dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Visual Quality Indices as Predictors for Quality on Low Quality Images In  [6] ,  [11]  the point was raised that the use of visual quality indices as a recognition index could not be properly evaluated because there is a lack of quality and recognition databases. We will use the evaluation methods from these papers and evaluate the VQIs on the database presented in this paper. This serves a two-fold purpose, 1) we extend the results from the given papers with the missing information and 2) we show (again) that the correspondence of VQIs to low quality images is low. The specific methods are Spearman rank order correlation (SROC) as well as confidence and signal shape scores which were introduced in  [6]. The SROC is simply the evaluation of the monotonous relationship between two scores.
E. Predicting Recognizability Classes via Linear Discriminant Analysis In order to test the potential for a fusion approach we can use a linear discriminant analysis (LDA), similar to the PCA above. The purpose of the PCA is to find the best dimension reduction of our data, i. e. , finding linear combinations of the input variables presenting the highest variation in the dataset. The main objective of the LDA is to optimize, for the representation domain provided, the best separation between various classes. The LDA attempts to separate the classes in the best way possible based on input features, in our case the VQI scores per image. As established before, a ranking for recognizability, or quality, is difficult and not likely to succeed due to the double error problem. To make the prediction more manageable we will split the images into three clusters (see section III-A and Fig.
3  Fig. 8 : LDA on the full dataset. The results of the LDA, based on the nine VQIs and the MOS Q , are given in Fig. 8. A relation between recognition and quality is undoubtedly given by the clear separation of the given clusters along the LD1 component. The MOS Q , which we do not have in practice, is used in the experiment to show that the LDA in principle can handle the prediction. In Fig. 8  the clustering along LD1 looks promising. To get a better view on the data we split the data by encryption type and repeated the LDA, shown in Fig. 9a. In order to better see the classification along LD1 we also gave the histograms of a projection of the data onto LD1 in Fig. 9b. The separation of the clusters overall is decent but far from perfect, H. 265 especially has large overlaps. The next step is to repeat the experiments without the MOS Q and apply the LDA to an ensemble of VQIs.
IV. CONCLUSION We have provided the community with a freely available database of recognition score amended with a quality estimate by human observers. We used this database to look into the relation between quality, recognizability and various visual quality indices. The overall relation between quality and recognizability can be summed up as "Where quality ends, recognition begins". However, there is a certain overlap. This means that the range of images, where quality scores should be applied versus where recognition scores should be applied, is not clearly separable. This is the unfortunate reality of using the human visual system, which, by default, is subjective and noisy. We found that the prediction of recognition by using visual quality indices does not yield good results. We have shown that there is a disparity between recognition and quality.
C. Reliance on Visual Quality Indices It should be noted that we used the visual quality indices to attempt to predict the recognizability purely due to availability. We have already discussed how the double prediction, from VQI to quality and from quality to recognizability, introduces additional errors. But the VQIs are diverse and readily available and thus are a natural choice to use as sources for the prediction. However, in the long term if a proper recognizability score is required it would be better to cut out the middleman and attempt to develop a recognizability score directly based on the recognizability databases. However, as this paper, and prior papers  [17] ,  [18] , have shown, the features used in VQIs are not a good fit for the recognizability task. The development of recognizability score therefore will need a careful analysis of image features which can be utilized.
We have also modified the CSF to give even more weights to the lowest frequencies. Unfortunately, none of these numerous attempts proved to efficiently link recognizability and quality. The only method presenting interesting results was actually the multivariate analysis which we have presented in this paper. The reason for that is probably because, by design, the PCA or LDA are able to benefit from heterogeneous data, discard irrelevant inputs, while enhancing the relevant ones. As already briefly mentioned in section III-D, each VQI spans a certain range of perceived qualities. Among the tested VQIs, one for instance has been specifically designed and tuned for optimized performance against data hiding scenarios (the CPA  [39] ). And indeed, the CPA exhibits a better performance than other metrics near the visibility threshold.
