dataesr/scientific-paragraphs-categorization huggingface dataset:
In a separate study, a large deletion in SOX6 was identified in a patient with global developmental delay and progressive parkinsonian symptoms including rest tremor  [77]. Interestingly, SOX6 has been identified as a determinant of gastric dopaminergic neuron development  [59] , which may suggest a role for this gene in the enteric nervous system dysfunction and pathology observed in PD. SOX6 may also have a role in melanoma. In a cancer cell line expression study, SOX6 was found to be highly expressed in melanoma cells but was not detectable in eight other cancers  [86]. Additionally, SOX6 was identified as a candidate melanoma driver gene  [72]  in a screen and SOX6 may be a melanoma stem cell marker  [51]. While we observe evidence for differential expression between neuropathologicallyconfirmed PD and controls for PIEZO1, TRAPPC2L, and SOX6 in at least one substantia nigra microarray dataset, these results should be interpreted with caution.
Neurodegenerative diseases like PD are characterized by dramatic changes in cell-type proportions  [49]  which will impact differential expression results. Thus, the PD-associated differential expression of the eGenes highlighted in this study should be confirmed in larger, RNA-sequencing-based datasets -as these become available -in order to allow for the inclusion of important covariates like cell-type proportions, sex, age of death, and RNA quality among others. Nevertheless, the fact we observe differential expression of SOX6 in the same direction as previously published  [70]  is reassuring. Investigating for differential expression of the eGenes highlighted in this study in the context of melanoma is challenging given our focus on the risk of developing melanoma. Nevertheless, a recent GTEx v8-based, multi-tissue TWAS resource (phenomexcan.
Our results support such future research efforts. 5 ). Disease-inferred gene expression profiles were generated from the processed melanoma and METAPD summary statistics using FUSION/TWAS software and correlation between these profiles was estimated using RHOGE software. METAPD is an inverse-variance-weighted meta-analysis of the three independent Parkinson disease summary statistic datasets. The red dashed line demarks the multiple test corrected P threshold of 1. 04 × 10 -03 (0. 05 / 48) while the blue dotted line demarks the nominal threshold, P = 0. 05. All GWAS summary statistic datasets were standardized and filtered using the same pipeline. We annotated all variants with dbSNP v151 rs-identifiers and gnomAD non-Finnish European (NFE) allele frequencies. We filtered variants as to only include bi-allelic variants with rsidentifiers and further removed variants with an effect allele frequency less than 0.
05, variants with strand ambiguous alleles, variants with limited support, i. e. those supported by a low sample or study number, and variants that were not reported in the HapMap3 study. Presented are the numbers of variants overlapping between each dataset. METAPD is an inversevariance-weighted meta-analysis of the three independent Parkinson disease summary statistic datasets. PD: Parkinson disease; AD: Alzheimer disease; FTD: Frontotemporal dementia. We inferred cross-tissue, eGene-disease associations based on standardized and processed melanoma and METAPD GWAS summary statistics using UTMOST software and the Genotype-Tissue Expression Project (GTEx) v6 reference panel. METAPD is an inversevariance-weighted meta-analysis of the three independent Parkinson disease (PD) summary statistic datasets.
Acknowledgements We thank  Dr. Susan Searles Nielsen  for helpful comments on a previous version of this manuscript. This work was supported by grants from the  National Institutes of Health  ( R01AG044546 ,  P01AG003991 ,  RF1AG053303 ,  R01AG058501 ,  U01AG058922 ,  K01AG046374 ,  K08NS101118  and  R01HL119813 ), the  Alzheimer Association  ( NIRG-11-200110 ,  BAND-14-338165 ,  AARG-16-441560  and  BFG-15-362540 ). This work was supported by access to equipment made possible by the  Hope Center for Neurological Disorders and the Departments of Neurology and Psychiatry at Washington University School of Medicine. We acknowledge the support of all participants, investigators, and researchers from the Melanoma-Meta-analysis Consortium; complete acknowledgements for this meta-analysis can be found in the supplemental data of Law et al.
