dataesr/scientific-paragraphs-categorization huggingface dataset:
0 mm ME-EPI 2000 ms 10, 25, 38 ms 77 72 × 72 240. 0 mm × 240. 0 mm 2x 34 3. 75 mm × 3. 75 mm 3. 8 mm 230 Word frequency. Log-transformed unigram frequency of each word in The Little Prince in English, Chinese and French was estimated using Google Books Ngram Viewer, Version 20120701 (http://storage. googleapis. com/ books/ngrams/books/datasetsv2. html).
parvum reference genome sequence [57] using BWA-mem 0. 7. 16  [58]  as defaults parameters. Sorting and indexing of the aligned sequences were performed with Samtools 1. 4  [59]. Picard 2. 2. 4 software (http://broadinstitute. github. io/picard/, accessed on 1 March 2018) was used to remove duplicates and Bamaddrg (https://github. com/ekg/bamaddrg, accessed on 1 March 2018) to edit read groups information. Coverage was evaluated using Bedtools 2. 17. 0  [60].
RNAseq Gene Expression Data Analysis and Gene Re-Annotation A public RNAseq dataset (GSE85079) obtained with N. parvum isolate Np-UCD-646-So  [36]  was used to improve Np-UCR-NP2-v3 gene annotation and to assess gene expression of mellein synthase and terremutin cluster genes. RNAseq reads from 3 PDAgrown samples were mapped against Np-UCR-NP2-v3 genome using HISAT2 v2. 2. 0 tool  [66].
The genome sequences of these two strains were compared using N. parvum genome sequence of isolate Np-UCR-NP2-v3 [57] as a cross-reference. Differences for fourteen nucleotides were detected between Np-B and NpB-UV9, but only two were located in coding sequences (Table  1 ). Each mutation affected a distinct gene encoding either a putative Zn(2)-Cys6 transcription factor conserved in fungi (Np-UCR-NP2_6692), or a putative regulator of G protein signaling conserved in fungi highly similar (78%) to the flbA protein of Aspergillus nidulans  [68]  involved in sporulation (Np-UCR-NP2_9007). Both genes were re-annotated using available RNAseq data (Table  S4 ). Newly annotated versions of genes UCR-NP2_6692 and UCR-NP2_9007 were named UCR-NP2_6692_v2 and UCR-NP2_9007-v2 respectively. Np-UCR-NP2_6692 mutation affected the protein sequence (P603A), while Np-UCR-NP2_9007 mutation was located in an intron and is likely silent (Table  1 ).
terreus involved in terremutin biosynthesis (https://www. ncbi. nlm. nih. gov/protein/BAA20102. 2?report=genbank& log$=prottop&blast_rank=25&RID=02GB07MS016, accessed on 1 July 2020). Both protein sequences were used to search for related fungal proteins using BlastP at NCBI with default settings. The 20 first non-redundant hits were recovered and aligned using MUSCLE at phylogeny. fr. The phylogenetic tree was constructed using default parameters at phylogeny. fr [64]. Np-UCR-NP2_6207 belongs to cluster of fungal PKS that is distinct from the cluster of the 6-MSA synthases involved in terremutin biosynthesis, although Np-UCR-NP2_6207 was wrongly assigned to 6-MSA synthases during N. parvum genome annotation. Figure  S3 : Phylogenic tree of N. parvum isolates used in this study. Available genome sequences of N.
Data Availability Statement: The whole genome sequences of the strains Np-B and Np-Bt67 have been deposited in the SRA NCBI database with the accession numbers SRX13878974 and SRX13878975, respectively. The sequences of genes subjected to re-annotation in this study have been deposited in the Genbank NCBI database. Sequences of genes Np-atB, Np-atE, Np-atD, UCR-NP2_6692_v2, and UCR-NP2_9007_v2 of strain Np-B have the accession numbers OM373092, OM373094, OM373096, OM677640, and OM677642, respectively. Sequences of genes Np-atB, Np-atE,.
Acknowledgments: We are grateful to  Cecilia Rego  ( Higher Institute of Agronomy, Lisbon University, Portugal ) and  Philippe Larignon  ( IFV Occitanie, France ) for pathogen gift, and to the  Vranken-Pommery Domain (Reims, France)  for the gift of Chardonnay material. Thanks are also due to  Isabelle Roberrini  for her technical assistance. This work was carried out within the Maldive Chair financed by the  Grand Reims.