The aim of this study was to perform an in silico analysis of the available wholegenome sequencing data to detect syntenic genomic islands (GIs) having homology to Salmonella genomic island 1 (SGI1), analyze the genetic variations of their backbone, and determine their relatedness. Eighty-nine non-redundant SGI1-related elements (SGI1-REs) were identified among gamma-proteobacteria. With the inclusion of the thirty-seven backbones characterized to date, seven clusters were identified based on integrase homology: SGI1, PGI1, PGI2, AGI1 clusters, and clusters 5, 6, and 7 composed of GIs mainly harbored by waterborne or marine bacteria, such as Vibrio, Shewanella, Halomonas, Idiomarina, Marinobacter, and Pseudohongiella. The integrase genes and the backbones of SGI1-REs from clusters 6 and 7, and from PGI1, PGI2, and AGI1 clusters differed significantly from those of the SGI1 cluster, suggesting a different ancestor.
To support this hypothesis, we explored GenBank R. Currently, thanks to the microbial whole-genome sequencing (WGS) database contains lot of data. They consisted of complete genomic sequences or partial genomes made up of multiple unassembled contigs. Identification of resistance genes and other specific sequences can be achieved using the basic local alignment search tool (BLAST). Here we report the results of an in silico analysis of SGI1-REs exhibiting homology with the backbones of SGI1/PGI1/PGI2/AGI1 islands. Only SGI1-REs, whose backbones could be completely assembled, were studied. We examined these SGI1-REs by searching for variations in these uncharacterized backbones. A single representative backbone was selected when multiple strains of the same species contained an identical backbone.
Bioinformatics Analysis The SGI1/PGI1/PGI2/AGI1 islands were identified with the nucleotide BLAST in the National Center for Biotechnology Information (NCBI) databases (nucleotide collection and wholegenome shotgun contigs databases) available until March 31, 2021 1 first using the integrase gene and second using the complete backbone of the GIs as a query sequence. We searched for SGI1/PGI1/PGI2/AGI1 islands in alpha-, betaand gamma-proteobacteria and retained only GIs with syntenic genes. Redundant backbone nt sequences found in the strains of the same species were excluded. SGI1-REs with the same int GI homology but with no or identical backbone variations (insertions, deletions, nt sequence exchanges, or ≥0. 2% of nt substitutions) were retained when they were detected in different bacterial species to determine the distribution of the SGI1-REs.
Phylogenetic Analysis Multiple alignments of nucleotide sequences, including the 37 fully characterized backbones to date and the uncharacterized GIs retained for this study, was performed using the online MAFFT software version 7 available at the Japanese Computational Biology Research Center (CBRC) 3. The 37 known SGI1-REs included 18 SGI1 variants: SGI1  (Boyd et al. , 2001) , SGI1-K1  (Levings et al. , 2007) , SGI2  (Levings et al. , 2008) , SGI1-V  (Siebor and Neuwirth, 2011)  recently renamed SGI-V (de Curraize et al. , 2020a), SGI1-PmMAT  (Siebor and Neuwirth, 2013) , SGI1-B2 and SGI1-Z  (Lei et al. , 2015) , SGI1-L  (Schultz et al. , 2017 ), SGI1-Pm2CHAMA (de Curraize et al. , 2018) , SGI1-PmJN40  (Bie et al. , 2018) , SGI1B-Ec1  (Cummins et al. , 2019) , SGI1-PmSC1111  (Wang et al.
, 2012). The int GI showed ≥98. 68% of nt identity with int PGI1-PmESC (Supplementary Table  3C ). Eleven SGI1-REs belonged to the PGI2 cluster: SGI1-REs in C. sakazakii (1), E. coli (1), K. pneumoniae (1), M. morganii (1), in V. cholerae (3), V. mimicus (2), V. parahaemolyticus (1), and for the first time, a GI of the SGI1-RE family was detected in P. aeruginosa (1). Their int GI showed ≥98. 94% of nt identity with int PGI2 (Supplementary Table  3D ). Only 7 new SGI1-REs were found in the AGI1 cluster, a group gathering recently discovered AGI1 variants in the GenBank R  (Siebor and Neuwirth, 2020). They were detected in V. cholerae (3) and V. fluvialis (1). For the first time, SGI1-REs belonging to the AGI1 cluster were found in 2 strains of P. mirabilis (int GI = 99. 65% of nt identity with int AGI1 ) (Supplementary Table  3E ).
