dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: The getSequenceInfo software tool allows to access sequence information from various public repositories (GenBank, RefSeq, and the European Nucleotide Archive), and is compatible with different operating systems (Linux, MacOS, and Microsoft Windows) in a programmatic way (command line) or as a graphical user interface. getSequenceInfo or gSeqI v1. 0 should help users to get some information on queried sequences that could be useful for specific studies (e. g. the country of origin/isolation or the release date of queried sequences). Queries can be made to retrieve sequence data based on a given kingdom and species, or from a given date. This program allows the separation between chromosomes and plasmids (or other genetic elements/components) by arranging each component in a given folder.
Background Sequencing technologies are widely used nowadays and sequencing data is increasing at a rapid rate. Whole genome sequencing (WGS) projects can be applied in various settings and scientific studies, fostering the analysis of a large amount of data which are generally deposited in public archives such as those belonging to the International Nucleotide Sequence Database Collaboration (INSDC), which comprises the DNA Data Bank of Japan (DDBJ), the European Nucleotide Archive (ENA), and GenBank  [1] [2] [3] [4]. Unlike GenBank sequences, RefSeq sequences are not part of the INSDC but are derived from INSDC sequences to provide non-redundant curated data representing our current knowledge of known genes. Differences between GenBank and RefSeq genome assemblies are well described (https:// www.
ncbi. nlm. nih. gov/ books/ NBK50 679/# RefSe qFAQ. what_ is_ the_ diffe rence_ betwe en_1). Several important and useful initiatives have been conducted to foster data retrieval from these public sequence repositories, and various programs or online tools are available for this task. Among these programs/tools, we can non-exhaustively mention the NCBI Genome Downloading Scripts (https:// github. com/ kblin/ ncbi-genome-downl oad), Kraken_db_install_scripts (https:// github. com/ mw553 09/ Kraken_ db_ insta ll_ scrip ts), Entrez Programming Utilities (https:// www. ncbi. nlm. nih. gov/ books/ NBK25 501/; https:// www. ncbi. nlm. nih. gov/ books/ NBK17 9288/), enaBrowserTools (https:// github. com/ enase quence/ enaBr owser Tools), NCBI genomes FTP site (https:// ftp. ncbi. nih. gov/ genom es/), and the recently released NCBI Datasets (https:// www.
ncbi. nlm. nih. gov/ datas ets/). Another interesting software tool named metatools_ncbi (https:// github. com/ farhat-lab/ metat ools_ ncbi) allows downloading Biosample and SRA run information metadata from the NCBI. SRA tools such as fastq-dump (https:// github. com/ ncbi/ sra-tools) or SRAdb  [5]  are also interesting for downloading FASTQ reads and associated information. Furthermore, a wide range of additional bioinformatics resources are available in the INSDC and elsewhere, adhering to the Findable, Accessible, Interoperable, Reusable (FAIR) data principles, which are fostering better practices in sequencing data analyses  [6]. Some research studies are becoming more and more complex due to a wide variety of biological data and formats. Sharing information in a more reproducible, understandable and pedagogical manner is important to make scientific research affordable and accessible to a greater number of scientists and/or students.
Dedicated strategies have been developed to better organize and analyze genomic data such as the notion of genome assembly  [7]. A wide variety of software tools have been developed to tentatively respond to specific or more general requests regarding sequence analysis. Nucleotide sequence data could be more or less complex to study, depending on the species of interest or the studied genomic elements/components (e. g. chromosomes, plasmids, etc. ). Approaches and methods facilitating comparative genomic studies have been implemented such as the well-known average nucleotide identity (ANI) which allows improving taxonomic assignments  [8]. Despite the usefulness of public genomic repositories, metadata related to genomic sequences are not always well annotated or available. As mentioned before  [9] , the World Health Organization (WHO) has repeatedly advocated open sharing of pathogen genetic sequences as well as the knowledge and benefits resulting from the genetic data (https:// www.
who. int/ bluep rint/ meeti ngs-events/ meeti ng-report-patho gen-genet ic-seque nce-data-shari ng. pdf ). We aim to develop intuitive software to obtain genomic sequences and their associated data when available in a relatively simple way through a graphical user interface (GUI) or using the command line. Such an approach could be of relevant significance for the design of specific biological databases or the improvement of existing ones.
