dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The percentage of correct responses was calculated for each condition. Mean accuracies are reported in Table  1. First, data were submitted to an analysis of variance (ANOVA) with Type of Presentation as between-subjects factor (comparison between control group 4, control group 8, and the preview group) and with Type of Configuration (C1, C2, C3, and C4) as within-subject factor. This analysis revealed a significant main effect of the type of presentation, F(2, 77) = 12. 89, p <. 001, η p ² =. 25. In order to analyze this global effect, planned comparisons were conducted. Performance of control group 4 was better than performance of control group 8, F(1, 77) = 24. 80, p <. 001, η p ² =. 24. More interestingly, participants of preview groups who previewed the distractors had better performance than participants of control group 8, F(1, 77) = 16.
Computer-aided diagnosis of Alzheimer3s disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models3 robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up).
Structural 1. 5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models3 between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols. In the ADNI set, the best AD/HC sensitivity/specificity (88. 6%/92. 0%test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine).
Finally, previous studies have successfully employed pattern recognition techniques to classify MRI images from different cohorts only within the combined sets  (Westman et al. , 2011; Lebedev et al. , 2014). The present study was planned as one of the first to assess classifiers3 between-cohort robustness in two independent large-scale datasets. We hypothesized that with the use of more disease-specific parcellation atlases (in this case, when the measurements are extracted from the predefined regions, known to be affected by Alzheimer3s disease), it would be possible to achieve AD-detection accuracy equivalent to that of the models trained with high-dimensional input without parcellation with shorter computational time. In addition, we hypothesized that it is possible to achieve good between-cohort generalization of the models if the MRI protocols are harmonized.
Subjects The study was based on two cohorts. The first set of clinical and MRI data was obtained from the Alzheimer3s Disease Neuroimaging Initiative (ADNI-1) database (http://adni. loni. ucla. edu). In short, ADNI-1 includes more than 800 subjects with up to 5 years of annual follow-up with comprehensive clinical, neuropsychological, imaging and laboratory evaluations, performed at the 57 specialized ADNI sites in North America. For details, see  Aisen et al. (2010)  and  ADNI-Core (2011). The present cross-sectional study is focused on baseline imaging data and longitudinal information regarding conversion to dementia. In total, 3D T1 baseline brain scans from 809 subjects passed our image quality control criteria. From this group we selected 575 subjects -185 AD, 225 healthy controls (HC) and 165 patients with mild cognitive impairment (MCI) and long term follow up informationwho met the inclusion criteria (see below).
Subsampling From the final ADNI cohort of 575 subjects, 150 AD patients and 150 HCs were randomly selected, forming the training dataset, with the remaining AD 35 and 75 HC (coupled with 165 MCI patients) subjects included in the testing dataset. MCI subjects were split into 6 subgroups according to the month of MCI-to-AD conversion during 4 years of follow-up (6th-, 12th-, 18th-, 24th-, 36+th-month converters and non-converters).
Image post-processing Image processing was performed at one site: Centre for Neuroimaging Sciences, IoP (KCL). Image quality control was performed using standardized procedures  (Simmons et al. , 2011; Simmons et al. , 2009). Next, the raw 3D T1 MRI data underwent processing for surface-based cortex reconstruction and volumetric segmentation using the Freesurfer image analysis software (http://surfer. nmr. mgh. harvard. edu/) version 5. 1 installed on a CentOS4 x86_64 cluster. There are several rationales for using Freesurfer in our study. Firstly, the surface-based registration approach incorporated into this software has been shown to have better reproducibility compared to Laplacian-or Registration-based methods for cortical thickness estimation  (Clarkson et al. , 2011). Secondly, this framework provides a range of different kinds of surface-based and volumetric measurements, as well as different parcellation atlases for extracting averaged morphometric data.
