dataesr/scientific-paragraphs-categorization huggingface dataset:
Strain dataset for the phylogenetic analysis of the Sma complex We collected the genome sequences of strains belonging to the Sma complex from various geographic regions and isolation sources (see Methods and Fig. S1 ). We isolated 108 Sma strains from soil samples collected in Japan and France and 117 Sma clinical strains from multiple hospitals in Japan and France and obtained their draft genome sequences. In addition, we collected the genome sequences of 644 strains from the NCBI database that were registered as species/subspecies belonging to the Sma complex along with their metadata (countries and sources). The type strains of the Sma complex species and completely sequenced strains were included in the dataset even if sufficient metadata were not recorded. After quality filtering and removing strain redundancy, the final dataset included 775 strains (225 sequenced in this study and 550 from the NCBI database; Table  S2 ).
sequences showed >99 % identity to that of strain SM39, and we collected the genome sequences of strains registered in the NCBI database as the species/subspecies belonging to the initially defined Sma complex using a >95 % ANI threshold. However, the minimum ANI scores between clades were below 95 % in several cases (indicated by black and grey filled circles in Fig. 3 ). This result indicated that our strain set included more diverse strains than we initially expected and highlighted the difficulty of the species identification of strains belonging to the Sma complex and its close relatives.
Core-gene-based phylogenetic analysis of the Sma complex and the relationships between clades and known species Next, we performed a core-gene-based phylogenetic analysis of our strain dataset (Fig. 4 ). By mapping the clade information to the phylogenetic tree, we confirmed that the strains belonging to the same clade clustered together and formed monophyletic clusters. The type strains of Sma (ATCC 13880 T ) and Sne (CGMCC 1. 6853 T ) belonged to clades 11 and 10, respectively. Although the type strains of Sma_sak (DSM 17174 T ) and Sur (CCUG 50595 T ) were not included in the strain set because their sequences were not available when we designed this study, the phylogenetic analysis of these type strains with representative strains of each clade revealed that the Sma_sak type strain belonged to clade 11 together with the Sma-type strain and indicated that the Sur-type strain belonged to clade 12 (Fig.
S2 ). The former finding is consistent with a recent report  [22]. Notably, strain CRK0003, which was registered as Sne in the database, belonged to clade 3 (not to clade 10) and showed 94. 95 % ANI to the Sne-type strain belonging to clade 10 (Fig. S2 ). On the other hand, strain K27, which was registered as Sma_sak, belonged to clade 10 and showed 98. 44 % ANI to the Sne-type strain but 96. 72 and 96. 69 % to the type strains of Sma and Sma_sak, respectively. Clades 13 (n=9) and 14 (n=1) were distantly related to the other clades; their ANI scores relative to all other clades were lower than 94 %. The phylogenetic analysis of these strains with representative strains of other clades and the type strains of species closely related to the Sma complex (Ssu and S. ficalia) revealed that clade 13 corresponded to Ssu (Fig.
Distribution of the pig gene cluster The production of a red pigment known as prodigiosin is a well-known characteristic of Sma  [1] , but it was reported that the prodigiosin biosynthesis gene cluster (pigA -pigN) is not present in all Sma strains  [54]. The analysis of the distribution of the pig gene cluster in our strain dataset revealed that the pig gene cluster was distributed only in clades 10 (corresponding to Sne), 11 (Sma and Sma_sak), 13 (Ssu) and 14 (Fig. 4 ). All pig-positive strains contained a full set of pig gene cluster comprising 14 genes in the chromosome region upstream of the copA gene encoding a cupper-translocating P-type ATPase. The nucleotide sequences of these gene clusters were highly conserved, showing >98. 2 % identity to the cluster of Sma strain ATCC 274  [39] , except for the genes of clade 14 (92.
4 %) and one clade 10 strain (ADJS-2C_Purple, SAMN08373108; 80. 4%), but notably lower similarities (<60 %) to the pig gene cluster found in the Serratia sp. strain ATCC 39006  [55]. The low sequence identity of the ADJS-2C_Purple genes appeared to be due to low sequence quality of this chromosome region. These findings indicate that the pig gene cluster was acquired by the common ancestor of the Sma complex and has been deleted from most clades in the Sma complex. The distribution of pig-negative strains in the phylogenetic tree shown in Fig. 4  indicated that the deletion of the pig gene cluster occurred at least five times in the strain set analysed in this study (indicated by arrows in Fig. 4 , including the deletions in three sublineages/strain in clades 10, 11 and 13).