DC2-4 where an ORF encoding an AAA family ATPase was found. No SGI1-REs in this cluster carried an MDR region. Variations occurred in the Sh024-res region. In the SGI1-RE of V. parahaemolyticus A1EZ952, the ORFs Sh024-Sh025 encoded a restriction endonuclease belonging to the XcyI family and a sitespecific DNA-methyltransferase (99. 36 and 98. 24% of nt identity with those of SGI1-REs from V. cholerae N2786 and N2757 in the SGI1 cluster, respectively), and the adjacent res gene (99. 66 and 97. 47% of nt identity, respectively) was only 71% identical to res SGI1-RE5SsW3-18-1 (Supplementary Figure  5C ). In the SGI1-RE of V. cholerae VN-2808, the Sh023-res region was replaced by a 2,482 bp nt sequence which exhibited no significant homology with sequences deposited in the NCBI database. The BLASTX alignment identified 2 ORFs encoding a YkgJ family cysteine cluster protein and a type II toxin-antitoxin system PemK/MazF family toxin.
DISCUSSION The screening of SGI1-REs in the WGS database had made possible the detection of GIs whose backbone presented the same gene synteny as these elements. SGI1-REs were mainly found in the bacterial species frequently isolated not only in the human and animal samples (Enterobacteriales and P. aeruginosa), but also in the water samples (V. cholerae) and belonged to the 4 groups of SGI1-REs already described. Most SGI1-REs in Enterobacteriales carried an MDR region (36/42), but only 8/19 in V. cholerae. This would certainly be due to the acquisition of resistance genes following exposure to antibiotics in Enterobacteriales which constitute the intestinal flora, whereas V. cholerae is a bacterium of the water environment. SGI1-REs were also found in the bacterial species isolated from crustaceans and marine molluscs, and more surprisingly from groundwater and seawater at great depths (V.
PCR fragments were purified using the Roche High pure PCR purification kit (Roche Diagnostics). Electroporation of plasmid DNA into E. coli was performed as described previously  31. Electrotransformation of B. breve UCC2003 was performed as previously described  32. The correct orientation and integrity of all constructs was verified by DNA sequencing performed at MWG Biotech (Ebersberg, Germany). Sequence data were obtained from the Artemis-mediated  33  genome annotations of B. breve UCC2003 6. Database searches were performed using non-redundant sequences accessible at the National Centre for Biotechnology Information internet site (http:// www. ncbi. nlm. nih. gov) using Blast. Sequence alignments were performed using the Clustal Method of the MEGALIGN program of the DNASTAR software package (DNASTAR, Madison, WI, USA).
Interestingly, the first product of each cluster, a pilin subunit precursor, has the Sec secretion signal and LPXTG motif encoded by two distinct ORFs separated by a sequence containing a homopolymeric G-tract. A potential re-framing mechanism at this G-tract to join the two ORFs into a single CDS was considered in the original annotated version of GenBank submission CP000303. 1 (updated 31st January 2014) of the UCC2003 genome, though was not considered in the most recent update NC_020517. 1 (on 30th March 2017). Here, the former B. breve UCC2003 ORFs containing the coding sequences [coordinates 164585-164608] and [coordinates 164607-169169] are re-designated as ORFs bbr_0113a (the 'upstream ORF') and bbr_0113b (the ex-bbr_0113), respectively; and those representing the sequences [2376304-2376500] and [2368283-2376305] are re-named as bbr_1889a (the 'upstream 65-AA encoding ORF') and bbr_1889b (the former 2674-AA encoding bbr_1889).
Adhesion of B. breve strains to HT29 epithelial cells. We next explored the biological properties of each of the two sortase-dependent pili to mediate cell surface adhesion. Strains B. breve UCC2003 (WT), B. breve UCC2003-113 and B. breve UCC2003-1889 were tested for their ability to adhere to HT29 epithelial cells. B. breve UCC2003 and UCC2003-1889 exhibit similar adhesion abilities with approximately 7% of bacterial cells adhering to the HT29 cells, while the adhesion ability of UCC2003-113 was significantly higher, with more than 20% of bacterial cells adhering to HT29 epithelial cells (Fig. 2 , panel F). This data indicates that expression of sortase-dependent pili, at least by B. breve UCC2003-113, promotes significantly enhanced adhesion to epithelial cells. This apparent pilus-mediated adhesion is likely to be an advantageous feature for bifidobacteria in the context of host colonization and host-microbe interaction.
