dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results For the demonstration of spectral reconstruction results, we selected 16 material surfaces from both "Kitchen" and "Textile_1" images, while 20 material surfaces from the "Textile_2" image. The reflectance data for these surfaces is acquired from hyperspectral camera and is used as ground truth for comparison of results. We present results from two imaging techniques in this paper; one is through the filter-wheel camera where the integration time for each channel is adjusted manually while the other imaging technique is snapshot camera where all channels are acquired with the same integration time. We get the snapshot like effect by normalizing the acquired image through filter-wheel camera with respect to sensor integration time for each channel. It is assumed that the camera response curve is linear.
Conclusion In this paper, we have provided practical demonstration and evaluation of the concept of multispectral constancy. Three different scenes are created in viewing booth and multispectral images are captured while using different illuminants. The acquired multispectral data is transformed into canonical representation through manual selection of white patch of ColorChecker in the image and by estimating the illuminant. Spectral reconstruction is performed by using Wiener estimation method and results are evaluated in terms of RMSE, cosine distance and spectral angle mapper. Results show a promising aspect of multispectral imaging as they can be used as a spectrophotometer for getting spectral information of a whole scene. We found that the spectral adaptation transform do not provide significant improvement in the results for this specific camera.
Methods Molecular Modeling and Energy Minimizations The NGF/TrkA-Ig2 complex was built by considering the atomic coordinates from PDB entry 1WWW  [17]  that contains the homodimeric structure of NGF complexed with the homodimeric Cterminal immunoglobulin-like domain in a stoichiometry 2:2. The few residue gaps in the structure were completed by using MODELLER 9. 15  [70]. The optimization is done by 10 4 steps of energy minimization using conjugated gradient (CG) method followed by 10 3 steps of molecular dynamics with simulated annealing, repeated 10 times for each model or until the objective function value reaches 10 8. Water molecules in the crystallographic structure were maintained in final models. The lowest energy model was kept for further calculations. PyMOL 1. 7  [71]  was used to generate R221W mutant from the wild type model.
The 200 lowest normal modes for all atoms (dismissing the 6 modes related to rigid body rotations and translations) were computed in vacuum using DIMB  [75]  module implemented in CHARMM. A distance dependent dielectric constant (ε=2r ij ) was employed to treat electrostatic interactions. NM and atomic fluctuations (root mean square fluctuations -RMSF) were computed with the VIBRAN module of CHARMM. Low energy conformations along the normal modes vectors were produced by using the VMOD module in CHARMM as previously described  [76]. It consists in performing a mass-weighted root mean square (MRMS) displacement of the structure along each normal mode through successive low-temperature molecular dynamics followed by energy minimizations. It uses restraining potentials for displacements along the normal mode coordinates, and for freezing the overall translation and rotation motions to the standard potential function, according the following equation: ( ) 1 where in the second term q j is a given mass-weighted displacement along normal mode j, q j t is the desired target distance along normal mode j, and k j is the force constant.
For the modes analyzed, it was observed that the M+ and M-matrices were highly related, thus only the (M + -M χ ) difference matrix was kept for analysis. The relative displacement matrices corresponding to all the modes considered were compared between each other through the Mantel correlation test, as implemented in the Vegan package version 2. 3-1  [77]  of R 3. 2. 3 software  [78]. Using this method, it can be assumed that two matrices sharing a correlation coefficient equal or greater than 0. 6 (p < 0. 001) describe closely related motions in 3D space  [49].
05 above maximal modularity) and rebuilt the network looking into partitions with modularity close to the maximal value but with an overall smaller number of communities. Also, communities with less than ten nodes were pruned. Hierarchical clustering was applied to aggregate highly correlated nodes into communities using the Girvan-Newman method  [47]. Suboptimal path calculation was performed to identify residues involved in the dynamic coupling of binding sites. Calculations were performed with Bio3D package version 2. 3-1.
