dataesr/scientific-paragraphs-categorization huggingface dataset:
01% [w/v] final concentration for chemical shift calibration) was added to each freeze-dried titrated extract. The mixture was centrifuged at 17,700g for 5 min at room temperature. The supernatant was then transferred into a 5 mm NMR tube for acquisition. Quantitative 1 H-NMR spectra were recorded at 500. 162 MHz and 300 K on an Avance III spectrometer (Bruker Biospin) using a 5-mm ATMA broadband inverse probe flushed with N 2 gas and an electronic reference for quantification (Digital ERETIC, Bruker TopSpin 3. 0). Sixty-four scans of 32,000 data points each were acquired with a 90°pulse angle, a 6-MHz spectral width, a 2. 73-s acquisition time, and a 20-s recycle delay. The assignments of metabolites in the NMR spectra were made by comparing the proton chemical shifts with literature  (Likes et al.
, 2007)  or database values from MeRy-B (a database of plant 1 H-NMR metabolomic profiles), the Human Metabolome Database, the Biological Magnetic Resonance Bank, and the Madison Metabolomic Consortium Database by comparing spectra of authentic compounds and by spiking the samples. An example of an annotated NMR spectrum is given in Supplemental Figure  S8. For assignment purposes, 1 H-1 H correlation spectra and 1 H-13 C heteronuclear single quantum coherence spectra were acquired for a selected sample. For absolute quantification, four calibration curves (Glc and Fru, 2. 5-100 mM; Glu and Gln, 2. 5-30 mM) were prepared and analyzed under the same conditions. The Glc calibration was used, for the quantification of all compounds, as a function of the number of protons of selected resonances, except for Fru, Glu, and Gln, which were quantified using their own calibration curve.
Statistical and Data Analysis All statistical analyses were performed using the statistical software program R v3. 6. 1 (R Core Team, 2018). The edgeR package v3. 28  (Robinson et al. , 2010)  was used for differential expression analysis. The Counts Per Million method was used with a threshold of five reads per million in a third of the samples. Libraries were normalized using the trimmed mean of M-values method  (Robinson and Oshlack, 2010; Dillies et al. , 2013). The differential expression analysis was performed with generalized linear models. To analyze the main effects of the grain developmental stage, the treatment, and the interaction between stage and treatment, and to identify genes differentially expressed between any of the groups, an ANOVA-like test was performed by specifying multiple coefficients to the glmLRT function of the EdgeR package.
researchsquare. com/article/ 84521cdd-9046-44ac-8f52-906dc21cf1a4/v1). The data, the R script, and the results are available at https://forgemia. inra. fr/GNet/einkorn_grain_transcriptome. The results of the differential analysis are in the folder /Results/ Einkorn_grain/DiffAnalysis, where a subfolder is dedicated to each question. The folder /Results/Einkorn_grain/Quality_Control contains a pdf file with exploratory plots to investigate the data quality. For further explanation, we refer readers to the DiCoExpress user manual. Differences in mass of metabolites per grain were analyzed using a two-way ANOVA with treatments and grain developmental stages (from 300°Cd to 950°Cd after anthesis) defined as factors. Effects of the grain developmental stage, the treatment, and the interaction between stage and treatment were judged to be significant at P , 0.
ACKNOWLEDGMENTS We thank  Richard Blanc  for his help with plant cultivation;  David Alvarez  and  Annie Faye  for organizing plant culture and planning grain sampling;  Marielle Merlino ,  Sibille Perrochon ,  Julie Boudet , and  Isabelle Nadaud  for help with grain sampling;  Mickaël Maucourt  for the metabolome analysis;  Florent Murat  for help in supervising the read mapping step during RNA-Seq data analysis;  Morgane B. Gillard  for her help with statistical analyses; and  Selver Babi  and  Romain De Oliveira  for their help with data analysis.
1 This work was supported by the  French Ministry for Higher Education and Research  (PhD grant to T. B. ), the  French National Research Agency (ANR ; grant nos. ANR-10-BTBR-03   BREED-WHEAT ,  ANR-11-INBS-0010   MetaboHUB , and  ANR-10-LABX-0040-SPS ),  France AgriMer , the  French Fund Supporting Plant Breeding (FSOV) , and the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 , under grant no  FP7-613556 ).