A 150 bp paired-end Illumina sequencing was performed to exclude sequencing errors within the G-or C-tract specified by either of the two DNA strands of the PCR product. Unexpectedly with the G-tract containing template, the counterpart sequence of the derived NGS reads through and beyond the G-tract template sequence, corresponds to repetitive G base(s), named in this current work "G's end (Fig. 4 ). This phenomenon is believed to be caused during Illumina sequencing of a G-tract-containing template due to either a G-tract-forming secondary structure thereby forming a physical roadblock for the DNA polymerase or to non-specific, repetitive binding of fluorescent Guanine nucleotide. Therefore, the bioinformatic analysis of the paired-end NGS data was performed with only one of the two NGS reads, R1 or R2.
Authenticity of G-tract sequences. Accurate assessment of the number of bases at a homopolymeric sequence, especially at a G-tract is challenging because in vitro DNA polymerase reactions used in slippage detection are also sensitive to slippage. Here, G/C tract length was determined using Maximum Depth Sequencing (MDS)  37  aimed at excluding errors introduced during PCR and sequencing procedures. Surprisingly, the distribution of G/C tract length, and corresponding frequency, can differ significantly from the annotated sequences (NC_020517. 1). This highlights the need for caution when considering the reality of annotation of homopolymeric G-tracts present in sequence databases, with potential significance for population heterogeneity of their nt lengths at specific genomic loci. Such plasticity of nt length is also supported by our experiments as one single purified bacterial colony was used in our in vitro culture conditions.
Acknowledgements The authors acknowledge the facilities provided at the  Electron Microscopy Unit at National University of Ireland Galway. We thank  Maurice O'Donoghue ,  School of Microbiology, UCC  for his assistance in performing the adhesion assays. The authors want also to deeply thank  Glycom A/S (Lyngby, Denmark)  for the provision of purified HMO samples under their donation program. We also acknowledge  Dr. Abdelhak El Amrani  and  Pr. Ivan Couee  for their comments on the manuscript.
Participants also were asked to distribute the survey invitation to other colleagues involved in MIS-C care. The professional background of respondents was evaluated to confirm relevant roles in care and/or policymaking regarding patients with MIS-C. IP addresses were verified to ensure the uniqueness of each participant. Possible overlapping data (eg, national data that included regional counts) were excluded after personal communication with the involved respondents. Given the study design, overall survey response rates and subsequently any comparisons between survey respondents with nonrespondents could not be performed. On an individual patient level, no identifiable information was collected. Data were collected from November 1, 2021, to December 15, 2021.
Results Health care professionals (n = 132) involved in the care of patients with MIS-C initiated the survey. After excluding incomplete entries, we collected complete data from 83 health care professionals from 32 different countries (Figure  1 ). Most respondents were clinicians (79/83) and/or clinical researchers (21/83). Medical specialties included pediatrics (53/83), pediatric infectious diseases (23/83), cardiology (13/ 83), immunology (8/83), intensive care and emergency medicine (8/83), rheumatology (7/83), and dermatology (1/ 83). Four entries were excluded because of probable or confirmed overlapping data. After we excluded these cases, information from 5673 patients with MIS-C was analyzed (Figure  2 ). In total, 3465 of these cases were accurately counted and 2208 were reported as estimated case numbers of patients with MIS-C; 44% of the patients had been admitted to intensive care units during their MIS-C episode.
Tolerability of SARS-CoV-2 Vaccine Most respondents (54 of 65 entries without contraindication for vaccination) could not provide specific data on mild or moderate AE, either because there was lack of formal registration and/or that respondents were not at all confronted with these specific AE in patients. Of registered data, mild or moderate AE was reported to a variable extent, including both localized reactions (swelling, redness, pain) and systemic responses (fever, chills, nausea, fatigue, headache, lymphadenopathy). As such, frequencies of AEs for individual entries ranged from 0 to 100% of patients, although importantly, all mild or moderate AEs disappeared after 1-3 days without requiring specific interventions. One 13-year-old male recipient was reported with acute-onset facial nerve palsy (Bell's palsy) 1 week after his second BNT162b2 (Pfizer-BioNTech) vaccine.