Hydrogen Bonds and Salt Bridges Analyses Hydrogen bond (hbond) and salt bridge formation was analyzed by using VMD 1. 9. 2 plugins  [80]. The hbond donor-acceptor distance was set to 3. 0 Å and the cutoff angle was set to 20º. Oxygen-Nitrogen distance was set at 3. 2 Å for salt bridges analysis. High occupancy interactions was set to a minimum of 50% within the trajectory of each motion type  [81]. To confirm the hbond changes we analyzed the structures using Baker & Hubard criteria with MDTraj 1. 2 software  [82].
EEG Recording The EEG activity was measured by using a 64-channel recording system (BrainAmp amplifier and BrainVision Recorder software, BrainProducts GmbH, Gilching, Germany). The Ag/AgCl sintered ring electrodes were mounted in an electrode cap (EasyCap GmbH, Herrsching, Germany) on the scalp according to the 10% equidistant system. During acquisition, electrodes were referenced to a scalp electrode placed between Fz and Cz electrodes. Horizontal and vertical eye movements were monitored by EOG channels. Three EMG electrodes to record muscle activity, and one ECG electrode to record cardiac activity were placed on the chin and the chest, respectively. All electrode contact impedances were kept below 10 k. EEG data was recorded with a sampling rate of 500 Hz, band pass filtered between (0.
3 and 70 Hz). In order to remove muscle and eye movement related artifact from the awake EEG data (Active Wake and Quiet Rest groups), EEG preprocessing was performed using the Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER) toolbox 1  (Nolan et al. , 2010)  implemented in EEGLAB  (Delorme and Makeig, 2004)  under Matlab (The Mathworks). The data was first re-referenced to the Fz electrode, notch filtered at 50 Hz, and band-pass filtered between 0. 5 and 45 Hz. Using a predefined z-score threshold of ±3 for each parameter, artifacts were detected and corrected regarding single channels, epochs, and independent components (based on the infomax algorithm  Bell and Sejnowski, 1995). This way, data was cleared from eye-movement, muscle and heartbeat artifacts. The data was then re-referenced to the average of the mastoid electrodes (M1 and M2).
Remaining epochs containing artifacts were removed after visual inspection on a 4-s long basis. In case of the sleep recordings (Nap group), data was re-referenced to the average of the mastoid electrodes, and sleep stages as well as conventional parameters of sleep macrostructure were scored according to standardized criteria  (Berry et al. , 2012)  by two experienced sleep researchers. Periods of NREM sleep (Stage 2 and SWS) were considered for subsequent analyses. Epochs containing artifacts were visually inspected and removed on a 4-s basis. Wrong channels (N = 6 in the dataset of the Nap group) were replaced by the average of the neighboring channels. Spectral power and sleep spindle analyses of artifact-free segments were performed by a custom made software tool for EEG analysis  (FerciosEEGPlus, © Ferenc Gombos 2008 -2017).
Statistical Analyses Statistical analyses were carried out with the Statistical Package for the Social Sciences version 22. 0 (SPSS, IBM) and R (R Core Team, 2014). The blocks of the explicit ASRT task were collapsed into epochs of five blocks to facilitate data processing and to reduce intra-individual variability. The first epoch contained blocks 1-5, the second epoch contained blocks 6-10, etc. We calculated median reaction times (RTs) for all correct responses, separately for pattern, random high and random low trials for each epoch and each participant. Note that for each response (n), we defined whether it was the last element of a high-or a low-frequency triplet. Two kinds of low-frequency triplets were eliminated: repetitions (e. g. , 222, 333) and trills (e. g. , 212, 343). Repetitions and trills corresponded to low frequency triplets for all participants and individuals often show preexisting response tendencies to such triplets  (Howard et al.
Acknowledgements This work was supported by the  Centre National pour la Recherche Scientifique , the  Université de Strasbourg  and the  Agence Nationale pour la Recherche  (Grant  ANR-12-JSV4-006-01 ). ASD was funded by a fellowship from the  Ministère de la Recherche  and by a fellowship from the  Fonds Paul Mandel pour les Recherches en Neuroscience. We thank  Ipek Yalcin  ( Institut des Neurosciences Cellulaires et Intégratives, Strasbourg ) and  Claire Gaveriaux-Ruff  ( Institut de Génétique et de Biologie Moléculaire et Cellulaire, Strasbourg ) for the kind gift of the dlx5/6-Cre mice and  Thomas Knöpfel  ( Imperial College, London ) for the kind gift of the Kv3. 1-EYFP mice. We thank  Sophie Reibel-Foisset  and the animal facility Chronobiotron ( UMS 3415 Centre National pour la Recherche Scientifique and Université de Strasbourg) , the  Plateforme Imagerie In Vitro-Neuropôle-Strasbourg , and  Aline Huber  for their technical assistance.
