dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, in the past 20 years, neither Koch's nor Falkow's hypotheses have been able to completely define virulence factors. The development of rapid and cost-effective genome sequencing technologies has provided the opportunity, among others, to discover a wide range of genes that could be linked to bacterial pathogenicity. This ability to determine the pathogenic capacities of a bacterium from its genome is known as pathogenomics  [2]. Virulence factor databases allow for fast and easy identification of putative virulence factors in whole-genome sequence data based only on sequence homologies. The Virulence Factor Database was released in 2005 and is an example of a regularly-updated database, of which a fourth version was released in 2018  [3]. Other updated databases exist, such as PHI-base and Victors  [4, 5].
Research Method and Results This systematic review was based on PubMed published articles in the English language. We used the following MeSH terms: "Virulence", "Virulence factors", "Pathogenicity", "Whole genome sequencing", and "Genomics". In particular, we searched for the following species: "Staphylococcus epidermidis", "Staphylococcus lugdunensis", "Staphylococcus capitis", "Staphylococcus caprae", "Staphylococcus haemolyticus", and "Staphylococcus saprophyticus". We excluded studies that were not based on whole-genome data, and those for which genome sequences were not deposited in public databases (e. g. , GenBank, European Nucleotide Archive). We identified eight studies for S. epidermidis  [29, 30, [40] [41] [42] [43] [44] [45] , four for S. capitis  [46] [47] [48] [49] , three for S.
Staphylococcus epidermidis S. epidermidis is by far the most prevalent CoNS in microbiological samples and the primary cause of CoNS-related infections, particularly in nosocomial settings  [56]. This species has been widely studied at a molecular level and has provided the largest amount of data regarding the presence of virulence factors that might be identified as pathogenicity factors  [16]. Biofilm formation is the main path by which S. epidermidis colonizes and infects prosthetic and medical devices as reviewed by Otto et al. [57]. The biofilm life cycle is a complex process which implicates MSCRAMMs, the proliferation of the exopolysaccharide matrix, and finally, biofilm dispersion, mostly due to phenol-soluble modulin (PSM) peptides  [57]. Nearly 500 genomes have been made available on GenBank, mainly draft genomes, but also 12 complete genomes.
Interestingly, Heilbronner et al. showed that duplication of the isd locus provided a selective advantage to S. lugdunensis in the case of iron limitation, possibly improving bacteria survival and pathogenicity. Recently, we identified and purified a novel metalloprotease named lugdulysin that could be implicated in osteoarticular infections  [61]. S. lugdunensis whole-genome-based studies to determine virulence determinants are listed in Table  2. The first complete genome of S. lugdunensis was published by Tse et al. in 2010  [68]. Since then, 17 complete genomes have been published, and eight additional draft genomes are available in GenBank. Nevertheless, the first exploitation of a complete genome was provided by Heilbronner et al. in 2011  [50]. The authors published the complete genome of strain N920143 and identified several putative virulence factors based on comparative genomics and homologies searches.
Staphylococcus saprophyticus S. saprophyticus is a commensal CoNS and can lead to lower urinary tract infections in young women  [79, 80]. Colonization of the genital area and the gut has been linked to this atypical type of infection, but most importantly, it was shown in vitro that this CoNS has an unusual capacity to adhere to urothelial cells and to produce urease. Nevertheless, those characteristics are not unique in CoNS and do not fully explain the capacity of this CoNS to cause urinary tract infections. S. saprophyticus whole-genome-based studies to determine virulence determinants are listed in Table  4. In 2005, Kuroda et al. established the first complete genome of S. saprophyticus, and until now, only six complete genomes for this species have been made available in GenBank  [55].
aureus genomes 2 Complete genomes from the study and from GenBank - - - S. epidermidis and S. aureus present syntenic genomes and horizontal gene exchange probably happen between those species in both directions, possibly mobilizing virulence factors from S. aureus to S. epidermidis. Comparative genomics between Complete S. epidermidis has an open pan genome, the variable genome 2012 [42] commensal and 30 genomes from - - - comprises mobile genetic elements, the fdh gene distinguishes pathogenic the study commensal from pathogenic bacteria. S. epidermidis.
epidermidis strains after osteoarticular infections 104 Complete genomes from the study - - - Some genes from S. epidermidis were associated with bad outcome in patients: bhp (biofilm formation), qacA (antiseptics resistance), ccrA-ccrB (cassette chromosome recombinase), IS256-like (transposase). Comparative Complete S. epidermidis pathogenic strains that produce a C-3 like 2018 [44] genomics between 3 S. epidermidis 3 genomes from the study and - - - enterotoxin bear a SEC-coding sequence located on a composite pathogenicity island SePI-1/SeCI-1 that suggest the genomes from GenBank existence of HGT from S. aureus to S. epidermidis. P1 to P6 Plasmids - Presence of multiple genes implicated in drug resistance atl Autolysin 2018 [45] Pathogenicity factor search 1 G6_2 ERR387168 Environmental strain ebh, ebp, sdr ssp, nuc Adhesins/MSCRAMMs Serine and cysteine protease, nuclease lip, geh Lipases ica Intercellular adhesion factors hlb, hld Haemolysin.
