dataesr/scientific-paragraphs-categorization huggingface dataset:
11. Switzerland: Compared to Germany, Switzerland had telemedicine services that were already well established and promoted by health insurers before the COVID-19 outbreak. With the spread of the disease, these services have received strong support from the population and consequently, their use increased by 30% by April 2020 41. However, these services are largely based on videoconferencing and do not support remote vital sign measurements. In the wake of COVID-19, several medtech startups have launched initiatives to bridge this gap. Leitwert GmbH (Zurich, CH) has partnered with the University Hospital Basel (Basel, CH) to remotely track vital signs with wearables, which are continuously stored using their internet of medical things device and database management solution. Third-party wearables can be easily integrated to transmit health parameters independent from location to a cloud-hosted dashboard  (Käch et al 2020).
In order to monitor and reduce the spread of COVID-19, the South African government, through the National Institute for Communicable Diseases, issued regulations that mandated the establishment of a centralized database to facilitate contact tracing  (Klaaren et al 2020). The centralized contact tracing system termed "COVID-19 Tracing Database" collects and aggregates information from a large range of sources including mobile phone operators, electronic communication services, testing laboratories and accommodation registries  (Klaaren et al 2020). The information collected in the COVID-19 Tracing Database may only be used by authorized persons and exclusively for the purpose of facilitating contact tracing and monitoring of COVID-19 73. Contact tracing efforts are further supported by Covi-ID, a mobile QR-code based location tracing application that was developed by the University of Cape Town (UCT) for the South African Government 74.
The National Broadband Network Corporation, which is a Government Business Enterprise, has recently upgraded Australian GP clinics to Internet connectivity speeds of 50/20 Mbps (download/upload) at no extra cost, for six months 86. This has increased the capability of clinics to deliver multiple consultations through telemonitoring simultaneously. MedicalDirector, a Sydney-based medical software provider, has recently made its fully integrated telemonitoring solution available for free (for at least three months) to support GP clinics. In the first three weeks of use (up to the 20 April 2020), more than 4. 3 million consultations were delivered to over three million patients due to the COVID-19 pandemic-driven telehealth expansion 87. Despite many obvious benefits of telemonitoring, the actual uptake and integration of it into mainstream practice has been slow  (Wade et al 2014 , Caffery et al 2018 , 2016).
In the case of noncompliance severe fine may be imposed. Within the scope of the pandemic there has been debates about how GDPR will influence data sharing and analysis of digital health data collected in Europe for research purposes  (McLennan et al 2020). This includes health data collected remotely. It has been put forward that the research exemption clause of the GDPR during COVID-19 should be used in order to facilitate research efforts using these data. This highlights important considerations relating to data protection laws and some of their implications within the area of remote health monitoring and how these laws may have exemption clauses that enable special usage of data under GDPR in certain extraordinary circumstances. Whether this will include the current pandemic is yet to be determined but we agree with the general call (McLennan et al 2020) for making these regulations more flexible within the context of the COVID-19 pandemic as far as it may benefit research and human health while preserving privacy.
KUV 00311-1938 Fermi/LAT data contemporaneous to the H. E. S. S. observations, i. e. in the period from 2009-11-10 (MJD 55145) to 2014-08-25 (MJD 56894) were analysed in the [100 MeV-300 GeV] energy range and in the [100 MeV-10 GeV] energy range. As the source has modest variability in the 4FGL catalogue (Section 1. 1), a 2-month binning Fermi/LAT light curve was computed assuming a PWL shape, leaving the index free to vary (see Fig. 1 ). [100 MeV-300 GeV]. No variability is detected during the H. E. S. S. observation period, as an excess variance calculation yields a value compatible with zero. The γ-ray emission from KUV 00311-1938 is therefore well described as constant in the Fermi/LAT energy range, implying that it is safe to combine the data from the full H. E. S. S. observation period into a single spectrum.
