dataesr/scientific-paragraphs-categorization huggingface dataset:
(2020). We map the distribution of the different baryonic phases around filaments by building phase fraction profiles, and we characterise their thermodynamical properties by means of temperature and pressure profiles. We remove the contamination of gas contained in nodes, and we identify the contribution of massive (M tot > 10 12 M ) galactic haloes in order to explicitly focus on the properties of Article number, page 1 of 16 arXiv:2010. 15139v2 [astro-ph. CO] 4 Mar 2021 A&A proofs: manuscript no. main the gas of filaments that is not associated with smaller-scales collapsed structures. In Section 2, we introduce the IllustrisTNG simulation and we describe the filament catalogue as well as the five different phases studied in this work. We show our results on the distribution of these phases around the different types of filaments in Sect.
The IllustrisTNG simulation We analyse the gravo-magnetohydrodynamical simulation Illus-trisTNG 1  (Nelson et al. 2019). This simulation follows the coupled evolution of DM, gas, stars, and black holes from redshift z = 127 to z = 0. IllustrisTNG is run with the moving-mesh code Arepo  (Springel 2010)  with the cosmological parameters from  (Planck Collaboration et al. 2016a) , namely Ω Λ,0 = 0. 6911, Ω m,0 = 0. 3089, Ω b,0 = 0. 0486, σ 8 = 0. 8159, n s = 0. 9667 and h = 0. 6774. In this work, we choose to use the largest simulation volume with the highest mass resolution of the IllustrisTNG suite in order to accurately describe large cosmic filaments and their gas content down to small scales. We therefore focus on the simulation box TNG300-1 at a redshift z = 0. It consists in a cube of around 302 Mpc side length with a DM resolution of m DM = 4.
Gas cell dataset The TNG300-1 simulation box provides a dataset of 15, 625, 000, 000 gas cells. As we want to study gas properties that are associated with filaments, we remove from the analysis the cells located within spheres of radius 3 × R 200 centered on the position of the maximum density critical points of the Delaunay density field (hereafter CPmax, see Sect. 2. 2), that are the topological nodes of the skeleton. This step removes 11% of the initial dataset. For illustration, the gas cells within 1 × R 200 of the CPmax are represented by the red points in the xy slice of Fig. 1. We also remove the contribution of galactic haloes, that we define as massive (M tot > 10 12 M ) collapsed structures hosting at least one galaxy of stellar mass 10 9 ≤ M * ≤ 10 12 M (see green points in Fig.
1 ). We therefore discard the gas cells located within spheres of radius 3 × R 200 centred on these structures. After these steps, our dataset contains 76% of the initial cells, the other 24% lying within 3 × R 200 of nodes (11%) or galactic haloes (13%). Furthermore, given the excess of critical points near the boundaries of the simulation box, we chose, following  Galárraga-Espinosa et al. (2020) , to completely disregard the regions of thickness 25 Mpc at the box boundaries (vertical and horizontal lines in Fig. 1 ), and to replicate the cell distribution at the borders, in order to reduce the effects of a volume-limited simulation in the gas profiles. By masking the CPmax and the galactic haloes, and by removing the cells at the borders, we obtain a dataset containing ∼ 6, 400, 000, 000 gas cells (i.
e. ∼ 40% of the initial dataset). This is still an extremely large number, and dealing with such an amount of information is computationally very expensive and time consuming. So, in order to perform our analysis in reasonable computing time, we sample the gas dataset by randomly selecting one cell out of 500. By doing so, we end up with a final set containing 12, 711, 272 cells. We have checked that this number is representative of the full cell dataset, as different random samples give exactly the same results in terms of gas distributions and statistical properties. Hence, the resulting gas dataset allows us to perform the analysis of properties of gas of the interfilamentary medium, without the contamination of gas in haloes and nodes. This gas dataset will be used in the following Sects.
The authors thank the anonymous referee for helpful comments. This research has been supported by the funding for the ByoPiC project from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program grant agreement ERC-2015-AdG 695561. (ByoPiC, https://byopic. eu). The authors acknowledge the very useful comments and discussions with all the members of the ByoPiC team (https://byopic. eu/team/), and thank Marius Cautun for providing constructive feedback. DGE is especially grateful to Victor Bonjean and Céline Gouin for stimulating discussions. We thank the IllustrisTNG team for making their data publicly available. Table A. 1. Fractions of cells in the five different gas phases considered in this work, for the full TNG300-1 and TNG300-2 simulations.