1 Knee prosthesis VISLISI_33 NZ_CP020769. 1 Liver abscess φSL2 to φSL4 Prophages VISLISI_37 CP020761. 1 Endocarditis 2018 [32] Comparative genomics with 15 S. aureus and 13 S. epidermidis genomes 15 All complete genome from GenBank - - - Identification of a closed pan genome in S. lugdunensis and multiple barriers to horizontal gene transfer (restriction-modification, CRISPR/Cas, and toxin/antitoxin systems).
New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i. e. , a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases.
Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain.
2015 , Gwiazdowski et al. 2015 , Oliveira et al. 2015 , 2016 , Park et al. 2015 , Savage et al. 2015 , Warne et al. 2015 , Xu et al. 2015 , Iftikhar et al. 2016 , Lobo et al. 2016 , Rimet et al. 2016 , Yang et al. 2016 , Bell et al. 2017 , Pennisi 2017 ), until complete reference libraries exist for all taxonomic groups and for all geographic locations, the comparison of a query sequence to sequences in public reference libraries such as the Barcode of Life Database (BOLD;  Ratnasingham and Hebert 2007)  or GenBank  (Benson et al. 2013) , or a researcher's private database, will result in one of the following four outcomes: (1) a high-percentage match to a specimen with a species-level identification, (2) a low-percentage match to a specimen with a species-level identification, (3) a high-percentage match to a sequence from an unidentified specimen (usually noted as some variation of "Undet sp"), or (4) a low-percentage match to a sequence from an unidentified specimen.
Data filtering As a first filter, sequences of less than 300 base pairs (b. p. ) were removed from the data set to ensure that included sequences had sufficient overlap to allow for pairwise distance estimates in downstream analyses. Each sequence was compared to published sequences in the NCBI GenBank database using the blastn search algorithm  (Altschul et al. 1990 ) run locally using the command-line interface and compared to the nt sequence database (downloaded 11 July 2016) with all default summary statistics retained in tab-delimited format for the top-match sequence, except that we also retained the taxids number for the top-math accession. The taxids number was then used to query the NCBI Taxonomy Database to provide higher level taxonomic information using a custom interactive python script blast2taxoninfo.
py (H. Krehenwinkel, G. de Kerdrel, J. C. Andersen, and R. Gillespie, unpublished data). The order-level morphological identification of each specimen was then compared to the order-level assignment from the top-match sequence in the NCBI GenBank Database, and any specimens for which there was disagreement between morphological-based order assignments and the order assignment for the top-match sequence were considered as "contaminant" sequences and removed from analyses. DNA sequences, locality collection information, field collection identification, and NCBI top-match information for each retained specimen are provided in Data S1: CollectionInformation. csv.
Species delineation and categorization For the spiders and for each order of insect, unique alignments were generated using the nucleotide alignment software MUSCLE  (Edgar 2004 ) as implemented through the EMBL-EBI web service  (Li et al. 2015). Alignments were subsequently refined in Geneious using the Translational Align sub-option for the Multiple Alignment tool based on visual examinations of the reading frame prior to refinement using the Invertebrate Mitochondrial genetic code. Refined alignments were inspected and edited by hand when necessary, and further filtered to remove any sequences that did not have >100 b. p. of sequence overlap with every other sequence in the alignment. Specimens were then assigned to species-groups using Automatic Barcode Gap Discovery (ABGD;  Puillandre et al.
Second, the labels for the remaining species that lacked species-level identifications were updated using the NCBI GenBank best-match subject sequences as follows: for any species for which a specimen had a ≥97% match to a published sequence with a species-level identification, that species was assigned the species-level identification from the published sequence; for any species for which a specimen had a ≥90 but <97% match to a published sequence with a species-or genus-level identification, that species was assigned the genus-level identification from the published sequence; for any species for which a specimen had a ≥80% but <90% match to published sequence with a species-, genus-, or family-level identification, that species was assigned the family-level identification sequence from the published sequence; the taxonomic IDs for all remaining species were then updated to include the order name followed by "sp.
Model development For each order, a distance matrix based on that order's nucleotide alignment was generated. Using a custom script (provided in Data S1: CustomRscript. R) written in the statistical language R v 3. 1. 3 (R Core Team 2014), we then used the distance matrix to estimate the average distance between individuals within a species (i. e. , average similarity) and average distance between individuals of nearest-neighbor species (i. e. , average distance). The data was then subset to include a training data set that included the above statistics only for species with species-level identifications and a biogeographical categorization of either endemic or introduced in  Ramage (2017) , and the full data set that included the statistics for all species regardless of whether or not they had a species-level identification or a known status of endemic/introduced.
