dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and future plans The puy de Dôme is a century-old meteorological observation site, and CO-PDD in its present configuration has now more than 20 years of microphysical, chemical, and remote sensing measurements. CO-PDD is dedicated to the longterm atmospheric survey in the context of climate change and offers a unique instrumentation and data set to document the complex connections linking precipitation and radiation, but also gas and particle cloud interactions. This allows CO-PDD to play an active role in European infrastructures such as ACTRIS or ICOS. Recent instrument acquisitions funded through regional, national, and European projects continue to strengthen the observation and research capacity of CO-PDD and will allow us in the future to continue to respond to the scientific air quality and climate change key questions and to provide data available for the scientific community, and develop new research activities, especially for linking the different atmospheric compartments.
An analysis using mesoscale model is necessary to investigate this point. https://doi. org/10. 5194/amt-13-3413-2020 Atmos. Meas. Tech. , 13, 3413-3445, 2020 Data availability. All data produced by CO-PDD are effectively accessible free of charge to a wide user community. Because CO-PDD is part of different national and international initiatives, including the European Research Infrastructures ACTRIS and ICOS, with specific data policies, or the international networks AERONET, not all CO-PDD information can be retrieved by a single data hub, but is split between local, national, and international data centers. Since 2020, a strong effort has been made to apply FAIR (findable, accessible, interoperable, and reusable) principles to some data produced at CO-PDD through the work done within the ENVRI-FAIR (H2020, 2020-2024) EU project that will facilitate uptake of data from different sources.
All data produced at CO-PDD are first stored on local servers including raw data. For optimizing their visibility and local use, some quicklooks, descriptions of measurement systems and near realtime data are accessible through the current OPGC internet portal  (OPGC, 2020a). A new website with a virtual observatory (OPGC, 2020b) allowing easier information on data is currently being developed. A second layer of dissemination is the national level. CO-PDD provides all data produced within national and international initiatives to the national Earth Science Data System and its specific atmospheric component AERIS (AERIS, 2020a). AERIS centralizes all information besides the GPS water vapor column measurements measured at PUY, Cézeaux and Opme which are accessible through the RESIF-RENAG databases  (RENAG, 2020; IGN, 2020)  and besides the radionucleides measurements available through the OPERA data base (IRSN, 2020) upon request.
GAWand EMEP-related information is also accessible through the EBAS data center. -AERONET-related information from sun-photometer measurements is available through the NASA Goddard Space Flight Center (NASA/GSFC, 2020). -ICOS-related information is accessible through the ICOS Carbon Portal (ICOS, 2020). -The E-Profile database contains the Opme and Aulnat wind profiler measurements (EUMETNET, 2020). Author contributions. JLB, LD and AC have written the manuscript, with contributions of many co-authors. JVB, NC, AF, and NH are current or past heads of the laboratory (LaMP) or observatory (OPGC).
The following co-authors are or have been responsible or in charge of CO-PDD instruments or data. Reactive gases: AgB, LB, AC, LD, JMP, MR, MR and StS. Greenhouse gases: MD, VK, ML, MR, MS, ET and CYK. In situ aerosol: LB, YB, AC, AF, EF, PL, AM, DP, JMP, MR, CR, KS and PV. Cloud measurements: PA, AnB, AgB, MB, TC, NC, AC, LD, AMD, RD, MJ, GM, MP, MS, MV and MW. Radionuclides measurements: LB and OM. Lidar: JLB, PF, MH, and NM. Wind profilers radar, rain gauges/radar and GPS: JLB, SB, CH, FP, YP and JVB. Wind tunnel: GF and CG. Database and data processing: SB, PC, JMP and YP.
Acknowledgements. We thank all the people who contributed to the functioning and development of the instrumented site CO-PDD without being a co-author of this article. Financial support. CO-PDD is an instrumented site of the  OPGC observatory and LaMP laboratory , supported by the  Université Clermont Auvergne (UCA) , by the  Centre National de la Recherche Scientifique (CNRS-INSU) , and by the  Centre National d'Etudes Spatiales (CNES). In addition, some instruments and operations have been founded by the  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)  and the  Commissariat à l'Energie Atomique  et aux énergies alternatives (CEA),  Région Auvergne  and  Auvergne-Rhône-Alpes ,  Conseil Départemental 63 ,  Clermont Métropole. The research activities have received funding from the  European Union 's  Horizon 2020 research and innovation programme  ( AC-TRIS2  (grant no.
