dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis was performed by comparing cases to age-matched controls using the statistical softwares R version 4. 0. 4 and SAS version 9. 4. Once the normal distribution of variables was veri ed, differences in the mean values between the case and control groups were analyzed using paired Student T tests. If not normally distributed, differences between measurements (cases vs controls) were compared using the Wilcoxon signed rank tests. Control group was strati ed by benign or stage 0 breast disease status One-way analyses of variances (ANOVA) or their non-parametric equivalents, Kruskall Wallis H tests, were applied to assess differences between the 3 resulting groups (benign, stage 0 and stage III). If found signi cant (p < 0. 05), a post-hoc Tukey test was performed to identify a group that signi cantly differed from the others.
Acknowledgements We express our gratitude to the staff of the  Core Laboratories of the Hôpital de l'Enfant-Jésus  and of the CHUL for their help with measurements of biomarkers available in clinical laboratories. This work has been supported by an establishment grant from the  Oncology Research axis of the CHU de Québec-Université Laval Research Centre  and the  Medicine faculty of Laval University. Plasma samples were provided by the tissue and data biobank of the  Cancer Research Centre , the  Fonds de recherche du Québec-Santé (FRQS) , and the  Quebec Breast Cancer Foundation , a liated with the  Canadian Tissue Repository Network.
Of the seven species that were introduced to the island, only four established self-sustaining populations: tilapia (Oreochromis niloticus), guppy (Poecilia reticulata), swordtail (Xiphophorus hellerii) and the non-anadromous form of rainbow trout  (Keith 2002). The presence of juvenile rainbow trout in the absence of introduction, which indicates wild reproduction, is the criterion used to define a self-sustaining population. The presence of self-sustaining rainbow trout populations is assessed by visual observation (e. g. presence of juveniles, acts of spawning, redds) and electrofishing surveys conducted by the local association for fishing and the protection of aquatic environments (hereafter, fishing association). The presence or absence of a self-sustaining population from 2016-2020 was assessed at each site where trout had been introduced, all sites where its presence was suspected, and at all sites where temperature data were available (see below).
The model was based on temperature and elevation data from all 33 sites (Table  S2 ). From this model, we estimated mean summer and winter water temperatures for 2297 locations along all of the perennial rivers. The elevation of each location was extracted from a detailed topographic map provided by the French digital elevation model (BD ALTI, 25 m resolution, https://geoservices. ign. fr/bdalti). We then calculated and mapped the predicted probability of the presence of rainbow trout at all locations, based on its seasonal temperature preferences as described in the Mtemp model. All analyses were performed using R v4. 0. 5 (R Development Core Team 2018). Maps were produced using QGis version 2. 8. 93. 10 (https://www. qgis. org/en/site/).
Datasets The public retrospective datasets The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM)  [36, 37]  and The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG)  [37, 38]  were used in our paper. They were used to develop the radiomics models, to predict the tumor grade, IDH mutation status, and 1p/19q codeletion status of the glioma patients, each corresponding to a binary classification task. We introduce hereafter the Magnetic Resonance Imaging (MRI) images and the molecular status of the datasets, and then describe how we clean the data before the analysis.
Clinical MRI Scans The clinical MRI scans of the TCGA-GBM and TCGA-LGG datasets were downloaded from the Cancer Imaging Archive (TCIA  [39] , https://www. cancerimagingarchive. net/, accessed on 18 July, 2021) platform, which de-identifies and hosts numerous medical images of different cancers for public download purposes. We downloaded the data of 243 glioma patients from the TCIA, including 135 patients (102 train data and 33 test data) from the TCGA-GBM dataset and 108 patients (65 train data and 43 test data) from the TCGA-LGG dataset. For each patient, four 3D-MRI images representing four different MRI sequences are available, including the native T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2weighted (T2), and T2 fluid attenuated inversion recovery (T2-FLAIR) volumes. These MRI images were originally collected from multiple institutions, using different MRI scanners and different clinical protocols.
The segmentation image in Figure  2  shows these subregions of a glioma tumor in different colors, as an example. Specifically, some glioma tumors in our dataset may not have an enhancing tumor (ET) or an edema (ED) zone. The four MRI sequences (T1, T1Gd, T2, T2-FLAIR) and the corresponding segmentation mask of an example patient "TCGA-02-0047". The whole tumor (WT) consists of the necrotic part (NCR, red color), the enhancing tumor part (ET, yellow color), and the peritumoral edematous tissue (ED, green color). The tumor core (TC) consists of the necrotic part (NCR, red color) and the enhancing part (ET, yellow color).
