dataesr/scientific-paragraphs-categorization huggingface dataset:
bivalvicida) plotted away from the rest of the 24*3 generated spectra (Fig. 2a ; spectra groups 1x and 1y). The flexAnalysis software was used to observe these spectra and showed that they were different from the others, including spectra within the same group (Fig. 2b ). Finally, the CCI matrix analysis confirmed the exclusion of spectra in cluster 1x (Fig. 2c ). Using these three tools, divergent spectra were discarded, and representative spectra were selected for each strain and each laboratory and were then merged into the MSP, each of which contains by mean 64 selected spectra (min 39, max 72 selected spectra). Bacterial isolates belonging to the same species were found to be grouped within the same clade on the MSP dendrogram (Fig. 3 ), in accordance with the Vibrio clades previously defined using genetic tools  (Sawabe et al.
2007 (Sawabe et al. , 2013)). However, one exception concerns the Coralliilyticus clade, which formed a cluster with part of Orientalis clade including V. orientalis, V. sinaloensis, and V. hepatarius as well as with V. europaeus and V. bivalvicida (Fig. 3 ). Bacterial identification with MALDI-TOF MS is dependent on the database queried Within the 25 species included in our database and selected as marine mollusk pathogens, 12 are already present in the Bruker TM database, and six can be found in the VibrioBase  (Erler et al. 2015)  but sometimes with only one representative strain (Table  1 ). In our mollusk pathogen VibrioBase (EnviBase), we included new isolates, especially "clinical" strains isolated from mollusk mortality episodes, such as V. aestuarianus, V. coralliilyticus, and V. rotiferianus (Table  1 ).
After the generation of 120 new MSP for these 25 Vibrio species (Fig. 1a , Supplementary Table  S1 ), we first tested them among the two available databases. Overall, using these databases, only 62% (n=74/120) of the MSP gave correct identifications (Supplementary Table  S2 ). However, when considering Vibrio species that were present in at least one of the two databases (the Bruker database and Erler's database), 95% of our new MSP (n=74) were wellidentified, among which we find V. aestuarianus, V. harveyi, V. rotiferianus, V. coralliilyticus, V. alginolyticus, V. mediterranei, V. ostreicida, V. mytili, V. natriegens, and V. brasiliensis (Fig. 4a , Supplementary Table  S2 ). In particular, 74% of the MSP created had a matching score over 2. 3 (highly probable species identification), and 21% had a score between 2.
0 and 2. 3 (probable species identification). It is important to note that 4% of the MSP had a score value between 1. 7 and 2. 0 for V. neptunius and V. orientalis and 1% gave a different identification for V. campbellii despite these species being given in the Bruker database (Fig. 4a , Supplementary Table  S2 ). The identities of the strains of V. neptunius (09/123 1T2; DSM 17183), V. orientalis (DSM 7897T; 8F5_42; 8H6_11; 8Z_12; 8Z_27), and V. campbellii (CIP 70. 67) we used were confirmed by PCR and DNA sequencing (data not shown). Moreover, as expected, all of the strains affiliated with species that are not present in the existing databases had a score below 2. 0 or were classified under a different identification. The mismatches between these species and Bruker's and Erler's databases show the input of the newly created MSP included in EnviBase.
For instance, V. bivalvicida, V. europaeus, V. sagamiensis, V. sinaloensis, V. tapetis, and that the V. tubiashii strains had a score below 1. 7 with Bruker's and Erler's databasesand Vibrio hepatarius, V. maritimus, V. variabilis, V. jasicida, and V. owensii were classified within different species (V. orientalis, V. mediterranei, V. mediterranei, V. harveyi, and V. harveyi, respectively) (Fig. 4b , Supplementary Table  S2 ). In cases where a species had a different identification, it should be noted that the databases recognized the closest species, or at least a species defined within the same clade.
00 for the first method and 0%, 3%, and 0% for the second. Very closely related species belonging to V. tubiashii/V. europaeus and V. owensii/ V. jasicida/V. campbellii (Table  S4A ) were incorrectly identified but still had a high matching score with the two methods. Two percent (2%) of the species obtained a score below 1. 7 with the protein extract method. For all of the tested species not present in the newly created database (n=13) and for the three laboratories, some species were not identified at all or had a different identification, showing the specificity of our database (Table  S4B ). The blind test performed with the three databases (EnviBase, Bruker's, and Erler's databases), revealed that the best matching scores were obtained with our new database, with an average rate of 98% for all of the strains found in all ofthe databases (52/53) for bothmethods (data not shown).
Discussion In this study, for the first time, a MALDI-TOF MS database was built collecting most of the Vibrio species involved in mollusk diseases. The EnviBase comprised 120 MSP containing 25 Vibrio species including 12 species that are currently absent from the other existing databases. This database was tested face to Bruker Daltonics database and the VibrioBase  (Erler et al. 2015). Another MALDI-TOF MS database called EMbaRC has been recently developed and includes 77 spectra of bacteria of Vibrionaceae family  (Ruvira et al. 2013 ), but as this database mostly contains Vibrio type strains already available in Bruker's one, it was not included in this study.
