dataesr/scientific-paragraphs-categorization huggingface dataset:
Odds ratios were considered significant if one 95% CI did not overlap with another  [104]. Means were reported with their standard deviation and associated range. All models were fitted in R software V. 4. 0. 2  [110]  using the glmmTMB package  [111] , the marginal effect was fitted using the ggeffects package  [107]  and usual validation diagnostics for mixed-effect models (see  [102] ) were performed using the DHARma package [112].
e. , from site scale to regional management unit scale [RMU]  [140] ). Because fibropapillomatosis appears to be associated with multifactorial risks  [39] , with sea turtles inhabiting a complex and dynamic cancer risk landscape  [23] , it is important to assimilate longitudinal datasets investigating multiple risk factors as a whole (e. g. , phytoplankton bloom occurrence, composition, and associated biotoxins), along with any other environmental variables of relevance at a given site (e. g. , other cancer promotors in the environment, ChHV5 shedding rates from environmental DNA  [100] , genetics or immune responses of sea turtles, emerging contaminants). The sharing of holistic datasets collected from multiple study sites within a strong collaborative network could facilitate rapid advances in our understanding of the disease, similar to those developed for cancer in humans  [141]  and transmissible cancers in wildlife  [46].
Data were acquired on the BioMark Real-Time PCR system and processed using the Fluidigm Real-Time PCR Analysis software to obtain a cut-off (Ct) value  (Michelet et al. , 2014; Gondard et al. , 2020). The BioMark real-time PCR system (Fluidigm, San Francisco, CA, United States) was used for high-throughput microfluidic real-time PCR for the most common bacterial and parasitic TBP species known to circulate or recently emerging in Europe. The real-time PCR system developed for the screening of known and potential TBPs in Romanian ticks included 47 sets of primers and probes  (Michelet et al. , 2014; Sprong et al. , 2019; Gondard et al. , 2020). Among them, 37 primers were used for the detection of pathogens to species level (n = 30 bacterial and n = 7 apicomplexan) and 8 primers to genus level (n = 5 bacterial and n = 3 apicomplexan).
Thus, the results obtained by the real-time microfluidic PCR assay for 89 Borrelia spp. , 63 Anaplasma spp. , 89 Rickettsia spp. , and 36 Bartonella spp. samples were re-tested by conventional or nested PCRs. Following the molecular analysis, 80 Borrelia spp. , 53 A. phagocytophilum, and 44 Rickettsia spp. samples were sequenced. Identity percentages of the sequences obtained with reference sequences available in GenBank (NCBI) are presented (Table  3 ). The sequences were compared to other GenBank entries by BLAST (Basic Local Alignment Search Tool) analysis and further submitted to the GenBank under the following accession numbers: Bo. afzelii (MW272725, MW272726, MW272727, MW272728, MW272729, MW272730, MW272731, MW272732, MW272733, MW272734); Bo. garinii (MW272735, MW272736, MW272737, MW272738, MW272739, MW272740); Bo.
R. aeschlimannii (MW272754). Five Borrelia spp. samples showcased equal identity percentages with both Bo. bavariensis and Bo. garinii reference sequences. Since this title formulation is not accepted by GenBank the two individual sequences were submitted as Bo. bavariensis. MW272750); Bo. miyamotoi (MW272748); A. phagocytophilum (MW272751, MW272752); R. helvetica (MW272753, MW272755, MW272756, MW272757); R. monacensis (MW272758); I. ricinus - - 3 - 3 Alexandru Borza Botanical Garden Erinaceus roumanicus (n = 3) I. ricinus 6 7 15 15 43 I. hexagonus 10 5 - - 15 Apodemus flavicollis (n = 1) I. ricinus - - - 5 5 Talpa europaea (n = 1) I. ricinus - - - 4 4 Turdus merula (n = 1) I. ricinus - - 1 1 2 H. punctata - - - 2 2 Hoia forest Apodemus flavicollis (n = 2) I. ricinus - - 1 5 6 Sorex minutus (n = 1) I.