Funding This work was supported by the  Leverhulme Trust's Philip Leverhulme Prize , and by  LABEX WIFI (Laboratory of Excellence  within the  French Program  " Investments for the Future")  under references  ANR-10-LABX-24  and  ANR-10-IDEX-0001-02 PSL*. I. S. and A. M. P-D. acknowledge support from  EPSRC  ( EP/L015331/1 ) through the  Centre of Doctoral Training in Metamaterials  ( XM 2  ). N. F. acknowledges financial support from the  French "Direction Générale de l'Armement" (DGA). I. S. , A. M. P. , and N. F. contributed equally.
PET data acquisition and processing All florbetapir-PET scans are acquired in a single session on a Philips Gemini GXL CT-PET scanner 50 (± 5) minutes after injection of approximately 370 MBq (333-407 MBq) of Florbetapir. PET acquisition consists of 3 x 5 minutes frames, a 128 x 128 acquisition matrix and a voxel size of 2 x 2 x 2 mm³. Images are then reconstructed using iterative LOR-RAMLA algorithm (10 iterations), with a smooth post-reconstruction filter. All corrections (attenuation, scatter, and random coincidence) are integrated in the reconstruction. Lastly, frames are realigned, averaged and quality-checked by the CATI team. CATI is a French neuroimaging platform funded by the French Plan Alzheimer (available at http://cati-neuroimaging. com). Reconstructed PET images are analyzed with a pipeline developed by CATI.
All significant r were adjusted for age, sex and APOE genotype (ɛ4 carriers versus non-carriers). All tests were two-tailed and p values <0. 05 were considered statistically significant. The Bonferroni correction was applied to adjust the false discovery rate (FDR) for multiple comparisons. Data analysis was performed using SPSS 23. 0 © IBM software for MacOS. Analysis of prediction performance for unseen time-series data. 264 subjects had no missing values at time-point 1, 212 subjects and 126 subjects had no missing value at time-point 2 and time-point 3, respectively. Only 66 subjects had no missing values for the three time-points and there was no significant difference in the distribution of plasma Aβ1-42 and ratio Aβ1-40/Aβ1-42 between time-points. To test whether the brain amyloidosis status might be better explained by taking into account several plasma biomarkers reflecting distinctive pathophysiological mechanisms of AD (herein Aβ1-42, Aβ1-40, Tau, NFL, BACE1, YKL40) and whether the performance of such classifier(s) might be conserved across subjects and time-points, we applied a tree-based approach involving the use of random forest analysis (RFA) for variable selection and that of Classification and regression trees (CART) analysis for selecting optimal classifiers.
Such analyzes were performed after over-sampling of time-point 1 data (i. e. to obtain a wellbalanced training set) as well as on true data. To perform oversampling, we used a Synthetic Minority Over-Sampling Technique (SMOTE) as implemented in the R package DmwR (https://cran. rproject. org/web/packages/DMwR/index. html). The resulting data set is herein referred to as set SMOTE. These analyzes also involved the evaluation of predictive robustness in unseen data, that is a test set including 140 subjects at time-point 2 and 54 additional subjects at time-point 3, The corresponding data set is herein referred to as set test. RFA was performed by using the library randomForest for R (https://CRAN. R-project. org/package=randomForest ). RFA included an evaluation of the predictive performance of the models where the numbers of predictors is sequentially reduced via a nested cross-validation procedure (N = XX?) with the function rfcv of the randomForest package.
