dataesr/scientific-paragraphs-categorization huggingface dataset:
Baclofen and other GABA B agents directly interact with CXCR4 expressed in lipoparticles To substantiate the data above that GABA B agents are directly binding the CXCR4 receptor, we used BSI, a molecular interaction photometer, to quantify ligand/receptor binding. We previously reported binding specificity of the native pairs CXCR4/ CXCL12 binding using red blood cell "ghosts" with human SUP-T1 lymphoma T-cells expressing CXCR4 and BSI  (Baksh et al. , 2011). Here, we expressed the CXCR4 receptor using lipopar-ticle technology: protein directly incorporated into virus-like particles with a lipid bilayer surface that provides concentrated protein (50 -200 pM/mg) in the native conformation  (Jones et al. , 2008). Equilibrium dissociation constants were calculated using saturation analysis. To demonstrate that the refractive index (RI) change is not due simply to the introduction of the pairs, we used dopamine as a control in each experiment (at five concentrations that span the concentration range in all the other curves), and measured no RI change.
Awareness indexes In order to explore patients' awareness of their memory symptoms, we calculated three awareness indexes. The first index compares subjective ratings in both patients and proxies. For each patient/proxy couple, the D(subjHD-subjProx) index was the result of subtracting the subjective rating by the proxy (subjProx) from the subjective rating by the patient (subjHD). A D(subjHD-subjProx) index lower than 225 suggests severe unawareness of memory deficits in the patient; an index greater than 25 suggests spared awareness  [10]. The second index compares subjective ratings in patients to patients' objective memory performance  [11, 32]. For objective performance (objHD), we calculated the mean z-score of the total free recall of FCSRT and the total recall of RAVLT; this data was available for all patients.
On post-transfer day 9, we harvested the total population of lymphocytes that had infiltrated the CNS and analyzed the cells by FACS. Consistent with our active EAE data, fewer lymphocytes infiltrated the CNS in the CD146 EC-KO mice with passive EAE compared with the wild type mice. Specifically, fewer CD45 high CD11b 2 lymphocytes (Fig. 3a ) and lower proportions of CD4 1 , T H 1, and T H 17 cells (Fig. 3a  and b ) and CD146 1 lymphocytes (Fig. 3c ) infiltrated the CNS in the CD146 EC-KO mice. These data suggest that BBB endothelial CD146 may actively facilitate the infiltration of pathogenic lymphocytes into the CNS, thereby promoting EAE pathogenesis. Endothelial CD146 is required for lymphocyte transmigration in an in vitro BBB model. To directly test whether BBB endothelial CD146 promotes MS pathogenesis by mediating the infiltration of lymphocytes into the CNS, we used an in vitro BBB model to measure the transmigration of the human brain endothelial cell line hCMEC/ D3 (BBBEC)  33.
The area of demyelination in each treatment group was measured using the ImageJ software program. BBBEC cells and transfection. The immortalized human brain capillary endothelial cell line hCMEC/D3 (here called ''BBBEC'') was obtained from INSERM, France. The generation of this cell line was described previously  33. For all experiments, BBBECs were used between passage 25 and passage 35. The cells were cultured in EBM-2 medium supplemented with growth factors, hydrocortisone, ascorbate, 2. 5% fetal calf serum (FCS), and antibiotics (Gibco Life Technologies Inc. , UK) in a 5% CO 2 humidified atmosphere at 37uC. Lipofectamine 2000 (Invitrogen, San Diego, CA)mediated siRNA transfection was performed according to the manufacturer's instructions. The p3XFLAG-CMV-14-CD146 construct was transfected in order to rescue CD146 expression.
The cells were stained with antibodies specific for mouse CD3, CD8 or the intracellular cytokines IFNc and IL-17 and then subjected to FACS analysis. Statistical analysis. All experiments were performed independently at least three times. The summary results are expressed as the mean 6 SEM (standard error of the mean). Statistical analyses were performed using the GraphPad Prism software program. The paired sign test or Student's t-test was used to compare the experimental and control groups. Differences with a p-value , 0. 05 were considered to be statistically significant.
Spike-driven chips and memristors could be several orders of magnitude faster, and thus could emulate the biological visual system much faster than real time. For example, switching times in the order of nano seconds have been demonstrated for some Hf-based resistive switches of 10 × 10 nm size  (Govoreanu et al. , 2011). This is particularly appealing for visual learning, which takes months, if not years, in humans. But there is no reason why memristors could not, for example, extract visual features from huge image databases in a few seconds. We thus speculate that this line of research will yield revolutionary results in the next decade.