New Discoveries Allowed by Genome-Mining Approaches Genome mining is a revolutionary approach to search for natural products synthesised by microorganisms, especially since high-throughput sequencing has become more accessible, and several pieces of bioinformatic software have become more and more powerful (Figure  2 , Table  2 ). Many websites and web portals, including the antibiotics and secondary metabolite analysis shell (antiSMASH) [42], the nonribosomal polyketide urmite (NRPPUR) database  [43] , the secondary metabolite unknown regions finder (SMURF)  [44] , the natural product domain seeker (NAPDOS)  [45] , antibiotic-resistant target seeker (ARTS)  [46] , and others were developed to identify and characterise NRP and PK in microbial genomes. They contain databases and tools to identify the secondary metabolites, mainly using BLAST and HMMer, hidden Markov models (HMMs) methods.
They search for the enzymatic domains responsible for the different biosynthetic activities in the assembly line process. Analysis of the genes encoded up-and downstream of the hit sequence, then, allows for the identification of whole operons or gene clusters. These websites are quite simple to use. They only require the genome to be submitted, and they produce results regarding the detection and characterisation of secondary metabolites shortly after. Anti-SMASH shows the location of the BGCs in the genome, giving a graphical representation and providing further information about the similarity of these detected BGCs with already known compounds. NRPPUR has a very rich database of PKS in particular, and it is a very useful way to detect the NRPS-PKS domains with high accuracy. NAPDOS and ARTS can be very interesting for phylogeny and self-resistance guided antibiotic discovery, respectively.
Recently, a new software named gene cluster prediction with conditional random fields (GECCO)  [47]  showed very high computational performance to identify de novo BGCs. All these pieces of software are, therefore, powerful tools that help to make a selection in silico of the interesting bacteria to be tested in vitro. The genome mining strategy defined as the "bottom-up approach"  [37]  is a futureoriented, high-throughput screening approach for bacteria that may be a source of new classes of pharmaceutically active molecules (Table  2 ). High-throughput screening would enable scientists to detect silent BGCs and to avoid the rediscovery of known metabolites, which is the main cause of the slowdown in search for antibiotics in the pharmaceutical industry  [48]. Moreover, the accessibility and ease of use of "bottom-up approach" can help to expand the spectrum of tested bacteria, bringing to light some bacterial genera that were not considered to be important metabolite synthesisers, such as the Burkholderiales, Janthinobacterium, and Lysobacter genera  [49].
Likewise,  Qin et al. (2017)  isolated a new species of filamentous Actinomycete bacteria  [56]  that has been shown to harbour many new and/or atypical BGCs using antiSMASH software. In vitro tests have shown that a strain of Streptomyces formicae KY5 demonstrates antagonistic activity against MRSA and against various pathogenic drug-resistant bacteria and fungi. The authors were able to demonstrate that the observed activity was due to a type II PK called formicamycin, using CRISPR/Cas9 gene editing  [57]. Genome mining would appear to be an interesting screening method that may help to select potential producing bacteria that deserve to be tested in vitro. This rapid method assists with and prepares the fastidious and time consuming in vitro methods. Moreover, the rapid screening of all bacteria for which the genome is available will help to reinvigorate the research and development of compounds with antimicrobial properties, using these bioinformatics tools.
When the EF-TU resistance gene sequence was searched against genome databases using the BLAST program, 21 Streptomyces sp. were found to harbour homologs to this gene. The search and the characterisation of BGCs using antiSMASH software revealed the presence of these EF-TU resistance genes located inside the synthesis cluster for three Streptomyces. One Streptomyces bacteria out of the three was found to be a phenelfamycin producer when tested in vitro. Antimicrobial testing showed an interesting activity of this molecule against multidrug resistant gonococci. Although this molecule was already known, this work enabled the identification of a previously unknown elfamycin producer as well as the identification of the BGC of phenelfamycin  [63, 64]. Other experiments adopting the self-resistance-guided genome mining strategy have also led to the discovery of new antimicrobial compounds.
Several easily accessible pieces of online software, such as NAPDOS  [45, 65] , can detect and extract the elongation domains for NRPS and PKS, the condensation and ketosynthase domains, respectively. This software makes it possible to build a phylogenetic tree from the submitted file by extracting the C and KS domains contained in its database. Results based on the condensation domain's phylogeny of NRPS give an idea of the aminoacyl binding type. A study analysing soil bacteria made it possible to isolate 50 Actinomycetes spp. , of which 40 had an antimicrobial activity against blood and food-borne pathogens  [67]. Of these, two bacteria, Micromonospora Rc5 and Streptomyces Ru87, showed interesting bacterial activity in vitro. Phylogenetic analysis of PKS genes suggested that if the metabolites responsible for antimicrobial activity are in the PK family, these would be novel molecules with a unique and previously unknown synthesis pathway.