PKS 1440-389 Fermi/LAT data analysis has been performed for the 3-month data set contemporaneous with the H. E. S. S. observations (MJD 55985-56074) in the energy range [300 MeV-300 GeV] and [300 MeV-10 GeV]. The low energy bound of 300 MeV was applied to avoid contamination from the bright, nearby quasar PKS B1424-418 due to the larger point spread function at low energies  (Ackermann et al. 2013). Since PKS 1440-389 has modest variability in the 4FGL catalogue (Section 1. 2), a monthly binning Fermi/LAT light curve was computed assuming a PWL shape, leaving the index free to vary (see Fig. 1 ). The excess variance calculation over the Fermi/LAT lightcurve gives a value of 0. 1 ± 0. 3. Therefore data from the whole 3-month dataset were combined into a single spectrum. [300 MeV-300 GeV]. All spectral models in this energy range result in an excess with a significance of about ≈ 9.
3-10 keV energy range. The analysis was performed using the standard HEASoft (v6. 16) and Xspec (v12. 8. 2) tools. Source counts were extracted using the xselect task from a circular region with a radius of 20 pixels (∼ 47 arcsec). Background counts were extracted from a 60pixel circular region with no known X-ray sources. The data were grouped, requiring a minimum of 20 counts per bin and then fitted with a power-law model including photo-electric absorption with a fixed value for the Galactic column density Sky-corrected images for all available Swift/UVOT filters were taken from the Swift archive, and aperture photometry was performed using the UVOT tasks included within the HEASoft package. Source counts were extracted using a 5 ′′ radius for all single exposures and all filters, while the background was estimated from different positions more than 25 ′′ away from the source.
ATOM The Automatic Telescope for Optical Monitoring 2 (ATOM) is a 75 cm optical support telescope for H. E. S. S. , located at the H. E. S. S. site. Operating since 2005, it provides optical monitoring of γ-ray sources. KUV 00311-1938 was regularly monitored from April 2008 to December 2012 in the R band (640 nm), while PKS 1440-389 was observed with high cadence during the whole 2012 H. E. S. S. campaign on the source. The ATOM data were analysed using aperture photometry with custom calibrators in the field of view. We were able to use up to four calibrators in the case of KUV 00311-1938 and five for PKS 1440-389. The resulting magnitudes were de-reddened in the same manner as done for the UVOT data. The uncertainty on the Power-law parameters describing the differential photon flux obtained for the available XRT observations.
E. S. S. VHE γ-ray spectrum, accounting for EBL absorption, we deduce an upper limit on the redshift of the source of z < 0. 53. Combined with previous results, this constrains the redshift to the range 0. 14 z < 0. 53. PKS 1440-389 is found to be a standard source within the VHE HSPs, fitting reasonably well into the current population. ture for Computing (SNIC) at Lunarc. Tomas Bylund and Yvonne Becherini wish to acknowledge the support of the Data Intensive Sciences and Applications (DISA) centre at Linnaeus University. Matteo Cerruti has received financial support through the Postdoctoral Junior Leader Fellowship Programme from la Caixa Banking Foundation (LCF/BQ/PI18/11630012). This research made use of the NASA/IPAC Extragalactic Database (NED) and of the SIMBAD Astronomical Database, of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration.
We acknowledge the use of public data from the Swift data archive. This research made use of Gammapy, a community-developed core Python package for gamma-ray astronomy  (Deil et al. 2017). This research has made use of data and software provided by the Fermi Science Support Center, managed by the HEASARC at the Goddard Space Flight Center. Part of this work is based on archival data, software or online services provided by the Space Science Data Center -ASI. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published in A&AS 143, 23.
ACKNOWLEDGEMENTS The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  Helmholtz Association , the  Alexander von Humboldt Foundation , the  French Ministry of Higher Education, Research and Innovation ,  the Centre National de la Recherche Scientifique  ( CNRS/IN2P3  and  CNRS/INSU) , the Commissariat à l'énergie atomique et aux énergies alternatives (CEA), the  U. K. Science and Technology Facilities Council (STFC) , the  Knut and Alice Wallenberg Foundation , the  National Science Centre, Poland  grant no. 2016/22/M/ST9/00382 , the  South African Department of Science and Technology and National Research Foundation , the  University of Namibia , the  National Commission on Research, Science and Technology of Namibia (NCRST) , the  Austrian Federal Ministry of Education, Science and Research and the Austrian Science Fund (FWF) , the  Australian Research Council (ARC) , the  Japan Society for the Promotion of Science  and by the  University of Amsterdam.