ACKNOWLEDGMENTS This work was supported by  Spanish  grants from the  Ministry of Economy  and Competitivity  TEC200-106039-C04-01/02  (VULCANO) (with support from the  European Regional Development Fund)  and  PRI-PIMCHI-2011-0768  (PNEUMA) coordinated with the  European CHIST-ERA program , and  Andalusian  grant  TIC6091  (NANONEURO). T. Masquelier was supported by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  269459  (CORONET).
Simulation Study Both the generalization ability (prediction error) and the convergence of model coefficients were studied in simulation experiments. The resulted prediction errors of the proposed approach were compared with ones generated by other state of the art methods. The computational experiments were performed on simulated datasets for matrix input and scalar output, the simplest case for simulation and comparison of tensor based algorithms. Keeping the essential properties, it simplifies the visualization and interpretation of results. A set of PLS family offline algorithms for tensor data analysis, namely, generic NPLS, Iterative NPLS, and unfolded PLS, were compared with new sequential RNPLS. Let us note that all these algorithms generate the set of projectors and the set of coefficients of linear regression in low dimensional spaces of latent variables to predict the output variable from the input one.
This model can be rewritten in the original variables. In the particular case of scalar y t ð Þ[< and matrix x t ð Þ[< I1|I2 , ŷ y t ð Þ~X i,j x i,j t ð Þc i,j , C~c i,j È É i,j , The matrix of coefficients C is estimated by each algorithm. To analyze the convergence properties of sequential blockwise RNPLS, the training data were split into K disjoint subsets. The RNPLS algorithm was consequently applied to these subsets, i. e. , each following subset was used to adjust the solution from the previous step. C k is the approximation of the regression coefficient matrix estimated at step k~1,. ,K. The variance and expectation of the coefficients evaluated by the different methods were examined in comparison with ''true'' coefficients. Prediction errors of algorithms mentioned above were compared on simulated datasets.
Comparison of Algorithms To study whether the recursive scheme of the RNPLS algorithm decreases the accuracy of modeling, the proposed method was compared with generic offline NPLS, iterative NPLS and unfolded PLS on the simulated data. The percentage of prediction errors (RMSE) was estimated depending on the level of noise and the number of factors. The variance of the coefficients was studied for the set of algorithms. Finally these coefficients were compared with the ''true'' ones. The entire simulated dataset (1600 points) was equally split into the training and the test datasets. The NPLS, iterative NPLS, and unfolded PLS algorithms processed the whole training set. For the recursive calculation, the training dataset was split into 20 disjoint windows, each one containing 40 points. For all conditions (different noise level and the number of factors), the experiment was repeated 10 times with new realizations of noise.
The percentage of prediction errors was estimated using 10 realizations of the test dataset depending on the level of noise and number of factors for all the algorithms. RNPLS demonstrated a significantly smaller number of factors which are necessary for efficient prediction (Table  1 ). Let us remember that the RNPLS showed better robustness, as the variation of the prediction errors was essentially smaller for the recursive algorithm for a small number of factors  [19]. To compare the variance of the estimated coefficients, RNPLS, NPLS, INPLS, and UPLS algorithms were applied to 10 realizations of the training dataset with the level of noise l~10 (1000%). The factor numbers were chosen equal to 9, 17, 17, and 13, for RNPLS, NPLS, INPLS, and UPLS, respectively, in order to minimize the average RMSE in the 10-fold cross-validation procedure.
In parallel, the means and the standard deviations of r C true l~0 , regression coefficients C RNPLS k È É 10 k~1 , C NPLS k È É 10 C NPLS lw0 À Á , r C true l~0 , C INPLS lw0 À Á and r C true l~0 , C unfolded PLS lw0 À Á were computed for every level of noise. All statistics were calculated for 10 realizations. As can be seen, the mean value of the distances between the RNPLS and ''true'' regression coefficients after 15 iterations (the first 20-100 points of the training set) is less than those between the NPLS, or UPLS and ''true'' regression coefficients computed for the whole training datasets (800 points), whereas RNPLS needs up to 14 iterations (280 points) to outperform the INPLS results. In addition, the standard deviations of the RNPLS results are generally less than those obtained by NPLS, INPLS, or UPLS.