, 2015. [45] We thank the  International Genomics of Alzheimer Project (IGAP)  for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i-Select chips was funded by the  French National Foundation on Alzheimer disease  and related disorders. EADI was supported by the  LABEX (laboratory of excellence program  investment for the future) DISTALZ grant,  Inserm ,  Institut Pasteur de Lille, Université de Lille 2  and the  Lille University Hospital. GERAD/PERADES was supported by the  Medical Research Council  (Grant n°  503480 ),  Alzheimer Research UK  (Grant n°  503176 ), the  Wellcome Trust  (Grant n°  082604/2/07/Z ) and  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant n°  01GI0102 ,  01GI0711 ,  01GI0420.
Constellation-level parameters such as the reliability of the de-orbitation system, altitude spread, or de-orbitation time can be accounted for in the model, and they can be varied to perform sensitivity analysis. Finally, the number of LEO objects is several orders of magnitude higher than above 2 000 km, with many different intersecting trajectories, which makes a statistical description more relevant. The INDEMN code was developed internally at Share My Space in Paris and uses a statistical model that derives the evolution of various populations at multiple altitudes. The model was initially inspired by the work of G. -L. Somma  [7, 8, 9] , improved by an object-oriented code architecture (the reader can refer to Appendix B for some details about the code architecture), that allows for an arbitrary number of different object populations and a more accurate description of the size distribution function of each population.
Overview INDEMN is an object-oriented Python 3. X code developed by Share My Space. It features a statistical model deriving the evolution of various populations in various altitude shells and it can be used both for scientific and industrial purposes. The model is based on source and sink terms that include future launches, explosions, collisions, drag, and de-orbitation policies and compliance. Many populations can be implemented and their interactions are computed at each time step. The collision frequency between two given populations depends on the local density of each population, at a given time. After being initialized using object databases for the larger objects (> 10 cm) and statistical models for smaller ones (< 10 cm) according to the NASA breakup model  [10] , the density of objects belonging to each population is computed dynamically with its radial dependence.
The overall operating principle of INDEMN is illustrated in Fig. 1  The main part of the code consists in the computation of the evolution of the density of various object populations depending on their altitude only. Choice was made to limit the boundaries of the LEO environment from 200 to 2 000 km. Below 200 km, the lifetime of an object is of a few days only and orbit propagation models are more relevant. Above 2 000 km, the drag compensation model does not apply anymore and the very low object density limits the relevance of any statistical approach. The whole model relies on the somewhat strong assumption that the orbital plane angles follow a chaotic law on a long time scale. Indeed, some objects are organized into specific orbit planes such as sun-synchronous satellites or constellations.
Space objects catalogs The main input of the code consists of the data of the space objects orbiting around Earth in the LEO environment. The main catalog is maintained by the US STRAT COM and made available on the space-track. org platform  [12]. This data is mainly stored in the Two Line Element (TLE) format and can be retrieved automatically by an Application Program Interface (API) request. The information for each object is stored in a Python data structure whose primary characteristics are the orbital elements directly initialized from the TLE data, and that includes various methods for secondary orbital properties, and for orbit propagation. However, orbit propagation of the objects is not used in the core of the code. A flag is added to the object definition that specifies the type of object (collision/explosion debris, rocket body, intact object, mission related object etc.
Atmosphere description The residual atmospheric density is extracted from the NRLMSISE-00 Atmosphere model  [14]  that provides angle and altitude resolved density data since 1960. The data was averaged over the orbital angles and fitted to a sinusoidal function. This fit tends to minimize the amplitude of the temporal variations of density compared to other models such as the ones implemented in ESAs MASTER or NASAs ORDEM. The effect of the drag varies considerably depending on the altitude. The residual atmosphere mass density is fitted to : ρ(t, h) = ρ m (h) + ρ A (h) cos 2π T s (t -t 0 ) (1) where T s is the period of solar activity (11. 2 years), and t 0 is a date of maximum solar activity (e. g. January 1 st , 2002). The parameters ρ m (h) and ρ A (h) were fitted using the NRLMSISE-00 atmosphere model.