Archive (SRA) accession numbers DRR253307 to DRR253531. †These authors contributed All supporting data, code and protocols have been provided within the article or through supplementary data files. Nine supplementary figures and twelve supplementary tables are available with the online version of this article. 000793 numbers DRR253307 to DRR253531. All supporting data, codes and protocols have been provided within the article or through supplementary data files. Twelve supplementary tables and nine supplementary figures are available with the online version of this article.
The wild-type laboratory strain GJV1 (strain 'S' in  [52] ) is derived from DK1622 via a small degree of sub-culturing and differs from it by five mutations  [48]. DK1622 (GenBank accession number CP000113) is a mutant of the natural isolate DK101 (also called FB) which was constructed to restore full function to a motility system that had acquired a mutation during the laboratory culturing process following isolation  [90, 91]. The sample of M. xanthus strain FB referred to as DK101 was obtained from the University of California, Berkeley culture collection  [90, 92, 93]. Strain FB (ATCC 25,232) was sourced from strain Beebe 1941 (ATCC 19,368), which was isolated from soil in the vicinity of Ames, Iowa around 1941  [94, 95]  and which has since been lost, making FB the new ancestral laboratory strain for this species.
Plasmids and mutant construction We disrupted csgA (NCBI DK1622 locus tag MXAN_ RS06255, old locus tag MXAN_1294) at the same nucleotide position in M. xanthus strains GJV1, Chihaya 20, Serengeti 01, GH3. 5. 6c2, and MC3. 5. 9c15 by plasmid insertion, without otherwise altering the native csgA sequence of each strain. We transformed each strain with a plasmid containing a fragment of the strain's own csgA allele. We amplified all csgA fragments for the different strains from the identical region of csgA using the same two primers, which pair to fully conserved csgA segments (see Additional file 1: Fig. S3A ). We selected PCR primer sequences in the conserved csgA segments by aligning the csgA sequence of the published M. xanthus DK1622 genome (NCBI:txid246197) with natural-isolate genomes (SRA accession numbers: SRR8298023, SRR8298022  [50, 51] ; Additional file 1: Fig.
Data analysis We performed all data analysis and statistical testing using R version 4. 0. 0 and RStudio version 1. 2. 5042. We visualized the data using the ggplot2 package  [99]. Original data files and analysis protocols, including statistical scripts, R Markdown files, and full results of statistical tests, may be accessed via Dryad (https:// doi. org/ 10. 5061/ dryad. fbg79 cnsb). For the csgA mutants in the developmental assays, we calculated the one-way mixing effect where "sporulation efficiency" refers to the fraction of cells inoculated that became spores, and the relative fitness in mixture as in Vos and Velicer  [28]. Here, 'i' refers to the mutant and 'j' refers to the cooperator. We analyzed our experimental results using general linear models followed by post-hoc Tukey HSD tests, Dunnett tests, or t-tests, as reported in the results.
Funding Open access funding provided by Swiss Federal Institute of Technology Zurich. This study was supported in part by Swiss National Science Foundation (SNSF) Grants 31003A_160005 and 310030B_182830 to GJV and an ETH Fellowship 16-2 FEL-59 to MV. The funding bodies played no role in the design of the study, the collection, analysis, or interpretation of data, or in the writing of the manuscript.
II. MATERIALS AND METHODS The visual search task is exactly similar to that described in the experimental setup for the original What/Where model  [16] : A random handwritten digit will be placed inside a screen with added clutter and noise, and the agent's mission will be to classify the digit; as in determining its label. However, the digit will be placed in a random position and the difficulty of the task will be modified according to two parameters, the eccentricity; the digit's distance from the center point of the image, and the contrast, or the digit's visibility relative to the background. The larger the eccentricity and the lower the contrast, the harder the task. Training datasets are prepared, and networks are implemented in Python, using the high-performance deep learning framework "PyTorch"  [22].
