dataesr/scientific-paragraphs-categorization huggingface dataset:
1 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 1. To facilitate user interaction, the initial replicon selection from the database is based on the first letter of the corresponding organism name. A selectable scrolling list of organisms with the same initial is then provided. 2. BAGET 2. 0 accepts the submission of user-provided annotated genomes in the GenBank GBFF format. Uploaded genomes are processed in real time and their features are exposed similarly to database entries. User-uploaded genomes are not added to the database and remain confidential. 3. Upon selection or user-uploading of a replicon, a corresponding gene list is created where individual genes can be selected to generate a complete report.
4. Text queries on the annotation of uploaded or database genomes provide a user-selectable gene list. 5. Gene selection generates a proportionally scaled color-coded context map covering a selectable genomic interval between 5 and 30 kb. The gene of interest is depicted in red at the center of the map in 5 0 to 3 0 orientation. Surrounding genes in the same orientation are shown in green while the antiparallel genes, in blue. Information on mapped genes is accessible by mouse hovering. Navigation through mapped genes is accessible by mouse clicking. 6. The DNA sequence of the gene of interest and the immediately surrounding genes is shown using the same color-code and genetic orientation as the context map. For protein-coding genes, the colored sequences refer to open reading frames. The intergenic regions are shown in black and gene overlaps are displayed using text background colors.
Results BAGET 2. 0 provides a fast and effortless access to every gene from over 54 000 replicons corresponding to >25 000 bacterial and archaeal genomes. Its user interface consisting of a single page loaded only once at startup does not require navigation through numbers of forms. In order to mimic user-friendly desktop applications, BAGET 2. 0 exchanges data with the server in asynchronous mode with a significant increase in performance. Only three mouse clicks were required to access the BD01_0175 gene sequence and genomic context from Thermococcus nautili 30-1 (Fig. 1 ). The major assets of this web service reside in its user friendliness, daily updated database and quality of the exportable results. To ensure user confidentiality, submitted data, generated results and PDF reports are never saved to disk but reside exclusively in volatile memory for the duration of the user session.
Acknowledgements This work was supported by  Inserm ,  Université Sorbonne Paris Nord , and  Université de Paris. A. D. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Action -Innovative Training Network (MSCA-ITN) DeLIVER , grant agreement no. 766181. C. L. B. acknowledges funding from the  "Recherche Hospitalo-universitaire" Innovations for Liver Tissue Engineering  ( RHU iLite ), grant no. ANR-16-RHUS-0005.
In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98. 73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy.
Thus, taking advantage of the available information of international resources without any political and/or economic borders, we propose an innovative system based on viral gene sequencing. Using a CNN to separate Coronaviruses belonging to different strains  35  , including SARS-CoV-2, we apply techniques inspired by XAI in computer vision to discover representative cDNA sequences that the network uses to classify SARS-CoV-2. We then validate the discovered sequences on datasets not used during the training of the CNN, and show how to exploit them to create a novel, highly informative set of sequence features (e. g. viral sequences). Such sequences can be later inspected and analyzed by human experts. Experimental results show that the new set of sequence features leads traditional, simple classifiers, to correctly assess SARS-CoV-2 with remarkable accuracy (> 99%).
Thus, we create a table of feature appearance of each of the sequences selected from the previous step. This results in a set of features able to differentiate SARS-CoV-2 from other viruses. The experiments presented in the following subsections to validate our method have different objectives and make use of different datasets. A summary of all the experiments and datasets used is shown in Fig. 1. Identifying SARS-CoV-2. Recapitulating the results of experiments 1-4 (see Fig. 1 ), we discovered 12 meaningful 21-bps sequences that best characterize SARS-CoV-2. For all the analyzed data, these sequences appear only in SARS-CoV-2 samples and not in any other viruses, as summarized in Table  1. Remarkably, our results outperform earlier publications using machine learning for identifying SARS-CoV-2 (see for example  26  ), with the added benefit of producing human-readable results instead of a plain black box classifier.
Laboratory validation of the candidate primer set. We calculated the frequency of appearance of different primer sets' sequences used in SARS-CoV-2 RT-PCR tests developed by World Health Organization (WHO) referral laboratories, and compared it to our primer design on the GISAID dataset (Table  2 ). All of the sequences have a frequency of appearance of > 99% , with the exception of CHINA-CDC-N-F, with a 68. 52%. This is consistent with the percentage of genomes with mutation in the primer region, as stated by latest GISAID update summary of August 11th, 2020  31. For the in-silico analysis of specificity, we compared all the primers sets' sequences with the NCBI-B and NGDC dataset, and the results show that HKU-N-F, HKU-N-R, Charite-E-F, Charite-E-R and US-CDC-N2-F are not specific to SARS-CoV-2, as they bind to SARS-CoV, too.