Programming language and modules The Perl programming language was used to develop the freely available getSequen-ceInfo (gSeqI) software. Perl/Tk graphical user interface toolkit has been employed to design the GUI in order to make an intuitive interface. Several other Perl modules are required to run the software, including BioPerl (http:// biope rl. org/)  [10]. The list of needed modules is provided in the software installation file (https:// github. com/ dcouv in/ getSe quenc eInfo/ tree/ master/ insta ll). Further details regarding programming are available on the software GitHub page (https:// github. com/ karub iotoo ls/ getSe quenc eInfo). A user manual is also available from the GitHub page to help using the tool. The software is compatible with Linux and Microsoft Windows operating systems.
How getSequenceInfo tool differs from other existing tools Unlike other existing tools, getSequenceInfo allows users to query specific information regarding wanted sequences. For example, users can download NCBI sequences from a given release date, and they can also download specific sequences (from the assemblies) such as plasmids, chromosomes, etc. Users can query both ENA and NCBI GenBank or RefSeq databases. Extraction of metadata associated to genome assemblies such as country, host, or isolation source is. Furthermore, one may notice the notion of "NucleScore" (further explanations are provided below) which could bring new information for classification/characterization of genome assemblies. This score is a reduction of the nucleotide information which is intended to be discriminating and informative.
Main options Users can select two methods for querying nucleotide databases managed by the gSeqI software: (i) the method based on NCBI databases (GenBank and RefSeq) designed to download FASTA and GenBank files associated to various metadata (when available), or (ii) the method based on ENA designed to download particularly FASTQ files (among others). Both methods provide different functionalities allowing users to download various sequence file formats. The tool provides a parser allowing to extract assembly metadata (such as country, PubMed ID, isolation source, host, etc. ) from FASTA and GenBank files. Specific string search and regular expressions were used to retrieve metadata (further details are provided in Supplementary_tools scripts "nucleScore. pl" and "genbank_info. pl"). Some options allow users to determine several features of their search.
Users can select the sequence database from which they want to download genomic data. For example, users can choose a NCBI sequence database with the option "-directory" or "-dir" followed by the database of interest (i. e. "genbank" or "refseq"). Once the database has been selected, the user can indicate the species with option "-species" followed by the wanted species (e. g. Escherichia coli). Users can also query the sequence database by indicating a NCBI Taxonomy ID (Taxid) with the option "-taxid". Furthermore, users can limit the number of NCBI assemblies they want to download (with the option "-n"). Regarding options dedicated to ENA sequence database, users can choose the option "-ena" to download FASTA sequence records from ENA according to an accession number (https:// ena-docs. readt hedocs.
io/ en/ latest/ retri eval/ gener al-guide/ data-class es. html) or the option "-fastq" to download compressed FASTQ files obtained from the ENA run accession numbers (starting with "ERR…" or "SRR…") provided (https:// ena-docs. readt hedocs. io/ en/ latest/ submit/ gener al-guide/ acces sions. html). Further options are available for both querying methods (i. e. based on NCBI databases or on ENA) and are visible in the provided user manual (https:// github. com/ karub iotoo ls/ getSe quenc eInfo/ blob/ master/ User_ Manual. pdf ).
Supplementary tools A set of additional or complementary tools is also offered to help users analyze their DNA sequences. These tools are available in a dedicated folder named "Supplementary_tools" on the GitHub repository. A dedicated program named "nucleScore. pl" was designed to get metrics corresponding to the aforementioned "NucleScore" directly from users' data. The code can be used as follows (considering a set of FASTA files available in the current repository): $ perl nucleScore. pl *. fasta. The result file will be a tabulated file containing sequence information corresponding to each FASTA file (nucleotide frequencies, GC-content, AT/GC ratio, etc. ). Another program named "countDifferences. pl" allows users to compare sequences from a multi-Fasta alignment file by calculating differences (in bp) and percentage identity.
