dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, we previously reported in U87-MG cells that anti-migratory effect of VFL was associated with the inhibition of EB1 accumulation at MT plus-end and with the decrease of C-ter detyrosinated form of EB1  [6]. This shift in post-translational modification of EB1 may participate to Vinca-alkaloid antitumor activity, if one considers that detyrosinated EB1 promotes tumor progression as described with detyrosinated tubulin  [35]. It is also conceivable that increased cell proliferation may sensitize the cells to the action of cell cycle-dependent anticancer drugs. Altogether, our data open new avenues of research on the role of EB1 in GBM tumor progression that has limited therapeutic options. Subgroups of glioma patients with EB1 overexpressing tumor should be identified. Furthermore, MT targeting drug activity should be evaluated in regard to EB1 status in cancer patients.
Immunofluorescence and western blot analysis Indirect immunofluorescence was performed as previously described  [5]  by using the anti-EB1 antibody and anti-mouse antibody Alexa 568 nm (Molecular Probes); and FITC-coupled anti-α-tubulin antibody (clone DM1A; Sigma-Aldrich). Cells were observed using a Leica DM-IRBE microscope, 100X magnification. All images were acquired using Metamoph software (Molecular Devices, Sunnyvale, CA) at identical acquisition settings, and were processed using Image J software. After cell lysis, 30 μg of total protein were loaded into a 12% SDS-PAGE gel. Anti-EB1 antibody, anti-α-tubulin and anti-mouse IgG-horseradish peroxidase (Jackson Immunoresearch) were used. Chemiluminescence detection kit (Millipore) was used for visualization of protein bands. Chemiluminescent signal was acquired on a G:BOX imaging system (Syngene, Cambridge, UK) and quantification was done with Image J software.
Tumor volume evaluation Frozen brains were serially sectioned using a Leica cryostat, and 20 μm sections were stained with Hematoxylin/Eosin (HE) for histomorphology and measures of the tumor volume. Images of HE-stained sections were captured with a Leica Z16APO macroscope using the Leica Application Suite 2. 8. 1 Software. The tumor area was manually outlined and measured using Image J software. Knowing the thickness and the number of sections, we calculated the total volume of each tumor. Tumor volumes were measured for three animals per group.
Analysis of microtubule dynamics from EB3-GFP and CLIP 170-GFP comet data U87-MG cells (6 × 10 4 cells/well) were grown for 24 hours on 8-well Labtek II chamber slides (Labtek, Thermo Scientific, Roskilde, Denmark) precoated 1 hour with fibronectin (10 μg/ml). U87-MG cells were then transfected with plasmid coding for green fluorescent protein EB3-GFP or CLIP 170 GFP using lipofectamine TM 2000 system (Invitrogen). Before microscopy analysis, cells were incubated with various concentrations of VCR for 4 hours. Time-lapse acquisitions for microtubule dynamic instability experiments were performed with a Leica DM-IRBE equipped with a 100X objective lens. Sixty images per cell were acquired at 2-s intervals using a digital camera (CCD camera Coolsnap FX; Princeton Instruments). EB3-GFP comets were detected using the plusTipTracker package  [49].
The software package can be downloaded from http://lccb. hms. harvard. edu. Comet detection requires no user intervention, as the detection algorithm automatically estimates locally optimal thresholds. Tracking and inference of complete MT trajectories by plusTipTracker requires user-defined settings of several control parameters (Watershed-based detection: sigma 1 = 3; sigma 2 = 4 and K = 1; Tracking search radius range = 5-10 pixels, minimum subtrack length = 4 frames, maximum gap length = 12 frames, max shrinkage factor = 1. 5, maximun angle forward = 30° and backward = 10°; fluctuation radius = 1. 5 pixel ; Post processing frame rate = 2s; pixel size = 130 nm). Correct tracking was verified by visual inspection of several overlay movies. The parameter settings were kept identical for all movies.