We furthermore set an inner annulus with a radius of 1. 5° to uniform gray. Gratings had a generative radius of 12. 5°, but were truncated by the vertical screen border at a radius of 11. 3°. The contrast of the reference grating was always set to 0. 5, while the contrast of the test stimulus varied and changed every 100 ms, as controlled by the staircase procedure (see above). Stimuli were generated using Psychtoolbox 3 for Matlab. They were back-projected on a transparent screen using a Sanyo PCL-XP51 projector with a resolution of 1920 x 1080 at 60 Hz. The luminance profile was linearized by measuring and correcting for the systems gamma curve. A doubling of contrast values therefore also produced a doubling of luminance differences. During the first training session stimuli were presented on a VIEWPixx monitor with the same resolution and refresh rate (also linearized).
Data acquisition We used a CTF MEG system with 275 axial gradiometer sensors and recorded at 1200Hz. Recordings took place in a dimly lit magnetically shielded room. We concurrently collected eye-position data with a SR-Research EyeLink 1000 eye-tracker (1000Hz). We continuously monitored head position by using three fiducial coils. After seating the participant in the MEG chair, we created and stored a template head position. At the beginning of each following session and after each block we guided participants back into this template position. We used Ag/AgCl electrodes to measure ECG and vertical and horizontal EOG.
Preprocessing, spectral analysis, and source reconstruction of MEG data We used beamforming to reconstruct the sources of activity observed at the MEG sensor level. First, we automatically labeled artifacts in raw MEG data by detecting blinks, muscle artifacts, sensor jumps and cars passing by in the vicinity of the building. Blinks were detected based on concurrently recorded eyemovement signals (SR-Research EyeLink 1000). Sensor jumps were detected by convolving each sensor with a filter designed to detect large sudden jumps and subsequently by looking for outliers in the filter response. Muscle and environmental artefacts were detected by filtering each channel in the 100-140Hz or <1Hz range and by detecting outliers that occurred simultaneously in many channels. To remove power line noise, we applied a notch filter.
In a final step, we epoched data, downsampled to 600Hz and discarded all epochs that contained artifacts. We computed time-frequency representations (TFRs) of single-trial data by using a multi-taper method. For low frequencies (1-9Hz in steps of 1Hz), we used a window length of 0. 25 s (frequency smoothing of 8Hz). For high frequencies (10-150Hz in steps of 5Hz), we used a window length of 0. 1 s (20Hz frequency smoothing). We used linearly constrained minimum variance (LCMV) beamforming to estimate activity time courses at the level of cortical sources  86. We constructed individual three-layer head models from subject specific MRI scans using fieldtrip ( 87 ; functions, ft_volumesegment and ft_prepare_mesh). Head models were aligned to the MEG data by a transformation matrix that aligned the average fiducial coil position in the MEG data and the corresponding locations in each head model.
Transformation matrices were generated using MNE software  88. We computed one transformation matrix per recording session. Third, we reconstructed cortical surfaces from individual MRIs using Freesurfer and aligned two different atlases to each surface  89, 90. In a fourth step we used MNE  88  to compute LCMV filters for projecting data into source space. LCMV filters combined a forward model based on the head model and a source space constrained to the cortical sheet (4096 vertices per hemisphere, recursively subdivided octahedron) with a data covariance matrix estimated from the cleaned and epoched data. We computed one filter per vertex, based on the covariance matrix computed on the time-points from stimulus onset until 1. 35s after stimulus onset across all trials. We chose the source orientation with maximum output source power at each cortical location.
In a final step, we computed TFRs of the epoched MEG data (same method as described for the sensor-level TFR decomposition) and projected the complex time series into source space. In source space we computed TFR power at each vertex location and then averaged across all vertices within a ROI. We aligned the polarity of time-series at neighboring vertices, because the beamformer output potentially included arbitrary sign flips for different vertices. For all analyses except the one for Figure  S7B , we collapsed power values within hemisphere-specific regions of interest across vertices. We then converted the power values into units of percent power change (i. e. , modulation) relative to the baseline power in each frequency  15. To this end, we averaged power estimates across trials and time in a pre-stimulus interval (-250 to 0 ms before test stimulus onset).
