dataesr/scientific-paragraphs-categorization huggingface dataset:
The longest ORF in each gene cluster was selected as the unigene. Expression levels were estimated as transcripts per kilobase million read (TPM) using Salmon 0. 7. 2  [44] , and unigenes with TPM less than 0. 5 were considered as non-expressed  [25]. The level of completeness of our eight assembled transcriptomes was evaluated using BUSCO (benchmarking universal single-copy orthologs) v1. 1b  [45]. Predicted protein sequences were annotated using BLASTp 2. 4. 0+ [46]  against NCBI's non-redundant (nr) database with an E-value of 1 × 10 -5. Gene Ontology (GO) function for each unigene was assigned using Blast2GO  [47]  with BLASTp nr input. Sequences were also submitted to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Automatic Annotation Sever (http:// www. genome. jp/kegg/kaas/) to determine their functional relationships using the bi-directional best-hit method.
References for the KEGG annotation included the default representative eukaryotic genomes as well as Table  1  A summary of transcriptome data from eight apple snails used for database construction. Tissues: albumen gland (AG), digestive gland (DG), foot (F), gill (G), lung (L), mantle (M), kidney (K), stomach (S), testis (T) and other tissues (OT; including DG, F, M and T) the genomes of several invertebrates: Helobdella robusta, Lottia gigantea, Crassostrea gigas, Octopus bimaculoides, Schistosoma mansoni, Nematostella vectensis and Hydra vulgaris. The annotation results are summarized in Table  2.
AmpuBase database construction AmpuBase is a relational database that provides public access to these newly assembled ampullariid transcriptomes and annotations. The database structure and layout are similar to those of PcarnBase  [48] , except that data from several species can be searched at the same time and that the GO and KEGG search pages are integrated. In brief, for each species, a relational database was developed using MySQL v5. 6. 34 and hosted on an Apache HTTP server. The BLAST search function is powered by ViroBLAST  [49]  using the PHP programming language. The database consists of DNA and protein sequences of all unigenes that are linked with associated NCBI nr, GO and KEGG annotations through unigene ID. The database consists of five entity tables ("NCBI annotation", "Proteins", "DNAs", "Gene Ontology" and "KEGG") and two relation tables ("NCBI_GO_relation" and "NCBI_KEGG_relation").
AmpuBase: Functions and applications AmpuBase is available online via web interface at http:// www. comp. hkbu. edu. hk/~db/AmpuBase/. The database can be searched with BLAST or other query terms. The BLAST search function allows users to blast query sequence(s) or fasta files against single or multiple DNA/protein datasets with default settings (under Basic Search option) or customizable settings (under Advance Search option) (Fig. 2a ). Upon submitting a BLAST search, matched sequences are returned with their Evalue and similarity score, and information on the corresponding annotation can be obtained by clicking "Unigene ID" (Fig. 2b ). Apart from BLAST search, the transcriptome data can be searched in two other ways (Fig. 2c ). General Annotation Search allows one to query the relevant annotations (i.
e. , NCBI annotation, GO and KEGG) either by using the unigene ID or unigene name (e. g. , perivitellin ovorubin). Each successful query returns a table that contains Unigene ID, NCBI's nr, GO and KEGG description (if available). The resultant sequences can be downloaded by selecting the Unigene ID and clicking "Submit" for further analysis, for example, phylogenetic analysis of perivitellin ovorubins, major and multiple functional proteins in PVF  [20, 24, 25]. In addition, GO and KEGG Annotation Search is also provided for searching GO and KEGG information using GO ID, KEGG ID or a keyword. All sequence data for these ampullariid transcriptomes are available for download under the "Downloads" menu, for transcriptome wide data mining and analysis of a specific gene.
Conclusions In this study, we have generated a large set of transcriptome data for eight species that represent five genera of Ampullariidae. These data are compiled in a relational database, AmpuBase, which greatly enhances the publicly available genomic resources for ampullariids. The database provides tools for sequence-or keyword-based query functions, which will facilitate in-depth ecological and evolutionary studies on ampullariids, and comparative studies with other invertebrates. AmpuBase will be updated when more genomic data become available in the future.