The code is used as follows: $ perl countDifferences. pl multi_alignment. fasta. It generates percentage_identity and distance matrices from the given multi-FASTA alignment input file. Then, a program named "SRArunInfo. pl" allows users to get information on running accessions using ID (e. g. SRR7693912). The program can be runned as follows: $ perl SRArunInfo. pl SRR7693877,SRR9850824,SRR9850830. A result summary information corresponding to the query is generated (Additional file 1). In addition, XML and CSV result files are generated for each queried accession. "removeChar. pl" tool allows to remove positions (or columns) from a multi-Fasta alignment file in function of a given character (e. g. 'N' or '-'). The program can be used as follows: $ perl removeChar. pl alignment. fasta -The result will be a multi-Fasta alignment file without the given character ('-') in the sequences.
This tool uses: (a) ABRicate (https:// github. com/ tseem ann/ abric ate) with ResFinder  [14] , PlasmidFinder  [15] , and VFDB  [16]  databases to predict resistance, plasmid, and virulence genes; as well as (b) MLST tool (https:// github. com/ tseem ann/ mlst). A percentage coverage of 90. 00% was used to screen resistance and plasmid genes, whereas a percentage coverage of 80. 00% was used to filter virulence genes. getSeqI and catchSequenceInfo tools were used to build a small dataset containing assemblies belonging to Escherichia coli (n = 1835), Klebsiella pneumoniae (n = 622) and Enterobacter cloacae (n = 61) species (results are provided in Additional file 2). Please note that due to our low data storage capacity, the total number of assemblies that can be downloaded has been limited to 50 in Galaxy (users can use the CLI interface to download further data).
Discussion The getSequenceInfo software tool attempts to allow users to easily download sequences as well as associated metadata from NCBI's GenBank or RefSeq and EBI's ENA without advanced computing knowledge or complex manipulations. This tool has been designed to work preferably on a small number of sequences of interest. It could be helpful in designing specific genomic databases or strains collection, by facilitating access to sequences using accession numbers and keywords. For example, users can easily download plasmid sequences from available strains belonging to given species (e. g. Escherichia coli). Although existing sequence downloading tools offer great functionality to download sequences in various manners, getSeqI provides a different way to download sequences making association with several metadata (such as country and host), and allowing users to make specific queries.
The main tool has also been made accessible through a Singularity container to facilitate its utilization (the GitHub page of the tool shows how to install the Singularity image)  [17]. The tool will also be deposited in a Docker container (https:// docs. docker. com/). Prospects for improvement of this tool will be performed. The fact that the software tool is for the moment available as a standalone program (although it is also available through a GUI) could represent a limit for the utilization of the tool. Therefore, efforts have been made to make the getSequenceInfo tool partly accessible through a Galaxy platform. Future developments of getSequenceInfo will consist in adding some functions for improving extraction of useful information and in adding other methods to make the tool more accessible.
Conclusion To conclude, getSequenceInfo offers an accessible way to get genome sequences (in FASTA, FASTQ or GenBank formats) and associated metadata in a programmatic way (command line) or using a graphical user interface (GUI). The software tool allows users to get multi-level information from queried genome assembly such as country of origin/ isolation, release date, host, etc. (using NCBI GenBank or RefSeq databases). getSequen-ceInfo could allow users to retrieve sequencing data in the function of a given kingdom or species, and from a given date. The separation between chromosomes and plasmids (or other elements/components) by ranging corresponding sequences in a given folder, is also possible. Some basic statistics are also performed by the program (such as the calculation of GC content for queried assemblies).
Furthermore, an empirically designed nucleotide ratio ("NucleScore") is proposed to tentatively provide a score explaining nucleotide diversity of queried genome assemblies. Supplementary tools are provided to help users with some occasional needs for sequence analysis and data retrieval. getSe-quenceInfo and tools are partly available from the recently released Galaxy KaruBioNet platform (http:// calam ar. univ-ag. fr/ c3i/ galaxy_ karub ionet. html). Finally, getSequen-ceInfo could be used as an educational tool allowing students to better comprehend sequence data.
(i) Coronavirus genome assemblies (example of 50 genomes available from December 1st 2019) Nucle Score = log 2 Var × GC content × ATG Cratio 3 length $ perl getSequenceInfo. pl -s coronavirus -k viral -date 2019-12-01 -n 50 -o COVID19. Please note that nowadays, a wide range of specific tools have been developed for querying SARS-CoV-2 data such as this NCBI resources page: https:// www. ncbi. nlm. nih. gov/ sars-cov-2/. (ii) E. coli complete genome assemblies from RefSeq and their associated plasmids (example of 50 genomes) $ perl getSequenceInfo. pl -k bacteria -s "Escherichia coli" -d refseq -level "Complete Genome" -c plasmid -n 50. (iii) 5 sequences belonging to Naegleria fowleri species from GenBank $ perl getSequenceInfo. pl -k protozoa -s "Naegleria fowleri" -d genbank -n 5. (iv) FASTA sequences and associated text reports from ENA $ perl getSequenceInfo.