Clinical Information and Molecular Status For patients involved in the TCGA-GBM and TCGA-LGG datasets, their molecular status (tumor grade, IDH mutation status, 1p/19q codeletion status, etc. ) and clinical information (age, sex) were downloaded from The Cancer Genome Atlas (TCGA, https: //www. cancer. gov/about-nci/organization/ccg/research/structural-genomics/tcga, accessed on 18 July, 2021) platform using TCGAbiolinks and R codes.
• Dropped two patients ("TCGA-06-0177" and "TCGA-CS-4941") because of their very poor image qualities and abnormal image histograms. • Dropped patient "TCGA-EZ-7265A" because the patient ID could not be found in the TCGA platform. • Dropped 23 patients who lacked IDH mutation statuses. Among the 23 patients, 22 patients were of the GBM type and 1 was of the LGG type. • Dropped five patients who lacked the 1p/19q codeletion status. Among the five patients, four patients were GBM with the IDH wild type, one patient was GBM with the IDH mutant. After dropping these patients, 212 patients were left for our analysis to predict the glioma subtype. We summarize the repartition size of the dataset regarding the tumor grade, IDH mutation status, and 1p/19q codeletion status in Table  1. It can be seen that, in our dataset, most low-grade gliomas were the IDH-mutant, while most glioblastomas were IDH wild type.
Extract Radiomic Features We extracted 106 radiomic features using the python package Pyradiomics  [44]  for each three-dimensional volume of interest (VOI), consisting of 14 3D shape-based features, 18 first-order features, 16 gray level run length matrix (GLRLM) features, 16 gray level size zone matrix (GLSZM) features, 14 gray level dependence matrix (GLDM) features, 5 neighbouring gray tone difference matrix (NGTDM) features, and 23 gray level cooccurrence matrix (GLCM) features. We extracted these radiomic features from all four MRI sequences (T1, T1Gd, T2, and T2-FLAIR). Notice that the shape features are the same for these four co-registered MRI scans, so for one VOI, we extracted the shape features only once from the four scans for the same patient. As shown in Figure  2 , the whole tumor consists of different parts of the tumor, including the tumor core (necrosis and enhancing tumor parts) and edema.
Impact of ComBat Harmonization The TCGA-GBM and TCGA-LGG datasets used in our paper include patients collected from different institutions with different image scanners or clinical protocols; thus, the nonbiological scanner effects might be introduced. We applied Z-score normalization on the MRI images as a preprocessing step; however, according to the conclusion of our recent study  [43] , only applying the intensity normalization might not be enough for radiomics models. Thus, we tested the ComBat method to see whether it helped improve the feature ability of the radiomic models. As displayed in Figure  7 , ComBat does not help in any of the three binary classification tasks. None of the ComBat variants (namely, standard ComBat without using empirical Bayes (EB), parametric ComBat using parametric EB, and nonparametric ComBat using nonparametric EB), appeared to help improve the median/mean cross-validation AUC values significantly, whether using "magnetic field strength" or "site label" as scanner setting labels, or keeping the age and sex information during harmonization.
Impact of Data Imbalance Strategy When predicting the 1p/19q codeletion status, the data are highly imbalanced (27 1p/19q codeleted patients vs. 185 1p/19q intact patients). When tuning the hyperparameters for the classifiers using the random search method, we already added the possible regularization parameters. Here, we further applied some other data imbalance strategies, such as setting the class weights or using data sampling-based methods (random undersampling/over-sampling, SMOTE  [48] , SVM-SMOTE  [49] , Borderline-SMOTE  [50] , etc. ) to deal with the data imbalance. The experiment results are shown in Figure  8. Surprisingly, these data imbalance methods did not further improve the prediction of the 1p/19q codeletion status significantly in our dataset. Here, the possible regularization was already applied for each classifier, so "base" means not further using any other data imbalance strategies.
Predict Glioma Subtype by the Final Pipeline In Section 3. 2, we summarize the final radiomics pipeline for predicting each label (tumor grade, IDH mutation status, 1p/19q codeletion status) of the glioma tumor (Figure  11 ). We used this pipeline and report the prediction performances on the hold-out test dataset, also based on the 51 random seeds for a robust analysis. Moreover, the performance of the basic radiomics pipeline (Section 2. 4) without our pipeline tuning process is also listed as a comparison. As shown in Table  3 , our final radiomics pipeline outperforms the basic radiomics pipeline significantly, indicating the great significance of our pipeline tuning process. In detail, the mean AUC on the hold-out test data computed on 51 random runs improved significantly, from 0. 8935 (±0.