Statistical analysis Statistical analysis was carried out using the R programming language (R Core Team, 2012), version 2. 15. 1, on R-Cloud built on EBI 64bit Linux Cluster  (Kapushesky et al. , 2010). Demographic and clinical features were compared using parametric and non-parametric tests as appropriate. Principal component analysis (PCA) from the R 'base' package was used with visual inspection of PCA score-plot for the outlier detection  (Esbensen et al. , 2002). One subject was excluded during this procedure (see Results). The 'randomForest' package  (Liaw and Wiener, 2002)  was used in further analysis.
Parameter selection and classification The R package 'caret'  (Kuhn, 2012a)  was used to implement recursive feature elimination (RFE) based on the Gini-criterion with 5-fold cross-validation (CV) within the context of RF  (Kuhn, 2012b). Each of the steps described below was performed for all modalities: cortical thickness, sulcal depth, Jacobian maps, non-cortical volumes, combined parcelled measurements of cortical thickness and non-cortical volumes. First, the measurements with near-zero variance were removed from the feature sets and the resulting output underwent stepwise RFE. 10,000 trees were used to "grow" the first forest (using full feature set), and afterwards RFE was performed based on feature importance vector (defined in Eq. 1) derived from the first forest, by removing the lowest-ranked 5% of the features at each step (gradually reducing the dimensionality as 100%, 95%, … etc.
See diagram in Appendix Fig. A2. The following parameters from the final models were reported to characterize performance: out-of-bag error (for the term definition see  Breiman, 2001) , area under the ROC curve (AUC), sensitivity/specificity and overall accuracy on the testing datasets of AD, HC and MCI subjects (see "Subsampling"). ROC-curves of the best models were compared using DeLong3s test for two correlated ROCs, as implemented in the 'pROC' R-package  (Robin et al. , 2011). The robustness of each model was also tested with respect to cohort differences (using a different cohort of AD and HC subjects from the AddNeuroMed study)  (Simmons et al. , 2011). Finally, variables of importance were mapped from the best model into the brain space in order to identify the regions, which were most relevant for the classification.
Use of different parcellation schemes To investigate the effect of different atlases, we selected cortical thickness as a measurement type that produced the most accurate models and applied two parcellations implemented in the Freesurfer package -Desikan-Killiany (DK) and Destrieux (D) atlasesto extract averaged values from the predefined regions. DK  (Desikan et al. , 2006 ) is a gyral-based neuroanatomical parcellation atlas that subdivides each hemisphere of the human brain cortex into 34 regions. One of the important features of this atlas, which is especially relevant for the present study, is that it has been developed using MRI scans not only from healthy controls (young, middle-and old-age groups), but also from patients with AD, and therefore this parcellation may be considered as more disease-specific.
Prediction of MCI-to-AD conversion The best ability to predict MCI-to-AD conversion based on imaging data only was observed for the model in which all RF ensembles were combined by a majority vote, and was achieved at 76. 6% in overall MCI-to-AD conversion detection sensitivity, 2 years before actual dementia onset (averaged value for 6th-, 12th-, 18th-and 24th-month converters) with a specificity of 75. 0% (see Table  3 ).
Discussion In the present study, we managed to produce robust and accurate models with good generalization across different cohorts. Our classifier ensembles demonstrated one of the best AD detection and prediction accuracy to date, superior over the reference model (linear SVM). It is also worth noting that performance of the best ADNI models on the AddNeuroMed dataset was equivalent to the cross-validated accuracies reported in Westman3s study  (Westman et al. , 2011). Of note, a recent study found no effect of the ApoE genotype on AD/ HC discrimination accuracy  (Aguilar et al. , 2013). This is in line with our results, which however demonstrated that adding this feature may be beneficial for detecting earlier stages of the disease (MCI-to-AD converters). To the best of our knowledge, this study is also one of the first to investigate the impact of different parcellation schemes and dimensionality of the imaging features on machine learning modeling accuracy, computation/memory and time costs.