654109 )). The authors are also very grateful for the financial support from the  Fédération des Recherches en Environnement  through the CPER funded by  Region Auvergne -Rhône-Alpes , the  French ministry, ACTRIS Research Infrastructure , and  FEDER European Regional funds. Some research projects funded by the  French national research agency (ANR)  also contributed to the data production of CO-PDD (fund nos. CHAIN ANR-14-CE01-0003  and  BIOCAP ANR-13-BS06-0004 ). Review statement. This paper was edited by  Johannes Schneider  and reviewed by  Matthias Beekmann  and one anonymous referee.
For example, AI-based CT scans were introduced to distinguish COVID-19 features from other diseases  [9]. Currently, the development of new diagnostic methods using deep learning algorithms is underway. Convolutional Neural Networks (CNNs) have been one of the deep neural networks (DNNs) used to screen for COVID-19 from patient data, leading to impressive results  [10]. Nowadays, the availability of large-scale CT and CXR image datasets for deep model training and testing purposes has encouraged the deep learning community to develop intelligent systems for the screening of coronavirus disease  [10, 13, 14]. For instance, the results obtained in  [10]  showed that the use of large CT datasets (618 samples in total) for the training model led to reliable COVID-19 diagnostic outcomes. Since the beginning of the pandemic, many researchers have proposed a range of deep learning-based methods for COVID-19 detection and diagnosis from X-ray scans, including  [18-22, 36, 37].
In  [18] , a deep model based on CXR data showed a high accuracy of up to 98%. The network parameters were loaded from a pre-trained ResNet50  [24]  model. Similarly, the authors of  [19]  used the transfer learning technique with CNNs to speed-up the detection of COVID-19 on X-ray samples. They achieved an accuracy, sensitivity, and specificity of 96. 78%, 98. 66%, and 96. 46%, respectively. In  [20] , the authors proposed a screening model based on ensemble learning techniques, called "DeepCOVIDExplainer", which was used to predict the COVID-19 pandemic from X-ray features  (16, 995  samples in total). The authors of  [21]  introduced a binary classification model (i. e. 2 classes to be predicted: COVID-19 or non-COVID-19, respectively) using ResNet-18 as the backbone network. The backbone network was used to extract visual patterns from CXR images following the transfer learning paradigm.
Dataset As part of this work, we collected a total of 3440 CXR samples (without data augmentation) for the training and testing of our model. Indeed, these samples are extracted and combined from three publicly available CXR datasets  [13, 14, 35] , in which COVID-19 patients, Pneumonia patients, and Normal cases Fig. 1  Temporal evolution of CXR outcomes in an older male patient with 3 consolidation changes in the lung periphery  [31]  are labeled for final prediction. More specifically, 754 COVID-19 confirmed cases, 1341 Normal cases, and 1345 Pneumonia cases are extracted from both the COVID-19 datasets  [13, 35] , and the CXR (Pneumonia) dataset [14], respectively. Pneumonia patient dataset consists of information on viral and bacterial infections from a total of 53 selected cases. The datasets mentioned are regularly updated with additional data items provided by external contributors (e.
g. , researchers, radiologists, etc. ). To reduce the imbalance in data distribution and improve generalizability, the conventional data augmentation technique is applied as a pre-processing step by rotating each CXR image by up to 20 •. Table  1  shows the statistical distribution of data obtained before and after increasing the number of data samples. We randomly divided the augmented dataset into two distinct sets; one set of 80% for training and validation and one set of 20% for testing. The validation set is used to select the best hyper-parameters when the model performs best. Fig. 7  illustrates an example of CXR images from Normal cases (Fig. 2.