Preliminary analyses based on Welch's two-sample t test using the training data set (Appendix S1: Fig. S1 ) indicated that there were highly significant differences between endemic and introduced species based on measures of average distance (t = 5. 897, df = 114. 1, P value < 0. 0001), while there were no significant differences based on measures average similarity (t = 0. 958, df = 112. 5, P = 0. 34). We then constructed four generalized linear mixed models (GLMMs), using the R package lme4  (Bates et al. 2015)  with species categorization as the response variable coded as binomials (i. e. , endemic = 1, and introduced = 0), average similarity and/or average distance as fixed effects, and we included order as a random effect because we were concerned that the model might have better fit for some orders than others.
Models were then compared based on AIC c scores using the R package AICcmodavg  (Mazerolle 2017 ) to determine the model with the best fit for the data set. Using the loadings from the best-fit GLMM calculated using the training data set, we then assigned scores from 0 to 1 to each species in the full data set using the predict command in R. Ninety-five percent confidence intervals (CI) from the GLMM assignment scores for endemic and introduced species in the training data set were then calculated (mean AE 1. 96 9 SE). Any species in the full data set with a predicted value ≤ the upper 95% CI for introduced species was assigned the label "GLMM-Nonnative," any species in the full data set with a predicted value ≥ the lower 95% CI for endemic species was assigned the label "GLMM-Native," and any species in the full data set with a predicted value that fell between the two CIs was assigned the label "GLMM-Undet.
Specimen sampling and data filtering After removal of sequences of <300 base pairs, and the removal of sequences identified as "contaminants" based on order-level mismatches between the morphological identifications and the order-level identification for their best-match sequence in GenBank, the final data set included cytochrome oxidase I (COI) sequences from 4,663 specimens. Species-level identifications were given to 2,172 specimens using a combination of morphologically based identifications, identifications published in  Ramage et al. (2017) , or identifications based on matches of ≥97% similarity to a published sequence in the NCBI GenBank database.
2007 , Gonzalez et al. 2009 ). Here we have presented a technique that can be utilized by any community DNA sequencing project to rapidly categorize unidentified specimens or specimens from species whose biogeographic status is unknown as representing either likely native or nonnative species. For example, using a traditional reference library approach (i. e. , combining morphological identifications with matches to sequences in public databases with species-level identifications), we were able to categorize 794 of the 4,663 sequenced specimens (n = 147 species) as being either endemic or introduced species. In contrast, using our GLMM approach, 3,497 of the 4,663 sequenced specimens (n = 707 species) could be categorized as either likely native or likely nonnative. We discuss several possible applications for this approach below.
ACKNOWLEDGMENTS The  Moorea BioCode Project  is a collaborative project between the  Gump Research Station, CRIOBE , the  Smithsonian Institute, IRD, The Bishop Museum, The University of Florida , and the  University of California Berkeley. This collaboration would not have been possible without the incredible efforts by  Werner Loher  to establish the  Gump Research Station. Major funding for the  BioCode Project  was provided by the  Gordon and Betty Moore Foundation. Funding to  J. C. Anderson  was provided by a  USDA NIFA   Postdoctoral Fellowship   2016-67012-24688. The authors would like to thank  R. Gwiazdowski  and  M. Labb  e for their comments on earlier drafts of this manuscript.
Data are simultaneously acquired during six runs lasting 5 minutes each for each freestream velocity and for each PIV plane by the previously presented sensors. The sampling frequency are 500 Hz (balance and Kulite), 50 Hz (microDAQ) and 1 Hz (stereo PIV). The profiles of the boundary layer upstream of the bluff body, of the underflow and of the upper boundary layer upstream of the flow separation are also measured by a one-component hot-wire probe.
Surface tension measurements Surface tension data were obtained using a DSA 100 Drop Shape Analysis System (Krüss GmbH, Germany). Approximately 25 ml drops of protein in water at various concentrations were suspended from a 1. 830 mm diameter syringe needle. Photos were taken at an average speed of 3 fps and the drop shape was monitored over 15 min. The shape factor was iterated from the photos of the drops by the DSA1 software. Times shown in the data denote the time since the first measurement. It takes ∼8 s, from the first depression of the syringe plunger (liquid exiting the needle), for the drop to be formed and to stabilise. Therefore, changes during the first 8 s are not measured using this system.