Spectrum selection and MSP creation In order to integrate the most specific MSP in the database, we defined a pipeline for spectrum selection via the different analysis tools. In the conventional spectrum selection method, the analysis focuses on peaks that have the highest intensity within the range of mass peaks from 3000 and 12,000 Da (MALDI Biotyper V1. 1 protocol). However, peaks with a lower intensity could reveal significant differences between isolates. These differences could have an impact on the specificity of the spectra, especially in very closely related species. Thus, we used a principal component analysis (PCA)  (Shao et al. 2012; Vallenet et al. 2019 ) and the composite correlation index (CCI matrix)  (Arnold and Reilly 1998; Carrasco et al. 2014; Magalhães et al. 2018)  to identify the spectra that were not specific enough due to these differences.
Our data processing allowed to create more specific MSP and thus to discriminate between very close species such as V. bivalvicida/ V. europaeus or V. neptunius/V. coralliilyticus. In general, the PCA clusters and CCI matrix were used to visualize the relationships between the spectra  (Shao et al. 2012). Hence, the application of this statistical method to select spectra could confirm the reproducibility of the spectra for the same strain in the same conditions (e. g. , for one laboratory). The procedure we recommend first consists of analyzing the spectra using flexAnalysis software to check the intensity of the peaks and the general shape of the spectra. In a second step, the spectra are loaded into Bruker's MBT Compass software where they are then identified one by one using existing databases to eliminate mismatched spectra.
Database assessment The new EnviBase was evaluated by testing the created MSP with the existing databases. Considering the species present in these databases, the majority of species were correctly identified except for certain strains like V. campbellii, which was indistinguishable from the closely related V. harveyi. Specific effort is needed to build highly specific MSP that can be used to discriminate between these very similar species  (Azevedo et al. 2003; De Montaudouin et al. 2009; Gomez-Gil et al. 2004 ). Most of the other strains belonging to species present in Bruker's database were correctly identified, although some low scores were observed for certain species such as the V. neptunius and V. orientalis strains (between 1. 7 and 2). These low scores could be explained by a poor representativeness of the bacterial species in the database (e.
g. , there is only one strain each for V. neptunius and V. orientalis in the Bruker database). Erler et al. (2015)  showed the importance of having a sufficient number of strains for a given species in order to encompass its intra-specific diversity. Future updates of the databases with diverse strains for each species could improve the identification of any given species. Validation of the database After the EnviBase assessment, we carried out a validation procedure with a blind test performed with two MALDI-TOF analysis methods: direct transfer (usually used in routine diagnoses) and whole protein extraction. No significant differences were observed between the two extraction methods with regard to species identification. The only differences observed were differences in the identification rates between the two methods for certain influence on the accuracy of their identification (Eck species.
The structure of the bacteria probably has an and Dayhoff 1966). Meanwhile, the direct transfer method is faster to implement and is, therefore, more suitable for routine diagnosis. Consequently, this method can be used and is even recommended in routine diagnoses. Some species, such as V. europaeus/V. tubiashii, were misidentified during the blind test given their high taxonomic proximity. It should be noted that V. europaeus and V. tubiashii have undergone numerous taxonomic revisions: V. europaeus was considered to be a sub-species of V. tubiashii before being reclassified as a distinct species  (Dubert et al. 2016). For other species, no conclusion could be drawn as to why V. jasicida was identified as V. harveyi when the databases were pooled. This is likely linked to the fact that V.
jasicida is not represented in Bruker's or Erler's databases or, possibly, that it has been incorrectly affiliated with some of the MSP for V. harveyi present in these databases. The difficulties to differentiate and characterize Harveyi clade species due to their taxonomic proximity were illustrated recently by  Ke et al. (2017)  who showed that a V. harveyi 1114GL strain affecting shrimp should be in fact affiliated with the V. campbellii species. The high similarity in the rDNA sequences and phenotypes made it risky to identify these species  (Ezaki et al. 1989; Yoshizawa et al. 2012). Furthermore, we encountered difficulties when attempting to discriminate V. owensii from V. jasicida as there is only one representative strain for the latter. This should be improved in future work. The majority of the sub-species were correctly identified (data not shown), but even if we do not have clear identifications, the MALDI-TOF MS technique could be sufficient enough to identify the correct sub-species if the specificity of the infected host is taken into consideration.