Both inputs produced models with equivalent performance. It is worth noting that tuning of the models with the parcelled input involved an exhaustive search for the optimal feature subset and m try -parameter, whereas tuning of the HD-models was carried out only partially. Therefore, we cannot be sure that exhaustive tuning of the HD-models would not outperform the parcelled approach. But clarifying this currently does not appear to be feasible and practically relevant, even given abundant computational and memory resources available for our study. Nevertheless, it is worth mentioning that the use of high-dimensional raw features may have advantages for certain tasks due to the absence of spatial constraints of ROIs. Thus, we would generally expect this   The classifiers were trained on the subset from the ADNI dataset and then validated on testing sets from both ADNI (same) and AddNeuroMed (separate) cohorts.
, 1995; Van Essen, 1997). However, further research is clearly needed to support or reject this speculation. Another interesting observation was that recursive feature elimination for the high-dimensional data improved performance of the model trained with cortical thickness, whereas other HD models did not seem to demonstrate substantial improvement. A possible explanation for this may be that the impact of neurodegeneration on cortical thickness is more localized to the entorhinal area, whereas its impact on sulcal depth and brain deformation is sparser, which complicates feature elimination. The main strengths of the present study are: (a) the use of two large imaging databases of Alzheimer3s disease with assessment of the classifiers3 between-cohort robustness; (b) optimized models with one of the best accuracy to date; (c) evaluation of several important factors influencing classification performance, such as morphometric data modality and dimensionality, parcellation schemes; (d) long-term follow-up available for the MCI subgroup from the ADNI cohort that allowed the appropriate definition of MCI non-converters and assessment of models3 sensitivity at different disease stages before the actual dementia onset.
Measurement of Barrier Functions: Transendothelial Electrical Resistance Transendothelial electrical resistance (TEER) reflects the permeability of intercellular tight junctions for ions. TEER was measured by an EVOM resistance meter using STX-2 electrodes (World Precision Instruments Inc. , Sarasota, FL, USA) and expressed relative to the surface area (V6cm 2 ). TEER values of cell-free inserts were subtracted from the measured data. The TEER of untreated human hCMEC/D3 brain endothelial cell monolayers was 43. 366. 1 V6cm 2 (mean 6 SD; n = 60) in agreement with literature data  [40]. Resistance measurements were carried out before and after treatments to check the barrier integrity. The TEER values are presented as percent of non treated control groups.
Holographic Phase Contrast Microscopy Digital holographic images were taken with a Holo-Monitor M3 instrument (Phase Holographic Imaging AB, Lund, Sweden). Endothelial cells were cultured on collagen coated culture dishes with borosilicate glass bottom (MatTek, Ashland, MA, USA). All treatments lasted for 4 hours. Holographic images of the same culture area were captured before and during treatments. Cell morphological changes were analysed by the Holostudio 2. 4 software provided with the microscope (Phase Holographic Imaging AB, Lund, Sweden). Each point in the box plot reflects the data obtained on a single cell  [48, 49].
Statistical Analysis All data presented are means 6 SD or SEM as indicated in the text. The values were compared using the analysis of variance followed by Dunnett or Bonferroni posthoc tests using GraphPad Prism 5. 0 software (GraphPad Software Inc. , San Diego, CA, USA). Changes were considered statistically significant at p,0. 05. All experiments were repeated at least three times, the number of parallel wells or inserts for each treatment and time point varied between 3 and 16.
Irreversible cell damage was observed at 800 and 1000 mM (Figure  1A ). To validate our data, 600 mM methylglyoxal concentration was tested using the colorimetric end-point MTT assay at 8-hour time point. The cell viability values after methylglyoxal treatment measured by the MTT test decreased to 56. 764. 6% (mean 6 SD, n = 50) in good agreement with the decrease of cell index at the same concentration of methylglyoxal at the same time point (58. 265. 8%, mean 6 SD, n = 10). The 600 mM concentration of methylglyoxal causing approximately 50% toxicity was selected for further tests. To support the relevance of our data on endothelial cell line, the effect of methylglyoxal was also tested on cultures of primary rat brain endothelial cells (for preparation of cultures see Text S1). The toxicity of methylglyoxal was investigated by the WST-8 colorimetric assay of cell metabolism and LDH release reflecting cell membrane damage.