The tensor included 400 points corresponding to event-related epochs (50 pressing events repeated 8 times) and randomly selected 1000 non-event epochs. The tensor of observation was split into the training and test tensors. Namely, 1000 randomly selected points (700 ''non-events'' and 300 ''events'') formed the training dataset, while 400 points (300+100) were used for the test. The proportion of the classes is chosen to avoid a class imbalance problem (e. g. [28] ). Generally, binary self-paced experiments  [29]  lead to imbalance classes: the ''minority'' class has a very small number of examples (class of events), while the ''majority'' class includes a large number of cases (class non-event). Most of the machine learning approaches are strongly biased toward the majority class (e. g. [28]  while the correct classification of samples from the minority class is the most important.
At the same time, the non-event class should be well presented for efficient algorithm learning. To avoid the problem, the classes were formed in a less biased way, following  [8]. To test RNPLS, the training dataset was split into disjoint subsets with 10 and 100 points, marked as RNPLS  (10)  and RNPLS (100), respectively. Next, the projectors and the predictive models were identified. Figure  9A  represents the first 2 factors calculated by RNPLS  (10). The total number of factors was estimated to be 5 by the 10-fold cross-validation procedure. Each factor consists of 3 projectors, corresponding to frequency, temporal, and spatial modalities, respectively. Absolute values of the projector's elements define the influence of these elements on the identified model. The weights of factors in the decision rule are different.
Continuous BCI The RNPLS method was also applied for decoding of the continuous three-dimensional hand trajectories from epidural ECoG signals of a Japanese macaque. The data is publicly available (http://neurotycho. org/data/20100802s1epiduralecogfood-trackingbkentaroshimoda). The ECoG signals were recorded (Blackrock Microsystems, Salt Lake City, UT, USA) with a sampling rate of 1 kHz from the 64 electrodes implanted in the epidural space of the left hemisphere of the monkey, which was trained to retrieve foods using the right hand. The hand motion was recorded by an optical motion capture system (Vicon Motion Systems, Oxford, UK) with a sampling rate 120 Hz. The length of the experiments was 15 minutes. During the experiment, the monkey was seated in the chair with restricted head movement.
The experimenter demonstrated foods at random locations at a distance of 20 cm for the monkey at random time intervals (3. 861. 0 times per minute), and the monkey grasped the foods. A precise description of the experiments can be found in  [31]. In this work, the unfolded PLS was applied for the continuous threedimensional hand trajectories reconstruction. To test the RNPLS algorithm one recording (15 minutes, sampling rate: 1000 Hz) was chosen randomly from the data base. To train the algorithm, 5000 time epochs were randomly selected among the recorded time moments. As a result, the training dataset includes 0. 5% of the entire recording. The test dataset The difference of RMSE for RNPLS and the other methods is not statistically significant (t,t 0:01 ) in the majority of the cases (10 from 12) according to the two-tailed Welch's t-test for 1% confidence level t 0:01.
79 for X -, Y -, and Z-positions are achieved for 150-175 factors. An example of the real and predicted trajectories is given in Figure  12. To test the RNPLS approach in the general case of tensor input and tensor output data, we applied the proposed method for simultaneous prediction of the monkey's right shoulder, right elbow, and right wrist coordinates. Both the training and the test input tensors X training and X test (calculated from ECoG) were not changed, whereas the output tensors Y training and Y test acquired one more modality (shoulder, elbow, wrist). Thus, the training dataset was X training k , Y training k n o 5 k~1 , X training k [< 1000|84|201|64 , Y training k [< 1000|3|3 and the test dataset was X test , Y test È É , X test [< 4500|84|201|64 , Y test [< 4500|3|3. The resulting correla- tions between the observed and predicted coordinates are represented in Table  2.
Conclusion In the current paper we consider the method of the adaptive multimodal data flow processing in the most general case of tensor-input/tensor-output data, whose properties were studied in detail for different aspects. A detailed pseudocode is provided which substantially facilitates the understanding and implementation of the proposed approach. Contrary to other multimodal data treatment methods, such as unfolded PLS, NPLS, INPLS, etc. , the RNPLS allows sequential blockwise adaptive learning. Due to significant variability of the neural data, this property is of great importance for the real-time BCI applications, where complete recalibration of the system is time-consuming and cannot be done frequently. For the obvious demonstration of the convergence properties, they were explored on the artificial datasets.
Acknowledgments The authors are grateful to  Prof. A. -L. Benabid ,  Drs. A. Wyss ,  C. Moro ,  N. Torres ,  T. Costecalde ,  G. Charvet ,  F. Sauter ,  J. Porcherot ,  A. Serpollet  and  C. Mestais , as well as to all members of the  CEA, LETI, CLINATEC team , who organized and realized the experiments as well as providing the recordings used in these studies.