Genome analysis and comparisons Average Nucleotide Identity analyses were performed using the ANIm method  [26]  with the Python module Pyani  [27]  using proposed threshold of ≈ 95-96% for species delineation. Genome annotation, including manual curation, and comparisons, including pan and core genome computation, were performed using the web interface MicroScope  [28] , which allows graphic visualization enhanced by a synchronized representation of synteny groups  [29]. Persistent, shell and cloud genomes were computed using the PPanGGOLiN 0. 1. 4 software  [30]. Considering the low levels of sequence divergence at typical core genome loci previously reported for T. maritimum  [15] , we chose a cutoff of 80% identity and 80% on the minimal coverage of the length between the aligned portions of two proteins to determine whether two CDSs were members of the same gene family.
GCA_902705425 The list of contributors is available in Additional file 1. tree reconstruction using Gubbins  [32]  and the Phylip package version 3. 6 released by Felsenstein in 2005, available online at  [33]  and originally released in 1980  [34]. Regions of high diversity (high number of single nucleotide polymorphisms [SNPs]) presumably linked to recombination events were masked at each Gubbins iteration. A Maximum Likelihood tree was built at the fifth iteration using non-masked polymorphic sites. Neighbor-joining and parsimony trees were obtained with Phylip suite v3. 696 (programs dnadist, neighbor, and dnapars) after removing positions with gaps in the alignment. Custom Perl and R scripts were used for the nucleotide diversity analysis (computation of the average pairwise nucleotide diversity and of the homoplasy index) and graphical representations (including R library "ape" for the drawing of phylogenetic trees).
MALDI-TOF MS data acquisition A MALDI Biotyper Microflex LT controlled by Compass flexControl software (version 3. 4; Bruker Daltonics, Billerica, Massachusetts, USA) was used to generate mass spectra for all isolates. Mass spectra were acquired using automatic mode and default settings (2000 to 20 0000 Da; linear positive mode; 240 laser shots). For each isolate, twelve spectra (four spots of each isolate extracted were measured 3 times) were recorded.
Peak shift characterization using genomic data Ribosomal protein sequences were retrieved from the MicroScope annotation platform using the 25 available T. maritimum genomes. The theoretical mass weight for each sequence was computed with the mw() function from the Peptides R-package and TermiNator  [37]  was used to predict the first methionine cleavage resulting in a theoretical loss of 131. 2 Da. The theoretical masses obtained were compared to the peak list and spectra were screened to retrieve peak shifts using the predicted masses of presumptive polymorphic biomarkers. Several other peak shifts were identified during this step but were not kept for typing purpose because of the stringent criteria used (see section "Results").
MALDI-TOF data analysis algorithm and implementation in R We used different R packages dedicated to mass spectrometry analysis from the BioConductor repository  [38] , i. e. MALDIquant Foreign, MALDIquant, MALDIrppa and MassSpecWavelet. Using these packages, any type of MALDI-TOF data can be loaded from any manufacturer. Raw data generated by Microflex ® Bruker were imported into R environment by MALDIquant Foreign. The spectra pre-processing steps (i. e. , intensity transformation, baseline correction, intensity correction, spectra alignment, peak detection), curve smoothing and peak detection on average spectra (mean spectra) were performed using MALDIquant, MALDIrppa and MassSpecWavelet  [39] , respectively. This method is based on local maxima detection at different scales, and the retained peaks are observed at many scales as true maxima (true peaks).