This is the first report on methylglyoxal-induced morphology changes in brain endothelial cells using holographic phase contrast imaging. cells measured by real-time cell electronic sensing method (A). Effect of co-treament with 600 mM methylglyoxal and different concentrations of edaravone (MG + E; 600-3000 mM) or aminoguanidine (MG + AG; 2 mM) (B). Cell index is expressed as an arbitrary unit and calculated from impedance measurements between cells and sensors. Data are presented as means 6 SD, n = 10. Triton X-100 was used at 10 mg/mL concentration. Statistical analysis: two-way ANOVA followed by Bonferroni test. Statistically significant differences (p,0. 05) from the control group (#) and from the methylglyoxal-treated group (*) are indicated. doi:10. 1371/journal. pone. 0100152. g001.
The two different cell viability assays we used were in complete agreement on the direct cellular damaging effect of methylglyoxal, impedance data reflecting changes in cell adhesion, cell shape and number were confirmed by MTT tests measuring the metabolic activity of cells. Our data lend support to and expand previous findings on the effect of methylglyoxal on human brain endothelial cells  [25, 28, 37, 53]. We selected the human hCMEC/D3 cell line as a simplified model of the blood-brain barrier. This cell line is widely used in different experiments, including pharmacological and drug studies  [40]. To support the relevance of our data on hCMEC/D3 endothelial cell line, the effect of methylglyoxal was also tested on primary cultures of rat brain endothelial cells. The observed effects were in agreement with our observations on the human cell line, indicating a similar sensitivity of primary endothelial cells and hCMEC/D3 endothelial cell line for the toxic effects of methylglyoxal.
Assessment of behavioral rhythms Locomotor activity was continuously recorded in 1 min intervals throughout the entire duration of the study using passive-infrared motion detectors mounted above each animal's cage. The motion captors were connected to a computerized data acquisition system (Circadian Activity Monitoring, INSERM, France; for details see  [27] ). Data were analysed using the Clocklab software package (Actimetrics, Evanston, IL, USA). We used Chi-squared periodogram method to calculate the period of behavioral rhythm which is defined as the time elapsed for one complete oscillation or cycle (the distance in time between two consecutive peaks or troughs of a recurring rhythm). The stability and the consistency of the rhythm are reflected by the value of the periodogram amplitude which corresponds to the zenith of a periodogram curve.
Lighting protocols Animals were recorded for periods of 8 months to more than 24 months. Prior and after MPTP administration, the animals were maintained under an LD cycle (12 h light:12 h dark) of approximately 450-500 lux during the light phase and 0 lux during the dark phase. The animals were periodically exposed for 12-15 days to conditions of constant light (LL) of 300 lux. Unlike rodents, diurnal primates do not tolerate constant darkness (DD) that induces stress and is considered unethical practice. On a few occasions continuous dim light (5-10 lux) was examined, but as no differences were found between the two lighting conditions, data was grouped together for analysis. The average time between the end of MPTP treatment and the first exposure to LL was 30-210 days (avg = 96 days) and for subsequent exposures was 60-367 days thereafter.
Analysis and Statistics The calculation of a significant behavioral rhythm was performed using Chi-squared periodogram method  [38]. For analysis of behavioural amplitudes at different clinical stages, cosinor analysis and a Linear mixed model (lme4 package) in R (version 2. 12. 2) were used. For analysis of hormonal rhythms, the dual harmonic regression model was used to estimate the amplitude and the acrophase of the rhythms (Sigmaplot 12, Systat Software Inc. ). Behavioral parameters were analysed using a twoway analysis of variance ANOVA (with treatments and light conditions as factors). Post hoc comparisons of interactions were performed with the Bonferroni correction. For analysis of the TH, melanopsin and orexin-A positive cell, a one-way analysis of variance ANOVA was used. P values less than 0.
