dataesr/scientific-paragraphs-categorization huggingface dataset:
proposed parameterization of the Rao's Q explicitly considers the dispersion of pixel values in a spectral space (and their relative abundance) by allowing the whole dimensionality of diversity to be captured. ACK N OWLED G M ENTSDR and DK were partially supported by the H2020 project SHOWCASE (Grant agreement No862480). DR was also supported by the H2020 COST Action CA17134 'Optical synergies for spatiotemporal sensing of scalable ecophysiological traits (SENSECO)'. RF was supported by the Swiss National Science Foundation (SNSF 175529). SV was partially supported by the H2020 ESHAPE project (grant agreement 820852). The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
We identified differentially expressed genes between disease and controls within the major brain tissue cell subtypes and the possible biological pathways affected by these changes. These findings point to distinct molecular signatures at the subcellular level, that accompany pathological diagnoses of these diseases and can help in improving diagnostic methods. Differentially expressed genes for PD cell types had statistically significant overlap with gene loci nearest to PD GWAS variants. The expression of genes associated with PD, SNCA and LRRK2, were found to be overexpressed in PD microglia. Moreover, the expression of MAPT, associated with several neurodegenerative diseases, was upregulated in PD oligodendrocytes and OPCs. By performing WGCNA, we identified a common gene module implicated in both PD and MSA which could have potential applications in drug discovery.
Gene Co-Expression Network Analysis Reveals Disease Correlated Gene Set in Neurons To gain more insight into the disease biology and to identify disease associated transcriptional changes, we performed Weighted Gene Co-expression Network Analysis (WGCNA) for neurons and obtained 12 gene co-expression modules as shown in Figure  3A. Pearson correlations of module eigen genes with one-hot encoded disease condition is shown in Figure  3B. Interestingly, we noticed that the Brown module had the strongest association with PD as well as MSA (p values < 1e-323). The corresponding Brown module eigen gene had strong positive correlation (0. 72) with MSA and a strong negative correlation (-0. 7) with PD (p value < 1e-323 for both associations). The Brown module contains 417 genes including genes DDX5, SPRED1, ATR involved in DNA damage response.
Given the motor dysfunction in these disorders, neurons play a critical role in their pathologies. Despite of the similarities, we observed contrasting molecular changes in PD and MSA compared to control subjects. D1 and D2 spiny projection neurons make up majority of striatal neurons, however they exhibit differential modulation of enriched biological pathways in PD and MSA (Figure  2B ). Dopamine-DARPP32 feedback signaling was activated in PD D1/D2 receptor neurons, whereas it was inhibited in the case of MSA D1/D2 neurons. Moreover, the Brown module of disease correlated genes, identified from WGCNA analysis, had opposite expression pattern in MSA and PD compared to Control neurons. PPI network genes of this module were enriched for dopaminergic synapse, MAPK signaling and mitophagy. These findings, together, suggest contrasting neuronal biology in MSA and PD regardless of the similarities in clinical manifestations, and warrant further studies for comprehensive understanding.
Pathway Enrichment Analysis For each of the sub-population of a given cell-type, differential gene expression for PD versus Control and MSA versus Control was performed using the FindMarkers() function in Seurat (v3. 2) and the MAST R package  [35]. Canonical pathway regulation analysis was generated from the result of differential gene expression analysis, through the use of IPA software (QIAGEN Inc. , https://www. qiagenbioinformatics. com/products/ingenuity-pathway-analysis). We used adjusted p-value cutoff of 0. 05 and log fold change cutoff of 0. 25 (fold change cutoff of ~ 1. 28, ~ e 0. 25 ) for core expression analysis in IPA. The Z-score reported by IPA assesses the match of observed and predicted up/down regulation patterns within gene sets  [36]. Missing Z-scores represent absence of a statistically significant activation state of the regulator.