Likewise, based on the phylogeny of KS domain,  Guo et al. (2016)  identified a new compound with antimicrobial activity, named talafun  [68]. Talafun is a PK produced by an endophytic fungus Talaromyces funiculosus isolated from a plant, Silicorna bigelovii. The phylogenetic analysis placed this PK in the same clade as compounds with unknown biosynthesis pathways. Testing for its antimicrobial activity showed that talafun has better antimicrobial activity than ampicillin, but less than gentamicin against E. coli. Genome databases provide an important source of information to be exploited in the search for new antimicrobial compounds. Targeted searches can be performed to find a particular type of compounds. Thus,  Li et al. (2018)   [69]  analysed as many as 7395 bacterial genomes and targeted cationic nonribosomal peptides (CNRPs).
The creation of syn-BNPs is very often inspired by the BGCs from bacterial genomes deposited in publicly available databases. Based on the adenylation (with regards to NRPS) or acetylation (with regards to PKS) domain, it is possible to predict the selected substrate and, consequently, the final composition of the molecules encoded by the BGC. This culture-independent approach is dependent upon robust algorithms such as the NRPS predictor  [31] , Minowa  [79] , and the Stachelhaus code  [30]. Some studies have managed to synthesise molecules based on these predictions and have demonstrated their biological activity  [80]. This approach allows for the elaboration of a good matrix for the production of molecules and helps to circumvent the difficulties due to silent BGCs. Moreover, it is no longer necessary to physically possess the strains but rather to work on the genomes available in public databases.
Syn-BNP may, therefore, represent an inexhaustible source of potential new antibiotics  [81]. This method has made it possible to identify many interesting molecules in recent years with various mechanisms of action and activity. Chu et al. (2016)  screened a large collection of bacterial genomes from the human microbial project database  [80]. They were able to synthetise 30 molecules for which they tested their antimicrobial activity against human pathogens. NRPS clusters from Rhodococcus equi and Rhodococcus erythropolis led to the discovery of the antibiotic humimycin  [80]. Humimycin has demonstrated antimicrobial activity against methicillin-resistant Staphylococcus aureus, and it has potentiated β-lactam activity. In another work,  Chu et al. (2017)  selected 96 linear peptides that guided the synthesis of 171 syn-BNPs  [81].
Vila-Farres et al. (2017)  [82]  synthesised a peptide with an antifungal activity inspired by a cluster found in the genome of Xenorhabdus nematophila, which could not be detected by culture methods. In the same study, Vila-Farres et al. (2017) synthesised a peptide based on an NRPS found in the genome of Paenibacillus mucilaginosus strain K02, which has been shown to be active against Gram-positive bacteria  [82]. The synthesised peptide, named paenimucillin A, also showed limited activity against Gramnegative bacteria. Further modifications made by changing the N-acyl at the N-terminal acyl led to the new compound gaining activity against multi-resistant Acinetobacter baumanii, while retaining limited toxicity. This new syn-BNP was named paenimucillin C and showed encouraging results in skin wound infections on multidrug-resistant (MDR) A.
baumannii in a rat model  [83]. This approach was revealed to be particularly amenable, while drawing inspiration from known BGCs, and it may also offer the possibility of optimising syn-BNP activity. Given the potential of this method in discovering new antibiotics, the use of classical fermentation or the culture of bacteria did not appear to be relevant. Thus, databases such as NCBI and genome sequencing became a source for the discovery of new antibiotics. Moreover, the laboratory conditions required to generate any experimental resistance against these syn-BNP products did not yield satisfactory results  [83]. These findings are very encouraging, because they guarantee that these future pharmaceutical products are effective, secure, and immune to bacterial resistance.
Technological advances and a better understanding of NRPS-PKS BGCs have made it possible to implement various approaches to target compounds that may be of interest to researchers. In addition, increasingly powerful analytical tools are available online, as well as databases, which can provide access to a huge amount of genomic data. These in silico approaches facilitate the identification of bacteria to be tested as priority. Furthermore, culture-independent methods  [81] [82] [83]  are an interesting way of circumventing difficulties, such as bacteria availability, problems related to culture, and silent BGCs. However, culture under multiple conditions, imitating reality as much as possible, remains a powerful tool for the expression of BGCs. To conclude, the combined use of these methods will expand our knowledge about the NRPS-PKS repertoire and will consequently enable the development of new antibiotics to fight antimicrobial resistance.
