dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, we emphasize how the local variability of the measurements acquired at the station closely relates to the dynamics of the vortex and prevalence of horizontal transport at stratospheric altitudes. We compare ERA5 and NCEP reanalyses to a IASI-derived temperature product and to local radiosondes. Related to the coastal location of the station, a significant spatial drift of the sondes during their ascent up to the stratosphere leads us to use temperature reanalyses in lidar data processing to ensure consistency. ERA5 proves to be the most convenient dataset to use locally, showing a satisfying agreement with DDU radiosondes from 2007 to 2020. In the near future, the IASI product should become an interesting option, especially for high latitude sites: the accuracy seems to be the same as ERA5, except in the upper troposphere.
Acknowledgements. The authors would like to thank the  French Polar Institute (IPEV, Institut polaire français Paul-Émile Victor ) which supports the science programme 209 operations on the  Dumont d'Urville  station, as well as the different operators of the DDU lidar who conduct measurements all year long in Antarctica and are essential to this work. Financial support. This research has been supported by the  French Polar Institute IPEV (Institut polaire français Paul -Emile Victor) science programme  209, the  EECLAT  project ( CNES-INSU) , and the  NDACC international  network (lidar instrument). Review statement. This paper was edited by  Matthias Tesche  and reviewed by three anonymous referees.
Data availability. The Dumont d'Urville lidar instrument is part of the NDACC international network and data are publicly available online at the NDACC/NOAA data archive (https://www-air. larc. nasa. gov/missions/ndacc/, last access: 29 December 2022;  Jumelet, 2022). ERA5 reanalysis data are available on the Copernicus Climate Data Store at https://doi. org/10. 24381/cds. bd0915c6  (Hersbach et al. , 2018). NCEP/NCAR reanalysis data are available at https://www-air. larc. nasa. gov/missions/ndacc/data. html?NCEP= ncep-list (last access: 11 January 2023; NCEP/NCAR, 2023;  Kalnay et al. , 1996; Jumelet, 2022).
1 ). In January 2023, the Global Biodiversity Information Facility (GBIF) had more than 51 million preserved or fossil specimens with an image. This number is expected to grow substantially. For example, digitisation of the Kew Herbarium, which holds over 7 million specimens, will add to already major digitisation programmes in Australia, China, Europe, USA and others  (Nelson and Ellis 2018 , Willis et al. 2018 , Borsch et al. 2020 , Chinese Virtual Herbarium 2021). With this increase in digital images, it is not surprising that computer vision techniques are now being applied to them. In recent years, machine learning, in particular, has become mainstream and has been built into workflows that start with digital images and metadata and result in statements about what is shown. Such workflows can extract information about biological specimens from typed or handwritten labels  (Allan et al.
2019 ). There are Progress in digitising natural history collections. A growing number of images are accessible from the Global Biodiversity Information Facility, iDigBio or BioCaSE. To examine the rate and volume of digitisation, we used six snapshots of these databases taken since 2019, using Preston, a biodiversity dataset tracker  (Poelen 2022 , Poelen and Groom 2022 , Elliott et al. 2022). Although likely to be an underestimate of specimen images, because not all are linked to the snapshot datasets, trends give an indication of digitisation progress. The number of available images is increasing approximately exponentially. There are seven times more plant specimens than insects in our most recent snapshot, though insects are far more numerous in nature, an estimated 5. 5 million species of insects (Stork 2018) vs.
The Vision We envisage a data space for biological collections with a centrally accessible image corpus with built-in processing. This will allow anyone to access digitised images of specimens, without having to concentrate on the logistics of corpus creation and maintenance. Building accessible interfaces would also remove technological barriers that prevent taxonomists, ecologists and others from using advanced analysis tools. Through supervised expert contributions, the system could integrate knowledge from many disciplines. Such a corpus would constantly be furnished with new images from publishing collections and support citation and reproducibility of workflows and their underlying collections, in alignment with FAIR Data Principles  (Wilkinson et al. 2016). It would make it easier to curate image datasets and use them for research (e.
Species identification Most experiments with species identification from specimen images have focused on herbarium specimens  (Carranza-Rojas et al. 2017 , Kho et al. 2018 , Pryer et al. 2020 , Hussein et al. 2022). This is because they are two-dimensional, follow a fairly standardised format and are highly available. Digitisation of herbarium specimens has preceded that of other organisms. Nevertheless, because insect specimens (Fig. 1 ), in particular, are much more numerous, there is clear demand for their automated identification  (Valan et al. 2019 , Høye et al. 2021). Insect colour and morphology are well preserved in specimens. This means that automatic identification trained on specimens may work on living insects and vice versa, having the possibility to create training datasets for rarely-seen organisms  (Goëau et al.
g. cm, mm). This can then be combined with specimen segmentation models, to automatically extract dimensions and specimen traits  (Triki et al. 2021 ). When rulers are not of uniform size, the distance transformation needs to be estimated by calculating the pixel distance between the measurement stripes or bars on the ruler (Bhalerao and Reynolds 2014). To extract the specific unit of measurement, the text denoting the unit on the ruler can be recognised or additional metadata about the specimen can be used to infer it.