The rest of the sequences, including our design, only appear in SARS-CoV-2. In summary, of the 8 different primer sets, 3 of them appear to not be specific to SARS-CoV-2; and by frequency of appearance, our design is the 3rd best option among the remaining 5, considering the lowest frequency between the -F and -R primer. This is a remarkable result, considering that the proposed primer set has been extracted by an almost completely automated procedure. To validate the data obtained in-silico by laboratory methods, a conventional PCR was performed on cDNA obtained from RNA from SARS-CoV-2 and other human coronaviruses. In addition, RNAs from nasopharyngeal swabs from six patients previously diagnosed with SARS-CoV-2 infection and four patients negative for SARS-CoV-2 by routine diagnostic method  5  were analyzed with the same conventional PCR (Fig.
We included HCoV-4408 in class 2 as well, as there were only 2 samples available: HCoV-4408 is subgroup A of the betacoronavirus genus, as HCoV-OC43  43. Finally, as we deemed important to distinguish SARS-CoV-2 from SARS-CoV, SARS-CoV was assigned to its own class (class 4) even if only 10 samples were available. We left out 30 SARS-CoV-2 sequences and then performed a 10-fold stratified cross-validation, with the remaining data divided into 80% training (8 folds), 10% validation (1 fold), 10% testing (1 fold). The stratified cross-validation preserves the ratio of classes in the original dataset for each fold, thus making the final accuracy more reliable. Identifying SARS-CoV-2. Experiment 1: Validation on the NGDC dataset. We downloaded the dataset from the NGDC repository 11 on March 15th, 2020.
We removed repeated sequences and applied the procedure to translate the data into the sequence feature space. This leaves us with a frequency table of 3827 features (21bps sequences) with 583 samples (Table  3  (left)). Next, we ran a state-of-the-art feature selection algorithm  45, 46  , to reduce the sequences needed to identify different virus strain to the bare minimum. Remarkably, we are then able to correctly differentiate all the coronavirus (MERS-CoV, SARS-CoV-2, SARS-CoV-1, etc) samples using only 53 of the original 3827 sequences, obtaining a 100% accuracy in a 10-fold cross-validation with a simpler and more traditional classifier, such as Logistic Regression. The list of the 53 features is available in the Supplementary Materials, Section 2. Experiment 2: Validation on the NCBI dataset.
We downloaded data from the NCBI 30 repository on March 15th, 2020, with the following query: gene = "ORF1ab" AND host = "homo sapiens" AND "complete genome". The query resulted in 407 non-repeated sequences (Table  3  (right)). We call this dataset NCBI-A, where 68 sequences belong to SARS-CoV-2. Then, we applied the procedure to translate the data into the set of sequence features, and we run the same state-of-the-art feature selection algorithm  45. The result is a list of 10 different sequences (Table  1 ), for which just checking for their presence is enough to differentiate between SARS-CoV-2 and other viruses in the dataset, with a 100% accuracy. Each of the sequences, in fact, only appears in SARS-CoV-2 samples. Table  3. Organism, assigned label, and number of samples in the unique sequences for the NGDC repository (left) and query: gene = "ORF1ab" AND host = "homo sapiens" AND "complete genome" in the NCBI repository (right).
Comparison of primer sets developed by WHO referral labs, against the primer set obtained by the proposed approach, listed as UtrechtU-ORF3a. The Frequency column indicates frequency of appearance of the sequence among samples of the GISAID dataset. The Specific (Yes/No) column shows whether the sequence appears to be unique to SARS-CoV-2 samples, or can also be found in other viruses, according to the evaluation on the NCBI-B and NGDC datasets.
Acknowledgements The daily board of the  Medical Ethics Committee Erasmus MC  ( METC-2015-306 ) of  Rotterdam ,  The Netherlands , has reviewed the above mentioned research. As a result of this review, the Committee informs you that the rules laid down in the Medical Research Involving Human Subjects Act (informed consent), do not apply to this research proposal. The samples are from anonymized leftover material from patients for the purpose of improving diagnostics. All experiments were performed in accordance with relevant guidelines and regulations.
