frequency characteristics with a minimal number of electrodes. In this method, two alternative criteria, time-frequency discrimination factor (TFDF) and F score, are proposed to evaluate the discriminative power of time-frequency regions. Distinct from classical measures (e. g. , Fisher criterion, r 2 coefficient), the TFDF is based on the neurophysiologic phenomena, on which the motor imagery BCI paradigm relies, rather than only from statistics. F score is based on the popular Fisher's discriminant and purely data driven; however, it differs from traditional measures since it provides a simple and effective measure for quantifying the discriminative power of a multi-dimensional feature vector.
The proposed method is tested on BCI competition IV datasets IIa and IIb for discriminating right and left hand motor imagery. Compared to state-of-the-art methods, our method based on both criteria led to comparable or even better classification results, while using fewer electrodes (i. e. , only two bipolar channels, C3 and C4). This work indicates that time-frequency optimization can not only improve the classification performance but also contribute to reducing the number of electrodes required in motor imagery BCIs.
However, ERD/ERS patterns are typically short-lasting (half to few seconds) and their frequency range may vary with subjects  [13]. Thus, only optimizing the position of electrodes may not be sufficient to achieve a good Figure  1  Positions of C3 and C4. This figure shows positions of C3 and C4 (indicated by ellipses) according to the international 10-20 system  [11]. classification, and a BCI system using bipolar recording also requires more precise user-specific time-frequency parameterization in the feature extraction step. To address this problem, a number of approaches were proposed to estimate time-frequency characteristics of motor imagery EEG  [13] [14] [15] [16] , but only a few were successfully applied to bipolar recording data. Among those methods, the filter bank CSP (FBCSP) method seems to be the most effective one, yielding the best BCI performances on BCI competition datasets  [17].
FBCSP was initially proposed only for frequency band optimization and then extended to include an optimal temporal selection process  [14]. However, FBCSP-based methods involve feature selection procedures based on mutual information, which require tedious iterative steps that greatly increase their complexity. Moreover, the latest version of FBSCP selects the optimal time segment from only a few different options, which did not yield better results on bipolar recording data (BCI competition IV dataset IIb) compared to previous versions  [14]. In this paper, we address the issue of time-frequency optimization with only two bipolar channels (C3 and C4) for the discrimination between right and left hand motor imagery tasks. On the contrary to the coarse selection of the time segment in FBSCP, we propose to take into account fine subject-specific time-frequency characteristics for feature extraction.
This classifier has a very low computational cost and usually yields good results for motor imagery BCIs  [19]. Two novel criteria are proposed for the evaluation step  (2). One is based on domain-specific knowledge of neurophysiology and is called time-frequency discrimination factor (TFDF), while the other is purely data driven and is named F score. Different from classical criteria (e. g. , Fisher criterion, r 2 coefficient) used for ranking one-dimensional observations  [20] , the proposed criteria are more suitable to quantify how informative a multi-dimensional feature vector is for distinguishing two classes. The comparison between these two criteria, as well as with state-of-the-art http://asp. eurasipjournals. com/content/2014/1/38 methods, is performed on a standard bipolar dataset (the BCI competition IV IIb), and their contribution to electrode reduction is evaluated on BCI Competition IV IIa dataset.
, M} , n ∈ {1, 2,. , N} (8) To examine the behavior of TFDF, we provide its possible values in Table  1  for different cases and present in Figure  2  the ERD/ERS maps and the corresponding TFDF values of 4 Hz, 2-s wide time-frequency regions for an example from a standard dataset. From Table  1 , we can see that  (1)  the values of TFDF are larger for PD C3 • PD C4 < 0 than for PD C3 • PD C4 ≥ 0, and (2) the values of TFDF are determined by min {|PD C3 | , |PD C4 |}. Thus, this method tends to seek the time-frequency region where PD C3 and PD C4 have different signs and large absolute values. In the ROI, the right hand motor imagery elicits more significant ERD at C3 compared to the left hand motor imagery, which leads to BP L C3 (ω * , τ * ) > BP R C3 (ω * , τ * ), while left hand motor imagery generates more significant ERD at C4 compared to the right hand motor imagery, so we have BP L C4 (ω * , τ * ) < BP R C4 (ω * , τ * ).
Data description and preprocessing In this study, we used data of the BCI competition IV dataset IIa  [26]  and IIb  [8]. Dataset IIa was recorded in a multi-channel monopolar setting (22 monopolar channels). The parameters of bipolar channels can be adapted to the experiments. Dataset IIb was recorded over three bipolar channels, C3, Cz, and C4. Details of these two datasets are provided in the following paragraphs.
Dataset IIa BCI competition IV dataset IIa  [26]  contains one training session and one evaluation session of EEG data from nine subjects who performed four classes of cue-driven motor imagery tasks (left hand, right hand, both feet and tongue). Each trial began with a fixation cross and an additional short acoustic warning tone. After 2 s, a cue in the form of an arrow pointing either to the left, right, down, or up (corresponding to left hand, right hand, foot, or tongue) appeared and stayed on the screen for 1. 25 s. The subjects were asked to carry out the motor imagery task until 4 s after cue on-set. No feedback was provided. The EEG signals were recorded by 22 Ag/AgCl electrodes (with inter-electrode distances of 3. 5 cm) using the left mastoid as reference and the right mastoid as ground (sampling rate 250 Hz).