Strikingly, most of the shell-and cloud-genome genes were encompassed in genomic islands (region of genomic plasticity). Each genome contained an average of 26 (SD = 4. 65) genomic islands (Table  1 ). Interestingly, strain FC isolated in Chile encompassed a unique (i. e. , not identified in other strains), 83-kb long (MARITFC_v1_10015 to MAR-ITFC_v1_10098) genomic island that displayed prophage characteristics (i. e. , located at an Arg tRNA and bordered with an integrase/recombinase encoding gene). This island contained many genes predicted to be involved in heavy metal resistance, including genes encoding proteins of the copper oxidase family and different cobaltzinc-cadmium efflux pumps. We focused on the two fully PacBio-assembled genomes (i. e. , strains NCIMB 2154 T and TM-KORJJ) contained in our dataset to accurately identify genes encompassed in these islands.
Identification of potential biomarkers in genomic data Because genomic data pointed to a cohesive species but still displaying variability to some extent, we aimed to evaluate the potential of MALDI-TOF MS for rapid screening and typing using specific sets of biomarker ions. Since half of the detected peaks in bacterial MALDI-TOF MS spectra correspond to ribosomal proteins  [40] , we focused on this protein set. Using genome sequence data, we first retrieved all deduced ribosomal protein sequences and predicted the first methionine removal using the Terminator software  [37, 41]. The molecular weight of each deduced ribosomal protein was computed. We retrieved ribosomal protein sequences without polymorphism (i. e. , invariant ribosomal protein sequences hereafter designed as monomorphic) that could serve as species biomarkers and for MALDI-TOF MS spectra internal calibration.
We also retrieved ribosomal protein sequences displaying polymorphism (i. e. , ribosomal protein sequences with variation hereafter designed as polymorphic) since they are likely relevant biomarkers for strain typing. Strikingly, one-third (18/54) of the ribosomal proteins were monomorphic while the others (36/54) displayed some degree of amino-acid polymorphism that mostly gave rise to a mass change (the resulting information is summarized in Additional file 6). Strikingly, the topology of the hierarchical classification tree deduced from this data set was globally congruent with that of the trees obtained using the core genome genes (Figure  1.
Selection of 9 polymorphic biomarkers for strain typing and MALDI-Type attribution Using our polymorphic biomarker candidates (Additional file 6), we performed visual inspection of the above-mentioned spectra to identify the corresponding peak shifts. We selected 8 polymorphic biomarkers corresponding to ribosomal proteins (i. e. , RpmD, RpmC, RpsP, RpsN, RpsO, RpsQ, RplX and RplT) with a molecular weight ranging from 6600 Da to 13 200 Da. An additional polymorphic biomarker, RpsT, was included after screening our collection of 131 isolates (see next paragraph). Indeed, strain Aq8-57 displayed an unexpected peak shift (not correlated to any amino-acid polymorphism observed in the 25 genomes data set). Sanger sequencing of strain Aq8-57 revealed that this peak shift corresponded to a 122A > G mutation in the sequence of the rpsT gene leading to a R41K change in the aminoacid sequence.
Using this scheme, we identified 20 MTs (Additional file 1). Using genome sequence data, one could predict the MT of a strain; for instance, strain TM-KORJJ was predicted to belong to MT3. This approach allowed unambiguous assignment for each isolate as well as the use of visualization and analysis tools developed for MLST such as the eBurst [43] and SplitsTree decompositions  [44]. Indeed, using eBurst on our dataset, the 20 MTs could be grouped in 4 clusters based on connection by single-locus-variants (Figure  3A ) that can be designated as MALDI-Groups (MGs). Of note, the criterion for the determination of MGs is analogous to the criterion for the determination of clonal complexes (CC) reported in the MLST strategy [45] but does not correspond to the same level of divergence between isolates.
Strain P2-27 belonged to MT19 whereas the type strain NCIMB 2154 T belonged to MT1 and strain NBRC 15946 belonged to MT4. These three strains were encompassed in MG3. Strain UCD SB2 belonged to MT8 (MG2) and also appeared more distantly related to the other strains encompassed in clade B in the core genome-based phylogeny. Finally, all the other strains belonged to MT3 (MG2) with only two exceptions, strains FC and DPIF 89/023-9 that were the only strains displaying incongruence between MT and core genome-based phylogeny. Although the 131 strains studied were from worldwide origin, our dataset was not suitable to highlight sound association between the isolation sources and the MT. We tried several statistical analyses (AMOVA and Fisher exact test, data not shown), but each tested variable (i.