5% gelatin, 0. 1 M PB; and cut on a cryostat (Leica) in six series of sagittal sections (15 m thick). Transplantation and analysis of grafted cells. Male C57BL/6 mice (8 weeks of age; 23-25 g; Charles River) were anesthetized and unilaterally injected with CNTF-secreting or control HEK cells (50,000 cells in 0. 5 l of HBSS) into CC at the following stereotaxic coordinates from bregma: 1 mm rostral, 1 mm lateral, 2 mm deep from the dura mater. Mice were immunosuppressed by daily injection of 20 mg/kg cyclosporine A (Sandimmun, Novartis), killed at 4 d postgrafting, and processed for histology. Analysis of RMS architecture 4 d postgrafting was performed using Axio Imager Z1 with the Apotome system (Zeiss) (magnification 20ϫ). The length of doublecortin (DCX) labeling interruptions (gaps) was measured using the Axiovision software within the first 800 m of the RMS starting from the SVZ.
Chemiluminescence was registered under the ECL Imager Chemi-Smart system (Fisher Bioblock Scientific). Semiquantitative analysis of CNTF expression was performed using Bio-1D Advanced Software (Vilmer Lourmat) using ␣-tubulin as a reference. Cell culture. Unless specified, all culture reagents were purchased from Invitrogen. HEK cell culture and transfection. CNTF-internal ribosome entry site 2-mono red fluorescent protein (mRFP) or mRFP alone sequences were cloned into an empty pCMV vector (Clontech). Constructs were stably transfected in HEK 293 cells (CRL-1573; ATCC), selected with Geneticin (450 g/ml; Life Technologies) and maintained in DMEM, 10% fetal bovine serum (FBS), MEM sodium pyruvate (1 mM), and 1% penicillin/ streptomycin. CNTF secretion was checked by Western blot using cell culture conditioned medium (data not shown).
Observation and pictures were done using Axio Imager Z1 with the Apotome system (Zeiss). For each experiment (n ϭ 4), pictures were collected in a unique session using a Plan-Apochromat 20ϫ/0. 8 objective (Zeiss), and the exposure time was constant and set up to avoid saturation for all the photographs. Fluores-cence intensity was calculated for each cell in the four experiments (2927 cells for control; 2413 in the presence of CNTF) using ImageJ software (National Institutes of Health). Boyden chamber assay. Dissociated neurospheres or purified OPCs were resuspended at 2. 10 6 cells/ml in neurosphere-defined medium supplemented with B27 and Cytosine ␤-D-arabinofuranoside (5 M; AraC; Sigma), and OPC-defined medium (DMEM), insulin (5 g/ml; Sigma), FBS (0. 5%; Thermo Fisher Scientific), apo-transferrin (100 g/ml; Sigma), putrescine (100 M; Sigma), progesterone (20 nM; Sigma), selenium (30 nM; Sigma), D-biotin (10 ng/ml; Sigma), AraC (5 M; Sigma), respectively.
Neurosphere and OPC migration assays were performed, respectively, on poly-DL-ornithine-coated (3 g/ml; Sigma) and poly-Llysine-coated (10 g/ml; Sigma) transwell polycarbonate membranes with 8 m pores (Corning). Assays were performed as follows: growth factors, including stromal cell-derived factor-1 (SDF1; 50 nM; Millipore), platelet-derived growth factor (PDGF-AA; 5 ng/ml; Peprotech), and CNTF (1, 10, and 100 ng/ml; Sigma) were diluted in the corresponding medium and added to the lower chamber. One hundred microliters of cell-containing solutions (including growth factor as indicated) was then placed in the upper chamber. Twenty-four hours later, membranes were fixed for 20 min in 4% PFA and mounted in VectashieldϩDAPI (AbCys). Concerning OPCs, membranes were also labeled against Olig2. Neurosphere and OPC migration assay data results from 10 and 8 independent experiments, respectively.
Cells that had migrated through the pores were counted in 10 -15 adjacent fields per membrane, and data (number of cells per field) were expressed relative to control. Video time lapse. Purified OPCs aggregates were placed on a poly-Llysine-coated dish, and 1 h later (i. e. , time for aggregates to adhere to the dish) aggregates were embedded with 10% Matrigel diluted in OPCdefined medium in the presence of 10 M AraC, and a bead soaked with CNTF (1 g/ml) was disposed close to them. Recording started 1 h later (i. e. , to be sure to record healthy aggregates) for 10 h (20 images/h, 5ϫ magnification) using an Axio Observer Z1 microscope (Zeiss) equipped with a thermostatic chamber (37°C, 5% CO 2 ). Efficient migration length, mean migration speed, and tortuosity (ratio of the total length of a curve followed by a cell to the distance between its two ends) were calculated using the manual tracking plugins added to the ImageJ software.