Three sub-species have been described for V. aestuarianus aestuarianus, V. aestuarianus francensis associated with cupped oyster diseases, and V. aestuarianus cardii, associated with cockle diseases  (Garcia et al. 2021). Further analysis could be applied to sub-type species or to discriminate between pathogenic and nonpathogenic strains, e. g. , the "MALDI-TOF-MS Multi Peak Shift Typing approach," highlighting a link between a peak shift in the mass spectra and the associated polymorphism in the genomic sequence  (Bridel et al. 2020; Rahmani et al. 2021). The combination of the MALDI-TOF MS technique with this new database is a powerful and revolutionary method within the context of surveillance programs and emerging diseases. Our database could also be used as a potent molecular epidemiologic tool during outbreaks  (Taneja et al.
2016 ). In the present work, we validate the direct transfer MALDIsamplepreparationmethod which is a real advantage in monitoring programs. In the future, we intend to expand this database by adding species of the fundamental and highly diverse Splendidus clade, which is associated with significant mortality events  (Bruto et al. 2017; Faury et al. 2004; Gay et al. 2004; Le Roux 2017; Lemire et al. 2015; Petton et al. 2015).
Acknowledgements This study was conducted within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX41 ). We wish to thank  Delphine Tourbiez  and  Mathilde Noyer  for their assistance with the bacteriological and molecular analyses. We are also grateful to  Dr. Frédérique le Roux  and  Dr. Delphine Destoumieux-Gazon  for their assistance and for sharing their Vibrio strains.
Funding This work was supported by Ifremer; by DGAL through the  National Reference Laboratory for Mollusc Diseases, Ifremer, La Tremblade ; and by the  European Commission  through the  European Union Reference Laboratory for Mollusc Diseases, Ifremer, La Tremblade. This work was also partly supported by the  EU -funded project  VIVALDI  (" Preventing and Mitigating Farmed Bivalve Disease") H2020 program , no. 678589.
Isolates and Identification From January 2012 to December 2013, non-duplicate S. aureus isolates were collected from MICU UHCC in the east of Algeria. All S. aureus isolates were cultured from clinical samples, including from the bloodstream, tracheal, drain, urine, pleural and urinary catheters, in infection contexts with or without the presence of polynuclear cells in the biological samples. HA-MRSA isolates are defined as isolates recovered from hospitalised patients 48 h after admission presenting resistance to methicillin. Demographic characteristics and clinical data were collected from the patient records. All isolates were grown on Chapman and Colombia blood agar media for 24 h at 37 • C. Isolates were pre-emptively identified using conventional methods. The purity of the S. aureus isolates transported was checked using a Trypticase Soja Agar medium at the IRD, APHM, MEPHI, IHU-Méditerranée Infection (Marseille, France), where their molecular identification was confirmed using matrix-assisted laser desorption-ionization time of flight mass spectrometry Microflex (MALDI-TOF-MS) (Bruker Daltonics, Bremen, Germany) as described  [63].
12 programme  [71]. Genes conferring antimicrobial resistance and virulence genes were investigated using the ABRICATE pipeline, which hosts different databases, including antibiotic resistance genes and virulence genes databases. The genetic environment of the SCCmec-III mercury element was investigated using the Easyfig version x2. 2. 3 programme (Available online: http://mjsull. github. io/Easyfig/, accessed on 15 June 2020).
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/pathogens10111503/s1, Supplementary Table  S1 : Predicted function of genes shown in Figure  2. Supplementary Table  S2 : Predicted function of resistance genes presented in Table  2  and their % of identities with described genes of resistance genes. Supplementary Table  S3 : Predicted function of virulence genes presented in Table  3  and their % identities with described genes. Supplementary Table  S4 : Primers and probes used in this study for the molecular characterisation of the clinical S. aureus isolates from the MICU at the UHCC. Whole Genome Sequences of the three sequenced S. aureus isolates here are deposited at DDBJ/ENA/GenBank under the accession numbers S. aureus SAUR1404 (PKSS01000000), S.
Database To validate the functioning of the algorithms proposed in this article, we had to compose a database. To do this, we can use videos from existing fixed cameras, but we preferred to make our own set of videos to have a greater flexibility in the filmed scenarios and thus to make stagings of escape, gathering, and dispersion. The shooting of these videos was done using an "OM-D EM-5 mk II" camera capable of filming in 4:2:0 8b, H. 264, BT-709, 29. 97 fps set at the same level as a city security camera. The filmed area is surrounded in red in Figure  1  and is located outside the laboratory in the center of Toulon, France. Sensors 2021, 21, x FOR PEER  REVIEW 3 of 19  this tracking brings to the current detection methods the possibility to determine abnormal behaviors and to have a tracking that is both efficient and integrates speed and instantaneous trajectory information.