Climatic Features and Sources of Variances of Three Years of Study The severity of Fusarium head blight differed considerably among years, reflecting climatic effects. The data in Table  1  show that the experimental season 2019 is the most favorable FHB compared to the others. It was characterized by a higher number of annual precipitation and lower maximal temperatures. By contrast, the experimental season 2017 was characterized by a lower amount of precipitation and a higher maximal temperature. Another difference between the two seasons is that wheat plants received 106. 2 mm of annual precipitations in May of 2019, while zero precipitation was recorded for the same month of 2017. This means that, unlike the 2017 season, the 2019 season was characterized by frequent precipitation. The analysis of variance (Tables  S1  and S2 ) revealed a highly significant effect of treatment (T), year (Y) and their interaction (T × Y) for FHB severity (%) and grain yield (GY) (p < 0.
Acknowledgments: We thank the  Unitat de Genòmica of the CCiTUB, JosepMatas (Servei de Camps Experimentals, University of Barcelona) , for the technical assistance. The participation of J. L. A. and M. D. S. in this study was supported by the  PID2019-106650RB-C21  project, from  Ministerio de Ciencia e Innovación, Spain. JLA also acknowledges support from  ICREA Academia, Generalitat de Catalunya, Spain.
Statistical evaluation All statistical tests were performed using SPSS (Version 27, IBM SPSS Inc. , Chicago, Illinois, USA). Graphs were created using STATA (Version 16, StataCorp LLC, College Station, Texas, USA). All tests were two-sided and a p value of < 0. 05 was regarded as statistically significant. Continuous data were reported as means and standard deviation, whereas ordinal data were reported as median and interquartile ranges. Interrater reliability was assessed with the intraclass correlation coefficient (ICC) type 2,1 according to Shrout and Fleiss  [21]. The second round of ratings was used to assess interrater reliability, and ICCs of less than 0. 5, between 0. 5 and 0. 75, between 0. 75 and 0. 9, and greater than 0. 9 were regarded as poor, moderate, good, and excellent reliability, respectively  [22].
[18]  tended to classify GBM patients and their survival outcomes into three distinct subgroups, based on the 2018 Brain Tumor Segmentation (BraTS) challenge datasets, their machine learning method achieved an accuracy of 87. 5%. These papers have provided a solid confirmation on the potential role of radiomics in predicting OS in GBM patients; nonetheless, they suffer from the lack of sufficient patient data and most importantly, radiomics signatures with ease of use in daily practice have not been validated in all these papers. To ensure the robustness of a potential radiomics signature, the evaluation of different datasets is eagerly needed. To our knowledge, the robustness and generalization error of radiomics signatures have never been evaluated on different datasets. In this study, machine learning (ML) models for survival prediction of GBM patients from the training dataset of the 2019 BraTS challenge were developed.
BraTS 2019 Cohort Data from the train dataset of the 2019 BraTS challenge  [23] [24] [25]  were retrieved to train a neural network for brain tumor segmentation, and to select a set of radiomics features to be evaluated on a distinct cohort for the survival prediction of GBM. The initial train dataset was composed of 259 patients with GBM and 76 patients with lower-grade gliomas (LGG) with a pathologically confirmed diagnosis. The OS of 210 GBM patients were available in the training data for the identification of a radiomics signature, to be used for survival analysis. For every patient, the dataset contained: the pre-(T1) and post-contrast T1-weighted (T1ce), the T2-weighted and fluid attenuated inversion recovery 3D MR images along with one ground truth mask containing 3 segmentation labels, covering 3 different anatomical parts of the tumor (the necrotic (NCR) and non-enhancing (NET) tumor core, the peritumoral edema (ED) and the enhancing tumor (ET)).
Validation Cohort This cohort was composed of 116 patients with histologically confirmed GBM (based on the World Health Organization [WHO] classification of central nervous system tumors, Grade IV  [26] ). All the radiological data were retrospectively collected from baseline MRI (at diagnosis) performed at Gustave Roussy Cancer Campus (Villejuif, France) between 2006 and 2016. All included patients, with an age ranging between 18 and 80 years old, had confirmed disease relapse and then received bevacizumab for the treatment of recurrent GBM. Disease relapses occurred after initial management by surgery (when possible) (70% patients) followed by post-operative chemo-radiotherapy or by chemoradiotherapy alone. This study was approved by the institutional review board as per RGPD provisions and was declared on the Health Data Hub site and the CNIL as per RGPD recommendations.