75 KCl, 2 CaCl 2 , 1. 3 MgCl 2 (pH 7. 4 with NaOH). Loose-seal cell-attached recordings (5-20 M seal resistance) were acquired continuously in the voltage-clamp mode. Signals were filtered at 3 kHz and digitized at 10 kHz. Pipette potential was maintained at 0 mV. Recordings were aborted if firing rate was sensitive to changes in pipette holding potential or if shapes of action current changed. Data were analyzed using Clampfit 9. 2 (Molecular Devices, Sunnyvale, CA, USA). Neurons with likely serotonergic specification were first targeted according to morphological criteria  (Brown et al. , 2008) : 5-HT neurons are clustered along the midline of the DRN and they have a larger soma (∼20-25 μm long-axis diameter) than nonserotonergic neurons (∼10-15 μm). Once loose-seal cell-attached recording configuration was established, 5-HT neurons were identified according to electrophysiological criteria  (Vandermaelen and Aghajanian, 1983; Allers and Sharp, 2003).
STATISTICAL ANALYSIS All the statistical tests were performed by GraphPad Prism version 5. 04 (GraphPad Software, San Diego, CA, USA). First, normality of data distribution was tested by D'Agostino-Pearson omnibus normality test. When the data were normally distributed, genotype effects were tested by one-way ANOVA [expressed as F (df1,df2) values] followed by Tukey's post hoc test. If not, data were analyzed by  Kruskal-Wallis test [expressed as H (df )  values] with Dunn's post hoc test. For testing effects of Trp in comparison to respective baseline, data (% change in firing rates) were analyzed by Wilcoxon signed rank test (two-tailed). In all cases, p < 0. 05 was considered statistically significant.
ACKNOWLEDGMENTS Supported by the  DFG  ( KFO 125 ,  SFB TRR 58/A1  and  A5 ),  BMBF (IZKF Wuerzburg ,  01KS9603 ),  Compagnia di San Paolo (Programma Neuroscienze -2008. 2265 ), and the  EC  ( NEWMOOD LSHM- CT-2004-503474 ). Naozumi Araragi  was supported by a grant of the  German Excellence Initiative  to the  Graduate School of Life Sciences (GSLS), University of Wuerzburg. The article publishing fee was funded by the  DFG  and the  University of Wuerzburg  in the funding program Open Access Publishing.
Acknowledgements. We sincerely thank  D. Gehring  and  F. Herrmann-Nießner  for their valuable help with treating the chickens. Funding statement. Supported by the  Deutsche Forschungsgemeinschaft  and the  Vereinigung der Freunde und Fo ¨rderer der J. W. Goethe-Universita ¨t Frankfurt am Main  (grants to R. W. ). C. N. was in part supported by the  Alfons und Gertrud Kassel Stiftung  through the  Frankfurt International Graduate School of Science. Support to M. A. was by  NSF  no. 12379 ,  ANR  and  HFSP-RGP0045.
ACKNOWLEDGMENTS The authors acknowledge financial support from the  INSERM, la Ligue Nationale Contre le Cancer, le Ministere de l'Education Nationale , the  Region Nord/Pas-de-Calais. Artem Kondratskyi  was supported by fellowship from  FRM (Fondation de Recherche Medicale). Maya Yassine  was a recipient of a PhD scholarship from  Erasmus Mundus. Kateryna Kondratska  is an IonTrac Project fellow.
A wide-angle varifocal lens (YV10x5B-2, Fujinon Europe) was used to monitor movement trajectories throughout free-swimming episodes, whereas a micro-inspection zoom lens system (Optem Zoom 70XL, Qioptiq Photonics) was used to record the eye motion during hindlimb swimming movements. Image acquisition frequencies were 60 -200 fps for filming of limb movements and 115-225 fps for eye movement recordings. Video sequences were collected on computer through a FireWire interface and stored using the AVI video file format with software FlyCapture 1. 8 (Point Grey Research). Subsequent data analyses were performed using free ImageJ 1. 44p software (National Institute of Health) and a plug-in that enabled manual tracking of eye position throughout a recorded sequence of swimming activity and extracting the x-y coordinates of predesignated marker points in relation to the corresponding hindlimb position.