Proposed method This section describes the proposed transfer learning-based method for end-toend screening of COVID-19 infected patients from CXR images. In practice, transfer learning, commonly known as cross-domain adaptation  [33] , aims to strengthen the generalization capacity of the target model by loading knowledge from the source one. Moreover, this paradigm can effectively contribute to overcoming the lack of available CXR datasets. So, in light of the specular success of transfer learning techniques as well as the generalizability of 2D and 3D CNN, we propose a hybrid framework, called Hybrid-COVID, for COVID-19 prediction. Therefore, we chosed the VGG16 pre-trained model as a baseline network for categorizing the CXR images into COVID-19, Normal, and Pneumonia. Fig. 3  show the workflow diagram of the proposed framework, which is composed mainly of six blocks, namely, respectively, a pre-trained 2D CNN (VGG16 network), an up-sampling step, a depth-wise separable convolution, a shallow 3D CNN (building block), a spatial pyramid pooling (SPP) module, followed by the classification scheme (dense layers).
Inspired by the work of  [32] , the VGG16 model is used as a backbone network for extracting generic features from a huge dataset (i. e. ImageNet). This pre-trained model has already achieved high performance for the image classification task. The up-sampling step plays a crucial role, as it is useful to reconstruct the loaded feature maps without skipping any spatial information. Besides, the depth-wise separable convolution ensure that useful features are preserved while reducing the model's computational cost. The computational complexity of stacking multiple layers of 3D convolution layers could be overcome by fine-tuning a pre-trained model, making it easier to learn deeper and more informative representations. This involves significantly reducing network parameters as well as retaining 3D spatial information that is otherwise missed by the 2D CNN.
Hybrid-COVID Hybrid-COVID is a novel transfer learning-based model architecture that aims to ensure a better compromise between accuracy and computational complexity while integrating both 2D and 3D CNNs. Table  2  shows the output shape for each level of our architecture. In the following paragraphs, each component of the model will be described in more detail. 2D CNN In computer vision, 2D CNN models have been one of the most popular deep models for image classification and understanding tasks  [26]. They allow the dynamic extraction of discriminating features directly from raw data (e. g. still images, etc. ) or from an intermediate level of the network. Visual Geometry Group Network (VGG)  [15]  is one of the deep CNN architectures that have proven their performance in extracting generic features from large datasets.
Hybridization process The goal of the hybridization process is to learn multi-domain features from multi-dimensional data cube using both 2D and 3D CNNs. The proposed Hybrid-COVID model mainly consists of two correlated sides: the 2D CNN side and the 3D CNN side. After removal of the last dense layer, the 2D CNN side (i. e. , a pre-trained VGG16 network) is used to load discriminating and abstract representations that are learned from a huge dataset (i. e. , 1000K-ImageNet). Furthermore, the 3D CNN side is fine-tuned by the 2D CNN one to learn high-level contextual features from the reshaped data. Thus, the Hybrid-COVID model is produced by integrating both 2D CNN and 3D CNN through reshaping operations. This operation can be formulated as follows: R f = f (R l 2D , R l 3D ) (5) where R l 2D and R l 3D represent the learned activations from 2D and 3D CNN sides, respectively.
Training implementation During the training process, the network parameters will be adjusted using the Back-propagation algorithm, which iteratively decreases the loss function until convergence. In this work, the categorical cross-entropy loss is employed as the loss function. Besides, we used the Adam optimizer  [28]  for a faster convergence of the algorithm. We fixed the maximum epochs at 30, the batch size at 16, and the initial learning rate at 0. 0003. Each training sample is resized to a spatial dimension of 112×112 pixels. The one-hot encoding scheme is then applied to convert the labeled data into numerical values. For model regularization, the dropout  [29]  and data augmentation  [30]  (see sub-section 2. 1) techniques are used to avoid the overfitting problem. The details of the algorithm of the proposed Hybrid-COVID model are summarized in Algorithm 1.
Metrics The proposed model shows acceptable experimental findings in terms of accuracy and efficiency on a collected dataset. To quantitatively prove this performance, therefore, standard measures are required for empirical analysis. These metrics include Precision (PREC), F1-Score (F-SCORE), Sensitivity (SEN)/Recall (REC), Specificity (SPE), and Accuracy (ACC). The PREC measures the correct prediction rate. The F-SCORE is the harmonic mean of precision and recall, which aims to strike a balance between the two. The SEN and SPE are used to measure true positive and true negative rates, respectively. The ACC determines the percentage of correctly predicted labels of total tests. These measures can be calculated as follows: P REC = T P/(T P + F P ) (6) F -SCORE = 2T P/(2T P + F P + F N ) (  7 ) SEN = T P/(T P + F N ) ( 8 ) SP E = T P/(F P + T N ) (  9 ) ACC = T P + T N/(T P + F P ) + (T N + F N ) (10) Note that T P , F P , T N , and F N , respectively, stand for true positives, false positives, true negatives, and false negatives.