A total of 212 patients from the public retrospective TCGA-LGG and TCGA-GBM datasets were used, so all of the experiment results and analyses in our study are replicable with publicly available codes. When tuning the settings in the radiomics pipeline, we acquired some interesting findings. First, Z-score normalization not only renders the MRI scans as more comparable among the patients, but it also significantly improves the classification performances for all three binary classification tasks. Second, the image filter applied to the MRI scans before extracting the radiomic features has an obvious impact on the classification performances. In our cases, we chose the relatively best one for each task, namely, the original image without any filters for predicting tumor grade, the square root filter for predicting IDH mutation, and the Laplacian of Gaussian filter for predicting the 1p/19q codeletion status.
For predicting the 1p/19q codeletion status, apart from the predicted tumor grade and IDH mutation labels, the first-order features and other features extracted from the LoG-filtered MRI images are of high importance. In summary, this paper provides a promising tool for the prediction of the glioma subtype as well as a better understanding of the radiomics model development and the feature interpretability. However, this paper also has some limitations. First, we only used the public retrospective datasets TCGA-GBM and TCGA-LGG in this study. The analyses and conclusions need to be further confirmed on other possible independent datasets. Second, the radiomics model we developed is not an end-to-end manner because the segmentation masks should be provided for the new patients. The automatic segmentation can be achieved by training a neural network, such as nnU-Net  [52]  with the public dataset of the brain tumor segmentation (BraTS) challenge.
The BraTS 2021 dataset  [42]  consists of 2000 patients from multiple institutions, corresponding to 8000 pre-operative multiparametric MRI scans (T1, T1Gd, T2, T2-FLAIR). We did not use the predicted segmentation labels from the nnU-Net trained with the BraTS 2021 dataset, because TCGA-GBM and TCGA-LGG are part of the BraTS2021 data and cannot be identified from the renamed patient IDs in BraTS 2021 (risking data leakage). We mention here that, although most of the segmentation masks used in our paper (downloaded from TCIA platform) have already been manually corrected based on the automatic segmentation masks achieved by GLISTRboost software, there is still room for improvement, especially when segmenting the necrosis and enhancing tumor parts. Third, the MRI scans downloaded from TCGA-LGG and TCGA-GBM datasets have already been preprocessed, including reorientation, co-registration, resampling, and skull-stripping, so the same preprocessing steps should be performed for the new patients when using our radiomics pipeline.
Conclusions In this paper, we investigated using the radiomics models to noninvasively predict the gliomas subtype as defined by the tumor grade, IDH mutation, and 1p/19q codeletion status. Several investigations were conducted on the public retrospective TCGA-GBM and TCGA-LGG datasets to evaluate the impacts of different settings in the radiomics pipeline on the classification performance, such as Z-score normalization, feature extraction strategy, image filters, ComBat harmonization, classifier chain strategy, etc. After tuning these settings, a final radiomics pipeline for the prediction of the glioma subtype was proposed. Furthermore, the characteristics of the various radiomic features were analyzed to tell which features distinguished the most for predicting the glioma grade, IDH mutation, and 1p/19q codeletion status, respectively.
Data Availability Statement: The data used in this paper are public available. Researchers can download the clinical MRI images of the dataset from The Cancer Imaging Archive (TCIA) platform, with the link https://wiki. cancerimagingarchive. net/pages/viewpage. action?pageId=24282666 (accessed on 18 July, 2021) for the TCGA-GBM dataset and the link https://wiki. cancerimagingarchive. net/ pages/viewpage. action?pageId=24282668 (accessed on 18 July, 2021) for the TCGA-LGG dataset. The corresponding gene status and clinical information can be downloaded from The Cancer.
Similar to HIV-1 and other  Intasomes containing biotin-and Cy3-conjugated vDNA were immobilized on a streptavidin-coated cover slip (light blue rectangle). IN and vDNA are shown as gray and black cartoons, respectively; biotin and Cy3 are depicted as a purple circle and yellow solar symbol, respectively. Following incubation with LEDGF/p75 (red cartoons) conjugated with Surf649 (red solar symbol) and a wash with buffer containing 0. 2, 0. 5, or 1 M NaCl, immobilized intasomes were observed by TIRF microscopy. The individual steps of Surf649 photobleaching during illumination with a 640-nm laser (hν) were counted. b Representative images of surface attached intasome (vDNA-Cy3, yellow; LEDGF/p75-Surf649, red) molecules. The field of view of 14. 4 by 14. 4 μm shown here is representative of the dataset, which included three areas of 81.