H. was funded by the VIB Grand Challenge Programs and FWO-TBM (T004721N). F. H. is supported by Ghent University research grant (BOF-UGent), VIB Grand Challenges Programs, and the Jeffrey Modell Foundation. A. S. -A. is supported by the Departments of Health and Education of the Government of Catalonia (Spain) for the coronavirus disease 2019 Sentinel School Network project. P. O. has received funding by the Instituto de Salud Carlos III (Contrato Juan Rod es, JR18/00042), Madrid, Spain. The authors declare no conflicts of interest. 0022-3476/$ -see front matter. ª 2022 Elsevier Inc. All rights reserved. https://doi. org/10. 1016/j. jpeds. 2022. 05. 028.
muscosa in its southern California range, but its relevance has not yet been fully evaluated. The goal of this study was to quantitatively assess the relevance of this target by predicting the likelihood of the species persistence at a 50-year horizon, if the 20/50 target was met. To put this criterion into perspective and further inform the recovery plan's objectives and strategy, we also assessed the species viability for a broader range of numeric targets, which could serve as alternative objectives under different risk acceptance scenarios  (Sanderson, 2006; Schwartz et al. , 2018). For this purpose, we first performed a demographic analysis using 19 years of mark-recapture data collected on the extant populations of R. muscosa, followed by a population viability analysis to simulate species trajectories under a defined set of alternative scenarios.
| Mark-recapture data collection We used mark-recapture data collected annually from 2001 to 2019 at eight different sites (see Figure  2 ): (1) Bear Gulch, (2) Little Rock Creek, (3) Vincent Gulch, (4) Big Rock Creek and (5) Devils Canyon, located in the San Gabriel Mountains; and (6) City Creek, located in the San Bernardino Mountains (7) Fuller Mill Creek and (8) Dark Canyon, located in the San Jacinto Mountains. We note that a typical R. muscosa site in our sample represents an intermittent stream section of ca. 2. 75 km in length. A ninth location, Tahquitz Creek located in the San Jacinto Mountains was excluded from the dataset because it was only rediscovered in 2009 and has been known to be occupied by only five adult female frogs. The historical population that once existed on Palomar Mountain appears to be extirpated.
This analysis provided us with estimates of the standard deviation (on the log scale), across year and across sites, in population growth rates. Full details on the model used for this analysis are provided in Appendix A2. Finally, we estimated the carrying capacity of a typical R. muscosa site (i. e. , a section of stream of ca. 2. 75 km in length), which was used as the maximum population size that could be reached at any site in the simulations. For this analysis we used data from Little Rock Creek, because this was the only site where the population had shown enough growth to get close to carrying capacity. This population displayed an exponential growth during several years, followed by a deceleration and stabilizing phase at $600 frogs (Figure  3 ). This pattern suggests that the Little Rock Creek population approached carrying capacity at that time.
To estimate the carrying capacity parameter K, we used Ricker's parameterization of density-dependence  (Ricker, 1954)  in a discrete-time logistic growth model  (Eberhardt et al. , 2008) , as explained in Appendix A3. All these analyses were done in a Bayesian framework using Markov Chain Monte Carlo algorithms (MCMC) implemented in program JAGS through the rjags package  (Plummer, 2013)  in R (R Core Team, 2020). For each analysis, we ran three chains initialized with a different set of initial values. We used a burn-in period of 5000 MCMC iterations followed by 100,000 MCMC iterations to sample the posterior distribution. For parameter inference, we thinned the posterior samples at a ratio 1/10 samples, for a total of 30,000 samples across the three chains. Chain convergence was assessed visually with sample path plots and using the Brooks-Gelman-Rubin diagnostic "BGR"  (Brooks & Gelman, 1998).
Population growth at each time step varied among sites and across years according to the spatial and temporal variance quantified from the data analyses (see above). Using 1000 simulations for each scenario, we quantified the risk of extinction of the species as a whole, and of populations, using the following metrics: (i) risk of species extinction (i. e. , the probabilities of full-and quasiextinction of the species after 50 years, using N = 10 as the threshold for quasi-extinction); (ii) the probability that less than two populations will be above the quasi-extinction threshold after 50 years; (iii) the proportion of populations that went fully extinct after 50 years; and (iv) population persistence; (i. e. , the expected number of populations left after 50 years). We define a quasi-extinct population as one retaining a few (<10) individuals and a fully extinct population as one with no remaining individuals.