The plusTipTracker software package contains modules for the parameterization of trajectories characterizing MT dynamics within one movie and for the comparison of the dynamics between movies. Many of the metrics are redundant and thus cannot be considered for comparative analyses without further dimensionality reduction. To assess the consistency we chose 5 metrics for pair-wise statistical testing: mean growth speed, mean growth lifetime, mean growth length, time-based catastrophe frequency and distance-based catastrophe frequency. A t-test was performed for each of these metrics. For each experimental condition, 5 to 8 cells were analysed (400-1200 tracks per cell).
Statistical analysis Data are presented as mean ± SEM. Cell counting, cellular viability data, tumor volumes were analyzed by Student's t test. Overall survival (OS) was defined to be the time from GBM diagnosis to death from any cause, censored at the date of last contact. Progression-Free Survival (PFS) was defined to be the time from GBM diagnosis to progression or death, censored at the date of last contact. PFS and OS were estimated by the Kaplan-Meier method. The log-rank test was used to estimate survival distributions. Prognostic factors with p < 0. 15 in univariate analysis were explored in multivariate analysis. Cox proportional hazards models were used for multivariate analyses and to estimate hazard ratios in regression models. Reported p-values are two-sided, and p < 0. 05 was considered statistically significant.
ACKNOWLEDGEMENTS We thank  AF. Blandin ,  A. Tchoghandjian ,  C. Boucard  for their technical help, and  E. Lopez  from the  CFREMC (Centre de Formation et de Recherches Expérimentales Médico-Chirurgicales , numéro d'agrément:  E 13-055-5 ), 13385 Marseille, France. This work was partly supported by  INCa-DGOS-Inserm 6038  ( SIRIC Label ),  A*MIDEX  project (n°  ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  ANR ,  Société Française de Pharmacie Oncologique and the French "Académie Nationale de Pharmacie ". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Real-time PCR was performed on the resulting complementary DNA using TaqMan microRNA specific primers and TaqMan Universal PCR Master Mix. All the experiments were performed according to the manufacturer's protocols, using a pipetting robotic platform epMotion 5070 (Eppendorf) and a LightCycler 480 Detection system (Roche, France). The expression of the U6B small nuclear RNA (RNU6B) was used as endogenous control for data normalization. Relative expression was calculated using the comparative threshold cycle (Ct) method.
Statistical Analysis. Patients' parameters were analyzed with the nonparametric Wilcoxon signed-rank test. Correlations with miR-125b expression levels were quantified with the Spearman's correlation test and the Fisher transformation was applied. All other data were analysed statistically using the Mann-Whitney  test. values less than 0. 05 were considered statistically significant. The Power and Precision V3 Software (http://www. power-analysis. com/) was used to calculate the 1- error (the probability of a  = 2 < 0. 01 not appearing at random) for the difference in sera levels of mir-125b between responders and nonresponders.
Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc. ). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures-based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.
, 1989) , mutual information  (Grassberger et al. , 1991) , transfer entropy  (Schreiber, 2000) , directed coherence, Granger causality  (Granger, 1969) , generalized synchronization  (Quiroga et al. , 2002)  and Bayes net methods  (Ramsey et al. , 2006)  etc. Methods for evaluating effective connectivity include Dynamic causal modeling (DCM)  (Friston et al. , 2003)  and structural equation modeling  (Mclntosh and Gonzalez_Lima, 1994). However, no evaluation framework exists to systematically compare the results of different methods for various types of datasets. We introduce here such evaluation framework, and we show its usefulness performing a systematic analysis of 42 methods using multiple datasets with different structures. Note that our goal here is not determine why a given method succeeds or fails in a specific condition.
