dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the default parameters, we finally obtained 5347 and 4507 small transcripts for S. enterica and E. coli respectively. These large amounts of detected small transcripts by APERO in S. enterica and E. coli also include presumably cleaved, processed or attenuated UTRs (5 UTR represent 12-13% of the total, 3 UTR 15. 5-24. 6%), antisense RNAs (34. 2-22%) and intergenic RNAs (which included orphans, divergent and primary small transcripts) that could be present in several isoforms (27. 5-25%) (Supplementary Figure  S5 ). Taking into account all these categories, the number of small transcripts detected by APERO is close to previous estimations (as an example >1000 antisense RNAs are detected in S. enterica and E. coli)  (39, 41, 42). Obviously, some of these small transcripts could correspond to transcriptional noise and  may be false positives.
However, among small transcripts detected by APERO in S. enterica, the highest quartile (271 highly expressed small transcripts) are expressed more than the large majority of the Kr öger et al. (32). Among these 271 small transcripts, 83% were not annotated by Kr öger et al. (Supplementary file S4) and we annotated 58 of them by querying Rfam or by searching similarity to E. coli (attenuators, riboswitches, etc. ). In addition, we evaluate the expression of these small transcripts in independent experiments by using the RNA-seq data from Kr öger (Supplementary file S4, supplementary Figure  S6 ): 64 are expressed at a higher level than adjacent genes. These highly expressed small transcripts are likely more than mere noise and may have important functions in gene regulation  (43). Identified transcripts have a median length of 181 nucleotides in E.
Comparison of small transcript detection methods We next compared APERO's performance with that of other available methods, including DETR'PROK  (28) , sRNA-Detect (  22 ), TLA from RNA-eXpress (  23 ), Rockhopper  (24) , ANNOgesic (  27 ) and the two in-house GL  (25)  and Nuss (26) methods, using the entire S. enterica and E. coli datasets and a list of experimentally validated sR-NAs (models) from these organisms (from ASAP/Ecogene, Ecocyc and RegulonDB databases for E. coli and BSRD database for S. enterica). We used these sRNAs as models for performance evaluation. Since several methods focused on sRNA identification from intergenic regions and antisense RNAs, only these two classes of small transcripts were analyzed (intergenic RNAs correspond to Orphans, Divergent and Primary RNAs in our annotation and antisense RNAs to Ai, 5 Ai and 3 Ai), i.
e. 3302 remaining RNAs in S. enterica and 2150 in E. coli. The corresponding experimental sets contain 84 sRNA models in S. enterica and 101 in E. coli (Supplementary File S2). We first compared the total number of small RNA candidates (intergenic and antisense) predicted by each method. This number varies widely according to the algorithm used, as described in Figure  2 , from a hundred (Rockhopper) to almost 20 000 (sRNA-Detect), with APERO providing intermediate figures. Identified transcripts have similar median sizes of 75 ± 20 nucleotides for S. enterica and 100±25 nucleotides for E. coli, except those of DETR'PROK, which are considerably longer. The transcript lengths found by APERO are generally longer than those of the other methods, which is expected if the latter tend to incorrectly cut the transcripts at internal sites where the coverage drops, as will be seen below.
Before looking at the transcript boundaries, we test how many experimentally validated sRNAs were detected by each method, by computing the recall (i. e. the fraction of detected sRNAs). Results are similar for both species, and APERO ranks second in terms of detec- tion ability, behind sRNA-Detect. However, the very high number of small transcripts detected by the latter indicates that many of them could be false positives; since we do not have the possibility to quantify the prediction specificity on these datasets, we now test the accuracy of the predicted sRNA boundary positions to evaluate the precision of each method, which was the primary objective of APERO.
This index represents the proportion of the predicted sRNA length that matches the corresponding annotation (see Materials and Methods), and thus allows representing the precision of each method over all model sRNAs in a single plot. Figure  3A  shows the recall values as a function of this number (i. e. retaining only the best predicted transcripts with an increasing threshold), which clearly demonstrates the accuracy of APERO. At a minimum Jaccard index of 0. 8, i. e. keeping only the most accurately predicted transcripts, it is the only method to recover well over 50% of the annotated sRNAs. Analyzing them individually (Figure  3B ) confirms that 8 S. enterica and 14 E. coli annotated sRNAs (out of 84 and 101 models respectively) are detected by AP-ERO alone, well over all other methods.
Among the larger S. enterica dataset of 203 sRNAs identified by Kr öger et al. (15, 32)  (not experimentally verified), 79 were detected by APERO with a Jaccard Index ≥0. 8, more than any other method (including ANNOgesic, Supplementary File S2) Taken together, these data demonstrate the preeminence of the APERO algorithm for full-length sRNA detection with accurate boundary identification and for detection of different isoforms of a single sRNA. A test of robustness was also carried by varying the parameters in APERO and other methods (Supplementary Figures  S9  and S10 ), which demonstrated its lower sensitivity to parameter variations.