Weighted Gene Co-expression Network Analysis (WGCNA) WGCNA affords identification of gene sets with similar expression patterns across different nuclei. We used the WGCNA R package in R  [38]. We used the cell x gene expression data from Seurat object, after correcting for batch effects using SCTransform function from Seurat with vars. to. regress parameter set to day of sample sequencing, and performed hierarchical clustering, dynamic tree cutting and subsequently identified 12 co-regulated gene modules. Furthermore, according to WGCNA protocol, we calculated Pearson correlations between module eigen vectors and one-hot encoded disease condition to check if any gene modules are related to disease condition. Positive correlation indicates higher expression of module genes, while negative correlation indicates reduced expression; and the absolute value of the correlation coefficient signifies the strength of association between module gene set and disease state.
After identifying modules that are highly associated with disease condition, we used the module genes to generate tissue specific (putamen) functional protein-protein interaction network (PPI) using NetworkAnalyst  [39]  and DifferentialNet database  [40]. For further investigation, network node genes were then analyzed for their association with biological pathways. Proteins from minimum PPI network were probed for overlap with genes within 100kb regions of PD GWAS variants (association p value < 1e-5) from the 2019 study by Nalls et. al. [18]. Statistical significance for overlap enrichment was examined using phyper() hypergeometric test function in R. Intersection overlap Venn diagram was generated using InteractiVenn  [41].
DATA AND CODE AVAILABILITY Single nuclei atlas (powered by cellxgene: https://chanzuckerberg. github. io/cellxgene/) is available at <link>. For a list of all supported features of the interface, visit https://chanzuckerberg. github. io/cellxgene/posts/gallery. R markdown code files of the analysis will be made available at the time of publication. Seurat v3 toolkit for single cell genomics: https://satijalab. org/seurat/ WGCNA package: Installation and documentation https://horvath. genetics. ucla. edu/html/CoexpressionNetwork/Rpackages/WGCNA/ NetworkAnalyst online interface: https://www. networkanalyst. ca/ Enrichr: Gene list enrichment analysis tool: https://amp. pharm. mssm. edu/Enrichr/ InteractiVenn: Analysis of sets through Venn diagrams http://www. interactivenn. net/.
As a single image can give a wide variety of information, the first step of image analysis consists of choosing a readout (i. e. , which data is worth collecting). For example, protein quantity can be assessed by measuring staining intensity, whereas tumor or bacterial colony growth can be followed by measuring the stained area. In the case of apoptosis, the most commonly found readouts are the "number of apoptotic cells" or an "apoptotic index" that has various definitions depending on the lab or experimenters  [4] [5] [6] [7]. When the readout is the "number of apoptotic cells", many studies use a manual counting, implying that an experimenter defines interesting spots and count them. Manual counting is reliable because the expert eyes of experimenters are able to distinguish the signal of interest from background noise better than any machine.
Software such as Imaris (Bitplane, Belfast, UK) or Matlab (MathWorks, Natick, MA, USA) display default functions for signal quantification  [8, 9]  which usually comprise image processing to yield a rapid result. However, these programs are not open source and the methodology used to obtain the values is often hard to access, meaning that users have only a limited control on their implementation. Moreover, they usually are computationally demanding and thus require powerful devices to run. For all those reasons, many researchers prefer to work with open-source software with which one can develop its own protocol for image processing and quantification. Several open-source image analysis software such as QuPath (University of Edinburgh, Edinburgh, UK) or Icy (Institut Pasteur, Paris, France)have been developed but ImageJ (NIH, Bethesda, MD, USA), and its implemented version Fiji (Fiji Is Just ImageJ, NIH, Bethesda, MD, USA), remain the most widely used as they are highly versatile tools  [10] [11] [12].
Once the readout has been chosen (i. e. , intensity, number of objects, area), the image has to be modified to get rid of background noise and artefacts in order to improve segmentation. Images typically display three major kinds of defects: 1. general background noise; 2. isolated background pixels with aberrant high intensities; 3. groups of background pixels with aberrant high intensities. Many functions are available to improve image quality on ImageJ/Fiji software, but they often involve the experimenter's appraisal. Unfortunately, the more the experimenter is involved, the more it is difficult to ensure that all the images have undergone the same process. However, most of the time, it is impossible to totally exclude experimenter's involvement. We previously showed in the Drosophila model that overexpressing rbf1, the homolog of the human tumor suppressor RB1, induces apoptosis in proliferating cells  [13].
