dataesr/scientific-paragraphs-categorization huggingface dataset:
ern-net. eu). The WP9, representing patient empowerment, has highlighted the need to take urgent care of the transition process. In 2019, MetabERN and SSIEM organized a webinar in which four MetabERN centers presented their own program on transition. Then, in the context of the WP4 on Guidelines, Care Pathways and Standardization for Medical Care and Transition, MetabERN established the Transition Project Working Group (TPWG), which is led by referral experts in the field in collaboration with the associations of patients. As a first step in its activities, the TPWG has investigated how the transition process is currently organized in European metabolic centers, in particular what transition programs currently exist, whether there is any reimbursement for metabolic patients available, and how the transition process could be supported further to facilitate smooth transition for metabolic patients.
Moreover, transition is a process involving several key players in the center of interest; therefore, the next survey to complement that of the HCPs should be targeted at patients and their associations to understand their point of view and especially to gather information on the social and psychological aspects of transition, which seems even more difficult in patients with multiple disabilities. Our survey focused on HCPs, which can provide information and suggestions regarding the more practical and administrative aspects of the transition process. In fact, no publications exist on the patient or caretaker point of view and indeed this is one of the next steps to be taken by MetabERN. Secondly, due to the design of the survey, we were not able to collect specific data regarding the percentage of pediatricians that are involved in the care of adult IMD patients.
ACKNOWLEDGMENTS We are grateful to all HCP representatives of the MetabERN collaboration group that replied to our survey:  Giovanni Ceccarini ,  Stefan Koelker ,  Sabine Fuchs ,  Luisa Diogo ,  Stephen Attard ,  Niklas Darin ,  Salvatore Grosso ,  Maria Alice Donati ,  Carlo Dionisi-Vici ,  Julia Hennermann ,  Madara Masinska ,  Anibh Das ,  Terry Derks ,  Dorothea Moeslinger ,  Pavel Jesina ,  Viktor Kozich ,  Ivo Barić ,  Athanasia Ziagaki ,  Ans van der Pleog ,  Per Mathisen ,  Birute Tumiene ,  Maria-Estela Rubio-Gozalbo ,  Mojca Zerjav Tansek ,  Angeles García Cazorla ,  Luis Aldámiz-Echevarría ,  Annet M. Bosch ,  Olga Azevedo ,  Svetlana Lajic ,  Sabine Grønborg ,  Sarah Grünert ,  Katalin Szakszon ,  Montserrat Morales Conejo ,  Pascale de Lonlay ,  Annalisa Sechi ,  Corinne De Laet ,  Ana Gaspar ,  Giancarlo Parenti ,  Kurt Ullrich ,  Janice Andersen ,  Alessandro Simonati ,  Marie-Cecile Nassogne ,  Dominique Paul Germain ,  François-Guillaume Debray ,  María Luz Couce ,  Klaus Mohnike ,  Serena Gasperini ,  Philippe Labrune ,  Esmeralda Martins ,  Eyskens Francois ,  Erle Kristensen ,  Allan M.
Examples are the STM32Cube. AI  [11]  by STMicroElectronics, which embeds human activity recognition and audio scene classification capabilities in micro-controllers; the Snapdragon Neural Processing Engine (SNPE) Software Development Kit (SDK)  [12]  released by Qualcomm, which enables DL into smartphones; and uTensor [13], built on Tensorflow and optimized for ARM processors. More distributed AI approaches can be also foreseen, which do not only spread learning and inference tasks between the cloud and the (deep) edge, but also enforce inference model splitting between device-edge, edge-edge, and edge-cloud tiers on a per-application basis  [14]. Having AI pervasively deployed over a distributed cloud along the cloud-to-things continuum  [7, 15]  would not come without challenges, as summarized in Table  1.