See Supplementary Tables  3  and 4  for statistical analyses of panel c, d datasets. Source data are provided as a Source Data file. tDNA conformation induced by diverse retroviral intasomes (Supplementary Fig. 4 ). Evidently, in each case, the observed deformation allows sufficient expansion of the tDNA major groove to afford intasomal active sites access to the scissile phosphodiester bonds. The propensity of retroviruses and retrotransposons to integrate into nucleosomes is supported by strong evidence  [71] [72] [73]  , and the PFV intasome in complex with a core nucleosome particle was visualized by cryo-EM  74, 75. However, wrapping around the histone octamer imposes constraints of the conformation and major groove accessibility of nucleosomal DNA  76. Thus, the differences in tDNA conformation during its capture by the retroviral intasomes strongly support the idea that retroviruses may have evolved genus or even species-specific ways of interacting with chromatinized target DNA  77.
Cryo-EM movie frames were collected on a Talos Arctica transmission electron microscope (Thermo Fisher Scientific) operating at 200 keV equipped with a K2 summit direct detector (Gatan). Data collection was performed using the Leginon software version 3. 0  80, 81  at a magnification of 45,000x, corresponding to a pixel size of 0. 92 A ̊/pixel in nanoprobe mode. The stage was tilted to 40°during data collection to account for the preferential orientation of the sample within the vitreous ice  82. Movies composed of 100 frames were collected in counting mode over 10 s (100 ms per frame). The total fluence was 43. 6 e -/Å 2 at a rate of 3. 7 e -/pix/s. Imaging parameters are summarized in Supplementary Table  1. The movie frames were motion-corrected and dose-weighted using MotionCor2 version 1.
2a ). The 3D FSC was obtained by using the 3D FSC server (https://3dfsc. salk. edu)  82 and the sampling compensation factor (SCF) and surface sampling plots were calculated using the graphical user interface tool (https://www. github. com/LyumkisLab/SamplingGui)  88, 89. Selected image analysis results are shown in Supplementary Fig. 10  and summarized  in Supplementary Table  1. MVV STC cryo-EM grid preparation, data collection, and image processing. A 4-μL drop of freshly prepared MVV STC at 0. 15 mg/mL in 310 mM NaCl, 3 mM CaCl 2 and 25 mM BisTris-HCl, pH 6. 5 was applied onto glow-discharged lacey carbon grids coated with 3-nm carbon film (Ted Pella, product code #01824). The grids were incubated for 60 s under 100% humidity in a Vitrobot Mark IV (FEI) at 20 °C. To reduce salt concentration, the grids were blotted for 0.
5 s, immediately rehydrated with a 4-μL drop of 75 mM NaCl and 25 mM BisTris-HCl, pH 6. 5 and blotted again for 3. 5 s, followed by plunging into liquid ethane. Cryo-EM data were collected on a Titan Krios electron microscope (FEI) operating at 300 keV equipped with a K2 Summit direct electron detector (Gatan) using EPU software version 1. 9 (FEI). A total of 12,679 micrograph movie stacks were acquired at a magnification of 36,232, resulting in a pixel size of 1. 38 Å at the specimen level, using a total fluence of 50 e/Å 2 spread over 50 frames with a defocus range of -1. 0 to -4. 5 μm. The movies were corrected for drift and beam-induced motion applying dose weighting as implemented in MotionCor2 version 1. 4. 0  83. The contrast transfer function was estimated using CTFFIND4 version 4.
1. 5  90 via Relion-2. 1 interface  91. Resulting frame sums were examined individually, and those containing mostly lacey carbon or showing evidence of crystalline ice contamination were discarded. A remaining 11,760 micrographs were used for further image processing (Supplementary Fig. 11a ). Particles, picked manually on a subset of micrographs using EMAN Boxer from Eman2 version 2. 07  92  , were subjected to reference-free 2D classification. Six well-defined 2D class averages, low-pass filtered to 20 Å, were used as references for automated particle picking in Relion-2. 1 of the entire dataset. Particles picked along carbon edges were removed using Eraser tool in EMAN Boxer. The remaining 2,022,321 particles were extracted, with 2-fold binning applied, and subjected to several rounds of reference-free 2D classification in Relion-2.