CTL, respectively (Figure  5C ). To identify signatures specific to each genotype, we then performed gene ontology and functional enrichment analysis using STRING, Reactome, KEGG, and Wikipathways databases. At D0 and compared to CTL cells, the transcriptome of Mlkl-KO cells is specifically enriched for genes involved in regulatory networks controlling extracellular matrix organization and ossification, suggesting that Mlkl-KO preadipocytes might be oriented more toward trans-differentiation to bone cells formation rather than adipogenesis (Figures  S9A  and S9B )  (Zhang et al. , 2012). Interestingly, Ripk3-KO pre-adipocytes present a completely different signature for which pathways are enriched for ''DNA replication'' and ''G1 to S cell cycle control'' (Figures  S9C  and S9D ). We further analyzed differences in gene regulatory network at D7 of differentiation.
Wikipathways enrichment analysis revealed that most of the significantly enriched pathways contained down-regulated genes in Mlkl-KO cells vs. CTL cells (Figure  5D ). The signature of Mlkl-KO cells indicates a clear reduction in the expression of genes involved in the early or late stages of adipogenesis (C/ebpa, Pparg, Adipoq, Plin1, Lpl, Fabp4, Lipe) (Figure  5E ), PPAR signaling pathways, fatty acid and cholesterol biosynthesis, fatty acid-beta oxidation and TCA cycle (Figure  S10 ). Interestingly, C/ebpa and Pparg down-regulated expressions in our dataset correlated with an increase in expression of Wnt10b a known inhibitor of adipogenesis (Figure  5E )  (Cawthorn et al. , 2012). We could further confirm by RT-qPCR that the expression of genes involved in fatty acid metabolism and PPAR signaling pathway was, indeed, reduced in Mlkl-KO cells at 7 days post-differentiation (Figure  5F ).
However, Wnt10b overexpression could be a mere consequence of the downregulation of the pro-adipogenic factors seen in these cells. Indeed, PPARg agonists induce b-catenin inhibition, while the inhibition of the canonical Wnt/b-catenin pathway activates PPARg  (Lecarpentier et al. , 2017). A similar cross-talk can be found in-between C/EBPa and the Wnt/b-catenin pathway  (Chung et al. , 2012). Interestingly, in our dataset Wnt10b is already up-regulated in Mlkl-KO pre-adipocytes, while Pparg is unchanged compared to CTL cells. This strongly suggests that the impact of Mlkl deficiency on the Wnt/b-catenin pathway may occur before the engagement of cells toward adipogenesis. Overall, we propose that MLKL can act as a molecular switch between these pathways in order to control adipogenesis. However, additional investigations are needed to decipher if the inhibition of MLKL regulates directly or indirectly the Wnt/b-catenin pathway.
6. 1  (Anders et al. , 2015). The differential expression analysis was performed using the DEseq2 package  (Dillies et al. , 2013). Genes with an adjusted p-value <0. 05 were further used for analysis. R software and ggplot2 v3. 3. 4 were used to generate volcano plots and perform the principal component analysis in-between Ripk3-KO, Mlkl-KO and CTL replicates. Gene ontology, pathways enrichment analysis and protein-protein interaction networks were generated using STRING Reactome (www. reactome. org) (Szklarczyk et al. , 2021) , KEGG  (Kanehisa and Goto, 2000)  and Wikipathways databases (https://wikipathways. org). RNAseq data are deposited on the NCBI Gene Expression Omnibus (GEO) repository under the accession number GSE201450. Real-time quantitative PCR (RT-qPCR) Total RNA was purified from cells using Nucleospin RNAkit (#740955, Macherey-Nagel, Du ¨ren, Germany).
The quantity and quality of RNA were determined spectroscopically using a nanodrop (Thermo Fisher Scientific). Total RNA (2 mg) was used to synthesize cDNA using the M-MLV reverse transcriptase kit (Thermo Fisher Scientific) according to the manufacturer's protocol. The cDNA samples (2 mL) were used for RT-qPCR in a total volume of 10 mL using SYBR Green Reagent (Roche Diagnostics, Meylan, France) and specific primers, on a LightCycler 96 Roche Instrument. All RT-qPCRs were performed in duplicate. Data were generated and analyzed using the LightCycler 96 software 1. 1. 0. All values were normalized for the level of hydroxymethylbilane synthase (Hmbs) or hypoxanthine guanine phosphoribosyltransferase (Hprt) mRNAs. The list of primer sequences is available in Table  S2.