Identifying and annotating new small transcripts We finally used the APERO method to identify sRNAs in D. dadantii from new sRNA-seq data (accession number Using the same parameter values as above on a standard dataset of size-selected RNA (<500 nucleotides), we detected 3974 small transcripts, with a median length of 132 nucleotides (Figure  4A ). We then tested the effect of Terminator EXonuclease (TEX) treatment on the detection of small transcripts. TEX was introduced to improve the detection of primary TSS positions in bacteria by selectively digesting fragments with monophosphate 5 ends, and thus enriching triphosphate 5 ends of RNA transcripts. Therefore, by using both +TEX and -TEX libraries, it is possible to discriminate between primary small RNA transcripts and processed small transcripts and to potentially identify the different isoforms of a single small transcript.
To quantify this amount, we reasoned that such degradation products would then tend to overlap one another more frequently than actual small transcripts, and we compared the effect of merging overlapping small transcripts in the output lists obtained from both datasets (+TEX/-TEX or -TEX). Indeed, only a small fraction (14%) of the 1703 primary small transcripts identified using TEX are eliminated by this procedure, this fraction is considerably higher (50%) in the 3974 small transcripts obtained in absence of TEX. In the following, we therefore consider that TEX increases the accuracy of small transcript 5 end detection and use the former dataset, keeping in mind that some true sRNAs produced by the endonucleolytic cleavage of mRNAs are discarded by this treatment. The 1703 remaining primary small transcripts could correspond to intergenic RNAs, 5 UTR or 3 UTR-derived RNA products (which may or may not be functional regulatory sRNAs in addition to attenuation or processing products), and antisense RNAs.
Hence, the APERO annotation module classified 526 of these 1703 as intergenic (div +P + O), 680 as antisense RNAs, 311 as 5 UTR and 108 as 3 UTR and 78 as both antisense and UTR (Supplementary Figure  S5 ). We now examine if some of these can be validated using existing annotations or experiments. 15 are detected with a Jaccard index >0. 9. PsrO is not detected (Jaccard index = 0). For the seven others, data analysis and previous observations suggest that the differences are due to discrepancies between the annotations inferred from E. coli and our data, rather than a failure of the algorithm (see text and Supplementary Figure  S11 ). ND = not detected. (B) Evaluation of APERO performance by RACE-PCR using 8 new predicted sRNAs. Jaccard indexes of the 8 sRNA candidates are shown. APERO clearly outperforms all other algorithms, with 6 accurate sRNAs (Jaccard ≥ 0.
coli annotations (Jaccard index>0. 9), whereas the remaining seven exhibit significant deviations. We examined each of these seven individuals, to understand if these deviations result from a failure of the algorithm or reflect a discrepancy between the annotation and our data. The summary of this analysis is given in Figure  5 , and snapshots of the RNA-Seq data are shown in Supplementary Figure  S11. One sRNA (CyaR) has a very low coverage, and thus presumably also a very low expression in our conditions. Another (RprA) is a RNAse E processed sRNA with a monophosphate 5 end  (19) ; accordingly, its 5 end was not detected after TEX treatment, but the shorter transcript detected by APERO is also present in the -TEX dataset and might thus correspond to an alternate form (Supplementary Figure  S11 ).
In three cases, in our data, the annotated sRNA is not transcribed alone, but co-transcribed with a neighbor gene: SraF is a pH-responsive riboregulator that resides in the 5 UTR region of the alx gene  (46) , and should thus be reannotated; PsrD and RyeB are co-transcribed with their downstream and upstream genes respectively (Supplementary Figure  S11 ), as previously observed  (12, 47) , and the boundaries given by APERO match those of the reported transcription units. For the two remaining sRNAs (RyeA and FnrS), the boundaries provided by APERO were validated by RACE-PCR. Altogether, the identified 5 and 3ends were accurate for more than 90% sRNA models (with Jaccard index > 0. 9). Discrepancies with the annotation deduced from E. coli thus indicate that the latter does not always match the transcriptional patterns present in our RNA-seq data and highlight the complexity of sRNA biogenesis in the living cell.
As a final test, we examine the validity of newly annotated small transcripts by RACE-PCR. We chose eight new candidates, whose boundaries were annotated differently depending on the algorithm used (Figure  5B  and Supplementary Figure  S12 ). Compared to RACE-PCR data, APERO annotations are accurate for six out of eight new candidates (Jaccard index > 0. 8), against two to three for other methods (Figure  5B , Supplementary File S5). Also, in contrast to APERO, all other algorithms strongly missed the boundaries of at least one small transcript (Jaccard < 0. 5). Distances between RACE-PCR and APERO annotations are mostly less than one nucleotide at the 5 -end and around twenty nucleotides at the 3 end (Supplementary File S5, Figure  5C ). Taken together, these validations clearly confirm the ability of APERO to accurately identify bacterial sRNAs.