Database To validate the functioning of the algorithms proposed in this article, we had to compose a database. To do this, we can use videos from existing fixed cameras, but we preferred to make our own set of videos to have a greater flexibility in the filmed scenarios and thus to make stagings of escape, gathering, and dispersion. The shooting of these videos was done using an "OM-D EM-5 mk II" camera capable of filming in 4:2:0 8b, H. 264, BT-709, 29. 97 fps set at the same level as a city security camera. The filmed area is surrounded in red in Figure  1  and is located outside the laboratory in the center of Toulon, France. It is necessary to have a good quality when we film our scenes, because the algorithm of detection of an individual will not be able to detect an individual if it is too blurred.
The study does not deal with the detection part, so we chose to get closer to the specifications of the cameras used by the city of Nice. It should be noted that the resolution of the camera will have an impact on the detection part in the sense that the detection will be much more effective on an image of the best possible quality. Our experiments are based on seven videos among about twenty that were initially shot. They were selected for their characteristics presented in    In order not to have to process the videos for each test, all the videos have been processed beforehand and the detections have been saved in a JSON file. The data is labeled with all the necessary information to replay the videos in real time. In the rest of this article, we will consider that the videos have been processed beforehand and that we therefore have a set of detection replay.
The points are grouped in a table whose size varies according to the number of individuals detected in the video and to which is associated a Timecode. The Timecode value is an integer whose unit is the millisecond, all the other values of position and size being taken directly from the video are in pixels. In this article, we will discuss how this point cloud evolution has been processed through two methods. All the processing done on these points was done and coded in Python3 under Windows with the Visual Studio code IDE. No proprietary library was used, and the matrix calculations were performed using Numpy and the calculations related to the Kalman filter were programmed in the laboratory. The results obtained after the different treatments can be reused by a system that would assemble both the image and the results if necessary to exploit the results visually.
Tracking by Proximity The most basic approach was to develop an algorithm whose goal is to find a correlation between two point clouds from two images following each other in a video. Indeed, as explained in the introduction, the data of the video and more particularly the positions and sizes of the persons present in each frame of the video were extracted to obtain a point cloud for each frame of the video where each point represents a person in the image to which we also attribute information on the size of the detection. The objective of this method is to search for the closest points considering that the evolution of a person between two images depends on three parameters: the distance at which the person is from the camera; the speed at which the person moves; the speed at which the camera records.
Tracking by Proximity The most basic approach was to develop an algorithm whose goal is to find a correlation between two point clouds from two images following each other in a video. Indeed, as explained in the introduction, the data of the video and more particularly the positions and sizes of the persons present in each frame of the video were extracted to obtain a point cloud for each frame of the video where each point represents a person in the image to which we also attribute information on the size of the detection. The objective of this method is to search for the closest points considering that the evolution of a person between two images depends on three parameters: the distance at which the person is from the camera; -the speed at which the person moves; -the speed at which the camera records.
Once processed, the result is stored as a JSON file. The first step is to convert the data in the JSON file into data that can be processed by the language used (in our case python3). If it is then the first data of the file, they are automatically integrated into the known points. The known points in our case are all the points with an identifier. If it is not the first message, the first step is to form the possible pairs between the old known points and the new received points. Three results are then possible: - The new point is associable with an old point; - The new point is not associable with an old point; -Several points are associable with an old point. In the first case, the solution is simple, there is an old and a new point perfectly associable, the new point is thus assigned information from the old one.
In the second case, if the new point can only be associated with an old one, it may mean that either the new point is a bad detection, or that the person represented by this point has passed behind an obstacle or has left the field of vision of the camera. In both cases, the point is saved in memory to be used later. However, this storage in memory must be temporary, because if it is not just a temporary disappearance, the point must not be reassigned. As explained earlier, the video stream is processed independently of analysis systems. Once processed, the result is stored as a JSON file. The first step is to convert the data in the JSON file into data that can be processed by the language used (in our case python3). If it is then the first data of the file, they are automatically integrated into the known points.
As shown in Figure  11 , the processing algorithm using Kalman filters is as follows: As shown in Figure  11 , the processing algorithm using Kalman filters is as follows: As explained above, the video stream is processed independently of analysis systems, once processed the result is stored as a JSON file. The first step is therefore to convert the data in the JSON file into data that can be processed by the language used (in our case python3). If it is the first data of the file, it is automatically integrated to the known points. The known points in our case are all the points with an identifier. If it is not the first message, the points "identified by Kalman filter" are updated. It is then possible to compare the new position of the points "identified by Kalman filter" and the new points received.
The first step is therefore to convert the data in the JSON file into data that can be processed by the language used (in our case python3). If it is the first data of the file, it is automatically integrated to the known points. The known points in our case are all the points with an identifier. If it is not the first message, the points "identified by Kalman filter" are updated. It is then possible to compare the new position of the points "identified by Kalman filter" and the new points received. Each new received point will be compared to the set of "Kalman filtered" points and will give one of the following results: - The point corresponds to one of the known points; -The point does not correspond to any known point; -One of the points "identified by Kalman filter" does not find a match.