At RFA, the group with no missing value at time-point 1 showed an excess of Aβ-PET negative subjects, which might have led to week performance of the RFA. To overcome this limitation, we applied a Synthetic Minority Over-Sampling Technique (SMOTE) before performing RFA. SMOTE generated a data set in which the Aβ-PET negative subjects are under-sampled (retaining 136/195 true subjects) and Aβ-PET-positive subjects are over-sampled (adding 68 virtual subjects). RFA after over-sampling the dataset displayed that the bet variable that best explains cerebral Aβ deposition is plasma ratio Aβ1-40/Aβ1-42 whether the model (training set) show for the latter 93% specificity and 85% sensitivity, with a balanced accuracy of 89% (Figure  2A  left panel). Of note, the predictive performance is preserved in the test running through the original data set (i.
ACKNOWLEDGMENTS We especially thank  X. Ai ,  N. Rouach ,  H. Kettenmann  and  B. Novitch  for their generous sharing of Sulf2 mutant, Aldh1L1-GFP, GFAP-GFP, and Olig2-Cre mice, respectively. We acknowledge  B. Guiard  for the gift anti-Connexin antibodies and  A. Pattyn  for providing valuable reagents and for critical reading of the manuscript. We thank the  ABC facility and ANEXPLO  for housing mice and the  Toulouse Regional Imaging Platform  ( TRI ) for technical assistance in confocal microscopy. We acknowledge the  Developmental Studies Hybridoma Bank , developed under the auspice of  NICHD  and maintained by the  University of Iowa ,  Department of Biological Sciences, Iowa City, IA , for supplying monoclonal antibodies. Work in C. S. ' lab was supported by grants from  ANR ,  ARC ,  ARSEP ,  CNRS , and  University of Toulouse.
Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen's d = -0. 13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use.
In the ENIGMA (Enhancing Neuro-Imaging Genetics through Meta-Analysis) consortium (http://enigma. ini. usc. edu), researchers from around the world collaborate to analyze many separate data sets jointly, and to reduce some of the technical heterogeneity by using harmonized protocols for MRI data processing. A recent study by the ENIGMA consortium's ASD working group showed small average differences in bilateral cortical and subcortical brain measures between 1571 cases and 1650 healthy controls, in the largest study of brain structure in ASD yet performed  14. Relative to controls, ASD patients had significantly lower volumes of several subcortical structures, as well as greater thickness in various cortical regions-mostly in the frontal lobes-and lower thickness of temporal regions. No associations of diagnosis with regional cortical surface areas were found  14.
Here, we made use of MRI data from 54 data sets that were collected across the world by members of the ENIGMA consortium's ASD Working Group, to perform the first highly powered study of structural brain asymmetry in ASD. Using a single, harmonized protocol for image analysis, we derived asymmetry indexes, AI = (Left-Right)/(Left + Right), for multiple brain regional and global hemispheric measures, in up to 1778 individuals with ASD and 1829 typically developing controls. The AI is a widely used index in brain asymmetry studies  41, 42. Age and sex are known to affect cortical-  43  as well as subcortical asymmetries  44  in healthy individuals. In addition, a recent structural imaging study of roughly 500 individuals with ASD, and 800 controls, found that case-control differences of bilateral cortical thickness were greater in younger versus older individuals, whereas also being related to ASD symptom severity, and with larger differences in individuals with lower versus higher full-scale intelligent quotient (IQ) scores  45.
Discussion In this, the largest study to date of brain asymmetry in ASD, we mapped differences in brain asymmetry between participants with ASD and controls, in a collection of 54 international data sets via the ENIGMA Consortium. We had 80% statistical power to detect Cohen's d effect sizes in the range of 0. 12-0. 13. We found significantly altered asymmetries of seven regional cortical thickness asymmetries in ASD compared with controls, predominantly involving medial frontal, orbitofrontal, inferior temporal, and cingulate regions. The magnitude of all regional thickness asymmetries was decreased in ASD compared with controls, whether it was reduced leftward, reduced rightward, or reversed average asymmetry. Rightward asymmetry of the medial orbitofrontal surface area was also decreased in individuals with ASD, as was leftward asymmetry of the lateral orbitofrontal surface area.