A. Datasets The MNIST database  [5]  is used for this task. It consists of a set of 70000 grayscale images of handwritten digits of size 28 × 28 split between 60000 training examples and 10000 validation examples. For the purpose of this application, three variants are prepared, the 28×28 Noisy dataset, where images keep their original size and are mixed with synthetic textures of random noise in the background  [16] ,  [26]  (see Fig. 1a  for some examples), the second one; the 128 × 128 Noisy dataset, where MNIST images are embedded randomly inside a larger 128 × 128 image that contains a circular mask (of radius 64) of random textures (Fig. 1b ), and finally, the compressed 128 × 128 noisy dataset, in which we use two banks of filters disposed on a log-polar grid to linearly transform the original feature map of size 128 * 128 = 16384 into a compressed form.
The first bank has 768 predisposed filters and the second one has 2560, this provides a compression rate of approximately 95% (1-768/16384) and 85% (1-2560/16384), respectively. It is also worth mentioning that the original What/Where model has a compression rate of about 83%  [16] , which can be helpful to test and benchmark Spatial Transformers on roughly the same compression rate and also on a higher one. In order to visualize the compressed version of the dataset, it remains possible to represent it in the visual space using the pseudoinverse of the transform (Fig. 1c ). The digit's eccentricity can vary between 0 and 40 pixels in all the datasets, forcing the digit to fit entirely inside the circular mask in the case of the 128 × 128 image and sometimes making the task impossible in the case of a 28 × 28 image.
B. Networks To natively compare the performance of the What/Where model with a Spatial Transformer Network (STN), i. e. , a Spatial Transformer augmented DCNN classifier, four different STN architectures are created. The first one; The STN 28x28, serves only as a comparison with the What module of the What/Where Network to test the robustness of a Spatial Transformer on a small generic dataset from the original task (e. g. , by classifying only a 28 × 28 pixels image), the other three compare to the full What/Where architecture and each one of them presents an important feature, one vanilla STN is parametrized to detect all types of affine transformations; scaling, rotation, and translation, the STN 128x128. The second one; The ATN (Attention-only spatial Transformer Network), is restricted only for attention, i.
A. Spatial Transformer Network Vs. The Generic "What" pathway After training, the STN 28x28 was able to achieve a central accuracy of 88% and a general accuracy of 43% on this dataset, compared to 84% and 34% from the Generic "What"  Pathway  [16] , the central accuracy is defined as the performance of the network when the digit's eccentricity is set to 0, the general accuracy is when the digit's shift can vary up to 15 pixels. Overall, this suggests that we have an improvement in the accuracy of the classifier. Next, digit's coordinates are fixed according to a grid of 55 × 55 pixels (for a 28 × 28 pixels image) and the accuracy value that corresponds to every possible position on this grid is measured, defining an accuracy map, like described for the What pathway  [16]. This accuracy map is calculated for the STN 28x28 and is represented on Fig.
B. Spatial Transformer Networks Vs. The What/Where model The three architectures; STN 128x128, ATN and POLO ATN, were benchmarked on eccentricities ranging from 0 to 40, on each of the three different following contrasts; 0. 7, 0. 5 and 0. 3. Classification accuracies are represented alongside the performance of the What/Where model on the same dataset parameters (see Fig. 3 ). Generally speaking, we can observe that the STN 128x128 and the ATN architectures have higher overall accuracies on all eccentricities and contrasts compared to the What/Where model (with 1 saccade). Next, and for these two networks, small to no difference in performance is observed between contrasts 0. 7 and 0. 5, followed by a decrease for a contrast of 0. 3. Another important feature that can be observed from these two architectures is that eccentricity does not affect the classification rate, i.
7 and 0. 5, providing a remarkable stable performance up to an eccentricity of 22 pixels despite the Log-Polar compression rate. In order to investigate the attentional mechanism and the inner workings of each of the three proposed architectures, dataset images with different varying eccentricity values (a maximum of 40 pixels) and different varying contrasts were transformed using the trained spatial transformer module. First, and in the case of STN 128x128, we can observe that the Spatial Transformer is going to center the digit by creating another warped 128 × 128 pixels version of the original feature map (see Fig. 4a ), even when the eccentricity is at its maximum and the task becomes harder, it is capable of centering the region of interest. Second, for the ATN, we can see that the transformer is capable of attending to the digit and centering it on the small 28 × 28 grid that will be fed later to the classification network (see Fig.