Vol. :(0123456789) Scientific Reports | (2021) 11:947 | https://doi. org/10. 1038/s41598-020-80363-5 www. nature. com/scientificreports/Experiment 3: Further validation on the NCBI dataset. We downloaded data from NCBI 30 on March 17th, 2020, with the following query: "virus"  AND host = "homo sapiens" AND "complete genome ", restricting the size from 1000 to 35,000 bps (NCBI-B). The query returns 20,603 samples, of which only 32 belong to SARS-CoV-2, and 20,571 are from other taxa, including  Hepatitis B, Dengue, Human immunodeficiency, Human orthopneumovirus, Enterovirus A, Hepacivirus C, Chikungunya virus, Zaire ebolavirus, Human respirovirus 3, Orthohepevirus A, Norovirus GII, Hepatitis delta virus, Mumps rubulavirus, Enterovirus D, Zika virus, Measles morbillivirus, Enterovirus C, Human  T-cell leukemia virus type I, Yellow fever virus, Adeno-associated virus, rhinovirus (A, B and C), for a total of more than 584 other viruses (not considering strains and isolates).
Then, we applied the procedure to translate the data into the sequence feature space and run the feature reduction algorithm 45. This results in 2 extra sequences of 21 bps: just by checking for their presence, we are able to separate SARS-CoV-2 from the rest of the samples with a 100% accuracy (Table 1). Experiment 4: Validation on the GISAID dataset. From the GISAID repository 31 , we downloaded 53,183 sequences available on August 10th, for SARS-CoV-2, from different countries, from there 52,645 have as < 1% Ns, high coverage and host = "homo sapiens". Then, we calculated the frequency table of the 21-bps sequences obtained from experiments 2 and 3, to verify which sequences remain and could be used for detection. The appearance frequency of the target sequences among the samples in the GISAID dataset is reported in Table 1, second column.
In addition, we downloaded 26 sequences from GISAID repository of other hosts (manis javanica, rhinolophus affinis, canine and felis catus) to make a comparison in the sequences from experiment 2 and 3. Laboratory validation of the candidate primer set. Experiment 5: Design of the candidate primer set. After the analysis carried out on the deep learning model, we ran an analysis with Primer3plus 47 , to see which of the sequences could be used as a forward primer, using sample  NCBI NC045512. 2as the reference SARS-CoV-2 sequence. We uncover the sequence TAG CAC TCT CCA AGG GTG TTC that shows a frequency of appearance of 99. 57% in viral genomes available from different countries in GISAID 31 and  100. 0%  in the NCBI 30 datasets. Using the reference SARS-CoV-2 sequence, we identify that this discovered sequence is located between nucleotides 25,604 and 25,624 in the ORF3a gene.
In SARS-CoV, this gene encodes a protein of 274 aa, that is related with necrotic cell death 48,49 , chemokine production like interleukin 8 (IL-8) and  RANTES/CCL5 , NFκB activation resulting in an inflammatory response 50 and may play an important role in the virus life cycle 51. We design a specific primer set for detection of SARS-CoV-2 using Primer3plus 47. We use TAG  CAC TCT CCA AGG GTG TTC as forward primer and GCA AAG CCA AAG CCT CAT TA  as reverse primer, obtaining an amplicon size of 179 bps. Then, we run an in-silico PCR test using FastPCR 6. 7 52 with default parameters in NC045512. 2used as a reference SARS-CoV-2 sequence, this yields Tm = 56. 2• C for the forward primer, Tm = 53. 1 • C for the reverse primer and Ta = 58 • C. In addition, we calculated the frequency of appearance of different primers sets' sequences used in SARS-CoV-2 RT-qPCR tests developed by WHO referral laboratories and compared it to our primer design sequences in 52,645 sequences from the GISAID repository and the 583 samples of different coronaviruses from the  NGDC  dataset from experiment 1.
Data availability All the necessary scripts to reproduce the experiments are stored on the public GitHub repository: https :// githu b. com/stepp enwol f0/prime rs-sars-cov-2. Due to storage limits, while the data to reproduce Experiment 1 is included in the repository, for Experiments 2-5 only the IDs of the samples used are listed in file sampleids. xlsx. Given their IDs, samples for Experiments 2-5 can be downloaded from the corresponding open repositories: : GISAID (https ://www. gisai d. org/), NCBI (https ://www. ncbi. nlm. nih. gov/), NGDC (https ://bigd. big. ac. cn/ncov/?lang=en).
In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo   et al. , 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a 5 database. Users can freely retrieve the data from this database through an online web portal (ismn. earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network.
As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present. The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database 10 continue to be updated. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.
, 2010) , boosted the establishment of new research networks  (Colliander et al. , 2017). While all networks are a valuable asset for assessing the skill of soil moisture products under various conditions and scales, their usage is hampered by the diversity of sensors, data formats, quality control, and accessibility mechanisms. The need of bringing together and harmonising available soil moisture data was recognised by the international soil moisture community and expedited by the Global Energy and Water cycle Exchanges (GEWEX) project of the World Climate Research Programme (WCRP) with support of the Commission on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observation (GEO). In the advent of the SMOS mission, ESA decided to provide the financial impetus to establish a global reference database of in situ soil moisture measurements for the purpose of satellite product development and validation.