• We realize a Proof-of-Concept (PoC) to showcase the viability of the conceived proposal when referring to an object detection application and leveraging the Leshan implementation of OMA LwM2M. We also measure the data footprint in terms of exchanged bytes to retrieve the output of an object detection inference task. The paper is structured as follows: Section 2 introduces the OMA LwM2M protocol as the enabler for IoT virtualization as well as CoAP to facilitate message exchange. Our proposal is discussed in Section 3 and the devised PoC is presented in Section 4. Final remarks and conclusions are drawn in Section 5, by providing hints on future works.
Proposal 3. 1. Reference Architecture Our proposal builds upon the successful IoT virtualization concept that is extended to the case of upcoming AI-empowered IoT devices. The resulting reference architecture is reported in Figure  2. At the bottom of the architecture, we have intelligent IoT devices, i. e. , devices equipped with AI capabilities. Through embedded sensing capabilities, they can collect data feeding the on-board inference engine. The latter one mainly consists of models pre-trained on massive datasets by more powerful platforms, e. g. , the remote cloud. A typical pre-trained ML inference model cannot be run on constrained IoT devices as it is, and must be converted to fit the target limited device resources. Quantization and pruning  [43]  are just a few examples of the techniques to be deployed by an ML compiler to build the optimized model for the specific software and hardware platform that the device is featuring.
Exchanged Data Traffic We measure the number of exchanged bytes to retrieve an inference result upon a request issued by a remote consumer application. In particular, results reported in Figure  5  refer to the following cases: (i) the request issued by the remote application is forwarded by the VIO to the physical device, since there is no cached inference matching the request (curve labeled as "No caching at the VIO") and (ii) the inference requested by the remote application is cached by the VIO (curve labeled as "Caching at the VIO"). To enable caching at the VIO, whenever a new inference result is received by the physical device, data are stored in the local database and sent to the requesting application. CoAP is leveraged over the link between the Leshan client and the VIO to better match network and device constraints.
Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM--a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact.
g. when considering proteins whose genes are located within the same genomic region as candidate partners of a multimeric complex. Establishing connectivity between the different proteins of a proteomics-derived list can hardly be carried out manually, as it requires processing large amounts of data from various sources. With this regard, three web servers are currently available to assist biologists in this task. The STRING database (https://string-db. org/) (3), one of the most cited resources for building PPI networks, is a precomputed global resource including both physical interactions as well as functional associations. It uses multiple types of information, such as genetic interactions, text mining, or experimentally and predicted PPIs from other databases. The GeneMANIA web server (http://genemania.
org) (4) uses lists of gene ID as input to perform functional annotation and prediction, by integrating data from PPI databases such as BioGRID (https://thebiogrid. org/)  (5)  or Path-wayCommons (www. pathwaycommons. org) (6). Lastly, In-teractome3D (https://interactome3d. irbbarcelona. org/) (7) , introduced the use of protein three-dimensional (3D) structures for modeling the protein complexes of a given organism and, thus, may shed light on the protein residues involved in a given interaction. It also includes information retrieved from external resources, such as IntAct (https: //www. ebi. ac. uk/intact/)  (8). These three web resources are capable of finding interologs, i. e. complex-forming partners predicted on the basis of the interaction between their respective homologous proteins, providing that this interaction has been conserved throughout evolution.
This task is performed either through identical gene names across different species or, in the case of Interactome3D, by homology search using BLAST  (9). Finally, other useful tools for studying protein complexes can be cited, such as PrePPI  (10) , ProtCID  (11) , ComplexPortal  (12) , CORUM  (13) , and the XlinkDB database and software tools, specifically dedicated to cross-linking data analysis  (14). However, these cannot be compared to the aforementioned servers, as most fall in the database category and focus on different aspects of PPIs. PPI3D  (15)  is another tool to search for interologs with a 3D interface modeling perspective, but is presently limited to only two sequences. Recently, we have proposed Proteo3Dnet  (16) , a new integrative pipeline which stands out from the state of the art mainly by its detection of distant homologies and combination of domain-domain interactions as well as motifdomain contacts.