Following three washes in PBST, TUNEL staining was performed according to manufacturer's instructions (ApopTag Red In situ apoptosis detection kit, merck-millipore, Temecula, CA, USA). Finally, wing discs were mounted in ProLong Diamond (Invitrogen, Waltham, MA, USA) and images were acquired using a Leica SPE inverted confocal microscope (Leica, Wetzlar, Germany) at 568 nm for TUNEL and 612 nm for anti-cleaved Dcp-1 stainings (28 discs for the vg > rbf1 genotype and 39 discs for the vg > rbf1, debcl E26 ). Image analysis was performed exclusively with the Fiji software and the exact same zone was selected for both assays.
Choosing a Readout According to the Biological Question The readout is the data used to translate the intensity of the biological effect into numbers. Therefore, the chosen readout should be consistent with the biological question and the tool used to study it. For instance, intensity can be measured to assess the amount of a stained component. In the study of apoptosis rate, regardless of the assay, a cell is apoptotic or not. Thus, quantifying the staining intensity, even if it can somehow make sense, does not seem to be the best option for accurate quantification of apoptosis. Conversely, as long as apoptotic cells can be separated from each other (low apoptosis rate, scattered pattern, or intracellular discrete staining), counting the number of objects equates counting the number of apoptotic cells, which constitutes a valid readout.
If this readout cannot be used, another valuable readout is the stained area. These count or area measurements can be used as a readout per se or can be used as primary data for calculation of an apoptotic index or score. For instance, such apoptotic index can correspond to a percentage of apoptotic cells obtained by dividing the number of apoptotic cells (count readout) by the total number of cells in the tissue section. As well, another apoptotic index can be obtained from the stained area by dividing it by the cells number (obtained by plasma membrane or nuclear co-staining), by the area of interest (surface of a cellular clone or of the tissue section). These apoptotic indexes do not indicate the actual number of apoptotic cells, which can be estimated using the count readout, but this number of apoptotic cells is rarely necessary, and those readouts provide estimates that are sufficient to compare the apoptosis rate between different samples.
Thresholds for these images were also determined using the 16 algorithms. As expected, algorithms inducing obvious unrealistic segmentation of the apoptotic signal yielded threshold values very far from the range of the ones determined by experimenters (Figure  4a, b ; compare IsoData and Intermode with Exp. Data not shown). On the contrary, Moments, Otsu, and experimenter threshold values are in the same range (Figure  4a, b ). From this global analysis, it could seem that algorithms can be as efficient as human eye for threshold determination (compare for instance Otsu and Exp in Figure  4b ). However, visually, Otsu capability to determine a relevant threshold seemed irregular. We thus further deepened our data analysis and compared the threshold values obtained not globally but for each image.
The question resides then in determining whether this amount of background noise is important enough to alter quantification. In the case of TUNEL staining (Figure  4c ), values are quite low, thus it certainly affects quantification rather significantly. By contrast, when images come from anti-cleaved Dcp-1 staining, the threshold values given by Moments are much closer to the ones obtained by experimenters, they actually seem very similar to the lowest values determined by experimenters (Figure  4d ). Therefore, one could assume that the variability of threshold values between Moments and an experimenter is comparable to the one that exists between experimenters. We tested this by comparing the relationship between the two most distant experimenters' batch of measurements to the one between Moments and its closest dataset.
Accurate brain tumor segmentation is crucial for clinical assessment, follow-up, and subsequent treatment of gliomas. While convolutional neural networks (CNN) have become state of the art in this task, most proposed models either use 2D architectures ignoring 3D contextual information or 3D models requiring large memory capacity and extensive learning databases. In this study, an ensemble of two kinds of U-Net-like models based on both 3D and 2. 5D convolutions is proposed to segment multimodal magnetic resonance images (MRI). The 3D model uses concatenated data in a modified U-Net architecture. In contrast, the 2. 5D model is based on a multi-input strategy to extract low-level features from each modality independently and on a new 2. 5D Multi-View Inception block that aims to merge features from different views of a 3D image aggregating multi-scale features.