Results were normalized to the cell protein content. Oil Red-O staining, image processing, and quantification Intracellular lipids were stained by Oil Red-O (#O0625; Sigma-Aldrich). Cells were washed with PBS and fixed with 4% PFA in PBS, for 10 min. Fixed cells were incubated with Oil Red-O solution for 1 h at room temperature and then with DAPI (#D1306, Thermo Fischer Scientific) for 5 min. Fluorescence images were generated with IX83 Olympus microscope, acquired with Cell-Sens V1. 6 and analyzed with FIJI software. Images of 8-10 different areas per condition were visualized by fluorescence microscopy using mCherry and DAPI filters, followed by computer image analysis using FIJI software. Briefly, analysis was performed by threshold converting the 8-bit Red-Green-Blue image into a binary image, which consists only of pixels representing lipid droplets (i.
Immunofluorescence Cells were plated on coverslips in 6-well plates. After differentiation, cells were washed and fixed with 4% PFA for 10 min. Cells were then quenched (NH 4 Cl) and permeabilized (PBS-0. 5% triton, 10 min). After blocking with a solution of PBS-0. 5% Tween-5% BSA, the cells were incubated overnight with primary antibody (see above key resources table ). All cells were counterstained with DAPI for 5 min. Fluorescence images were generated with IX83 Olympus microscope (using EGFP and DAPI filters), acquired with Cell-Sens V1. 6 and analyzed with FIJI software.
Statistical analysis Data are presented as means G SEM (standard error of the mean). GraphPad Prism software (GraphPad Software) was used to calculate statistical significance. Gaussian distribution was tested with the D'Agostino-Pearson normality test. p values were determined by analysis of variance (ANOVA) with the Geisser-Greenhouse correction and Tukey's multiple comparisons test. Differences between 2 groups were determined using Student's t test. p < 0. 05 was considered statistically significant.
Red represents high FPKM values and blue represents low FPKM values. Color descending from red to blue indicates log 10 (FPKM +2) from high to low expression. (C) Volcano plots of DEGs comparing Mlkl-or Ripk3-KO to CTL at 7 days post-differentiation. Red and green dots represent up-and down-regulated DEGs, respectively, with FDR <0. 05. (D) Significantly enriched Wikipathways in Mlkl-or Ripk3-KO compared to CTL datasets at 7 days post-differentiation. (E) Protein-protein interaction network generated using STRING-db for Wikipathways WP447: Adipogenesis genes in Mlkl-KO vs CTL cells at D7. (F) Fasn, Pparg, Fabp4, Scd1, and Wnt10b mRNA levels were assessed by RT-qPCR. Results are expressed as means G SEM. ****p < 0. 0001; **p < 0. 01; *p < 0. 05. (G) Protein-protein interaction network generated using STRING-db for Wikipathways WP1269: Fatty acid beta-oxidation in Ripk3-KO vs CTL cells at D7.
ACKNOWLEDGMENTS Jeremie Gautheron is funded by the  Fondation pour la Recherche Medicale  ( FRM -ARF20170938613 & EQU202003010517 ), the  SocieteFrancophone du Diabe  `te ( SFD -R19114DD ), the  Mairie de Paris  ( Emergences -R18139DD ) and the  Agence Nationale de la Recherche  ( ANR-21-CE18-0002-01 ). Cecı ´lia  M. P. Rodrigues  is funded by the national funds through Fundaca ˜o para a Ciencia e a Tecnologia to CMPR (grants  SAICTPAC/0019/2015 -LISBOA-01-0145-FEDER-016405  and  PTDC/MED-FAR/29097/2017 -LISBOA-01-0145-FEDER-029097 ) and the  EU   H2020 Marie Sklodowska-Curie 722619 grant.
This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images.
Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets. Either for clinical diagnosis, prognosis, and therapy assessment of brain pathologies or neuroscience research, magnetic resonance (MR) imaging is of prime importance. However, MR images are subject to wide quantitative variations inherent to this imaging modality, i. e. MR data acquired for the same patient but on different sites or scanners yield to different MR images 1-3. Additional differences can also be attributed to artifacts such as bias field inhomogeneities, noise, motion, ghosting, or spike  [4] [5] [6] [7] [8].
subsequent robust models. To voluntary ignore scanner-induced data heterogeneity, most neuroimaging studies have traditionally been limited to datasets from a single center  23. In recent years, there has been an increasing trend towards the collection and sharing of neuroimaging data through the establishment of multi-institutional databases  24, 25. This effort to collect data covering a wide range of machine types and broad spectrum population (demographic) is essential for the development of diagnostic and prognostic biomarkers to enable robust translation of research into clinical practice. As a consequence, there is a strong and pressing need for standardization and/or harmonization  26, 27. Compensation for these effects can be seen at three different levels: (i) Image acquisition, (ii), Image processing and, (iii) Feature adjustment.