Search for homologs in the Protein Data Bank using HHsearch. We use the precalculated banks available from the HHsuite repository, in which a non-redundant subset of PDB entries at 70% sequence identity is maintained. To reduce the chance to identify irrelevant homologs, only hits associated with a probability of >95%, a cutoff, that in our experience fits well the objective of lowering the risk to miss some remote templates while identifying the relevant ones. 2. Proteo3Dnet then enlarges the collection of hits with the protein chains belonging to the same cluster. Because the initial search is performed on a subset of the PDB and because it could occur that non-representatives of the clusters correspond to PDB entries containing the structures of complexes of interest, this step extends the search to all homologs of the PDB.
Complex identification is based on the search for PDB entries for which several--if not all--chains of the input are homologous. To avoid incorrect complex assignment that could occur in the unit cell of the crystals, the search is performed considering the biological unit specified by the authors, or ranked one otherwise, as provided by the PDB entries in the mmCIF format. Information about homo-oligomers is also collected from these entries. 4. Complexes are then ranked according to the number of chains of the input they encompass, and conditional chain binding stable core complexes are identified. We emphasize that this search procedure covers the entirety of the PDB and, thus, is not per se biased by the number of structures of complexes that are evolutionarily conserved, neither is it regarding species specificity.
These are usually located in disordered regions of proteins, often in missing parts of resolved structures. To overcome this limitation, Proteo3Dnet relies on the ELM database that links the motifs with interacting Pfam domains. Occurrences of pairs involving SLiMs-Pfam domains correspond to potential PPIs. The ELM database currently gathers 291 linear motifs ('ELM classes') which are reported to bind one or several Pfam domains, according to published experiments ('ELM instances'). To identify transient interactions, Proteo3Dnet searches for these motifs and their corresponding Pfam domains in the proteins of the input list. The resulting connections are then filtered, based on the location of the motifs within intrinsically disordered regions, as predicted by the ANCHOR2 score of IUPred2A  (20) , computed and averaged for all positions of the motif.
Implementation Server input. The Proteo3Dnet input consists of a specification of the sequences identified during an interactome experiment. These can be specified either in the form of the UniProt identifiers or full UniProt sequences in the FASTA format. Note that presently, it is not possible to enter data without specifying the UniProt identifier. Output. The outputs consist of three main sections, that encompass interactive graph exploration, tables reporting information about the hetero-and homo-multimers identified, along with raw information on the homologs identified, and for each input protein, a list of experimentally verified interactions from the IntAct database (8) --which may include additional protein partners--presented as an interactive PPIs matrix (https://github. com/ebi-uniprot/ interaction-viewer/).
We focus here on the main output that corresponds to the interactive exploration of the groups of sequences identified as in interaction (Figure  1A ). This facility is based on cytoscape. js, a JavaScript library for interactive network visualization (https://js. cytoscape. org/) (21). The graph consists of nodes that correspond to proteins and edges that represent the interactions between them. Three types of nodes are distinguished in the graph and correspond to: (i) input proteins, (ii) the proteins not present in the input but in interaction with those in at least one complex structure, (iii) proteins with interaction suggested by BioGRID (see upper). In terms of AP-MS, the second type can correspond to either proteins that could be detected during the experiment but were for instance below the cutoff, or to proteins that interact conditionally with a 'core' complex.
It typically ranges from several minutes for small datasets of less than 20 sequences to up to several tens of minutes when the sequence number exceeds one hundred. For example, the 62 proteins of the Pragmin dataset  (23)  were processed in 15 minutes. For the larger proteasome 20S dataset (n = 192)  (24) , the calculations were completed in only 20 min. These times were obtained using the 'Normal' (i. e. slowest) mode of the server. To avoid very long runs, the maximum number of input proteins currently accepted is 400.