Acknowledgements We are grateful to  Nalin Rastogi  for helpful discussions and reading of this manuscript. We thank  Erick Stattner  and  Wilfried Segretier  (from  LAMIA laboratory of the Université des Antilles ) for their help regarding the NucleScore. We also thank  Vincent Guerlais ,  Isaure Quétel ,  Gélixa Gamiette ,  Gaëlle Gruel ,  Youri Vingataramin  and  Degrâce Batantou  (as well as other members of the  TReD-Path unit)  for their help.
Methods: This study developed a new deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network and perceptual loss is adopted with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra PET/CT. The patients were randomly split into a training dataset of 60 patients, the validation dataset of 10 patients, and the test data set of 10 patients. The 3D sinograms were converted into 2D sinogram slices and were used as input to the network, and the vendor reconstructed images were considered as ground truths. The proposed method was compared with DeepPET, a benchmark deep learning method for PET reconstruction.
Patients & imaging Clinical patient list mode data were collected using Biograph Vision Quadra (Siemens Healthineers) at the University of Bern, Switzerland. This system is with a FOV of 106 cm. Preliminary assessments of this scanner's characteristics reveal a sensitivity of 174 cps/kBq and a time of flight (TOF) resolution of 219 ps in ultra-high sensitivity mode  [19]. The selected patients were injected with 18 F-FDG and performed PET/CT examination, including 80 cases (median age, 63 years; age range, 50-76 years; 44 females; BMI, 73. 9±17 kg/m 2 ) of patients In all cases, subjects fasted for more than 4 h and had a blood glucose of no more than 200 mg/dl, all patients were injected with 18 F-FDG with an uptake time of 90 min ± 10%, and the patients without complete PET/CT scan images from above head to below thigh, or poor image quality because of patients' movement were been excluded.
The 80 patients were randomly split into a training dataset of 60 patients, the validation dataset of 10 patients, and the test data set of 10 patients (median age, 67 years; age range, 40-81 years; 5 females; BMI, 23. 85±2. 38 kg/m 2 ). The study was conducted in accordance with the requirements of the respective local ethics committees in Switzerland (Req-2021-00517).
Data pre-processing List-mode data obtained from the scanner was reconstructed with industry software (e7-tools, Siemens Healthineers) with CT-based attenuation correction. The used reconstruction method was PSF-TOF with 4 iterations and 5 subsets. 644 slides for each patient were reconstructed and used as ground truth in this work. The image size was 440×440 with voxel dimensions of 1. 65×1. 65×1. 65 mm 3. Furthermore, the list-mode data was transferred and uncompressed into 3-dimensional (3D) sinogram data. The 3D sinogram was converted into 2-dimensional (2D) slices by single-slice rebinning (SSRB)  [4]. 644 2D sinogram slices were obtained for each patient, corresponding to 644 reconstructed images. The input dataset of the network was 2D sinogram slices, and the reconstructed images from e7-tools were used as training targets.
The convolution blocks in VGG19 are consist of a convolution layer followed by ReLU. The sizes of feature maps are reduced by pooling layers. VGG19 network weights pre-trained on the ImageNet database(image-net. org) are used in this work. The outputs of the first 3 pooling layers are extracted to be used as feature reconstruction loss as expressed below: 𝐿 𝑉𝐺𝐺 = 1 3 ∑ |𝑉𝐺𝐺(𝑥) 𝑖 -𝑉𝐺𝐺(𝑦) 𝑖 | 3 𝑖=1 (1) where VGG(x) i and VGG(y) i represent the output of i-th pooling layer in VGG19 with the input of ground truth and predict image of image transform network. The other two parts are also involved in the loss function, mean square error (MSE) loss and structural similarity (SSIM)  [23] loss. The functions are shown as follows: 𝐿 𝑀𝑆𝐸 = 1 𝑛 ∑ (𝑥 𝑖 -𝑦 𝑖 ) 2 𝑛 𝑖=1 (2) where x is the ground truth, y is the predicted image of the image transform network, and n is the total number of image pixels.