The quickest and easiest way to acquire MALDI-TOF MS data from a bacterium is direct transfer method. We evaluated this protocol with 104 bacterial isolates arbitrarily sampled among the 135 above-mentioned isolates. Among these, only 9 strains were not fully typed (8. 65%) because of about 1% peak loss. We also assessed our method with ethanol-fixed bacteria, a strategy that could facilitate the MALDI-TOF MS typing of isolates from distant locations. We evaluated the effects of long term (up to 1 month) ethanol conservation followed by the regular protein-extraction protocol. Fifty-one out of 62 isolates (82%) were fully typed after 7 to 15 days ethanol storage. However, only 15 out of 25 isolates (60%) were fully typed after 16 to 26 days ethanol storage. Hence, the conservation time significantly affected typing reliability.
A web-based tool for MALDI-Type assignment We felt that a web-based tool for MT assignment of T. maritimum strains would facilitate data interpretation and help comparisons at a global scale in the same way as previously proposed for MLST schemes  [47]. Hence, we developed a friendly application named MALDIquant-TypeR for Tenacibaculum MALDI-TOF data analysis that contains 3 tools. Firstly, in order to avoid misinterpretation with spectra from bacteria that do not belong to the species T. maritimum, we added reference spectra from 24 out of the 29 described Tenacibaculum species, including all the fish-pathogenic or fish-associated species (i. e. , T. dicentrarchi, T. discolor, "T. finnmarkense", T. gallaicum, T. ovolyticum and T. soleae). Each reference spectrum is composed of a peak list obtained with Tenacibaculum type strains following Bruker's recommendations (> 20 independent acquisitions).
Indeed, important copper concentrations in sediments because of disposal of copper mine tailings has been documented for bays of northern Chile, the region where strain FC was isolated from. A copper-resistant Vibrio sp. strain was retrieved from cultured scallop [52] from the same geographical area suggesting convergent evolutionary mechanisms for heavy metal resistance in these phylogenetically unrelated marine bacteria. In addition to providing a global picture of the genomic diversity, the use of genomic data has been a key step in the MALDI-TOF MS scheme proposed in this study by allowing a better understanding of the spectral composition. The scheme follows Sauget et al. [46] MALDI-TOF MS recommendations by using the genomic information for selecting relevant biomarkers, by choosing ribosomal proteins that should be unambiguously identified within the spectra and by focusing exclusively on pic shifts for the typing purpose.
maritimum could be enriched in the future by considering additional biomarkers and/or by the identification of additional IF in the retained biomarkers. New, yet unobserved, combinations of isoforms may also be identified in the future. The definition of new MTs will follow the same incremental process as previously proposed for MLST schemes. Applied to our collection of T. maritimum isolates, this method identified 20 MTs grouped in 4 MGs. Because our goal was to propose and to evaluate a MPST scheme dedicated to the typing of T. maritimum strains, we used isolates from broad geographical, temporal and host-fish species origins to cover as much of the species diversity as possible. We are aware of the inherent bias of such a sampling choice. Indeed, the variables (i. e. country, year of isolation, host fish species) were highly correlated with each others; therefore, this dataset is not suitable for highlighting sound associations between isolation sources and MTs.
Indeed, the peak shifts caught by MALDI-TOF MS are highly correlated to the genotype as defined in any MLST analysis. MALDI-TOF spectra databases and processing tools can be easily exported on the Internet as a webtool application in the same way as MLST web-based tools (e. g. , PubMLST. org). MALDI-quantTypeR is one example of such application available online  [23]  as well as other dedicated websites such as the Mass Spectrometry Identification platform  [53]. Largescale studies could easily benefit from worldwide data collection. We have shown that ethanol (at least shortterm) conservation can be used for MALDI-TOF identification and typing. This method also suppresses biological hazard and should facilitate international transportation of bacterial samples. Other solutions may be designed for international collaboration, such as direct shipment of MALDI-TOF plates with fixed biological material, ready for acquisition.