Dataset IIb This dataset consists of two classes (left vs. right hand) cue-driven motor imagery BCI data from nine subjects. The EEG data are recorded in three bipolar channels, i. e. , at positions C3, Cz, and C4 (see Figure  5B ). The distances between the two bipolar electrodes forming a channel are pre-determined in this dataset (for details, see  [8] ). For each subject, five sessions are provided, including three training sessions and two evaluation sessions. The first two training sessions consist of 240 single trials (120 single trials per session) totally without visual feedback. Each trial started with a fixation cross and an additional short acoustic warning tone. Later, a visual cue was given to guide the subject to execute the corresponding imagination of hand movement over a period of 4 s.
Experimental goals The proposed time-frequency optimization methods based on different criteria will first be applied on dataset IIb using only two bipolar channels (C3, C4). The goal of this experiment is to evaluate the effectiveness of the methods in improving the performances of BCI based on few channels only. We first train the methods for each subject on the training data and then evaluate them on the testing data for this subject. Note that training and testing sessions are recorded on different days  [8, 26]. This is called session-to-session transfer  [25]. The results of our methods in session-to-session transfers from training sessions to testing sessions will be compared with the winners on this dataset in BCI competition IV. Then, we will apply our methods on two bipolar channels (C3, C4) selected from the 22 monopolar channels of dataset IIa.
The classification results obtained on dataset IIa will be compared with those obtained by CSP algorithms using 22   [8]. The arrows between the EEG electrodes show the bipolar derivation, where ⊕ is the signal electrode and is the reference. The distance between two bipolar electrodes forming a channel for each subject is pre-determined (for details, see  [8] ). Only C3 and C4 (marked by ellipses) are used in this study for discrimination between imaginations of left and right hand movements. http://asp. eurasipjournals. com/content/2014/1/38 monopolar channels to evaluate the interest of the method for electrode reduction.
Data preprocessing for time-frequency optimization Electrooculogram (EOG), originating from ocular activities (e. g. , eye movement, blinks), is the most important source of artifacts in BCI. To prevent the influence of EOG artifacts on the classification results, an automated EOG removal method (for details, see  [29] ) is first applied on the data as required in the BCI competition  [30]. Then, for each bipolar channel, 5th order Butterworth filters are applied to compute 19 successive 4-Hz wide frequency bands of signals (F = 4 Hz, F s = 1 Hz): 8 to 12, 9 to 13, 10 to 14 Hz,. , 26 to 30 Hz, and 15 successive 8-Hz wide frequency bands of signals (F = 8 Hz, F s = 1 Hz): 8 to 16, 9 to 17, 10 to 18 Hz,. , 22 to 30 Hz. Then, 36 overlapping time segments in each frequency band were obtained through 2-, 2.
5-, and 3-s wide (i. e. , T = 2, 2. 5, and 3 s, respectively) sliding windows (12 segments for each sliding window) with 0. 2-s step (i. e. T s = 0. 2) moving from 0. 5 s after the cue on-set. Those parameters are set based on the experience from competitors reported in BCI competition IV  [31]  and led to good performances in our previous work  [18]. Therefore, there are (19 + 15) × 36 = 1, 224 timefrequency areas for subject-specific selection. It has to be mentioned that the selection procedure is not very timeconsuming (2 min and 21 s in average, using Matlab 7. 10. 0 on Window 7 Professional 64bits, CPU 2. 66G Hz, RAM 2. 0G) and is done offline, so the computational cost is acceptable.
Improving classification performance for dataset IIb The time-frequency ROI (ω ROI × τ ROI ) of each training dataset is obtained by maximizing the TFDF and the F score criteria, respectively. Results are reported in Table  2. These results show that (1) the estimated time-frequency ROIs vary among different subjects, (2) even for the same subject, the estimated ROIs vary among different training sessions, and (3) the two criteria picked out different ROIs for the same training session. The fact that the estimation results depend on the subjects is also reflected in the individual differences of timing and frequency of ERD/ERS patterns. Even for the same subject, the timing and frequency of ERD/ERS may shift across sessions  [12] , which leads to the intra-subject difference in the estimation of ROIs between sessions.
A typical example of time-frequency maps displaying significant ERD (red) and ERS (blue) in a training session (session 3) for a subject (subject 6) in the dataset is shown in Figure  6. The ROIs estimated by the TFDF are marked out by solid rectangles (10 to 14 Hz, 0. 7 to 2. 7 s), while the ROIs selected by the F score are displayed as dashed rectangles (11 to 15 Hz, 1. 1to 4. 1 s). Although the ROIs estimated by the two criteria are different, both ROIs contain discriminative ERD patterns between the two classes, indicating that these two criteria could successfully capture the discriminative part of the signal. To evaluate the contribution of the proposed timefrequency optimization to classification, ten repetitions of cross-validation are performed on each training session for each subject, using the BP features extracted from the estimated time-frequency ROIs by TFDF and F score, respectively.
In each run, we randomly separated the data into calibration (90%) and test (10%) sets and classified the test data using the Fisher's LDA obtained from the calibration set. The classification accuracy (Acc) is defined as the observed agreement between classification outputs and true labels  [32]. The cross-validation results are obtained by averaging Acc over 10 runs. The results are compared to those obtained by using the BP features from the broad band (8 to 30 Hz) EEG segments (0. 5 to 2. 5 s) with or without CSP filtering. The number of spatial filters used in CSP-based classification for this dataset is two (one pair) because only three bipolar channels (C3, Cz, and C4) are provided in this dataset. The comparisons between different methods are shown in Figure  7  using scatter plots.