Funding JWQ was supported by  Shenzhen Science and Technology Innovation Committee  ( JCYJ20170307161326613 ) and  General Research Fund of Hong Kong  ( HKBU 12301415 ). HH was supported by  China  and  Agencia Nacional de Promoción Científica y Tecnológica, Argentina  ( 0850  and  0122 ). JCHI received a PhD studentship from  Hong Kong Baptist University. BVB was supported by a postdoctoral fellowship of the  FWO Vlaanderen  ( 12N3915N ) and a grant from the  French Agence Nationale de la Recherche  ( ANR-JCJC-EVOLINK ).
Availability and requirements All clean reads are deposited in the NCBI Sequence Read Archive (SRA) with accession numbers listed in Table  1. The assembled and annotated transcriptomes are available on the AmpuBase website (http://www. comp. hkbu. edu. hk/~db/ AmpuBase/). The transcriptome data and phylogeny data are deposited in the Dryad Digital Repository at https://doi. org/10. 5061/dryad. 117cf.
Five-micron-thick sections of paraffin were prepared then stained with hematoxylin and eosin dyes  [30]  and examined microscopically. 2. 12. Statistical Analysis. All data were expressed as the mea n ± standard error of mean (SEM) and were statistically analyzed by the SPSS/PC software (2001) using one-way analysis of variance (ANOVA), followed by post hoc Tukey's test. A p value < 0. 05 was considered statistically significant.
Third, to increase its outreach to non-ECMM countries, particularly LMICs, the academy membership fees have recently been substantially decreased for non-ECMM countries with Gross National Income (GNI) per capita converted to international dollars using purchasing power parity (PPP) rates between 10 000 and 20 000 (source data derived from http://data. worldbank. org/indicator/ NY. GNP. PCAP. PP. CD), to meet the fees that were previously applicable only to countries with a GNI PPP < 10 000. These changes led to an increased number of qualified applications from countries with GNI PPP rates between 10 000 and 20 000; the ECMM Academy was recently expanded with new FECMM members from Brazil, Iran, and Lebanon and as of mid-July 2018 counts 64 FECMM from 29 countries in total (Figure  2 ). Furthermore, fees for FECMM from all other high-income non-ECMM member countries have been adjusted accordingly.
Centres will only be designated as ECMM EC after careful onsite auditing. At least one centre per country is  The audit takes place by collecting data on how laboratory diagnosis is performed and on how clinical management of IFD is effectively executed. As a basis of the laboratory audit, the best practice recommendations for the diagnosis of serious fungal diseases will be used. 33 esently, five ECMM EC with diamond status and one with silver status (Zagreb, Croatia) are already implemented, and one EC (Houston, USA) is under evaluation (Figure  3 ). F I G U R E 2.
The collected epidemiological data on incidence of the various mycoses in different geographical areas may foster awareness of local as well as of international governing organisations to the significance and relevance of fungal diseases. Together with other leading organisations in the field, the ECMM will continue to spearhead innovative initiatives that serve the long-term goal to reduce IFD-related morbidity and mortality. TA B L E 4 Outline of how the ECMM guideline process addresses the domains of AGREE 1. Scope and purpose All diseases and their corresponding patient groups are predefined and appropriately covered by the guideline.
Brucella are highly infectious bacterial pathogens responsible for a severely debilitating zoonosis called brucellosis. Half of the human population worldwide is considered to live at risk of exposure, mostly in the poorest rural areas of the world. Prompt diagnosis of brucellosis is essential to prevent complications and to control epidemiology outbreaks, but identification of Brucella isolates may be hampered by the lack of rapid and cost-effective methods. Nowadays, many clinical microbiology laboratories use Matrix-Assisted Laser Desorption Ionization-Time Of Flight mass spectrometry (MALDI-TOF MS) for routine identification. However, lack of reference spectra in the currently commercialized databases does not allow the identification of Brucella isolates. In this work, we constructed a Brucella MALDI-TOF MS reference database using VITEK MS.