Dataset. Preoperative scans previously extracted from the The Cancer Imaging Archive (TCIA)  36  including both glioblastoma (TCGA-GBM, n = 135) and low-grade-glioma (TCGA-LGG, n=108) were considered  24, 37, 38. All methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). Selected DICOM files were pre-and post-contrast T1-weighted (T1w and T1w-gd), T2-weighted (T2w), and T2 Fluid-Attenuated Inversion Recovery (FLAIR) volumes (T2w-flair). These data presented high heterogeneity as they were collected from 11 different centers. Data from the three centers showing the lowest sample numbers were removed to ensure at least 5 samples in the training set after stratified data splitting  39, 40  (see section Batch effect adjustment method and subsection Empirical Bayes method).
To determine the optimal number of clusters, the elbow method of the Yellowbrick library was used  46. The elbow method runs the K-Means constrained clustering on the dataset for all possible values of K. Then, for each value of K, a metric is computed to evaluate quality of the clusters. By default, the scoring metric is the distortion, which calculates the sum of the squared distances from each point to its assigned cluster center. However, two other metrics can be used: the Silhouette score and the Calinski-Harabasz score. The Silhouette score calculates mean ratio of intra-cluster and nearest-cluster distance, while the Calinski Harabasz score calculates the ratio of dispersion between and within the clusters. The optimal value of K was determined automatically using the "knee point detection algorithm" which allows to determine the elbow, i.
e. the point of inflection  47. To use a reference batch in ComBat, our approach estimated the most relevant cluster for this role as the one with the lowest within-cluster sum-of-squares (WCSS), defined as the sum of the squared distances between each member of the cluster and its centroid. We implemented ComBat and AutoComBat in Python compatible with scikit-learn  48  to facilitate subsequent machine learning projects. ComBat can use EB or more simpler L/S method. When EB is chosen, adjustments can be done in a parametric or non-parametric way. A reference batch can also be set in case the user prefers to use the modified version of ComBat. AutoComBat benefits from the ComBat inheritance. The code is available at the following address: https:// github. com/ Alxal ine/ ComSc an. To extract the image quality metrics and the metadata from the DICOM files, we have also developed a Python package available at the following address: https:// github.
The modalities were then skull-stripped to keep only the brain  57. Finally, the z-score normalization was applied to the brain voxels by setting the mean to zero and the variance to one. The package used for preprocessing is the cBrainMRIPrePro Python package available at the following address: https:// github. com/ Alxal ine/ cBrai nMRIP rePro. This in-house package uses ANTsPy  58  and HD-bet  57  and enables the preprocessing of anatomical MR images in the form of a straightforward pipeline.
Radiomic feature extraction. The extraction of radiomic features was performed using the Python library Pyradiomics 59 v3. 0. 1. A total of 91 features were extracted including 18 first-order and 73 second-order features compliant with the Image Biomarker Standardization Initiative (IBSI), except for the first-order feature ( F = n i=1 v f i n ) with v f i , i th foreground voxels. B is Background intensity voxels ( B = n i=1 v b i n ) with v b i , i th background voxels. F P is Foreground random patch voxels (n = 5000, with a 5 × 5 × 5 patch-size). B P is Background random patch voxels (n = 5000, with a 5 × 5 × 5 patch-size). f 1 = -1 -1 -1 -1 8 -1 -1 -1 -1.
When multiple methods had overlapping CIs, they were all counted. In some cases, none of the methods met all three criteria, and in some cases more than one did, which explains the potential mismatch between the sum of the figures and the total number shown at the top of the column. In addition, the total number of significant features per method compared to the raw method is given. Comparing WM RSD of all methods to raw RSD, the preprocessing method showed the highest harmonization capabilities for T1w-gd (82%), while it was ComBat for T2w-flair (79%), very similar to preprocess (78%). For T1w and T2w, ComBat was found to be superior (96%-T1w; 92%-T2w), followed closely by the preprocessing method (93%-T1w; 85%-T2w). For AutoComBat, these values varied according to the characteristics used for the clustering (metadata and quality metrics, metadata only, quality metrics only) and the MR image type, but never outperformed ComBat, except for T1w-gd.