We then projected training and test set data into the space defined by these components. We then evaluated a linear support vector machine with L1 penalty (C=10/number of features) and discarded all features whose weights were below 1e-5. Finally, we trained another support vector machine (L2 penalty) on the final reduced feature set (C=1/2) and plotted the performance of this classifier on the test set (Figure  S7B ). Principal component analysis and feature selection were performed exclusively on the training set and then applied to the test set. In all three approaches, we evaluated decoding performance by means of 10-fold cross-validation and computed ROC-AUC values to evaluate each classifier and averaged across folds. We split all data per subject into ten folds, keeping the same percentage of choices in each fold.
We then used nine folds to determine parameters of the classifier and computed prediction scores on the tenth fold, which we used to compute ROC-AUC values. We used Bayesian inference to estimate uncertainty around average decoding performance (error bars in Figure  2B ). We assumed that a participants AUC value (subscript s below) at time point t were samples from a T distribution, here ignoring boundedness of AUC values because values were far from the bound, and placed weakly informative priors on all parameters of this distribution. We obtained posterior estimates using pymc3  93  : 𝑎𝑢𝑐 #,H = 𝑆𝑡𝑢𝑑𝑒𝑛𝑡𝑇(𝜇 H , 𝜎 H , 𝛾), (4)  H ~Ν(0. 5, 1), (  5 )  H ~(0, 5), 𝛾~𝐸𝑥𝑝 f 1 29 h + 1, (6) We used the NUTS sampler and two MCMC chains with 3000 iterations. We checked convergence visually and by ensuring that  ^ was below 1.
The code for all analysis steps is available at https://github. com/DonnerLab/2020_Large-scale-Dynamics-of-Perceptual-Decision-Information-across-Human-Cortex. Temporal correlation between the band-power time courses of V1 from Figure  S3  and the psychophysical kernels from Figure  1B. Each dot is one participant. Black lines, average correlation; *, p < 0. 05; **, p < 0. 01; ***, p < 0. 001; two-sided t-tests. These correlations were computed excluding the first two samples so as to eliminate the non-specific onset transient response that spanned all frequency bands (see Figure  S2B ). Correlations were qualitatively identical for gamma-and high frequency-bands when including all samples, but then also significantly positive for theta-and alphabands, presumably driven by the onset transient followed by power-suppression.
Acknowledgments We thank  Stanislas Dehaene ,  Mariano Sigman , and  Genis Prat Ortega  for discussion during this project, and  Klaus Wimmer  and  Alan Stocker  for thoughtful comments on the manuscript. This work has been funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), grants  DO 1240/3-1 ,  DO 1240/4-1 , and SFB 936 -Projekt-Nr. A7  (all to T. H. D).
It is important to determine the exact cell (sub)types and their interactions at play in atherosclerosis to devise novel therapeutic strategies. Here, we describe the total cellular composition of atherosclerotic plaques taken from carotid arteries of a broad cohort of patients. Our data suggest that the main immune cell subset consists of T cells, which can be subdivided by activation status. Macrophages are found in distinct populations with diverse activation patterns, inflammatory status, and foam cell characteristics. We shed light on plaque endothelial and smooth muscle cell gene expression and show cell clusters with gene expression patterns pointing towards characteristics of endothelial to mesenchymal transition. To further investigate the dynamic intraplaque niche, we assessed ligand-receptor interactions driving our cell communities and investigated potential transcription factor activity underlying myeloid and T-cell populations in the plaque by studying chromatin accessibility at the single-cell level.
Finally, we identified cell types enriched for cardiovascular susceptibility genes by integrating available genome-wide association studies data. Together, our data provide an in-depth map of the human atherosclerotic plaque and give valuable insights into cell types, pathways, and genes that are relevant for future research aiming at the development of novel therapeutic strategies. We assigned a cell type to each cluster based on differential expression of established lineage markers (Figure  1B  and 1D). Cluster composition did not differ between sexes (Figure IE in the Data Supplement) and cluster identities were confirmed by correlation with bulk RNA-seq datasets (Figure  1E ). 8 We observed 3 nonimmune cell clusters (clusters 8, 9, and 10; expressing CD34 and ACTA2 [actin alpha 2, smooth muscle]) 9,10 and 11 leukocyte clusters (Figure  1B  and 1D).