4b ), in the same manner as the STN 128x128, and even when the contrast is fixed to 0. 3 and the digit is barely visible, the ATN network will be able to localize the digit inside the 128 × 128 screen. Finally, and to test the POLO ATN architecture, the conv POLO ATN 85 was tested on the hardest setup for this particular dataset, a totally random image, taken between 0 -40 pixels and 30 -70% for the eccentricity and the contrast, respectively. For the majority of cases that were taken, the network was able to bound the digit inside its sampler, centering it perfectly in some cases and close calling its position for the remaining, and sometimes totally missing it out (see Fig. 4c ).
IV. DISCUSSION When comparing the vanilla "What" Network to the STN 28x28; both using a LeNet architecture for classification, we have demonstrated the effectiveness of using a Spatial Transformer module on the classification rate for this dataset, a significant improvement is obtained in the accuracy map giving the classifier robust spatial invariance to affine transformations in feature maps, which explains higher accuracies over translated digits. Although placing this module at the beginning of the "What" network for future applications should yield its benefits in performance, it should be noted that it accumulates a certain computational complexity on the architecture overall (by adding more layers). However, this problem can be solved by sharing parameters between the classification network and the module itself  [11].
Next, and although STN 128x128 and ATN perform exceptionally well on this dataset and outperform their counterpart; the What/Where model, they are more computationally costly as they process the full 128 × 128 image instead of the log-polar compressed version. It should be emphasized that although the ATN architecture limits the number of transformations to attention only and introduces a downsampling mechanism, the difference of performance with the STN 128x128 is considered minimal, meaning that this architecture should be privileged when thinking in terms of localizing the object in visual space with foveated vision like in mammals. Regarding the POLO ATN architecture, the loss of information in the peripheral zone for log-polar coordinates explains the decrease in performance with the eccentricity, this is similar to the What/Where model and to all architectures that use Log-Polar entry coordinates.
When using a DCNN classifier inside the Spatial Transformer, the POLO ATN architecture was able to surpass the What/Where model and to gain a stable performance for lower to mid eccentricities, even when the loss of information in the Log-Polar visual space is considered, all this inaugurates the POLO ATN as a viable candidate for a "Where" network that localizes objects in this particular visual search setup. Finally, the difference between the two paradigms should be considered, on one hand, the What/Where model uses an actor/critic framework to determine a focal accuracy-seeking policy while training the agent, and works on the dataset in a sequential manner, i. e. , scanning the visual environment, followed by the best action (a saccade) that maximizes its information gain. On the other hand, Spatial Transformer Network follows the classical Deep Learning paradigm, totally differentiable, they learn end-to-end how to map each input to its appropriate linear spatial transformation in an unsupervised manner and solely based on a classification criterion during training, this does indeed make learning the task at hand easier but adds more hyper-parameters to be controlled to the learning process.
Most of these functionalities are strain-dependent suggesting that the wide range of health-promoting properties of STH results in a high degree of variation in the content of strain genomes. Analysis at the genome level is, thus, required to better understand strain features. Thanks to the democratization of lowcost sequencing systems, the number of sequenced bacterial genomes has boomed in the last decade. For instance, among the genus Streptococcus, around 8,000 Streptococcus pneumoniae, 2,000 Streptococcus pyogenes, 1,000 Streptococcus agalactiae, 100 S. salivarius, and 10 Streptococcus macedonicus genomic sequences are available in the NCBI genome database (https://www. ncbi. nlm. nih. gov/genome/). The rst complete genome sequence of STH dates back to 2004  (15)  and ever since more than 100 genomes are now publicly available at NCBI.
Results & Discussion General genomic features of STH Among the STH available genomes in NCBI database in January 2020, 45 were selected according to their nishing level to explore the STH health-promoting properties: 27 genomes have been fully sequenced, 8 have been completely sequenced at the chromosome level, and the remaining 10 genomes have nished draft (Table  S1 ). We completed this dataset with the complete genome sequences of strains from CIRM-BIA collection (31 genomes) and MicroScope database (3 genomes). The 79 genome sequences selected correspond to strains isolated over a long time period (1954 to 2018) from diverse products (including cheeses, yogurts, fermented milks, starters, ke r grains, and digestive tract of marine sh), and different geographic locations (19 countries) providing a good sample of the overall species diversity of STH (Table  S1 ).