1b ), and local resolution was calculated using cryoSPARC (Supplementary Fig. 2a ). The 3D FSC was obtained by using the 3D FSC server (https://3dfsc. salk. edu)  82  and the sampling compensation factor (SCF) and surface sampling plots were calculated using the graphical user interface tool (https://www. github. com/ LyumkisLab/SamplingGui)  88, 89. Real-space refinement. The STC model was constructed from the original lowresolution strand transfer complex model published previously (PDB code 5M0R)  8  and coordinates for LEDGF/p75 IBD (PDB code 3HPH)  33. The coordinates were docked into the cryo-EM map using UCSF Chimera  97. Adjustments were made to the model interactively using Coot version 0. 9. 8  98  and the coordinates were subjected to real-space refinement in Phenix dev-4213-000 employing C2 NCS constraints  99.
Details of the map and model statistics are given in Supplementary Table  1. Model-vs-map FSCs for both structures are shown in Supplementary Fig. 1c. The final cryo-EM reconstructions were deposited with the EMDB under accession codes EMD-26322 (CSC) and EMD-14453 (STC) and fitted coordinates with the PDB under accession codes 7U32 (CSC) and 7Z1Z (STC). SEC-MALLS. All measurements were conducted using a Jasco chromatography system equipped with a DAWN-HELEOS II laser photometer and an OPTILAB-TrEX differential refractometer (Wyatt Technology). MVV IN was diluted to 1, 2, 4, or 8 mg/mL in 1 M NaCl, 7. 5 mM 3-[(3-cholamidopropyl) dimethylammonio] -1-propanesulfonate, 2 mM DTT, 25 mM BisTris propane-HCl, pH 7. 0 prior to injection of 20 µL samples onto a Superdex 200 Increase 3. 2/300 column (GE Healthcare) equilibrated in 1 M NaCl, 3 mM NaN 3 , 25 mM BisTris-HCl, pH 6.
5. Chromatography was performed at 20 °C and a flow rate of 150 µL/min. Weightaveraged molecular masses of eluting species were calculated using the data from both detectors in ASTRA-6. 1 software (Wyatt Technology). In vitro MVV IN strand transfer assays. Strand transfer assays were carried out as previously described 8 using double-stranded oligonucleotide mimicking the terminal 29 bp of the processed MVV U5 vDNA end (Supplementary Fig. 5 ). The vDNA substrate was prepared by annealing synthetic HPLC-purified oligonucleotides 5′-CCGTGCAACACCGGAGCGGATCTCGCA and 5′-GCTGCGAGA TCCGCTCCGGTGTT GCACGG. Reactions were initiated by addition of 1. 1 μM MVV IN to 1. 5 μM LEDGF/p75, 0. 75 μM vDNA substrate and 7. 5 ng/μL supercoiled target pGEM9zf DNA (Promega) in 25 mM BisTris-HCl, pH 6. 0, 40 mM KCl, 5 mM MgSO 4 , 5 μM ZnCl 2 and 2.
All data sets were initially processed with NIS Elements (Nikon) for noise reduction ("advanced denoising" at a value of 5 for both analyzed channels) and background subtraction ("rolling ball" algorithm with r = 0. 48 μm). For each field of view, Cy3 (intasome) spots were detected in the 5-frame initial dataset, using "bright spot detection" (0. 78 μm radius and 14. 5 contrast) within NIS Elements. On average, 400 spots were detected per 512 × 512 pixel field of view. Next, for each identified intasome spot, a Surf649 (LEDGF/p75) fluorescence trace was obtained from the 3-min continuous sampling acquisition. Spots with Cy3 fluorescence intensity above 200 arbitrary units were not considered for analysis as they are likely to represent overlapping intasome complexes (the 200 arbitrary units threshold was chosen based on Cy3 intensity distribution for all analyzed data).
Similarly, data with no Surf649 photobleaching steps or unstable bleaching profiles were excluded from further analysis. In order to obtain the number of Surf649 photobleaching steps per intasome, firstly, pairwise intensity differences were calculated for each photobleach trace: 4I ij ¼ 4I t i À Á À 4Iðt j Þ for all data pairs, for which t i > t j , followed by local maxima identification. These calculations were performed in MATLAB version R2019a (MathWorks). For each salt condition, the number of photobleaching steps per intasome was statistically analyzed for all three fields of view. Prism version 7 (GraphPad) was used for statistical analysis and data plotting. Cell lines and tissue culture. Cells were cultured at 37 °C in 5% CO 2 atmosphere in Dulbecco's modified Eagle medium (DMEM, Life Technologies) supplemented with 10% heat-inactivated fetal bovine serum (FBS) and antibiotic/antimycotic solution (Sigma-Aldrich).