Our goal is to provide neuroscientists with a useful tool to make informed choices about the method one wishes to use according to the type of data. The evaluation framework uses different connectivity architectures and biologically realistic models of various levels of complexity and time scales. We included 42 methods to span the domain of application of functional connectivity schemes. In this paper, we focus on the taxonomy of functional connectivity schemes and the generative models used to create simulated data, with a special focus on the sensitivity of different schemes to analysis parameters, the architecture of the generative models and different types and levels of noise. As an example of the proposed systematic evaluation framework, we benchmarked 42 methods against more than 10 4 datasets generated under five classes of simulated models.
METHODS Figure  1  shows how our systematic evaluation framework works. We simulated five types of data (first column) to cover the dynamic characteristics (time scales, complexity, and multi-scale properties) of signals obtained from EEG and fMRI modalities, including linear and non-linear systems with linear or non-linear coupling. In terms of connectivity architectures, we considered at least 154 underlying structures (all possible topologies for five nodes and five edges), with varying connection strengths, noise levels and time delays (third column). We applied 42 connectivity analysis methods (fourth column) to the ensuing datasets and compared the resulting connectivity with the ground-truth structures. Using this setup, we (1) identified the valid (optimal) parameter range for each method.
(2) Using this parameter range, we evaluated the relative performance of each method and, (3) finally, we characterized the accuracy of each method in the face of different levels of system noise (innovations or random fluctuations producing simulated neuronal signals) and four types of observation noise (random fluctuations added to signal to simulate data). In what follows, we consider each of these components in detail. An open source MATLAB code is available to test other methods and identify optimal analysis parameters. Our numerical simulations were performed using The Virtual Brain (TVB) high performance computing platform using 240 Intel Xeon E5-2670 based computing cores 1.
These properties are generally not known in real datasets. Hence our goal was to cover all possibilities. Another motivation was to facilitate comparison with previously published work on the assessment of analysis methods  (Quiroga et al. , 2002; Ansari-Asl et al. , 2006; Wendling and Ansari-Asl, 2009). Figure  2A  illustrates five types of simulated time series with different signal dynamics and different time resolutions for a given connectivity structure. Because the different types of signals have different sample frequencies, we used the number of time points as a measure of time to define analysis parameters, such as the size of sliding window, the length of the time series and time delays.
The direction i → j is determined if past of i has a stronger correlation with current j than past of j has with current i. BCorrU is the bivariate correlation method for measuring undirected connectivity by selecting the The color code corresponds to the strength of the connections between nodes (set to 1 for all connections in this example). Connection matrix (CM1) was obtained with PDC method. ROC 1 is the receiver operating characteristic (ROC) curve, which displays true positive rate as a function of false positive rate, for the PDC method, measured for all connection strength values (strength threshold). The AUC is the area under the curve. If AUC is 1 (as for CM2 and ROC2), it means that the method is able to find the "ground truth," if given the correct threshold. (C) For the undirected methods, such as BCorrU, the computed connection matrices are symmetric, thus we took the symmetrized designed matrix as a ground truth for calculating their ROC and AUC.
Granger causality family The Granger causality (GC) family calculates the directed connectivity (j→i) based on the notion that information in the past of j helps to predict the future of i with greater accuracy than by only considering the past of i itself  (Granger, 1969). We used two methods from "Granger causal connectivity analysis (GCCA)" Matlab toolbox  (Seth, 2010) , GC and CondGC. These methods are based on multivariate autoregressive models (MVAR): X (t) = p r = 1 A r X (t -r) + (t) (1) With state vector X (t) = [x 1 (t) , • • • , x n (t)] n×1 and prediction errors (t) = [ξ 1 (t) , • • • , ξ n (t)] n×1 for n nodes. p is the model order (maximum number of lagged observations included in the model) for the MVAR and r is the time delay. Omitting x j from X, we obtain a second group of MVAR with another prediction errors.