Results The main objective of the proposed deep learning-based framework is to categorize CXR images into three categories: COVID-19, Normal, or Pneumonia. We emphasize that the proposed Hybrid-COVID is a 2D/3D hybrid architecture designed to detect patients suspected of having COVID-19 by analyzing CXR images for early screening of the disease. This section assesses the proposed model on the collected dataset and compares it with state-of-the-art methods.
DarkCovidNet  [36]  achieved an ACC rate of 87. 02% on a CXR dataset of 1125 images, divided into three statuses: 500 Normal cases, 500 Pneumonia cases, and 125 COVID-19 positive cases. In  [19] , a screening model based on the transfer learning technique and the VGG19 network was reached an ACC rate of 93. 48%. The X-ray images they collected were split into three sets, one set of 504 Normal cases, one set of 700 Pneumonia cases, and one set of 224 COVID-19 patients (1427 cases in total). The authors of  [37]  obtained 96. 9% SEN, 97. 5% SPE, and 95% ACC, which represents less than 1. 43%, 1. 18 %, and 1. 91% of our results, respectively. CapsNet  [43]  reached 84. 22% ACC for multi-class classification purposes. Table  4  shows the superiority of our method compared to the aforementioned methods.
Discussion The proposed transfer learning-based framework consists of a hybrid 2D/3D CNN architecture that allows the automatic detection of COVID-19 using CXR modality. The implemented Hybrid-COVID model classifies each sample in a multi-class manner by predicting a class label for each testing example according to whether it belongs to the COVID-19 class, Normal, or Pneumonia. In this paper, Hybrid-COVID exploits the potential synergy between the pretrained VGG16 model (i. e. , a 2D CNN) and a shallow 3D CNN for high-level contextual feature learning for each data sample. The proposed hybridization pipeline shows its proficiency in capturing both visual appearance and semantic detail from multi-dimensional feature space. The pipeline mainly consists of two correlated branches: the 2D CNN branch and the 3D CNN branch.
On the one hand, the 2D CNN branch encodes features along the spatial axes of the data based on the transfer learning paradigm. On the other hand, the 3D CNN branch takes advantage of pre-trained parameters to capture and extract any inherent contextual information within the boundaries of SEN and SPE. In practice, the high SEN and SPE rates of a screening test are crucial for many reasons. For instance, a false negative result can lead to a critical situation where a patient with COVID-19 will not be isolated based on this test. However, this can lead to the widespread transmission of the virus to other healthy people, both indoors and outdoors. Compared to recent work in the literature, the experimental results produced by the Hybrid-COVID model show promising findings in terms of SPE and SEN.
Ancestral and Derived Status of Alleles Ancestral and derived states of alleles were assigned by comparison to outgroup sequences. For sites polymorphic in one population, the other population was used as outgroup, and for sites with fixed differences within populations, the North American sequence was used (sites where the status was uncertain were classified as "missing"). Codon positions and 0-, 2-, and 4-fold degenerate sites in the alignment were annotated based on the common ancestral sequence for both populations. Nucleotide Diversity, Recombination, and Frequency of Sex Data were processed and analyzed using custom scripts in R and software in JMP (SAS Institute Inc. ). Recombination was analyzed using the LDhat pairwise software with default values (https://github. com/auton1/LDhat; last accessed July 21, 2020;  McVean et al.
Distribution of Fitness Effects The distributions of fitness effects were estimated using the est_dfe software (http://www. homepages. ed. ac. uk/pkeightl; last accessed July 21, 2020;  Schneider et al. 2011). The program first uses the category of "neutral" sites, here, the 4fold sites, to estimate parameters for a demographic model that best describes the population and then uses it to make a prediction for neutral sites, against which the distribution of "selected" sites is tested. A mean estimate for deleterious effects, 4N e S d , is given as a product of the effective population size, N e , and derived as a mean of an assumed gamma distribution of deleterious effects (mean of gamma is equal to b/a¼N e S d , where b, a are the shape and scale parameters of the gamma distribution, respectively, and S d is the estimate of deleterious effect); the proportions of sites at four different categories of severity of deleterious effect are also given.