Network training and test procedure The network was implemented in TensorFlow(www. tensorflow. org) [24] , the training of the network was performed using a GPU (Tesla V100-PCIe-16GB, NVIDIA) and tested using GeForce RTX 2080 Ti (NVIDIA). Adam optimization method  [25]  was used as optimized with a learning rate of 10 -4. All images in the training dataset (35940 2D sinogram and reconstructed images) were used as input to the network by the batch size of 50 and trained with 300 epochs. The trained network was tested on 10 patients' data (5590 2D sinograms and reconstructed images).
Clinical evaluation The results on the test data set were further evaluated by 2 nuclear medicine physicians. For each patient, a typical lesion was selected and delineated manually. Among the 10 patients, 1 patient was proved to have no lesion. The average standardized uptake values (SUVmean) and max standardized uptake values (SUVmax) were measured of the tracer uptake in a region of interest of selected lesions. The relative errors between ground truth and DeepPET results and this work were calculated and compared.
IV Discussion This study followed the mainstream of AI development for PET reconstruction and focused on the direct reconstruction from Sinogram. In contrast to most previous research on Sinogram data from phantom-based simulation  [15]  or anthropomorphic simulation by projecting real patient data  [26] , the training and test in this study were directly on real PET measurements. A critical concern for AI development is its reproducibility and extensibility to complexity in real applications  [27]. Compared to the development on simulated sinogram data, the development on real measurement data in this study can better tackle the challenges of physical and physiological complexity and enhance the translational potential of data-driven methods. An advanced LAFOV PET scanner was employed in the development and test of the AI-based direct reconstruction from Sinogram data in this study.
But with more oblique LORs used, there could be a 40-fold increase  [31]. Remarkably, the prompts count rate peaked at 10 million events, a few orders of magnitude larger than for a traditional PET scanner  [28]. Conventional PET reconstruction algorithms are inefficient in the processing of the vast data of LAFOV PET reconstruction. Although our test was performed on Sinogram data for reconstruction, the results confirmed that deep learning can significantly shorten (up to 36 times) the reconstruction time for whole-body imaging compared with a conventional iterative algorithm. The potential in accelerating the computational speed may bring advantages for the practice of LAFOV PET in the clinical routine. The preliminary results demonstrated that the improvement of deep learning architecture can improve the performance of AI-based reconstruction.
The use of more varied and larger capacity training sets can make contributions to improving the accuracy of predictions. Another limitation of this work was the loss in the conversion of 3D sinograms to 2D sinograms. Due to the limited GPU memory, we transferred the vendor output of the 3D sinograms to the 2D sinograms as the input of this network. In the conversion process, certain noises were introduced, and part of the spatial information in the axial direction was lost. This resulted in the inability to fully recover all the details of the ground truth images. The development on 3D sinogram data with more data and computational resources may improve the performance.
TABLE. 1. Quality evaluation results of the test database, MSE, rRMSE, PSNR, and SSIM. The quantitative results of the two networks were statically analyzed with paired t-test This work DeepPET Paired t-test MSE 132. 6±75. 0 161. 7±84. 8 -3. 1 (p<0. 01) rRMSE 0. 60±0. 04 0. 63±0. 04 -6. 9 (p<0. 01) PSNR 82. 4±0. 87 82. 1±0. 90 7. 1 (p<0. 01) SSIM 0. 95±0. 02 0. 93±0. 03 7. 3 (p<0. 01).
Introduction Social media memes can be defined as "pieces of culture, typically jokes, which gain influence through online transmission"  (Davison, 2012). More specifically, memes are visual templates usually associated with a textual caption. Analysing memes involves many unique challenges that differ from classical multimodal tasks such as image captioning and visual question answering. While unimodal models can often perform well on multimodal datasets  (Agrawal et al. , 2018) , memes involve a lot of entanglement -stylistic or semantic -between the two modalities, such as the caption contradicting the image. This makes memes intrinsically multimodal. Furthermore, pragmatics -the context's contribution to meaning -plays a key role in the interpretation of memes. In particular, phenomenons such as irony are challenging to detect.