EVALUATION OF THE METHODS For each simulated dataset, each method computes a functional connectivity matrix, which assigns a connection value between 0 and 1 between any two nodes (Figure  2B ). When analyzing real datasets of unknown structure, the connection strength can be thresholded to produce a network graph. For example, in the connection matrix 2 in Figure  2B , choosing a connection threshold of 0. 4 will produce a graph with many more links (those in orange color) than the ground truth structure. However, with the threshold of 0. 5, the method identifies the ground truth structure. Choosing the most appropriate threshold value is an unresolved problem. However, simulated data is generated with a designed structure or graph, which we refer to as a ground truth structure. Based on these ground truth structures, we can calculate the number of false positive and false negative edges as a function of threshold.
ANALYSIS PARAMETERS To analyze a given dataset, we used a sliding window (with a given overlap) to analyze each timeseries. In other words, the timeseries was analyzed in N windows and a connection matrix was calculated for each window. These N matrices were then averaged into a mean connection matrix. We then compared this mean connection matrix with the designed one to obtain an AUC value. Each method requires setting some parameters, which affect their performance. These parameters include the size of any sliding window, the number of windows, the length of time series analyzed, the frequency bands for methods using the frequency domain, the maximum lag for the model-free methods, and the model order for the model-based methods. We therefore first performed a parametric study to determine the valid range of values for a given method using NMM and fMRI signals from the biophysical generative models.
Size of sliding windows Experimental datasets can be obtained with different sampling frequencies. In order to take this into consideration, the analysis platform uses time points rather than specific time units (these can be obtained by a simple conversion). We varied the size of sliding windows from 50 (0. 4 s) to 800 (6. 4 s) time points for NMM and from 50 (100 s) to 600 (1200 s) time points for fMRI datasets, testing different CS values. The sliding windows overlapped by half their length and the timeseries length was 8000 (64 s) time points for NMM and 5120 (10240 s) time points for fMRI. Figure  4A  shows that for NMM signals with a strong connection strength, (CS= 1), 25 methods had an AUC= 1, when the size of widow was 600 (4. 8 s) time points, while 7 methods were successful with a 150 (1.
Length of time series The length of the time series is also a crucial factor. It needs to be as short as possible since, in real datasets, functional connectivity may change dynamically over time. The minimum length of signal can be directly derived from the previous analysis, and is shown in Figures  5C, D. For NMM signals, 2000 (16 s) time points were sufficient for GC, BTED, BCohF, PDCF, and dDTF methods when using a 350 (2. 8 s) time point window. For fMRI signals, 1750 (3500 s) time points were sufficient for 5 chosen methods (BCohF, BCurrU, Bh2U, GC, and PDCF) for windows larger than 350 (700 s) time points.
In summary, these evaluations-performed using the NMM and fMRI simulations-suggest that analysis parameters can have a profound effect on the ability of functional connectivity measures to recover the underlying effective connectivity. Most of the effects we described above can be understood intuitively. However, the main contribution of these analyses is to note that, using simulated data, the parameters can be optimized for any empirical data set at hand. MATLAB code is available to reproduce the analyses reported above-to find the valid parameter range for any functional connectivity measure or configuration of the generative models. Using appropriate parameters, we now turn to the relative performance of the various measures on different simulated models.
The level of system noise was quantified as the ratio between the amplitude of system noise over the amplitude of deterministic input. Figure  11  shows that most methods were stable up to a ratio of 2900 for NMM and 16 for fMRI. Out of these ranges, the simulation models became unstable. This reflects the fact that system noise contributes to the signal and discloses functional dependencies or connectivity among neuronal sources. In fact, such system-like noise can be regarded as exogenous inputs. In another word, the strength of exogenous input determined the amplitude of the signals but did not change the coupling of signals. In real datasets, experimental procedures may cause observation noise and also the environment can constitute a major difficulty for data analysis. After generating NMM or fMRI signals, we added four types of observation noise to each recording site (node).