Whenever feasible, patients were informed of their enrolment in the study. Patients' characteristics are summarized in Table  1. MR acquisitions were all performed on 2 imaging machines (MRI) from the same manufacturer (General Electric, Milwaukee, WI, USA): Optima MR450w 1. 5T and Discovery MR750w 3T. MRI data included a post-contrast (gadoterate meglumine, Dotarem, Guerbet, Villepinte, France) three-dimensional T1-weighted Fast Spoiled Gradient Recalled (FSPGR) acquisition (post-contrast 3DT1), post-contrast 3DT1, and fat-suppressed FLAIR images. MR images were only used as inputs of the radiomics classifier. To ensure image quality, neuro-radiologists analyzed all the available imaging sequences. Table  2  details the MRI parameters for both machines.
Image Processing 2. 2. 1. Pre-Processing The whole pre-processing and post-processing pipelines are summarized in Figure  1. As the BraTS cohort was already pre-processed (pixel-wise normalization, co-registration and skull-stripping), the same pre-processing pipeline was applied on the validation set thanks to the BraTS preprocessor from the BraTS toolkit  [27]. First, the T2-FLAIR images were registered on the T1ce images using the Advanced normalization tools (ANTs) software. Then, T1ce images were registered on the T1-weighted BraTS atlas and the same transformation was applied on the previously registered T2-FLAIR images to have every imaging modality from both cohorts in the same spatial coordinates. Afterwards, the HD-BET brain extraction tool  [28]  was used on all images of the validation cohort for skull-stripping.
Tumour Segmentation The nn-Unet [29] framework was used to segment the tumours on the MR images of the validation cohort. This neural network ranked first in the 2020 BraTS challenge on an identical dataset as the 2019 challenge's segmentation task. nn-Unet offers a pretrained brain tumour segmentation model. However, it needs four MRI modalities for the creation of the 3-labeled mask, while our validation cohort had only T1ce and T2-FLAIR. Therefore, the use of the pre-trained inference model available on the GitHub repository of the authors was not possible. Consequently, a new training task was carried out on the BraTS cohort using only these two modalities. The architecture of the neural network was kept untouched. This newly trained nn-Unet model was then applied for the segmentation of the validation cohort.
Radiomics and Features Extraction Technique On both cohorts, radiomics were extracted from the two sub-masks (NCR/NET/ET and ED) on the T1ce and T2-FLAIR MRI modalities using the Python library Pyradiomics  [30]. The sub-masks from the BraTS ground-truth were utilized for the BraTS cohort while the radiomics extraction of the validation cohort was performed on the images automatically segmented by the nn-Unet model. The radiomic set of features included: 18 first-order statistics, 14 shape-based features, 24 grey level co-occurrence matrix features (GLCM, texture), 16 grey level run length matrix features (GLRLM, texture), 16 grey level size zone matrix features (GLSZM, texture), 5 original neighboring grey tone difference matrix and 14 grey level dependence matrix features (GLDM, texture), 5 neighbouring grey tone Difference Matrix (NGTDM, texture).
BraTS Cohort Survival Analysis Several ML algorithms were evaluated to process the BraTS cohort along 3 binary classification tasks: surviving less or more than 9, 12, and 15 months, respectively. GBM patients of the BraTS cohort were divided into train and test datasets. The proportion of each class in every classification model was kept in the train and test datasets. Crossvalidations (CV = 5) were applied on the train datasets to avoid overfitting and to find the best feature-scaling method, classifier and hyper-parameter. Then, the best ML model found was trained on the overall train-set and evaluated on the test-set. Using the Scikitlearn Python library, 3 scaling methods (standard scaler, min-max scaler and robust scaler) and 7 classifiers (KNN, random forest, logistic regression gradient boosting, AdaBoost, naïve Bayes and SVM) were tested.
Discussion In this study, ML algorithms based on radiomics from MR imaging were designed for the survival prediction of GBM patients. A total of 448 radiomics from T1ce and FLAIR images were extracted from two independent cohorts of GBM patients. A radiomic signature was identified on the first cohort of 210 patients, arisen from the train dataset of the 2019 BraTS challenge. Then, the robustness of this signature for survival prediction of GBM was tested on a validation cohort composed of 116 patients. The results of this study suggest that these models can provide some valuable insights into the OS of GBM patients. The novel classification models successfully stratified patients into two significantly distinct populations, thus suggesting a possible role in the identification of GBM phenotypes.