Intraplaque T Cells Are Defined by Activation Status Lymphocyte clusters consisted of one small, but homogenous cluster of B cells (cluster 11; expressing CD79A, FCER2, CD22, and CD79B)  [27] [28] [29]  (Figure  1B  and 1C, Table  II  in the Data Supplement), and 4 T-cell clusters. To define the T cells in more detail, we assessed the CD4 + T cells (expression CD4>CD8) and the CD8 + T cells (expression CD8>CD4) from CD3 enriched clusters 0, 1, 3, and 4. Isolating and reclustering the CD4 + T cells revealed 5 subclasses (CD4. 0-CD4. 4, Figure  3A , Table  II  in the Data Supplement) of which the primary difference was their activation state rather than the transcription factors and cytokines commonly used to define CD4 + T-helper (T H ) subsets (Figure  3B  and 3C). CD4. 0 and CD4. 1 exerted a cytotoxic gene expression profile exemplified by expression of GZMA, GZMK, and PRF1.
Intercellular Communication Drives Inflammation Within the Plaque We next examined potential ligand-receptor interactions between cell types to predict intercellular communication within the lesion based on CellPhone DB v2. 0. 50 ymphocytes and mast cells showed the lowest absolute numbers of potential interactions while myeloid, endothelial, and SMCs displayed higher numbers of interactions (Figure  5A ). The low interaction between myeloid and T cells may be a consequence of the apparent lack of detection of TCR-related genes (TRA, TRB, TRG) in our scRNA-seq dataset and the fact that CD4-class II and CD8-class I interactions are not included in this database. Subsequently, we specifically examined the top unique interactions within the myeloid populations, split by myeloid ligands (Figure  5B ) and receptors (Figure  5C ).
Apart from confirming the cytotoxic, T H 1-like phenotype within the plaque, this also suggests that the proinflammatory macrophage subclasses we observe in our dataset may be primed for classical activation by secretion of IFNγ by the T cells. 74 These T H 1 cells acting on macrophages may, in turn, be driven by activated CD1c + dendritic cells that were characterized by an active IL12 gene (ie, open enhancer), which has previously been found on protein level in plaque lysates  75, 76  and the enrichment of HLA-DR (major histocompability complex, class II, DR beta 1) subtypes. 49, 77 ach of the macrophage clusters seemed to have been activated differently, one expressing TNF and TLR4, which can be activated by oxLDL and IFNγ,  78  as well as IL1B, and the other more selectively expressing IL1B, which correlated with caspase expression suggesting inflammasome activation.
Acknowledgments Corresponding authors:  Johan Kuiper Leiden Academic Centre for Drug Research, Division of Biotherapeutics, Leiden University , Einsteinweg 55, 2333  CC Leiden , The Netherlands. Email: j. kuiper@lacdr. leidenuniv. nl;or  Menno P. J. de Winther :  Amsterdam University Medical Centers-location AMC, University of Amsterdam, Experimental Vascular Biology, Department of Medical Biochemistry, Amsterdam Cardiovascular Sciences, Amsterdam Infection and Immunity, Meibergdreef 9, Amsterdam, The Netherlands. Email: m. dewinther@amsterdamumc. nl; or  Michal Mokry: Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht , The Netherlands Email: m. mokry@umcutrecht. nl; or  Gerard Pasterkamp: Laboratory of Clinical Chemistry and Haematology, University Medical Center, Heidelberglaan 100, Utrecht, The Netherlands.
This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.