As a result, the International Soil Moisture Network (ISMN) went online in 2010  (Dorigo et al. , 2011b, a). The primary objective of the ISMN is to collect in situ soil moisture data sets shared by various data organisations on a voluntary basis and make them available in a harmonized format through a centralised free and open web portal (ismn. earth). While 10 years after its launch the core objective of the ISMN remains valid, its functionality has expanded since then. This new functionality includes the integration of advanced quality control methods  (Dorigo et al. , 2013) , the provision of additional metadata and ancillary variables (e. g. , precipitation, soil and air temperature), ongoing automation, the provision of software code to users, and the implementation of various tools to promote the information exchange between users, the ISMN, and the data providers.
The variables contained in the ISMN (Table  1  and 2 ) originate from networks that were built for various purposes, which consequently do not all contribute the same information. Since the ISMN was initially founded as a validation database for satellite (surface) soil moisture data, each station in the database provides soil moisture for the upper soil layer (≤ 0. 10 meter depth). Soil moisture data sets in the ISMN can go as deep as two metres, but generally with a decreasing number of measurements locations with depth (Table  D1 ). Some stations deploy more than one sensor at a certain depth, either as replacement in 90 case of failure of one of the sensors or to characterise local soil moisture variability. The availability of meteorological data like precipitation, soil and air temperature, is even more heterogeneous, depending on the scope of the network or the data sharing policy of the data providing organisation.
It is also quite common that single time series in the database are composed of the consecutive measurements of two or more different sensors, when a sensor is replaced after failure. Metadata information can be divided into two categories, i. e. , mandatory metadata, which allow for an unambiguous identification of each network, station, and measurement in the ISMN database (Figure  E1 ), and optional metadata, shared by data providers to allow more in-depth analysis of their data sets. To be consistent between sites, the mandatory variables climate, land cover, and soil characteristics are taken from external databases: Climate classification is taken from the Köppen-Geiger database with a resolution of 0. 1°  (Peel et al. , 2007) ; Dynamically evolving land cover for 2000, 2005, and 2010 is obtained from ESA's Climate Change Initiative (CCI) land cover v1.
6. 1 with 300 meter resolution; Soil information is retrieved from the Harmonized World Soil Database (HWSD v1. 1; FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) with a 30" sampling, although the actual resolution may strongly vary from location to location. If available, data providers can optionally share their own, more detailed, characterisations of land cover, soil, and quality flags with the ISMN. These are stored in addition to the same variables from external sources. All static variables per measurement site and depth are listed in Table  2.
g. , m and °C). Then, following the recommendations of the World Meteorological Organization for weather observation and forecasting  (Williams, 2010) , all data are resampled to hourly UTC reference time steps. Data that are available at higher sampling rates are thinned by selecting the individual measurements at the hourly UTC reference time step or the ones that are closest in time within a window of +/-0. 5 h. If there is no measurement available within this interval, the respective time step is not stored in the database but can be recreated and filled with a no-data value upon download. The temporal resampling scheme is valid for all included dynamic variables, except precipitation. Since precipitation is a flux and not a state variable, all measurements within the hourly interval are summed to represent the full amount of precipitation within the preceding hour  (Dorigo et al.
, 2011b). All soil moisture measurements provided to the ISMN are converted to volumetric soil moisture in m 3 m -3. Since the majority of networks already shares their measurements in volumetric soil moisture units often no unit conversion is needed. For the other (mostly historical) networks, measurements are converted to volumetric units using metadata on soil properties (in case measurements are provided as saturation level or plant available water) and/or the thickness of the soil layer represented by the measurement (in case measurements are provided as water height or mass)  (Dorigo et al. , 2011b). The harmonisation of measurement depths is particularly challenging, as different networks adopt different measurement depths, similar sensors are positioned differently (horizontally vs.
vertically), or their measurements represent different observation volumes, which may even differ according to the soil wetness (as for cosmic ray probes). Thus, harmonising soil moisture measurements to one or several reference depths would require either assumptions on the measurements and soil properties or supplemental soil modelling. Additionally, since there are lots of potential uses for the data, there is no common agreement on the optimum sampling depths. For example, satellite calibration-validation generally requires observations of the 0-5 cm layer while land surface model evaluation requires reference measurements that are representative for the layers defined in the model  (Dorigo et al. , 2011b). Consequently, the ISMN does not harmonise measurement depths. After data harmonisation, the data sets are submitted to extensive quality control procedures (Section 3.