We generated 590 spectra from 84 different strains (including rare or atypical isolates) to cover this bacterial genus. We then applied a novel biomathematical approach to discriminate different species. This allowed accurate identification of Brucella isolates at the genus level with no misidentifications, in particular as the closely related and less pathogenic Ochrobactrum genus. The main zoonotic species (B. melitensis, B. abortus and B. suis) could also be identified at the species level with an accuracy of 100%, 92. 9% and 100%, respectively. This MALDI-TOF reference database will be the first Brucella database validated for diagnostic and accessible to all VITEK MS users in routine. This will improve the diagnosis and control of brucellosis by allowing a rapid identification of these pathogens.
Its accuracy however largely depends on the coverage of the database of the commercially available MALDI-TOF MS systems. With regards to Brucella, identification was not possible because this genus was not represented in the databases of the two main MALDI-TOF MS system manufacturers (i. e. bioMerieux and Bruker)  [11] [12] [13]. Only the Bruker Security Relevant (SR) database, or custom databases developed in some laboratories, can identify these highly pathogenic bacteria, but access to these databases is not possible in some countries due to export restriction regulations  [13] [14] [15]. Moreover, only B. melitensis is included in the SR database.
Bacterial strains The bacterial strains used for the construction of the database are listed in Tables  1  and S1. Each of these strains was cultivated on several different media (S1 Table ). The bacterial isolates used for the external evaluation and their culture conditions are listed in Tables  2  and S2. All strains used in this study were previously characterized using an established workflow (phenotypic assays, Multiple-Locus Variable number tandem repeat Analysis -or MLVA-, wholegenome sequencing)  [16].
MALDI-TOF MS samples Samples used to build the spectra database were prepared according to a previously established inactivation protocol  [19]  consisting in resuspending two full loops of bacteria (i. e. multiple colonies) in 200 μL of solvent mix, vortexing (10 sec), centrifuging (10,000 g, 2 min) at room temperature, removing 190 μL and resuspending in the 10 μL of solvent left in the tube. For the external evaluation study, this protocol was simplified by suspending only one loop of bacteria in 100 μL of solvent mix, vortexing (10 sec) and incubating at room temperature (20-25˚C, 3 minutes). Bacteria were efficiently inactivated by this method and the biomass concentration of the samples allowed identification by MALDI-TOF MS, demonstrating that the centrifugation step in the original protocol was not required.
MALDI-TOF MS analysis One μL of each sample was applied to a single well of a disposable, barcode-labeled target slide (VITEK MS-DS, bioMerieux), overlaid with 1 μL of a saturated solution of alpha-cyano-4-hydroxycinnamic acid matrix in 50% acetonitrile and 2. 5% trifluoroacetic acid (VITEK MSCHCA, bioMerieux) then air dried. For the database construction, several independent measurements were recorded for each strain (see S1 Table for the different culture conditions). For instrument calibration, an Escherichia coli reference strain (ATCC 8739) was directly transferred to designated spots on the target slide using the procedure recommended by the manufacturer. Mass spectra were acquired using a VITEK MS Plus (bioMerieux, Marcy l'Etoile) and the Launchpad v2. 8 software program (Kratos, Shimadzu group Compagny, Manchester, UK).
Construction and optimization of the database The database was built as previously described  [20]. Briefly, peak lists were binned by assigning each peak within the mass range of 3. 000-17. 000 Da to one of 1,300 bins. A predictive model was then established for each species using the Advanced Spectra Classifier (ASC) algorithm developed by bioMerieux (La Balme les Grottes, France). The outcome of this procedure provided an assignment of a dimensionless weight for each bin and for each species. As a result, a specific pattern of weights for the 1,300 bins was obtained and combined for all species in a weighted bin matrix. For optimization, the spectral data were partitioned into 5 complementary subsets. One round of cross-validation involved a learning phase on 4 subsets ("training set") and a validation of the predictive model on the remaining subset ("testing set").
Five rounds of cross-validation were performed by permutation, and the results from the five rounds combined. To assess the accuracy of the database and calculate its performance in cross-validation, individual spectra were re-used as template for identification. The ASC algorithm compares the acquired spectrum to the specific pattern of each organism/organism group in the database and calculates a percent probability, or confidence value (%ID), which represents the similarity in terms of presence/absence of specific peaks between spectra. A perfect match provides a % ID of 99. 9%. %ID >60 to 99. 8% are considered as good. Scores <60% are considered to have no valid identification. The VITEK MS system renders the following types of identification results: "Single Choice", when the spectrum acquired presents a high level of similarity (%ID >60 to 99.