General parameters The input parameters are sorted in Table  1  according to the level of expertise of the user. The mission time, the ballistic coefficient, and the explosion rate can be defined for each population independently. The date of the catalog t cat corresponds to the date when the data was retrieved from the NORAD database. The relative velocity between two colliding objects v re f is assumed constant, which is a quite strong assumption that was however historically accepted  [15]. The relative velocity primarily depends on the orbital plane angles. However, since the model depends only on the altitude, this influence is averaged out. The fraction of catastrophic collisions is fixed to 0. 3187,  according to former studies of past collision events  [18]. The relative velocity should also depend on the altitude but the difference is not critical when only the LEO environment is treated.
Source terms Constellation objects and intact objects populations are increased according to predictions of future launches. The default background launch profile is calculated from a fit of the number of launches performed in year 2016 as a function of the altitude. Even though the fit was performed with the data of year 2016, Fig. 3  shows that it matches well with the launch data for the period 2009-2019, which indicates that the background population of satellites in LEO is replenished in a quite steady manner. Specific constellations can have customized launch scenarios implemented, as a function of time and altitude. Explosion objects are generated from populations of intact objects and constellation satellites. The number of objects generated by one explosion for a population of explosion objects bounded by (l min , l max ) is: N e = 6 l -1.
Conclusions Some of the first results obtained with the INDEMN software were presented in this paper. Among all the parameters of the constellation, the de-orbitation time was identified as a key driver of the total collision risk for end-of-life satellites with trackable objects. The cumulated collision probability for constellation satellites was estimated over five decades, based on size distribution functions for fragmentation debris. For industrial purposes, it would be useful to implement best and worst case evolution scenarios, and to perform an uncertainty quantification analysis. The worst case scenario would include all the constellations that have been announced since 2015, which mounts up to 15 000 more satellites in LEO. The figures presented in this paper are the results of a 1D model where all the objects have the same relative speed when they collide.
Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i. e. , constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github. com/vivek231/Skin-Project.
Recently, researchers have used state-of-the-art image segmentation based deep learning models to obtain more accurate skin lesion segmentation. In  [17] , a CNN-based fully convolutional residual network (FCRN) and multiscale contextual information were proposed to segment skin lesions. However, this method is not able to properly segment low contrast dermoscopic images and that includes hairs and irregular skin lesion shapes. Also, this method cannot fully utilize the discrimination capability of the deep CNN with limited training data, according to the authors of the paper. Furthermore, Bissoto et al. [18]  used the U-Net network to segment skin lesions. They assessed their model on the ISIC2018 skin lesion dataset and achieved an IoU score of 72. 8%. The main limitation of this method is that it requires several pre-processing steps for removing noise present in dermoscopic images.
To accurately segment the lesion boundaries, Vesal et al. [24]  proposed the SkinNet model, which is based on the U-Net architecture. The authors replaced standard convolution layers at every level of both the encoder and decoder with densely connected convolution layers. The SkinNet model was evaluated on ISBI2017 dataset and achieved a Dice coefficient of 85. 10% and an IoU score of 76. 7%. To extract rich features from a dermoscopic image, Sarkar et al. [25]  utilized a residual network weighting and a spatial pyramid pooling network. They also proposed the use of a loss function called End Point Error (EPE) to preserve the lesion boundaries. Furthermore, Jahanifar et al. [26]  integrated a multilevel segmentation algorithm, regional contrast, background descriptors, and a random forest regressor to create saliency scores for each region in the image.