Support was provided by the following:  Fondation de France ,  Fondation Caisse d'Epargne Rhone Alpes Lyon, Rhone-Alpes Cible/CMIRA ,  ANR-09-MNPS-040 ,  Retina France ,  Volubilis, GDRI Neurosciences , and  Cluster Handicap Vieillissement Neurosciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS This work was supported by funding from  INSERM ,  CNRS ,  IMPRT, University of Lille Nord de France, Lille County Hospital (CHRU-Lille) ,  DISTALZ (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) (Excellence Laboratory) ,  FRM (Fondation pour la Recherche médicale) ,  AFM , and  ANR Neurosplice of Tau. The authors would like to thank  Dr. de Munain  for the DM1 tissue slices,  Raphaelle Caillierez  and  Sabiha Eddarkaoui  for assistance with immunohistochemistry.
Thus, the extracellular domain of EphA4 is necessary and sufficient for its function in the notochord. (I) Inhibition of separation by EphB4 MO targeted to the paraxial mesoderm. Left boundary is missing (arrows). (J) Rescue by the BA chimera. The injection was targeted to the right boundary. (K) The AB chimera fails to rescue. The injection was targeted to the left side (arrows). The right boundary is intact (dashed line). (L) Quantification of boundary disruption by ephrin/Eph depletion. Individual boundaries were scored as follows: 1, intact boundary; 0. 5, partially disrupted boundary (rare cases); 0, fully disrupted boundary. The first column (t) compiles data of all embryos, (no) and (pm) the number of boundaries where injection was mainly targeted to the notochord or to the paraxial mesoderm.
Reaggregation Assay Cells from dissected mesoderm and inner layer ectoderm were dissociated in alkaline calcium-free buffer (88 mM NaCl, 1 mM KCl, 10 mM NaHCO3, pH 9. 3). For reaggregation, cells were transferred to agarose-coated Petri dishes containing MBSH and incubated for 1 h under mild rotation (10 rpm) on an orbital shaker. Images were taken under a dissecting microscope at a126 magnification using a Micropublished RTV3. 3 camera (Qimaging) and were analyzed for object size using ImageJ software. Two parameters were measured: average object area and area/ perimeter ratio. Results of six independent experiments were normalized using wild-type ectoderm or mesoderm as reference.
Live Imaging Tissues expressing membrane-targeted Cherry or GFP as well as various mRNAs/MOs were dissociated in dissociation buffer, cells were transferred to glass-bottom petri dishes (Fluoro dish, World Precision Instruments) coated with 0. 01 mg/ml Fibronectin, and cell behavior was filmed for 2 h. Cells were imaged with a WaveFX spinning disc confocal (Quorum Technologies) mounted on an automated DMI6000B Leica microscope, controlled with Volocity 3DM software (Improvision), using a 406HCX PL APO CS, NA = 1. 25 oil objective. Images were acquired every 2 to 5 min using an EM CCD 5126512 BT camera. Image processing was performed with Metamorph (Universal Imaging Corporation) and Adobe Photoshop7 software. Processing consisted of merging two to three planes from z stacks, assigning pseudo-colors, and adjusting image contrast.
PROTEIN EXTRACTION The proteins were extracted from pig frozen tissues by phase partition using chloroform/methanol (v:v, 2:1) on ice. The resulting samples were centrifuged (15,000 g for 15 min at 4°C) and the methanol phase was collected then evaporated in a Speed-vac concentrator. Aliquots were tested by native-polyacrylamide gel electrophoresis as already described  (29)  in order to obtain a standard quantity of proteins for each tissue (1X corresponds to 1 microg/band). The relative quantification of protein bands was calculated by using Image J software. Dried samples were stored at -20°C.