Indeed, this data is in accordance with previous results of our group showing that D-Ctl is a new HDP with undetectable susceptibility to resistance and potentiation of the efficiency of several antibiotics (cefotaxime, amoxicillin and methicillin)  25. The D-isomer of Ctl is also active against E. aerogenes and C. freundii with a MIC of 32 µg/ml (17. 2 µM). In this second part of the study it appears that the D-isomer of Ctl is the most potent antibacterial Ctl-derived peptide. Recently, the activity of D-Ctl on two strains of E. coli has been examined by using biophysical technics. For all concentrations of D-Ctl, membrane permeabilization was shown, but no pore was observed  46. S. aureus is an important agent responsible for the majority of human skin and soft tissue infections  47. We built up a non-toxic triblock peptide (DOPA 5 T-Ctl) for the on-demand self-killing of S.
The A620 nm value of control cultures growing in the absence of peptide and antibiotics was defined as 100% growth. A620 nm value with the antibiotics (Tetracycline and Cefotaxime) was taken as 100% inhibition. Absence of bacterial growth was verified by agar plate spreading. Each assay was performed in triplicates. Data were shown as mean + /-standard deviation of three independent experiments; p < 0. 05.
Antibacterial assays against resistant Cells culture. H9c2 cells (rat embryonic cardiomyocytes) were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA) (Cat# CRL-1446_H9c2, RRID:CVCL_0286) and maintained in Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F-12, Gibco, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% fetal bovine serum (FBS, Gibco), supplemented with 1% penicillin/streptomycin (Thermo Fisher Scientific), and incubated at 37 °C in humidified atmosphere with 5% CO2. When the cells reached a density of 80% in 100-mm dishes, they were digested using 0. 25% Trypsin-EDTA (1X) (Gibco) according to a 1:2 ratio following manufacturer's instructions (ATCC)  50.
Multiskan EX Microplate Reader Lab (Thermo Fisher Scientific, Waltham, MA, USA) at 570 nm. The means of absorbance values of three wells in each experimental group were expressed as the percentage of cell viability. Cell viability was reported as percentage of cells survival relative to positive controls. Each assay was tested in triplicate and data are shown as mean + /-standard deviation of three independent experiments; p < 0. 05.
Acknowledgements The authors thank  Drs Pierre Schaaf  and  Philippe Lavalle  ( INERM_UMR 1121 ) for their contribution to the design of the Dopa 5 T-Ctl peptide,  Dr Evelyne Schaffer  ( CNRS UPR3572 ,  Immunopathologie et Chimie Thérapeutique, Laboratory of Excellence Medalis, Institut de Biologie Moléculaire et Cellulaire, Strasbourg, France ), the  Plateforme Imagerie In Vitro du Neuropôle, (CNRS UPS   3156 ,  Strasbourg, France) , the  EFS (Etablissement Français du Sang)  for the preparation of immune cells, the  Institute of Bacteriology of Strasbourg  for providing S. aureus strains,  Atlangram company (Nantes, France)  for antimicrobial assays against several resistant microorganisms and  Dr. Nancy Grant  for writing assistance.
Funding This study was financially supported by  INSERM , the  University of Strasbourg  and the  Proteomic French Infrastructure (ProFI ;  ANR-10-INSB-08-03 ). Francesco Scavello was supported by the  University 'Italo-Francese ' in the context of ' Vinci  Project  2014 ' (n°  C2-72 ) attributed to the  Department of Biology, Ecology and Earth Science University of Calabria, Arcavacata di Rende, Italy  and the  INSERM_UMR 1121, Biomaterials and BioEngeneering, Strasbourg, France.
Data source and inclusion criteria The EARS-Net is a surveillance network that collects routine clinical antimicrobial susceptibility testing (AST) results and some patient data from EU and EEA countries. The AST results are ascertained according to agreed protocols  [12, 13] , and the general quality and comparability of the data are evaluated through an annual external quality assessment exercise offered to the participating laboratories  [14]. The network has been coordinated by ECDC since 2010. Data on S. aureus BSI reported to EARSS/EARS-Net for the period 2005 to 2018 were extracted from The European Surveillance System (TESSy) database at ECDC. Resistance to meticillin in S. aureus isolates was determined by the reporting laboratories by phenotypic susceptibility tests, molecular methods or the latex agglutination test, as specified in the EARS-Net reporting protocol  [12] , and BSI episodes were classified as either MRSA BSI or MSSA BSI.