This method gives poor segmentation results with low contrast dermoscopic images. To add image appearance information as well as contextual information, Mirikharaji et al. [20]  employed the U-Net based method to predict the pixel-wise probability of a skin lesion segmentation. It achieved a Dice coefficient of 90. 11% and an IoU score of 83. 30% on ISBI2016 dataset. Furthermore, Li et al. [21]  proposed a transformation consisting of a self-ensemble model, which enhances the regularization effects by utilizing the unlabeled data. It achieved a Dice coefficient of 87. 40% and IoU scores 79. 87% on ISBI2017 dataset. In turn, Venkatesh et al. [22]  used the U-Net model that is based on multi-scale input with a shortcut connection at each block of the U-Net. The suggested method has evaluated on ISBI2017 dataset, obtaining a Dice coefficient and IoU scores of 85.
60% and 76. 40%, respectively. Galdran et al. [23]  also exploited the U-Net architecture and used color constancy methods to normalize the color throughout the dataset images while retaining the estimated illumination information, enabling them to randomly change the color and illumination of normalized images during the training process. They achieved a Dice coefficient of 82. 40% on ISBI2017 dataset. Bi et al. [27]  proposed a FCN-based class-specific training to extract visible features of different kinds of skin lesions and a probability-based step-wise combination of the derived class-specific segmentation maps to guarantee visible persistence of the segmented regions. Also, Yuan  [6]  introduced a FCN-based model, which contains 29 layers to segment skin lesions from dermoscopic images.
C. GAN-BASED METHODS Recently, several GAN-based segmentation models have been applied to medical images. For instance, cGAN has been used in  [11]  and  [36]  to segment breast cancer subtypes, in which a Dice loss was used in the generator network to refine pixel-wise segmentation results. To segment skin lesions, Xue et al. [14]  proposed an adversarial-based model with residual blocks and skip connections. The model was evaluated on ISBI2017 dataset, achieving a Dice coefficient of 86. 70% and an IoU score of 78. 50%. Moreover, Bisla et al. [19]  introduced the Deep Convolutional Generative Adversarial Network (DCGAN) and ResNet-50 models to jointly segment the skin lesion and classify the lesions into benign and malignant. They exploited pre-processing steps to suppress the artifacts from the skin images.
With ISBI2017 and ISIC2018 test datasets, they obtained IoU scores of 77. 00% and 70. 20%, respectively. Most of the methods stated in Table  1  have utilized data augmentation/preprocessing techniques in the training phase while others applied postprocessing techniques (e. g. , morphological operations) on the resulting masks. In turn, during the training of the proposed FCA-Net, we utilize the original images of ISBI2016, ISBI2017, and ISIC2018 datasets without applying any data augmentation technique. For a fair comparison with the state-of-the-art methods, we separately trained and tested the proposed model on the training and testing sets of the aforementioned datasets. The proposed cGAN model that includes FCA blocks and a multi-scale stage highlights the most important features (of a highly receptive field) and disregards the artifacts from images.
IV. EXPERIMENTAL RESULTS AND DISCUSSION Datasets: To assess the efficacy of the proposed model, we use three skin lesion challenge datasets, which are publicly available: ISBI2016 1  [40], ISBI2017  2  [13] and ISIC2018 3   [41]. The images of the datasets were acquired using different devices at several medical centers worldwide. In the ISBI2016 dataset, the training set has 900 annotated images while the testing set has 379 annotated images. The size of the images varies from 542 × 718 to 2848 × 4288 pixels. The ISBI2017 dataset has three sets: the training set (2000 images), validation set (150 images) and testing set (600 images). In the ISIC2018 dataset, the training set has 2594 images, the validation set has 100 images and the testing set contains 1000 images. Ground truth of mask images (provided for training and used internally for scoring validation and test phases) were generated using several techniques, but all data were reviewed and curated by practicing dermatologists with expertise in dermoscopy.