Image metadata in the repository will include a reference to the image object located in the storage layer (Component 2), along with annotated training image data. Different kinds of image annotations will be supported, including geometric-based regions of interest (ROI), taxonomic or ecological traits and textual representations of label data. For interoperability, data standards supporting machine readability of these annotations are required. As different standards exist for these annotations and not all are equally suitable for any model, the platform should ensure support for multiple standards, such as COCO (JSON), Pascal VOC (XML) and image masks (rasterised or vectorised images). Multiple annotations can be made on a single specimen record, making persistent record identifiers vital.
g. dog) or using an image as the search term. This function is also available on Google Photos, where a user can search their personal photos for specific people, different types of objects, places, ceremonies and so on. Although different, all those systems share similar logic: (1) they include models trained for specific tasks (e. g. object detection) that have been created offline using massive datasets in large GPU clusters (e. g. Model Zoo and COCO dataset); (2) when a new image is added to the collection (or possibly all, when new models are deployed), in addition to the submitted user tags, the images are processed with these models (inference/prediction pipeline) and tags are extracted; (3) the extracted information is saved and indexed and made available as searchable data. The envisioned system should provide similar functionality, with the added complexity of the myriad of different models and images illustrated by the use cases in the previous section.
Storage of images for analysis Another function of the processing component is using 'finished' models for image analysis, resulting in annotation of newly-uploaded images with or without metadata (such as classification or identified regions of interest). For this purpose, images will be uploaded by researchers, after having selected a model or models from the repository to run on the images. Uploaded images will be stored in the storage component and kept there for the duration of the experiment. Responsibility for running these experiments, including the loading and execution of the selected models, lies with the processing component. Actively making available the images to the models is facilitated by orchestration logic. Once experiments have been completed, these images will be moved to a lowperformance part of the media storage component (archive storage), where they are stored with the newly-acquired metadata, in line with relevant governance policies.
Component 3: The Processing The processing component encompasses all the services and pipelines to compute tasks on batches of data, incoming or already existing in the system, such as those stored in the repository and storage components (Fig. 5 ). In other words, it supports a myriad of computational-intensive tasks, from ingesting new data, to the automated extraction of information from media, as well as exporting new datasets or scheduling the training of new models or the retraining of old ones. This component requires a considerable amount of computing power to handle all the scheduled tasks in the system, which can even be elastic (i. e. cloud principles) given the fluctuating demand. These are delegated by the orchestration logic component, a set of services that are responsible for handling external requests, such as those from front-end applications or other external services using public APIs, serving as both gateway and manager to the main internal components -repository, storage and processing (Fig.
Data ingestion Data ingestion is the process of adding new data to the system, encompassing tasks, such as crawling, parsing, validating and transforming information to be indexed. This process includes several data types, including metadata, images, annotations, analytics pipelines (which includes services and models) and so on. To this end, specific tools should handle incoming data to the infrastructure, following different paths depending on the data's source and type. When a new dataset is submitted, each entry undergoes a series of tasks to parse, validate and transform the information to facilitate a standardised entry. This may include crawling additional data from external services like Wikidata or to compute metrics, validate geographic coordinates and map them to locations. Additionally, this process will check for duplicate entries, based on the existing data in the infrastructure.
Image annotations One of the key features of the system will be the ability to provide annotations for the existing images. When a set of annotations is supplied, these need to be ingested, validated and transformed into standard data types and structures, depending on the problem (e. g. classification, object detection, natural language processing and optical character recognition). After preprocessing, the set of annotations will be additionally validated to find whether they duplicate existing annotations, if the attached labels make sense, if the tagged region falls inside of the image and so on. This information will then be indexed and provided by the repository component and can be included in datasets, which will serve to improve existing inference tools and develop new ones.
This involves the system indexing the data (i. e. link to the exact dataset) and code with the exact environment (e. g. by using conda and venv under Python or renv in R), the pre-trained models and all the required parameters, hyperparameters and similar, as well as controlling the randomness of such models (e. g. initialising seed state). Secondly, the entire analytics pipeline should be documented as code, from infrastructure to application level. This allows for the exact replication of the build, test, package and deployment. Over the last decade, several technologies and sets of practices have appeared to attain such goals, normally linked to software development concepts, such as DevOps, MLOps and GitOps. GitHub provides Actions to attain continuous integration and deployment, allowing the automation of the entire workflow of a software service, from building to testing and deploying, based on simple text files (YAML).