The Asymmetric Ensemble of Asymmetric U-Net (AE AU-Net) based on both is designed to find a balance between increasing multi-scale and 3D contextual information extraction and keeping memory consumption low. Experiments on 2019 dataset show that our model improves enhancing tumor sub-region segmentation. Overall, performance is comparable with state-of-the-art results, although with less learning data or memory requirements. In addition, we provide voxel-wise and structure-wise uncertainties of the segmentation results, and we have established qualitative and quantitative relationships between uncertainty and prediction errors. Dice similarity coefficient for the whole tumor, tumor core, and tumor enhancing regions on BraTS 2019 validation dataset were 0. 902, 0. 815, and 0. 773. We also applied our method in BraTS 2018 with corresponding Dice score values of 0.
Since accurate tumor segmentation is determinant for surgery, followup, and subsequent treatment of glioma, finding an automatic and reproducible solution may save time for physicians and contribute to improving the clinical assessment of glioma patients. Based on this observation, the Multimodal Brain Tumor Segmentation Challenge (BraTS) aims at stimulating the development and the comparison of the state-of-the-art segmentation algorithms by making available an extensive preoperative multimodal MRI dataset provided with ground truth labels for three tumor tissues: enhancing tumor, the peritumoral edema, and the necrotic and non-enhancing tumor core. This dataset contains four modalities: T2-weighted (T2), fluidattenuated inversion recovery (FLAIR), T1-weighted (T1), and T1 with contrast-enhancing gadolinium (T1c)  (3) (4) (5) (6) (7).
In addition, we propose that each of these four paths contains what we define as a 2. 5D Multi-View Inception module (Figure  1C ) that allows the extraction of features in the different orthogonal views of the 3D image: axial, coronal, and sagittal planes and different scales, merging them all in each forward pass. The design of the 2. 5D Multi-View Inception module is inspired by the Inception module of GoogLeNet  (39). Unlike the Inception module, we use 2. 5D anisotropic filters instead of 3D or 2D isotropic filters, and we add all the resulted feature maps instead of stacking them. This module has two main characteristics: the first one is the use of convolutions with different receptive field orientation on each image planeaxial, sagittal, and coronal planes-by using anisotropic kernels oriented in each direction.
Considering the different nature of the input data, we call the proposed method Asymmetric Ensemble of Asymmetric U-Net (AE AU-Net). The proposed ensemble model is based on the training of seven models. According to a 5-fold cross-validation strategy, both the 3D AU-Net and the 2. 5D AU-Net were trained five times each with different subsets of the dataset and varying weights of initialization. The seven best-performing models were selected; we finally chose four from 3D AU-Net and three from 2. 5D AU-Net, this mainly due to memory constrictions. The ensemble was obtained by averaging the output probability estimates of the labels for each voxel of the image.
BraTS Dataset We validated our model in the 2019 BraTS dataset, which consists of pre-operative MRI images of 626 glioma patients. Each patient's MRI images contain four modalities T2-weighted (T2), fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1), and T1 with gadolinium-enhancing contrast (T1c). All images were segmented manually by one to four raters, following the same annotation protocol. Experienced neuro-radiologists approved their annotations. The data are divided into three sets by the organizers of the BraTS challenge: training, validation, and test dataset. The training set is the only one provided with expert manual segmentation and the grading information of the disease. The training dataset contains images of 335 patients, of which 259 are HGG and 76 are LGG. The validation and test datasets include the same MRI modalities for 125 and 166 patients, respectively.
In the training dataset, the ground truth labels are provided for three tumor tissues: enhancing tumor (ET-label 4), the peritumoral edema (ED-label 2), and the necrotic and nonenhancing tumor core (NCR/NET-label 1). From these classes, we defined the following tumor regions to train our models: • The whole tumor (WT) region. This includes the union of the three tumor tissues ED, ET, and NCR/NET (i. e. , label = 1∪ 2 ∪4). • The tumor core (TC) region. This is the union of the ET and NCR/NET (i. e. , label = 1 ∪4). • The enhancing tumor (ET) (i. e. , label = 4).