For example, using all available, metadata only, and quality metrics only features for clustering, the number of significant features showed an improvement of 54%, 62%, 75% compared to raw, respectively. Looking at top features, AutoCombat using QM only obtained the best results for T1w-gd with 75% of features obtaining the lowest RSD. The preprocess method showed the best performance for T2w-flair with 78% of features. For MRI sequences not shown here (T1w and T2w in Supplementary), ComBat was superior, with AutoComBat QM presenting very similar values. These findings on the test set agreed with the validation (5) L = 1 n n f =1 RSD f = 1 n n f =1 σ f | µ f | × 100 set, except for AutoComBat (all) in the T2w-flair images, which had shown the best performance in the validation set but did not generalize the same way in the test set.
77 (min: 0. 76, max: 0. 81). For second-order features, ComBat also ranked first with a value of 0. 73 (min: 0. 52, max: 0. 79), while all other methods ranged from 0. 64-0. 68. Raw data gave a performance of 0. 67 (min: 0. 62, max: 0. 75). For the T1w-gd images and first-order features, image preprocessing gave the best performance with a value of 0. 87 (min: 0. 83, max: 0. 90). The other methods ranged between 0. 72-0. 75 and use of raw images yielded a median balanced accuracy of 0. 77 (min: 0. 73, max: 0. 79). For second-order features, preprocessing obtained the best performance with a value of 0. 79 (min: 0. 69, max: 0. 85). Direct use of raw data and AutoComBat led to performance between 0. 72 and 0. 77, while ComBat underperformed with a value of 0. 66 (min: 0. 43, max: 0. 74). For the T2w MR images and first-order features, AutoComBat (all), i.
e. , using all features available for clustering, ranked first with a value of 0. 80 (min: 0. 71, max: 0. 85). Here, preprocessing performed worse with a value of 0. 63 (min: 0. 60, max: 0. 68), while the others ranged between 0. 74-0. 77. Considering the second-order features, Auto-Combat (all) took first position again with a value of 0. 75 (min: 0. 66, max: 0. 78). Combat performed the worst with a value of 0. 70 (min: 0. 58, max: 0. 75), while the rest including raw data ranged between 0. 72-0. 74. Finally, the development of a ML model based on T2w flair-extracted first-order features with AutoComBat (Metadata) and without applying any processing yielded values of 0. 70 (min: 0. 53, max: 0. 73) and 0. 71 (min: 0. 66, max: 0. 73), respectively. Here, AutoComBat (QM) performed worse with a median balanced accuracy of 0.
61 (min: 0. 52, max: 0. 64). The value was equal to 0. 61 (min: 0. 64, max: 0. 65) when preprocessing was applied. For the second-order features, AutoComBat (all) and AutoComBat (metadata) provided the best results with 0. 78 (min: www. nature. com/scientificreports/ with median balanced accuracies respectively equal to 0. 75 and 0. 85 for T1w and T1w-gd images respectively. For T2w-flair MRI, the results are reversed in favor of ComBat. Interestingly, for this MR weighting, AutoCombat whether considering metadata plus quality metrics as inputs or metadata alone outperformed the traditional Combat, with median balanced accuracies equal to 0. 77, 0. 76 and 0. 75 respectively.
Discussion The aim of this study was to analyze the impact of the harmonization approach in an MR-based radiomics context, i. e. , either upstream as image processing or downstream after the extraction of radiomic features. In addition, a clustering method that aims to automatically define the batch to which an image should be assigned, using information from the DICOM file metadata and/or quality metrics deduced from the raw images themselves, was proposed. In this work, a highly heterogeneous dataset including conventional MR images (T1w, T1w-gd, T2w, T2w-flair) from a large number of centers was voluntary considered to evaluate the generalizability of the proposed solution, and both classes of radiomic features (first and second-orders) were analyzed separately first and in combination in a second step.
However, preprocessing underperforms the other strategies for T2-weightings, which by nature suffer from limited intensity ranges. AutoComBat (based either on Metadata or all features) has shown interesting properties, especially on the T2w-flair image with the best median value of balanced accuracy equal to 0. 78, considering second order radiomic features only as inputs. For this image weighting and feature class, AutoComBat has demonstrated a good generalization compared to other methods, i. e. , constant performance between the validation and the test sets (only 5% percentage Table  3. Counts (%) of features for each harmonization method with a RSD (95% CI) lower than the one corresponding to the raw images for the T1w-gd and T2w-flair MRI on the test set. The main part of the table gives the number of features for which the considered method is evaluated as the best one, which is called "Top".