On the other hand, Docker images and similar solutions allow services to be containerised using similar simple definitions and shared across various environments, enhancing consistency and portability, while simplifying deployment and scaling processes. Going a step further, it is nowadays possible to define both the infrastructure and how services interact as code too (e. g. used under Docker compose or with Terraform and Kubernetes). Such concepts must be exploited by the processing component, allowing submission of novel analytics pipelines. As the number of annotated datasets grows over time, the system might schedule the retraining of models and associated pipelines, reporting results and, if desired, replacing the existing analytics pipelines. Moreover, all the details, code and pre-trained models can be provided, so anyone can reuse them anywhere.
Data export The system will catalogue millions of specimens, each with variable amounts of metadata. These data can be filtered with complex queries, based on several parameters and fields. As an example, a user might want to search for records of a specific species, containing images and annotate them regarding the presence of signatures within a specific timespan. Requesting the generation of an image dataset, based on the result of such query, requires several processing tasks for scheduling, from the extraction and merging of the relevant metadata into the desired format, to resizing images if needed, assigning a persistent identifier, generating a dataset page and notifying the user. Moreover, if images and annotations for the same search criteria are updated in the following months, the user might request the dataset to be updated, generating a second version and assigning a new or versioned persistent identifier.
2013). TDWG have worked in close collaboration with GBIF to develop standards on biodiversity and one could imagine similar alliances would benefit the envisaged infrastructure and its users. We suggest that the most intractable obstacles to a shared, global infrastructure are sociopolitical. We envisage an infrastructure without institutional and national borders, in which people, organisations and nations are co-beneficiaries of a system, in which knowledge, skills, financing and other resourcing are acknowledged  (Pearce et al. 2020). Furthermore, tracking the provenance of resources is also needed to ensure reproducibility and replicability of the system  (Goodman et al. 2014). Experiments so far lack scalability, often have manual bottlenecks and experience significant time lag in production of results due to limited access to computational and physical resources and to human resources to create and curate training datasets (Wäldchen and Mäder 2018).
Funding program This work was supported by  European Cooperation in Science and Technology (COST)  as part of the  Mobilise Action   CA17106  on  Mobilising Data, Experts and Policies in Scientific Collections. Heliana Teixeira  was supported by  CESAM -FCT /MCTES UIDB/ 50017/2020+UIDP/50017/2020. Renato Panda  was supported by  Ci2 -FCT/MCTES UIDP/ 05567/2020. Elizabeth Ellwood  is supported by the  National Science Foundation  ( DBI 2027654 ). This work was also facilitated by the  Research Foundation -Flanders  research infrastructure under grant number  FWO I001721N , the  BiCIKL  (grant agreement No  101007492 ) and  SYNTHESYS+  (grant agreement No  823827 ) projects of the  European Union 's  Horizon 2020 Research and Innovation  action.
2022). A large repository of images would facilitate this research by allowing the development and curation of the two types of dataset necessary for self-supervised representation learning: large training corpora and smaller, task-specific benchmarking datasets(Van Horn et al. 2021). For instance, White et al. (2019) used representations of specimen images learned by a neural network trained to classify fern genera to directly compare specimen morphology and test biogeographic hypotheses. Similarly, Hoyal Cuthill et al. (2019) trained a network to th estimate the.
https:// doi. org/10. 1093/biosci/biaa044 • Pigot A, Sheard C, Miller E, Bregman T, Freeman B, Roll U, Seddon N, Trisos C, Weeks B, Tobias J (2020) Macroevolutionary convergence connects morphological form to ecological function in birds. Nature Ecology & Evolution 4 (2): 230-239. https://doi. org/ 10. 1038/s41559-019-1070-4 • Poelen J (2022) jhpoelen/specimen-image-index: 0. 0. 1. Zenodo. Release date: 2022-8-29. URL: https://doi. org/10. 5281/zenodo. 7032668 • Poelen JH, Groom Q (2022) Preserved specimen records with still images registered across biodiversity data networks in Period 2019-2022 hash://sha256/da7450941e 7179c973a2fe1127718541bca6ccafe0e4e2bfb7f7ca9dbb7adb86. Zenodo. Release date: 2022-8-29. URL: https://doi. org/10. 5281/zenodo. 7032574 • Pollock L, Morris W, Vesk P (2011) The role of functional traits in species distributions revealed through a hierarchical model.