Small-angle neutron scattering measurements Small-angle neutron scattering (SANS) experiments were performed with the Sans2D time-of-flight instrument at ISIS located at the Rutherford Appleton Laboratory in Oxfordshire, U. K. The detector length was 4 m and the incident wavelength of the neutron beam was 1. 75-16. 5 Å. This gave a Q range of 0. 005-0. 6 Å -1. QS quartz banjo cells with an optical path of 1 or 2 mm were used. Temperature control was carried out using 5 kW circulating fluid baths and set to 7°C. Typically, data were collected for 30 min and then corrected and normalised to an absolute scale by background subtraction. Data were analysed using the SasView-2. 2. 1 program (www. sasview. org). The freeze-thaw experiments were performed using 0. 25 or 0. 5 ml samples contained in 1.
5 h, depending on the contrast. For younger surface ages, the data were collected for 30 s. The RASCAL standard discrete layer approaches customarily used to fit neutron reflection data were insufficient to accurately describe our system. Therefore, a new custom layer model approach was used. This new, more flexible model contains 'thickness' as a fitted variable and assumes that there is no protein at either the top (i. e. the air boundary) or below the surface protein layer (the bulk). The fitting was done on all contrasts simultaneously. For each data set, 11 parameters were designated: the thickness of the entire surface layer measured and, for each protein, the protein concentration at five equally spaced points within the surface layer (10 parameters in total). Five points allowed for sufficient flexibility in the model while keeping the number of fitted variables low.
All parameters were allowed to fit freely between 0 and 100%. For each protein concentration point, the SLD was calculated by simply summing the SLD contributions from each protein ( protein concentration multiplied by the protein SLD) with the SLD contribution of the bulk SLD. The in-between protein concentrations were constructed using PCHIP interpolation and the entire protein concentration curve was then fit with the data and the Chi-squared measured. The fits were performed using alternating Simplex and Covariance Matrix Adaption Evolution Strategy procedures until the minimum Chi-squared was reached. For each dataset, the fitting was repeated six times with different initial points and the result with lowest Chi-squared was selected.
Far-UV circular dichroism spectroscopy Circular dichroism (CD) spectra of H-AavLEA1 and D-AavLEA1 at a concentration of 8-12. 3 mM in H 2 O, 42% D 2 O and D 2 O were recorded over the range 190-250 nm using an AVIV 410 spectrometer. Spectra were recorded at 25°C in a 1 mm path-length cuvette with a data pitch of 1 nm, an averaging time of 2 s and baseline corrected against the appropriate solvent before applying smoothing. Measured ellipticity values were converted to mean residue ellipticities (MREs) with units deg cm 2 dmol -1 to normalise for differences in concentration. Secondary structure analyses were carried out using the DichroWeb online server  [17]  with the CONTIN-LL algorithm  [18, 19]  and reference dataset seven  [20].
Consequently, we proceeded to examine the individual proteins and the mixtures of CS with D-AavLEA1 both before and after freeze-thaw stress. Before freezing, the scattering profiles of the two proteins in the mixture corresponded to those of the individual proteins, consistent with previous data showing that AavLEA1 does not stably associate with client proteins  [7]. Samples were then subjected to one or three rounds of freezethaw and scattering profiles recorded. Samples containing CS alone were seen to visibly aggregate following freeze-thaw. Consistent with this, SANS curves (Figure  2d ) showed an upturn at low Q which was progressive with successive rounds of freeze-thaw, indicative of aggregation. Interestingly, there were no major changes in the high Q region of the scattering curve, suggesting that any conformational changes in the CS dimer are only minor.
Neutron reflection analysis of the surface layer conformations of disordered LEA and globular CS proteins at air-water interfaces The dynamic surface tension data provided evidence that AavLEA1 preferentially adsorbs to the air-water interface; however, the method was unable to easily differentiate the contributions of each protein in the mixtures. Therefore, to provide a fuller picture of the system, neutron reflection was used. D-AavLEA1 protein was produced recombinantly as described in the Materials and Methods section. This allowed the different components in the protein mixture to be individually examined. In 100% D 2 O, only contributions from the air-water interface and hydrogenated proteins are detected (deuterated proteins are 'matched out'). In 42% D 2 O, only contributions from deuterated proteins and the substrate are recorded (hydrogenated proteins are 'matched out').
Finally, using 'air contrast matched water' (8% D 2 O), contributions from the substrate (water) can be 'matched out'. By simultaneously fitting the data from multiple different contrasts, density profiles for each protein can be calculated. Thorough attempts were made to fit the neutron reflection data using the standard discrete layer fitting approach in RASCAL. However, the inability to provide a good fit to the data indicated that the proteins were not present in distinct layers of single proteins. Therefore, a new, more flexible, custom layer model was written to fit the reflectivity profiles to provide protein concentration versus depth profiles. The reflectivity profiles and fitted curves are shown in the insert to each protein density curve. First, the two proteins were examined individually.