Again, this post hoc finding remains tentative in the context of multiple testing, and is reported here for descriptive purposes only. It is clear that most of the AIs that showed significant changes in ASD were not correlated with ADOS scores. We found no evidence that medication use affected any of the asymmetries altered in ASD, although our medication variable was rudimentary. The role of specific medication usage should be investigated in future studies. As mentioned above, data on comorbidities were only available for 54 of the ASD subjects, precluding a high-powered analysis of this issue. This is a limitation of the study. We did not analyze handedness in the present study, as this had no significant effect on the same brain asymmetry measures as analyzed here, in studies of healthy individuals comprising more than 15,000 participants  43, 44.
For detecting cerebral asymmetries with automated methods, some groups have chosen to work from artificially created, left-right symmetrical atlases, e. g. , ref. 65. However, our study was focused on comparing relative asymmetry between groups. The use of a 'real-world' asymmetrical atlas had the advantage that regional identification was likely to be more accurate for structures that are asymmetrical both in the atlas and, on average, in our data sets. By defining the regions of interest in each hemisphere based on each hemisphere's own particular features, such as its sulcal and gyral geometry, we could then obtain the corresponding relationships between hemispheres. To this end, we used data from the automated labeling program within FreeSurfer for subdividing the human cerebral cortex.
Methods Data sets. Structural MRI data were available for 57 different data sets (Table  1 ). Three data sets comprising either cases only, or controls only, were removed in this study (Table  1 ), as our analysis model included random intercepts for 'data set' (below), and diagnosis was fully confounded with data set for these three. The remaining 54 data sets comprised 1778 people with ASD (N = 1504 males; median age = 13 years; range = 2-64 years) and 1829 typically developing controls (N = 1400 males; median age = 13 years; range = 2-64 years). All data sets were collected during the period when DSM-IV and DSM-IV-TR were the common classification systems, between 1994 and 2013, and the clinical diagnosis of ASD was made according to DSM-IV criteria  69. The data sets were collected in a variety of different countries, and intended originally as separate studies.
Nonetheless, all subjects were diagnosed based on clinical diagnosis by a clinically experienced and board certified physician/psychiatrist/psychologist. This was a criterion for admission of a data set into the ENIGMA-ASD database. For each of the 54 data sets, all relevant ethical regulations were complied with, and appropriate informed consent was obtained for all individuals. Total scores from the Autism Diagnostic Observation Schedule-Generic (ADOS), a standardized instrument commonly used in autism diagnosis  70  , were available for a majority of cases (N = 878). Cases from the entire ASD spectrum were included, but only 66 cases had IQ below 70 (cases: mean IQ = 104, SD = 19, min = 34, max = 149; see Supplementary Fig. 1D ). The presence/absence of comorbid conditions had been recorded for 519 of the cases, but only 54 cases showed at least one comorbid condition (which could be ADHD, OCD, depression, anxiety, and/or Tourette's syndrome  14  ).
Numbers related to DSM-IV subtypes of ASD were not collated by the ENIGMA ASD working group, as this subtyping scheme has been dropped from DSM-V due to low reliability  71. There was not a homogeneous assessment/recruitment process for controls across the 54 data sets, but the overwhelming majority were typically developing/ healthy at the time of MRI, and no controls showed features that might have met criteria for a diagnosis of ASD. Only 19 controls had IQ > 70. In these subjects the exclusion of ASD diagnosis was performed by a senior child psychiatrist/physician. Eighteen of these were from the FSM data set and were clinically diagnosed with idiopathic intellectual disability. Amongst all controls the mean IQ was 112, SD = 15, min = 31, max = 149; see Supplementary Fig. 1D. Structural MRI.
Structural T1-weighted brain MRI scans were acquired at each study site. As shown in Table  1 , images were acquired using different field strengths (1. 5 T or 3 T) and scanner types. Each site used harmonized protocols from the ENIGMA consortium (http://enigma. ini. usc. edu/protocols/imaging-protocols) for data processing and quality control. The data used in the current study were thickness and surface area measures for each of 34 bilaterally paired cortical regions, as defined with the Desikan-Killiany atlas  64  , as well as the average cortical thickness and total surface area per entire hemisphere. In addition, left and right volumes of seven bilaterally paired subcortical structures, plus the lateral ventricles, were analyzed. Cortical parcellations and subcortical segmentations were performed with the freely available and validated software FreeSurfer (versions 5.