Specific attention has been given to subsequent tasks: • creating a consistent and representative training dataset for each class • benchmarking different models and configurations • producing a relevant land cover map from model predictions As databases (BDTOPO® or OpenStreetMap®) do not describe all the objects of each class, manual annotation is needed. We chose to create full ground truth on specific areas of interest (AOI) in the production zone to facilitate the labelling process. In the end, only 10% of the targeted zone is used for training. Semantic segmentation of aerial images with convolutional neural networks is then performed from this training dataset. Since the publication of an encoder-decoder deep convolutional network architecture  (Ronneberger et al. , 2015) , scientists have tried to improve semantic segmentation on medical images  (Ibtehaz, Rahman, 2019)   This article also describes some post-processing techniques to convert model predictions to a relevant land cover map.
Loss functions Several loss functions are evaluated in this work. • Cross-Entropy (CE) loss takes into account the variation between prediction and target. But it is evaluated on individual pixels, thus large objects contribute more to it than small ones which can be an issue when dealing with land cover objects. • Weight-balanced Cross-Entropy (WCE) loss is used to tackle the problem of an unbalanced training dataset. Under-represented classes get higher weights to assure that all classes contribute equally to the loss value. • Binary Cross-Entropy (BCE) is used to evaluate the benefits of allowing the network to perform a multi-label classification task (each pixel could belong to multiple classes). • A combo loss with BCE + Jaccard. Jaccard loss is computed from the Jaccard index defined by: (1) with a predicted segmentation and a y pred y true ground truth labelling.
Training dataset elaboration The dataset specifications are issued from some practical choices during its production. Most of the time, when training datasets are not provided for experiments, they are generated by picking a large number of random samples among the targeted area. We decided instead to create our dataset focusing on 5 consistent areas of interest carefully selected to best represent the overall territory characteristics (Figure  6 ). This choice has been determined by two main reasons: • It is easier to automatize or semi-automatize the training label production, with standard machine learning tools, on large images tiles than on many small patches. • It brings more flexibility in the choice of relevant size and resolution for training samples.
AOIs characteristics: This dataset is mainly dedicated to urban area segmentation. Figure  6. Training zones So, areas where land cover is mainly forest, natural or cropland have not been taken into consideration. But in order to catch urban area diversity, territories with different urban morphology have been manually selected. The chosen urban morphology is coastal and touristic areas (dark green in Figure  6 ), dense downtown (red in Figure  6 ), residential suburban zones (orange in Figure  6 ) and small towns in agricultural territories (light green and yellow in Figure  6 ).
Classical machine learning for vegetation: To build the training dataset for vegetation classes (forest and grassland), classical machine learning is used. We combine a pixel-based classification through random forests and a large-scale mean shift segmentation. Vegetation indices NDVI and NDWI, as well as RGB and Near-Infrared bands, are used as input of a supervised random forest classifier. We finally combine the pixel classification with regions of a large scale mean shift segmentation. Thanks to a majority voting on pixel class among each region, we get larger homogenous regions. Taking advantage of DHM and NDVI to detect vegetation and trees, the results are acceptable as is for ground truth. All those processings are done using functionalities of orfeo-toolbox  (Grizonnet et al. , 2017)  command-line tools.
Benchmarking configurations The dataset is split in train|test|validation parts: • training set represents 64% of the total • validation set (16% of the total) used between each epoch to prevent overfitting and decide when convergence is reached • test set (20%) is used to provide the metrics reported in the following paragraphs The experiment is run on a desktop computer with the following characteristics: Intel(R) Core(TM) i7-6950X CPU @ 3. 00GHz and 16Gbits of RAM and a GPU NVIDIA GeForce RTX 2070 with 8Gbits of RAM. The source code of the processing pipeline is based on pytorch and will be made available soon. Training hyper-parameters are: • training on 300 epochs with a patience of 20 (if loss do not decrease on the validation set for 20 epochs, the training is stopped) • learning rate starts at 0.
3. 2. 1 Configuration performances: The measure used to compare configurations is the Intersection Over Union (IOU) index also known as the Jaccard index described by formula 1. This index measures the overlap between prediction and ground truth. It is preferred to pixel accuracy as it is not affected by class imbalance. The following The overall result is quite good even if we note a severe defect for mineral material class which is known to be problematic. The training dataset is not very reliable for this class and it is a real challenge even for a human eye to distinguish between concrete and mineral material on proposed images.