This result is higher than previously reported (10%) and shown by a PCR-based screening of the presence of gadB on a set of 191 strains mainly isolated from Italian raw-milk cheeses  (80). The gadBC genes are surrounded by two copies of IS6 (IS1216) family transposase (98% identity at the nucleotide level) and are part of a larger locus anked upstream by luxS coding for AI-2 synthesis protein, and downstream by rnY coding for ribonuclease Y (Figure  5A ). The gadBC-containing locus, which is predicted as a region of genomic plasticity by the MicroScope database, appears to be variable in STH. It contains numerous IS transposases and speci c genes in a strain-dependent fashion. A chromosomal region containing only genes luxS and rnY is present in S. salivarius and S. vestibularis, two species that share a common ancestor with STH (2).
The capability of STH to synthesize the seven other B-group vitamins was also evaluated. All STH strains with complete genome sequence have incomplete de novo thiamin (vitamin B1), ribo avin (vitamin B2), pantothenate (B5), pyridoxine (vitamin B6) and biotin (vitamin B8) biosynthesis pathways (data not shown). This analysis con rms at a larger scale vitamin B2 and B5 auxotrophy of STH determined by single omission experiments  (66, 96). However, the presence of parts of the biosynthetic pathways suggests that STH can produce certain vitamins from extracellular intermediates provided by other bacteria in mixed cultures. For example, the presence of genes coding for pyridoxal kinase (EC 2. 7. 1. 35), pyridoxamin-phosphate transaminase (EC 2. 6. 1. 54) and pyridoxal 5'-phosphate synthase (EC 1.
Resistome and Virulome The resistome of 79 STH genomes was investigated using ABRicate with multiple databases. Only two genes potentially conferring resistance to antibiotics were detected in nine strains. Four strains, B59671, CIRM-BIA1047, CIRM-BIA1050 and KLDS 3. 1003, encode mefE which is 83% identical with 95% coverage at the nucleotide level with mefE of Streptococcus pneumoniae. The mefE gene codes for a proton e ux pump that confers resistance to macrolides including erythromycin  (149, 150). S. pneumoniae mefE gene is localized on a conjugative transposon-related element with mel, which is required for the maximal mefmediated e ux of erythromycin  (149). No mel homologs were evidenced in STH genomes, suggesting that mefE is likely not able to confer high-level macrolide resistance in STH (Table  S5 ).
A tet(S) antibiotic resistance gene was detected in ve other STH strains (CIRM-BIA368, CIRM-BIA961, CIRM-BIA967, CIRM-BIA998 and M17PTZA496) by four databases (NCBI AMRFinderPlus, Res nder, ARG-ANNOT, CARD). Tet(S) confers resistance to tetracycline with a ribosomal protection protein  (151). The STH tet(S) gene is 100% identical with tet(S) from multiple species and is anked by some mobile element proteins in STH genomes, which argue for an acquisition via HGT (Figure  S4B ). Resistance to tetracycline of strain M17PTZA496 has been previously demonstrated suggesting that the four other tet(S)-containing strains are also tetracycline resistant  (152). The possibility of antibiotic resistance gene transmission from food bacteria to microbiota is a serious issue. However, no transfer of tet(S) from strain M17PTZA496 was detected due to its chromosomal location  (152).
Genomic features MicroScope Pan-genome analysis tool was used to identify core, accessory, and unique protein families within 79 selected strains with the 80% amino acid identity and 80% alignment coverage option. Protein sequences were extracted and submitted to eggNOG-mapper to obtain COG classi cation of core and accessory genome  (154, 155). A presence / absence gene heatmap was created from accessory genome composition with R libraries pheatmap and dendextend  (156). Protein sequences of core proteome were gathered from Microscope and proteins duplicated in at least one strain were discarded. Sequences were concatenated strain by strain and aligned with MAFFT online version in auto mode  (157). Alignment was submitted to MEGA software to calculate a pairwise distance matrix  (158, 159).