9%) with only one specific pattern in the database; "Low discrimination", when the spectrum acquired presents a high level of similarity with 2 to 4 specific patterns in the database; or "No Identification", when the spectrum acquired either does not match with any pattern in the database, or presents a high level of similarity to more than 4 specific patterns. During cross-validation, identification was considered as correct when the result was consistent with the reference identification. Low discrimination results were considered as correct if the expected identification was included in the matches. A misidentification was defined as discordant organism identification between the cross-validation result and the reference identification.
Results To update the MALDI-TOF MS VITEK database, we used 84 Brucella strains, either reference strains or well characterized clinical/veterinary isolates (Tables  1  and S1 ), to generate independent spectra covering the Brucella genus. After initial selection based on quality criteria such as peak resolution, signal to noise ratio, number of peaks, absolute signal intensity, and intraspecific similarity, 590 spectra were retained and submitted for biomathematical analyses using an iterative system (bioMerieux patented ASC algorithm). Using an optimization process, we next evaluated the possibility to discriminate between different Brucella species and biovars. Discrimination between the different species was obtained, with the exception of B. ceti and B. pinnipedialis. These two species could not be clearly separated, as illustrated by the intertwining of their spectra on a dendrogram (Fig  1  After optimization, cross validation was performed to evaluate the performance of the updated database, which contains 37,902 spectra covering 1,095 bacterial species including Brucella.
This mathematical method is used to assess how accurately the database can perform. Correct identification at the genus level was obtained in 97. 29% of cases (Table  3 ). Importantly, the remaining 2. 71% of results corresponded to "no ID", but never to an incorrect identification. At the species level, the performance varied between the different classes. For the three main zoonotic species (B. melitensis, B. abortus and B. suis), correct identification was obtained with 96. 06%, 100% or 89. 34% of spectra, respectively. Finally, as an external validation, the database was challenged with the MALDI-TOF spectra from 48 independent Brucella isolates, and 2 strains of Ochrobactrum, which are "near neighbors" of the Brucella genus (Tables  2  and S2 ). The implemented database allowed correct identification at the genus level in 88.
Discussion A major asset of this MALDI-TOF MS database is its ability to identify Brucella isolates at the species level, which is essential for following epidemiological outbreaks. Obtaining such a resolution was very challenging for this genus, as highlighted in previous studies  [29] , because of the high similarity between species at the genetic level  [30]. Discrimination between species was made possible using a patented approach to differentiate closely related species using internal calibration and a two-step algorithm. This was not sufficient to distinguish the two species of Brucella from marine mammals (B. ceti and B. pinnipedialis). This is in agreement with a recent Multi-Locus Sequence Analysis (MLSA) showing that the taxonomy is inconsistent with the phylogeny of these two species, and that taxonomic rearrangement should be envisaged  [31].
This MALDI-TOF MS database is however able to discriminate eight different Brucella species, which include the most common in human or animal disease. The updated database allowed correct identification of Brucella isolates at the genus level in 88. 4% of cases. It is important to mention that none of them was identified as Ochrobactrum spp. , a misidentification that is common with other standard identification methods  [32] [33] [34]  and recently reported using the VITEK MS database currently available  [35]. Analysis at the species level gave only one discordant result, corresponding to cross identification between two Brucella species. Such result would have no consequence for human medicine, as identification at the genus level is sufficient to prescribe the appropriate treatment. As for all MAL-DI-TOF databases, the limitation of this system is its inability to identify non-clinically validated species or species not included in the database.