DISCUSSION We studied these 42 methods because they cover a wide range of approaches for characterizing functional connectivity. Of the 42 methods, 28 assume underlying linear models of statistical dependencies; 4 (h 2 family) use piece-wise linearization and the last 10 methods (mutual information and transfer entropy) use entropy measures to account for nonlinearities. Within the Granger and Since functional connectivity analysis is routinely performed on electrophysiological, encephalographic and fMRI signals, we selected neural mass models and associated fMRI models to generate datasets. However, when they were first introduced, most functional connectivity methods were tested on (biological) mathematical models. For example, the Granger family  (Seth, 2010)  and AH  (Baccalá and Sameshima, 2001)  families were tested on linear models, while non-linear methods were tested on non-linear models (Lopes da  Silva et al.
PARAMETRIC OPTIMIZATION Both evaluation and application of a functional connectivity method require valid parameters. The proposed framework attempts to provide a systematic way to find the valid parameters. After choosing candidate methods, it is necessary to select a model, which is related to the simulated data. Using the provided code and referring to parameter ranges presented here, it is then possible to choose the possible parameter ranges for the candidate methods on the simulated data. One can thus find the valid parameters. Note that we assumed that the performance of connectivity analysis methods has the same characteristics on both empirical data and the simulated data. Recovering the underlying (effective) connectivity graph requires sufficiently long time series. Our results suggested using a window size larger than 350 time points and at least 15 windows (with a half-window overlap) are necessary for most methods.
PERFORMANCE OF THE METHODS Although our goal was not to understand why methods succeed or fail to recover graphs, our systematic analysis of 42 methods provide some clues regarding their performance. In the case of model-based methods, the Granger family recovered most underlying structures. Barnett et al. (2009)  show that, in theory, Granger causality and transfer entropy are equivalent for Gaussian variables. In the present study, we demonstrated that transfer entropy was as good as Granger family in most cases, except for linear and Rössler systems. The AH family also performed quite well on most datasets (except Rössler systems), but was not robust to variations in connection strength. In the case of model-free methods, Fourier coherence performed quite well for recovering undirected graphs.
Wavelet coherence only performed well on fMRI datasets. The correlation family performed slightly better than h 2 on NMM and fMRI, but worse on non-linear mathematical models  (Rössler and Hénon). Mutual information methods only worked on fMRI datasets when the connection strength was strong (and on Hénon systems). The relative performance of the methods we considered also depends upon the structure and complexity of the signal. It is generally assumed that biological systems are inherently non-linear. All methods performed better on the neuronal models (NMM and fMRI) than on the purely mathematical non-linear models  (Rössler and Hénon). Most methods also performed better on fMRI than on NMM. One possible reason is that the number of hidden states within each node of a NMM (13 states) is larger than for fMRI (1 state).
In order to assess the possible origin of these differences, we analyzed the datasets used in  Smith et al. (2011)  (Supplementary Figure  7  and parameters in Supplementary Table  2 ). Differences with the present study can be noted: for example, undirected methods from both correlation and coherence families can identify the exact truth structures, while these methods failed in  Smith et al. (2011). This discrepancy may stem from the fact that we performed a systematic parametric search to identify the optimal parameter range for each method. This protects against false negative conclusions (i. e. , when a method fails due to an inappropriate parameter choice). Second we averaged the results across the sliding windows rather than using a single window. Directed methods such as Granger and PDC methods performed poorly for the datasets in  Smith et al.
VARYING NUMBER OF EDGES, HIDDEN NODES, AND NON-STATIONARITY Our analysis is based on networks comprising 5 nodes and 5 links. However, our (open source) MATLAB code allows one to vary the number of nodes and links, as well as the architecture of the network. The appropriate range of parameters (in particular the size of the sliding window) can then be optimized for any particular architecture defined in terms of graph size. When analyzing an experimental dataset, part of the real network could be hidden from observation. The framework presented in this paper allows one to characterize the influence of hidden nodes on connectivity estimates. For example, in fMRI, it is possible to simulate data with and without the influence of (hidden) nodes and compare the associated connectivity estimates.