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A recent phase three clinical trial (NCT04469114) found that tofacitinib treatment in hospitalized patients with COVID-19 pneumonia led to a lower risk of death or respiratory failure, which supported the US National Institutes of Health (NIH) to recommend tofacitinib as an alternative when baricitinib is not available. 48 In IBD patients with COVID-19, the use of tofacitinib was not associated with an increased risk of hospitalization, ICU admission, or death when compared with other treatment regimens. 49 However, the available data on its safety in COVID-19 patients remain limited and controversial. 34 Current efforts to explore JAK inhibitors for treating COVID-19 exclusively based on their anti-inflammatory activity. However, we found that tofacitinib and the new generation inhibitor filgotinib can directly inhibit coronavirus replication.
NL63 stock was produced by consecutively inoculating the virus onto LLCMK-2 cells. OC43 and 229E were bought from ATCC (USA) and amplified in Huh7 cells. SARS-CoV-2 (isolate BetaCoV/Munich/BavPat1/2020; European Virus Archive Global #026V-03883, GenBank: MT270101; referred as a wild-type strain), SARS-CoV-2 B. 1. 617. 2 Delta variant (isolated from a patient) and SARS-CoV-2 B. 1. 1. 529 Omicron variant (isolated from a patient) were used. Cell lines were analyzed by genotyping and confirmed to be mycoplasma negative.
ACKNOWLEDGEMENTS The authors thank  Dr. Lia van der Hoek  ( Amsterdam UMC location AMC, University of Amsterdam, the Netherlands ) for providing the stock of human coronavirus NL63. The authors also thank  Dr. Bart Haagmans  and  Dr. Mart M. Lamers  ( Department of Viroscience, Erasmus MC ) for providing the stock of SARS-CoV-2 and technical support. This study is supported by a  VIDI grant  (No. 91719300 ) from the  Netherlands Organization for Scientific Research (NWO)  to  Q. Pan , and the  ZonMw   COVID  project (114025011) from the  Netherlands Organization for Health Research and Development  to R. R.
NF-YA, the regulatory subunit of the trimeric CCAAT-binding transcription factor NF-Y, is present in vertebrates in two major alternative spliced isoforms: NF-YAl and NF-YAs, differing for the presence of exon-3. NF-YAx, a third isoform without exon-3/-5, was reported only in human neuronal cells and tumors. These events affect the Trans-Activation Domain. We provide here evidence for the expression of NF-YAx and for the existence of a new isoform, NF-YAg, skipping only exon-5. These isoforms are abundant in Aves, but not in reptiles, and are the prevalent transcripts in the initial phases of embryo development in chicken. Finally, we analyzed NF-YAg and NF-YAx amino acid sequence using AlphaFold: absence of exon-5 denotes a global reduction of β-stranded elements, while removal of the disordered exon-3 sequence has limited effects on TAD architecture.
In this respect, NF-YA, considered as the regulatory subunit of the trimer, appears to be informative, precisely because of its uniqueness: based on the Human Transcription Factors database (http://humantfs. ccbr. utoronto. ca/index. php), only 7/77 DBD families have, like NF-YA, a single member. The gene of NF-YA is composed of 10 exons, located in humans on the short arm of chromosome 6. The protein coding sequence starts within exon-2 and ends within exon-10. Most protein coding genes are involved in alternative splicing (AS), whose importance is being progressively understood in various species. AS dramatically variegates the emporium of RNA and protein products: together with initiation/elongation of transcription, it is the crucial step that regulates all developmental processes and responses to external stimuli  [10] [11] [12].
A new NF-YA isoform in birds (and alligator) A scheme of the NF-YA gene structure common to vertebrates is shown in the upper part of Fig. 1  and Fig. S1. Previously, we generated multiple sequence alignments -MSA-of NF-YA proteins from vertebrates retrieved from the Pfam database using as query the 56 amino acids HAP2 domain shared by all eukaryotes  [21]. We did notice few sequences skipping exon-5, but not exon-3, among others, from chicken, turkey, and Chinese alligator, which we previously placed in the collective branch of reptiles and birds (Sauropsida) (See Fig. S1C  in  [21] ). Similarly, we noticed 2 sequences potentially corresponding to NF-YAx from the bony fishes Anabas testudineus and Labrus bergylta, and one from Vombatus ursinus, a mammal (Fig. S1C  and Fig. S1B , respectively, in  [21] ).
Because of the paucity of these "outliers", and the possibility of erroneous annotations (See below), we initially did not pay attention as to the actual origin of exon-5-less sequences. Thanks to the ongoing efforts of the Vertebrate Genomes Project  [22] , the number of available annotated vertebrate genomes vastly increased since our report  [21]. This prompted us to further investigate the presence and distribution of potential novel NF-YA isoforms based on our previous observations. Fig. 1  shows an updated MSA of currently available Sauropsida NF-YA sequences, based on the InterPro database IPR001289 (>8000 unique sequences). We included in the shown MSA only species with a minimum of 3 protein sequences: we obtained 112 NF-YA sequences from 30 species of Aves and 42 from 7 species of reptiles.