However, the large coverage of the Brucella genus (in particular the most common species) in this database makes this risk is very minor. Diminution of the performance at the genus and/or species level was due to "no ID" results for some rare and/or atypical Brucella spp. (B. neotomae strain 5K33, B. microti strain CCM4915, and the rodent isolate NF2653), several strains from marine mammals, and the vaccine strain B. abortus RB51. These results were not due to the quality of MALDl-TOF spectra, which was good (based on the number of spectral peaks, Table  5 ). In the spectra for RB51, we found that several masses characteristics of the B. abortus class were less frequently present, in particular the masses of 5,920. 63, 6,040. 32 and 7,467. 89 Da were present in only 14. 3% of spectra (vs. in 75-95% of the spectra of other B.
abortus isolates, with a tolerance of 800 ppm). The only discordant result in our assay was obtained with B. canis Mex51, which was identified as B. suis. This was due to the presence in its spectra of additional masses that are common with the B. suis class in addition to the major peaks characteristics of the B. canis class. This finding is consistent with an exhaustive MLSA showing that B. canis strains are very close to B. suis biovars 3 and 4  [31]. Importantly, the MALDI-TOF database allowed the correct identification as Brucella of several recently discovered "atypical" isolates  [5, 6, 28, 36]. These strains represent a serious problem for diagnosis laboratories, as they are not identified as Brucella using classical phenotypic tests. It is possible that similar strains have been isolated in the past but misidentified.
Very little is known concerning the ability of these new species to cause disease in humans or livestock. The possibility to identify these isolates as Brucella will thus be important for both human and animal health. Overexpression of an exogenous protein (GFP) did not affect the identification of B. melitensis 16M. This is important since recombinant Brucella strains are common tools in research laboratories and could potentially infect lab workers. Moreover, the use of such Brucella strains as vaccines was proposed, since the presence anti-GFP antibodies would allow distinguishing vaccinated animals from naturally infected ones  [37]. In conclusion, this updated MALDI-TOF MS database is a new diagnostic tool that allows the identification of Brucella. It combines precision of identification (broad coverage of the Brucella genus together with species-level identification) and widespread availability.
After integration in the VITEK MS (v3. 2), this will be the first Brucella database validated for diagnostic with CE accreditation and accessible to all users in routine. This will allow accurate diagnosis and timely treatment in brucellosis. These highly infectious pathogens also causing one of the most frequent laboratory-acquired infection  [38] , their rapid identification by MALDI-TOF MS will decrease the risk of accidental infection of laboratory workers. A paradox of global health however is that the countries where brucellosis is endemic may not have access to MALDI-TOF MS. This could be circumvented by the use of the in-tube inactivation method described earlier  [19] , which will allow the shipment of erstwhile infectious samples to mass spectrometry platforms.
). Distinguishing the different biovars of B. melitensis and B. abortus was not possible. Discrimination between several of B. suis biovars was obtained (S1 Fig), but biovars 1 and 4 gave crossidentifications. Classes representing the different Brucella species were thus created by grouping together the different biovars of B. melitensis, of B. abortus and of B. suis, and the two species B. ceti and B. pinnipedialis. The eight species represented in the MALDI-TOF database are thus: B. melitensis (biovar 1, 2 or 3), B. abortus (biovar 1, 2, 3, 4, 5, 6 or 9), B. suis (biovar 1, 2, 3, 4 or 5), B. canis, B. ovis, B. ceti/B. pinnipedialis, B. inopinata and B. papionis.
Drug susceptibility tests using ETEST ® confirmed rifampicin resistance [minimal inhibitory concentration (MIC) > 32 mg/L] and resistance to isoniazid (MIC > 4 mg/L), streptomycin (MIC > 1,024 mg/L) and ethambutol (MIC > 256 mg/L). On drug-supplemented By the end of September 2016, one sputum specimen was cultured in Marseille on Coletsos medium (Bio-Rad, Marnes-la-Coquette, France). After a two-month incubation period at 37 °C, one rough colony was observed by the naked eye and microscopic examination after auramine-O staining was positive, the strain was then referred to as strain FB-527. Subcultures in BACTEC MGIT medium (Becton-Dickinson) and Middlebrook 7H10 (Becton-Dickinson) were positive after 7 and 10 days, respectively. Colonies tested negative for the GeneXpert ® MTB/RIF test and yielded no identification by MALDI-TOF-MS analysis (Bruker database, version December 2015).