Monochromatic Cu Kα 1 (λ = 1. 54056 Å) radiation was selected by means of a quartz monochromator. Cubic phase Na 2 Ca 3 Al 2 F 4 was used for external calibration. The analysis of the diffraction patterns (fitting of diffraction peaks by means of the Materials Studio software  29  ) was carried out using the published monoclinic (P2 1 /c) structures of TETRA,  30  DIA,  31  and NOR. 32 Hirshfeld surface analyses were performed by means of the CrystalExplorer software (https://crystalexplorer. scb. uwa. edu. au/) Broadband dielectric spectroscopy (BDS) measurements were carried on the amorphous form (supercooled liquid and glass states) of the drugs, by means of a Novocontrol Alpha analyzer. The samples were placed in a stainless steel parallel-plate capacitor specially designed for the analysis of liquid samples, with the two electrodes kept at a fixed distance by means of cylindrical silica spacers of 50 μm diameter.
Acknowledgements This work was supported by the  Spanish Ministry of Science and Innovation  (Grant  FIS2017-82625-P ) and the  Catalan Government  (Grant  2017SGR-42 ). We thank  Daiichi Sankyo France SAS, Bouchara-Recordati (France)  and  Neuraxpharm (Spain)  for providing samples of Tetrazepam, Nordazepam and Diazepam, respectively.
6 (95% CI 3. 3 to 3. 9) vs 4. 8 (95% CI 4. 5 to 5. 2) and 1. 0 (95% CI 0. 9 to 1. 1) vs 1. 4 (95% CI 1. 2 to 1. 6); HR 0. 79 (95% CI 0. 70 to 0. 89) and 0. 77 (95% CI 0. 61 to 0. 98) respectively), with similar risk of nonhaemorrhagic stroke/SE (rates/100 patient-years 0. 8 (95% CI 0. 7 to 0. 9) versus 1. 0 (95% CI 0. 8 to 1. 1); HR 0. 96 (95% CI 0. 73 to 1. 25). Conclusion Important benefits in terms of mortality and major bleeding were observed with NOAC versus VKA with no difference among NOAC subtypes. Trial registration number NCT01090362.
4 7 In a meta-analysis of data from 71 638 participants in these pivotal phase III trials, RE-LY (dabigatran),  4  ROCKET AF (rivaroxaban), 7 ARISTOTLE (apixaban)  6  and ENGAGE AF-TIMI 48 (edoxaban),  5  NOACs reduced all-cause mortality (relative risk 0. 90, 95% CI 0. 85 to 0. 95) and stroke/SE events (relative risk 0. 81, 95% CI 0. 73 to 0. 91) in comparison with warfarin with a borderline difference in risk of major bleeding (relative risk vs warfarin 0. 86, 95% CI 0. 73 to 1. 00). 8 In a systematic review and meta-analysis including phase II and phase III RCTs comparing NOACs with warfarin in a total of 77 011 patients with AF, NOACs reduced.
Acknowledgements We would like to thank the physicians, nurses and patients involved in the Global Anticoagulant Registry in the  FIELD-Atrial Fibrillation (GARFIELD-AF) registry. Programming support was provided by  Madhusudana Rao  ( Thrombosis Research Institute (TRI), London, UK ). Editorial support was provided by  Kate Ackrill  and  Dr Surekha Damineni  ( TRI ).
Machine-learning framework for identifying a molecular predictor of PE risk. Step 1: Normalization. First, all HPA variables and biological traits were normalized and scaled to have 0 mean and 1 variance to avoid major artificial influence of variables with large range of variations. Step2: Edited nearest neighbors. As our aim was to identify new molecular markers associated with PE, we hypothesized that conducting our discovery phase on isolated PE, an expected less heterogeneous class of VTE patients than the class of patients with both DVT and PE, will increase our chance to identify novel relevant molecular players. As a consequence, we decided to build our ANN model only on patients with isolated PE (N = 95) or with DVT (N = 1105). However, due to the imbalance nature of this dataset with ~ 10 more samples in the DVT class than in the PE class, we applied the edited nearest neighbors (ENN) algorithm, an under sampling method usually used in the field of pattern recognition or classification in presence of unbalanced samples  17.