Recombinational Diversity and the Frequency of Sex To compare rates of recombination in the two lineages we calculated the recombinational diversity q for each chromosome using the computer software LDhat 2. 2 (McVean et al. Estimated recombination parameter (q) and frequency of sex (S) for European and UK-only populations, plotted against Far East. Again, points are above the 1:1 line, showing higher rates of recombination and frequency of sex, in both Europe and UK; note one point is omitted from the recombination plot (but included in all analyses), as it represents an outlier for recombination in Europe (but not in UK or FE). Population Size, Sex and Purifying Selection 2002). We find q greater in Europe than Far East (chromosome averages weighted by length: 0. 000815 vs. 0. 000133 M/bp, 6.
Purifying Selection To analyze purifying selection in more detail we compared the distribution of fitness effects in the two populations. These can be estimated from the distributions of DAs among two different categories of sites, one that is assumed to be neutrally evolving and one that is under selection (using the est_dfe software;  Schneider et al. 2011). For both populations, the demographic history is best described with a model that includes at least one change in size during their history (P < 0. 0001 for both populations). For both populations, the change is an increase, consistent with the Tajima's D result above. Considering purifying selection, the mean estimate for deleterious effects, N e S d , is larger in Europe than in Far East (347. 2 vs. 207. 2, respectively, 1. 67-fold higher in EU; table  3 ), indicating stronger selection against deleterious effects in Europe, consistent with the predictions above.
Acknowledgments We gratefully acknowledge  A. Møller ,  G. Milinevsky ,  I. Chizhevsky ,  S. Kireyev ,  A. Nosovsky , and  M. Ivanenko  for logistic support and help in organizing fieldwork in Ukraine, partly supported by the  CNRS , the  Samuel Freeman Charitable Trust , and the  University of South Carolina, USA. Sampling and sequencing in the Sampaio lab  was supported by  Fundac¸ão para a Ciência e a Tecnologia (Portugal)  (Grant Nos. PTDC/BIA-MIC/30785/2017  and  UID/Multi/04378/2013  to J. P. S. ).
, 1992)  or the MDS clinical diagnostic criteria  (Postuma et al. , 2015). As detailed in the Statistical Analyses subsection, due to the differences in age and sex, statistical analyses were adjusted and included age and sex as co-variates. To account for ethnical heterogeneity of the Columbia cohort, an ethnicity covariate was also introduced for the analysis of this cohort (GWAS data was not available, therefore the reported ethnicity was used and not principal components). All three cohorts were sequenced in the same lab (McGill University), using the same protocol.
Data processing and quality control Reads were mapped to the human reference genome (hg19) with Burrows-Wheeler Aligner  (Li and Durbin, 2009). Post-alignment quality control and variant calling was performed using Genome Analysis Toolkit (GATK, v3. 8)  (McKenna et al. , 2010). Variant annotation was done with ANNOVAR  (Wang et al. , 2010). Data on the frequency of each variant in different populations were extracted from the public database Genome Aggregation Database (GnomAD)  (Lek et al. , 2016). The quality control pipeline that was used in this study is available on GitHub (https://github. com/gan-orlab/MIPVar. git). All calls with less than 25% of the reads containing the observed allele have been removed. The remaining quality control (QC) was performed using GATK and PLINK software v1.
Statistical analysis The burden of rare variants in each of the target genes was tested using the optimized sequence Kernel association test (SKAT-O, R package)  (Lee et al. , 2012). In addition, we examined the burden of specific variant subgroups including: i) Funct. : Potentially functional included all nonsynonymous mutations, stop gain/loss, frameshift mutations, splicing variants located within two base pairs of exon-intron junctions, and variants that were flagged by ENCODE (explained below). ii) NS: nonsynonymous variants. iii) LOF: loss-of-function variants included stop gain/loss, frameshift, and splicing variants located within two base pairs of exon-intron junctions. iv) CADD: variants with the Combined Annotation Dependent Depletion (CADD) score ≥ 12. 37; this threshold indicates the top 2% variants predicted to be the most damaging in the genome  (Amendola et al.