Data were de-duplicated taking only the first S. aureus BSI per patient and year into account. Data were stratified by patient age group (< 1 year, 1-24 years, 25-49 years, 50-64 years, 65-79 years and ≥ 80 years), sex (male or female) and hospital unit (intensive care unit (ICU) or other inpatient hospital unit). The stratum ICU included patients in adult ICU, paediatric ICU and neonatal ICU. A separate analysis was performed for outpatients, defined as patients not admitted to a hospital at the time of sampling of blood cultures. We used two datasets for the analyses: dataset A included all BSI reported during the study period, while dataset B only included BSI from laboratories that reported data consistently for all years during the period 2005 to 2018. The purpose of dataset B was to have a fixed cohort of laboratories for studying the trends in the percentages of MRSA and the number of MRSA and MSSA BSI.
Surveillance data before 2005 were excluded because of low numbers of laboratories and reported BSI. For each country and year, a blood culture set rate, defined as the number of blood culture sets per 1,000 days of hospitalisation, was extracted from TESSy. We included these data in the analysis as blood culture sampling practices may have changed during the study period as a result of increased awareness of the health impact of sepsis  [15]. The blood culture set rate was therefore considered as a potential confounder in the evaluation of the BSI trends.
Analysis Annual EU/EEA crude and population-weighted MRSA percentages were calculated for both dataset A and B in order to ascertain representativeness of dataset B. The EU/EEA population-weighted mean MRSA percentage was determined using as weights the population proportions of each country over the total population of the included countries. Annual population data were retrieved from the Eurostat online database  [16]. For both datasets A and B, we calculated the ratio between the numbers of MSSA BSI and MRSA BSI in each year. Based on dataset B, we carried out further analyses, stratified by age category, sex and hospital unit and for outpatients, on the percentage of MRSA BSI out of reported S. aureus BSI, the number of MRSA and MSSA BSI and their annual variation using 2005 data as a baseline.
For the stratified analyses, cases with missing information for the strata of interest were excluded. Annual EU/EEA weighted means of blood culture set rates were calculated for dataset B. The country proportions of S. aureus BSI cases over the total cases of dataset B were used as weights. For dealing with missing data, country rates from the previous or following year were used, if available. When these were not available, the blood culture set rate was considered missing and the concerned countries were not considered in the calculation of the EU/EEA weighted mean. To check for the impact of these exclusions, we conducted a sensitivity analysis by selecting the countries for which the rates were estimated for all the years. The effect of the annual blood culture set rate as a potential confounder of the temporal trends of the number of S.
Results Between 2005 and 2018, a total of 573,951 S. aureus BSI cases (96,918 MRSA and 477,033 MSSA) were reported to EARS-Net from 1,295 laboratories in 30 countries (dataset A) (Supplementary Table  S1 ). The number of BSI cases reported per year increased from  27, 125   data from all countries that reported data to EARS-Net except Croatia, Germany, Lithuania, Poland and Slovakia) (Supplementary Tables  S1  and S2 ). S3 ). The sensitivity analysis conducted by selecting only the countries for which the blood culture set rates were estimated for all the years 2005 to 2018 (13/25 countries) showed a temporal trend of blood culture set rates from 33. 1 per 1,000 hospital patient days in 2005 to 71. 8 in 2018, comparable to that observed for the entire dataset B (Supplementary Table  S3 ).
Overall trends Statistically significant decreasing trends in the MRSA percentage were observed for both the crude and the population-weighted EU/EEA means, and in both dataset A and B (   For dataset B, the total number of S. aureus BSI increased by 57% from 2005 to 2018, with a significant trend (p < 0. 001). This general increase was the result of an 84% increase in the number of MSSA BSI cases and a 31% decrease in the number of MRSA BSI cases (Table ; Figure  1 ). Based on the Poisson model, the mean annual crude changes in the number of cases were +3. 5% for S. aureus BSI, +4. 8% for MSSA BSI and -3. 3% MRSA BSI. After adjusting for the blood culture set rate, these changes were +1. 0%, +2. 9% and -5. 6%, respectively. All these trends were statistically significant (p < 0. 001).