This method relies on under sampling unit of analysis, in our case individuals, from the majority class by removing the most heterogeneous units. It consists in computing the Euclidean distance between each pair of individuals from their proteomics and biological data and to remove samples whose clinical phenotype (here DVT) is not consistent with that of his/her k nearest neighbors (k = 3 in this work). This led us to the selection of the so called ANN dataset composed of N = 497 DVT and N = 95 PE patients for building our ANN model.
Step3: Derivation of an ANN model for PE prediction. To build our ANN model, the ANN dataset was divided into a training set composed of 576 patients (487 DVT and 89 PE) and a testing set of 16 patients (10 DVT and 6 PE), the latter being used for testing the accuracy of the ANN model derived from the former. This allocation was chosen so that the number of PE cases used for training was sufficiently large. Because the application of a standard ANN methodology to our training set would lead to unstable network for predicting PE due to the imbalance nature of the input data with ~ 5 times more DVT than PE patients, an interactive ANN framework was adopted: At each iteration i, • A random sample of 30 PE patients and 100 DVT patients is selected from the training set and a sample of 70 synthetic PE samples are generated using the ADASYN algorithm  24.
ADASYN is an adaptive synthetic data generation method where new samples are generated based on the weighted distribution for minority class samples with two main advantages, resolving data imbalance and forcing classifiers to be more sensitive to the minority class. This strategy led to a balanced dataset Di of 100 PE and 100 DVT (synthetic) patients on which a ANN is built. • Using the Di dataset further splitted randomly into 90%/10% training/testing subsamples, a two hidden- layers feed forward neural network was implemented (see Supplementary data for an illustration of the neural network's structure). In addition to the input layer corresponding to the number of proteomic and biological variables (n = 395), the proposed neural network included a first hidden layer with 395 neurons, a second layer with 128 neurons and an output layer with 2 neurons, representing the DVT and PE classes respectively.
Genome wide genotyping. As previously described  16, 21  , both MARTHA and EOVT participants have been genotyped with high-density genotyping Illumina arrays and imputed for single nucleotide polymor-phisms (SNPs) from the 1000G Phase I Integrated Release Version 2 Haplotypes using MACH (v1. 0. 18. c) and Minimac (release 2011-10-27) imputation software.
Level of respiratory support and oxygen supplementation were set at the discretion of the treating physician. For the current study, 339 samples collected from 112 patients were available, with at least 2 samples for 71 of the patients (63. 4%). Baseline characteristics are shown in Supplementary Table  4. The HPA antibody HPA052141 targeting the PLXNA4 protein was used to measure PLXNA4 levels in the 339 available samples from the COMMUNITY study, following a similar protein profiling protocol as that described above. Association of PLXNA4 plasma levels with respiratory index at baseline was tested using linear regression models. To handle multiple time point measurements, association of PLXNA4 plasma levels with RI was further investigated using all available longitudinal measurements using a linear mixed effect model as implemented in the nlme R package.
After normalization and quality control, qualified libraries were sequenced on a HiSeqX5 instrument from Illumina (Illumina Inc. , CA, USA) using a paired-end 150 bp reads strategy. One lane of HiSeqX5 flow cell was used per sample specific library in order to reach an average sequencing depth of 30× for each sequenced individual. Sequence quality parameters have been assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ file for each sample. FastQ sequences were aligned on human genome hg37 using the BWA-mem program  35. Variant calling was performed using the GATK HaplotypeCaller (GenomeAnalysisTK-v3. 3-0, https:// softw are. broad insti tute. org/ gatk/ docum entat ion/ artic le. php? id= 4148).
Results Data description. The MARTHA proteomics substudy was composed of 1,388 VTE patients among which 1105 were diagnosed for DVT, 95 with isolated PE and 188 with both DVT and PE (Table  1 ). Exploration of this dataset using high-dimensional visualization techniques including principal component analysis, t-distributed stochastic neighbor embedding (t-SNE)  41  and Uniform Manifold Approximation and Projection (UMAP)  42  did not reveal any specific stratification in the data nor outliers (Fig. 2 ) but rather illustrates that the three class of patients (DVT, PE, DVT + PE) could not be easily separated. Artificial Neural Network for PE-As the accuracy/efficiency of any ANN strongly depend on the quality/ homogeneity of the input data, we first applied the edited nearest neighbors algorithm  17  to perform under sampling of the majority class (DVT) and obtain a more homogeneous set of DVT patients, and further discarded the DVT + PE class to avoid adding noise in discriminating between PE and non PE patients.