Our model has been trained on randomly chosen 2546 skin lesion images from the ISIC 2018 dataset and validated on the rest 48 images. The model is then evaluated on the validation and testing sets. This process has only been used to tune the hyperparameters of the model. Note that the trained model is evaluated on the testing sets of ISBI2016 and ISBI2017 datasets. Our model is further assessed on the ISIC2018 validation and test sets. Evaluation Metrics: Assume A is the ground truth and B is the segmented mask (using our model). The true positive (TP) rate is defined as TP = A ∩ B, which is the region of the segmented part common in both A and B. The false positive (FP) rate is defined as A ∩ B, which is the segmented area not belonging to A. The false-negative (FN) rate is defined as A ∩ B, which is the true area missed by the segmentation model.
The operating system was Ubuntu 16. 04 using a 3. 4 GHz Intel Core-i7 with 16 GB of RAM. The main required packages involve Python 3. 6, CUDA 9. 1, cuDNN 7. 0 and PyTorch 0. 4. 1. The codes of the proposed model are publicly available at https://github. com/vivek231/Skin-Project.
A. ABLATION STUDY To demonstrate the effect of each part of the proposed block, an ablation study has been done. We firstly trained a baseline (BL) model without adding the channel attention or factorized convolution blocks. Then, we added the channel attention block to the encoder layers of the generator network (called the BL+CA model). Furthermore, the factorized kernel was also separately added to the encoding layers (called the BL+FK model). Finally, the proposed model (FCA-Net) is constructed by adding both CA and FK blocks with and without multi-scale. Note that all models used in this ablation study have been trained on the ISIC2018 dataset and tested on the ISBI2016 and ISBI2017 datasets. We did not consider the testing images of ISBI2016 and ISBI2017 that appear in the training set of the ISIC2018 dataset.
In summary, the combination of the two branches of the FCA block helps each other to provide the best results of the four variants. In our experiments, we added the proposed FCA block to different state-of-the-art image segmentation methods (FCN8  [33] , U-Net  [34] , SegNet  [35] , LinkNet  [43]  and RefineNet  [44] ) and evaluated them on ISBI2016 and ISBI2017 dataset. Tables  3  and 4  present the results of all models with and without the proposed FCA block. It is important to note that all compared models have a multi-scale input layer (they have the same configuration of the multi-scale input layer of our model). We can see that all models give better results when adding the FCA block. Besides, our model gives the best results among all evaluated methods. To demonstrate the effectiveness of the proposed model, we provide descriptive statistics of Dice and IoU scores.
In Fig. 6 (a, b ), we show the boxplots of the Dice and IoU scores of the proposed model, FCN8, U-Net, SegNet, LinkNet, and RefineNet models with the ISBI2016 dataset. As shown in Fig. 6  (a), among the tested models, the proposed model has the highest mean Dice score and the smallest standard deviation with few outliers. Indeed, with the 379 images of the test set of ISBI2016, the proposed model produces 13 outliers of the boxplot with IoU scores while the LinkNet and SegNet models have 18 and 17 outliers, respectively. In turn, Fig. 6 (c, d ) shows the boxplots of Dice and IoU scores of the six models with ISBI2017 dataset. The FCN8 and U-Net models have no outliers but the deviation of IoU metric is much bigger than the one of our model as shown in Fig. 6  (d ). With the 600 images of the test set of ISBI2017, our model has 10 outliers with IoU scores while the RefineNet model has 14 outliers.
B. COMPARISON WITH THE STATE-OF-THE-ART METHODS In Table  5 , the FCA-Net model is compared with 8 state-of-the-art skin lesion segmentation methods on ISBI2016 dataset. For fair comparisons, we have also trained and tested FCA-Net with the ISBI2016 training set and evaluate them with the ISBI2016 test set. As shown, FCA-Net gives a Dice score of 92. 80% and an IoU score of 86. 41% that is the second best value. Li et al. [45]  achieve a sensitivity score higher than our model but we obtain better specificity and accuracy. Table  6  shows a comparison between the results of FCA-Net and 11 state-of-the-art skin lesion segmentation methods on ISBI2017 dataset. For fair comparisons, we have also trained FCA-Net with the ISBI2017 training set and evaluate it with the ISBI2017 test set. The IoU score of  [21]  is slightly better than our FCA-Net method, our score is the second-best.