Use case of yeast precursors to the small ribosomal subunits We have queried Proteo3Dnet with a list of interactants obtained by tandem-affinity purification of Saccaromyces cerevisiae precursors to the small ribosomal subunits (hereafter termed pre-40S particles;  (25). These 62 proteins include highly abundant 39 Ribosomal Proteins of the Small ribosomal subunit (RPS) and 8 ribosome biogenesis factors (RBFs) together with 14 much less abundant Ribosomal Proteins of the Large ribosomal subunit (RPL), and the Lrg1 protein. Proteo3Dnet, identified templates for all the 62 proteins (Figure  2A ). 56 of them were identified in Saccharomyces cerevisiae. There is currently no structure of yeast Dim1 and Nob1 RBFs and these were matched to their human (PDB: 1ZQ9) and archaeal (PDB: 2LCQ) counterparts.
A human model (PDB: 3EAP) was also proposed for Lrg1. It is important to note that the presence of RNA molecules within ribosomal particles (or any other type of structuring molecule) is not issue for our pipeline, since it only considers proteins. the 145 complexes retrieved, ∼80 correspond to the full ribosome (either from yeast, as c006, or from other related species like c130 (human), c144 (M. tuberculosis) or c065 (pig). They do not correspond but rather include the pre-40S particles, with low maximal completeness--from 7% (c107) to 60% (c001). On the contrary, ∼60 complexes with maximal completeness ranging from 30% (c016) to 100% (c009) and corresponding to small ribosomal subunits were identified, either from yeast (as c009) or other species. The c009 actually corresponds to the S.
cerevisiae pre-40S particle (PDB: 6FAI) and served as a positive control here (Figure  2B ). It contains 35 (out of 62) entries with 100% sequence identity. The c027 is a subcomplex corresponding to the dimeric Enp1/Ltv1 RBFs (PDB: 5WWO). Finally, a couple of complexes (c095, c098. ), matched large ribosomal subunits that were co-purified traces rather than true partners of the pre-40S particles. Very similar results were obtained on this example using interactome3D although some discrepancies exist. For ex-ample, only one interaction is found for Rps6 (with Rps4A) in interactome3D, whereas Proteo3Dnet reports interactions with all the other 38 RPS. In interactome3D, the model of Rio2 is based on a distant similarity with an archeal protein (PDB: 4GYI) while the 3D structure from S. cerevisiae is known (PDB: 6RBD)  (26).
ACKNOWLEDGEMENTS The authors would like to thank  Célia Plisson-Chastang , for careful proof-reading of the pre-40S data analysis. The authors are also grateful to the referees for their constructive feedback. FUNDING Investissement d'Avenir Infrastructures Nationales en Biologie et Santé  [ ANR-10-INBS-05-01  ( French Infrastructure for Integrated Structural Biology -FRISBI ),  ANR-10-INBS-08  ( Proteomics French Infrastructure -ProFI ),  ANR-11-INBS-0013  ( French Institute of Bioinformatics -IFB ),  ANR-18-IDEX-0001  ( IdEx Université de Paris )]. Funding for open access charge: INSERM [U1133].
Here, we developed Localization Model Fit (LocMoFit; Fig. 1 ), a general framework to fit an arbitrary model to coordinate-based SMLM data. Based on maximum likelihood estimation (MLE), it identifies the most likely model from a class of models and estimates the most likely parameters of the model that describe the experimental structure. If the underlying geometry cannot be guessed, LocMoFit can be used for particle averaging to calculate an average model under the assumption of identical structures. Advanced visualization routines and a simulation engine allow for efficient validation and quality control. LocMoFit, written in MATLAB, has an API for integration into own code and can be easily extended by user-defined models. Seamless integration of LocMoFit in the open-source analysis platform SMAP  21  provides access to plenty of tools for SMLM for localization, post-processing and quantification.
To validate this idea, we fitted different models to each NPC in the Nup96 dataset (Fig. 2a, b ). These models were rotationally symmetric with different symmetries (from 6-fold to 10-fold, see insets in Fig. 3a ). The 8-fold symmetric model clearly shows the lowest AICC overall, in line with the known symmetry of the NPC  25. To further validate the model-selection functionality of LocMoFit, we used its simulation engine to generate NPCs with different rotational symmetries. We demonstrate that the cumulative distributions allowed identifying the correct symmetry as a matching symmetry always had the lowest AICC (Extended Data Figure  6a ). The differences in AICC decreased with increasing similarity of the models, rendering the distinction of a 10-fold symmetric model from a 9-fold pore more challenging (Extended Data Figure  6a ).