Pre-processing The multimodal scans in the BraTS challenge were acquired from multiple institutions, employing different clinical protocols, resulting in a non-standardized distribution. The challenge organizers performed several preprocessing steps to homogenize the dataset. The images from different MR modalities were first co-registered to the same anatomical template. The SRI24 multichannel atlas of a normal adult human brain template (42) was used. The template was obtained by affine registration using the Linear Image Registration Tool (FLIRT)  (43)  developed by the Oxford Center for Functional MRI of the Brain (FMRIB) and available in the FMRIB Software Library (FSL)  (44). The original images were acquired across different views and variable anisotropic resolution. All the images were re-sampled and zeropadded to the same isotropic resolution (1.
Post-processing We implemented the post-processing of the prediction maps of our proposed model to reduce the number of false positives and enhance tumor detection. A threshold value representing the minimum size of the enhancing tumor region was defined, as suggested in Isensee et al. (19) , and the label of all the voxels of the enhancing tumor region was replaced with one of the necrosis regions when the total number of predicted ET voxels was lower than the threshold. The threshold value was estimated as the one that optimizes the overall performance in this region in the validation dataset. Besides, as proposed by McKinley et al. (20) , if our model detects no tumor core, we assume that the detected whole tumor region corresponds to the tumor core. We have relabeled the region as a tumor core.
Implementation and Training The Dice loss function was used to cope with class imbalances and weighted Dice coefficient (WDC) as the evaluation metrics to look for the best-performing model  (12). Since ground truth segmentations were only provided for the training dataset, we randomly selected 20% from the training set as our internal validation set, taking the same percentage of images from LGG and HGG patients. We trained our models using the remaining 80%. The networks were trained for 100 epochs using patches of 128 × 128 × 128 and Adam optimizer (47) as a back-propagation algorithm, with an initial learning rate of 0. 0001 and a batch size of 1. The learning rate was decreased by five if no improvement was seen, on the validation set, within 10 epochs. Our model takes around 8 h to be trained.
Qualitative Results Figure  4  shows an example of segmentation results obtained on two HGG patients with 3D AU-Net, 2. 5D AU-Net, and AE AU-Net. For simplicity of visualization, only the T1ce and FLAIR images are presented. The three orthogonal views, axial, coronal, and sagittal, are displayed for better representation of the volumetric segmentation. The green, yellow, and red regions correspond, respectively, to the whole tumor (WT), the enhancing tumor (ET), and the tumor core (TC). The ground truth provided with the BraTS dataset is presented on the third row, followed by 3D and 2. 5D AU-Net results on rows 4 and 5, and the results of the proposed AE AU-Net are presented in the last row. It can be observed that the segmentation results of the 3D and 2. 5D single networks provide proper segmentation, although with some defects that are highlighted with white arrows.
Looking at the different views and patients, it can be noticed that the performance of the 3D and 2. 5D approaches are varying and that both can present accurate segmentation or defects. In contrast, the proposed AE AU-Net appears globally more accurate, benefiting from both the models to decide more correctly the labeling of the voxels. In Figure  5 , we present two examples of segmentation made by our AE AU-Net model ensemble. Both patient images were taken from the 2018 validation dataset. In the top, we show the patient identified as CBICA_AZA_1 in the BraTS dataset. We selected the same example reported in Isensee et al. (19)  for a qualitative comparison. This case example has been reported as a difficult case for segmentation due to blood vessels close to the enhancing tumor, as can be seen in the T1ce image (arrow 1).
Quantitative Results The corresponding quantitative criteria obtained with the different approaches are presented in Table  1. For both the 3D and 2. 5D AU-Net, the results correspond to an ensemble from 5-fold cross-validation models. Results for all methods are presented as averagea ± SD obtained over the BraTS 2019 validation dataset. It can be observed that 3D and 2. 5D AU-Net got relatively similar scores, with better Dice achieved by 3D AU-Net on the enhancing tumor, and better Hausdorff distance obtained by 2. 5D AU-Net on the enhancing tumor and tumor core. The proposed AE AU-Net improved all the scores, reaching the best values on five criteria out of six, illustrating that each architecture learned different features to address the segmentation problem and complement each other. It achieved Dice scores of 0.