Integrating large datasets into an average representation in the form of protein localization maps can be a useful and complementary approach to a statistical analysis of parameters extracted from individual sites. LocMoFit can calculate such average representations by determining the precise position and orientation of a reference structure and use this to align target proteins, imaged in a second channel. By additionally evaluating a parameter that changes monotonously over time, LocMoFit can extend this approach to dynamic, timeresolved localization maps. However, whenever templates or models are used for registration, the averages might be biased towards the model  36. Thus, care should be taken when interpreting the averages of the reference structures. Target structures that were not considered during the alignment, on the other hand, should be free of such model bias.
Currently, a fit to a single site takes seconds to minutes (5-10 seconds for an NPC and CME site, 10-20 minutes for a micrometer-long microtubule, depending on the complexity of a model and the number of localizations of a site), allowing even large data sets with hundreds of sites to be analyzed in overnight runs on a standard CPU (e. g. , Intel core i5-4460). In the future, deploying LocMoFit on clusters or GPUs could further improve performance. Published as open-source, LocMoFit is readily useable as part of the SMLM software platform SMAP  21  , allowing users to easily fit their own data with any of the numerous pre-defined models using a graphical user interface. To this end, we provide detailed documentation, tutorials, and example files. Alternatively, LocMoFit can be run independently of SMAP and provides an API for integration in own software.
Endocytic structures: For fitting endocytic sites, we used a composite model formed by a two element model: projections of a 3D hemispheroid and a thick ring onto the 2D imaging plane (Fig. 4g ). This model was implemented as a discretized continuous fluorophore distribution. In the imaging plane, the base of the hemispherical projection is limited to below the thick-ring projection. The hemispherical projection is parameterized by the half long and half short axes  and  of a hemispheroid. The thick-ring projection is parameterized by the thickness  and the inner/outer radii  and  of the ring. This model was fitted to the yeast endocytic sites in the dual-color dataset (Fig. 4e-i ). The hemispheroid was only fitted to Abp1 localizations and the thick ring to Las17 localizations, respectively.
To remove unbound dye, coverslips were washed three times for 5 min in PBS. At this point, the sample was ready for single color superresolution imaging. For simultaneous dual-color imaging with immunostaining, samples were further blocked with 5% [v/v] normal goat serum (NGS) (catalog no. PCN5000, lifeTech) in PBS for 1 h. Binding of primary antibody (Elys (catalog no. HPA031658, Atlas Antibodies, 1:50), Nup133 (catalog no. HPA059767, Atlas Antibodies, 1:150), Nup62 (catalog no. 610498, BD Biosciences, 1:150), Nup153 (catalog no. ab24700, Abcam, 1:60)) was achieved by incubation with the respective antibody diluted in 5% [v/v] NGS in PBS for 1 h. Coverslips were washed three times for 5 min with PBS to remove unbound antibody and subsequently stained with CF660C labeled anti-rabbit antibody (catalog no.
Microscope setup and imaging All SMLM data in mammalian cells were acquired on a custom built widefield setup described previously  6, 45. Briefly, the free output of a commercial laser box (LightHub, Omicron-Laserage Laserprodukte) equipped with Luxx 405, 488 and 638 and Cobolt 561 lasers and an additional 640 nm booster laser (iBeam Smart, Toptica) were collimated and focused onto a speckle reducer (catalog no. LSR-3005-17S-VIS, Optotune, Dietikon) before being coupled into a multi-mode fiber (catalog no. M105L02S-A, Thorlabs). The output of the fiber was magnified by an achromatic lens and focused into the sample to homogeneously illuminate an area of about 1,000 µm 2. The laser was guided through a laser cleanup filter (390/482/563/640 HC Quad, AHF) to remove fluorescence generated by the fiber.