DISCUSSION AND CONCLUSION In this paper, we presented a novel tool named APERO to detect small bacterial transcripts from paired-end sizeselected RNA-seq data. Rather than focusing on coverage depth as most of the available state-of-art tools, AP-ERO takes advantage of paired-end sequencing to localize the ends of fragments derived from the analysed transcript, with high precision. Because of this improvement, the method is less impacted by the strong coverage variations appearing within a transcript, and it identifies the 5 and especially 3 ends with more accuracy. An additional specificity of APERO is its capacity to identify precursor and matured sRNA isoforms of a single sRNA. Among the existing tools, Rockhopper (24) and DETR'PROK  (28)  were the first published methodologies. Rockhopper was not specifically designed for sRNA detection and DETR'PROK was designed for the use of short unpaired reads, and consequently exhibit the worst performances for the latter.
This last issue is especially corrected by the APERO algorithm, leading to the significantly better performances exhibited in the Results section. As an example, APERO not only identified 144 sRNAs of the 185 experimentally validated sRNAs in both E. coli and S. enterica, but in addition, for >75% of these sRNAs, the distances between annotated boundaries and APERO detected boundaries are <20 bp. The APERO algorithm was designed to be applicable on standard RNA-Seq data after selection of small transcripts (and no fragmentation step), but also preferentially after Terminator Exonuclease (TEX) for improved TSS identification (as demonstrated using the D. dadantii dataset). It is also less sensitive to variation in parameter values, and altogether, can thus be considered as the most robust and accurate method for the detection of bacterial small RNAs.
ACKNOWLEDGEMENTS S. Leonard  received a doctoral grant from the  French Ministère de l'Education nationale de l'Enseignement Supérieur et de la Recherche. The authors thank  Vincent Lacroix  and  Hubert Charles  for helpful discussion and  Camille Villard  for her helpful technical assistance. S. Lacour  would like to thank  Fasteris SA (Geneva)  for its valuable technical assistance in the preparation of the sequencing library of small RNAs.
DATA AVAILABILITY APERO R package: https://github. com/Simon-Leonard/APERO; https://zenodo. org/record/2536767#. XDc6xlxKhPY APERO Galaxy module: http://bioinfo. insa-lyon. fr S. enterica dataset: SRA accession number SRX1036363. E. coli dataset: SRA accession number SRX4670654. D. dadantii dataset: SRA accession number SRX4664132.
e88 15 PAGEFigure 3. 153 Figure 3. Performance assessment on different datasets. (A) Recall of each method is plotted against the Jaccard index (indicating the accuracy in boundary identification) for S. enterica (left) and E. coli (right) data. (B) Venn diagrams of models detected by APERO, ANNOgesic (27) , TLA from RNA-eXpress (23) , GL (25)  and Nuss (26) algorithms, with a Jaccard index ≥0. 8. 8 models in S. enterica (out of 84 models) and 14 models in E. coli (out of 101 entities) are detected only by APERO.
Pulse field gel electrophoresis (PFGE). PFGE using XbaI restriction enzyme was carried out with a CHEF-DR III system (Bio-Rad), according to the PulseNet Protocol  18. Salmonella enterica serotype Braenderup H9812 was used as the molecular size marker in the PFGE experiment  19. Gels were stained with ethidium bromide and banding patterns were visualized under UV light, using the Gel Doc XR and Quantity One software (Bio-Rad). DNA patterns were analyzed with BioNumerics software (V 7. 6. 3, Applied Maths, Kortrijk, Belgium). Algorithms available within the program were used to compare patterns. Dendrograms were produced, using the Dice coefficient and the unweighted pair group method with arithmetic averages (UPGMA), with a 1% tolerance limit and 1% optimization  20. Each PFGE pattern differing by at least one band from a previously recognized type was considered to be a new pattern.
Bioinformatics analyses. Illumina sequences were processed using the FROGS pipeline (Find Rapidly OTU with Galaxy Solution)  24  implemented on a galaxy instance (http://migale. jouy. inra. fr/galaxy/). Bacterial 16S rRNA paired-end reads were merged with a maximum of 10% mismatches in the overlap region using FLASH  25. Denoising procedures consisted in discarding reads with no expected length and the ones containing ambiguous bases (N). After dereplication, the clusterisation tool ran with SWARM  26  with an aggregation distance equal to 3. Chimeras were then removed using VSEARCH  27  and sequences were filtered to keep OTUs (also called clusters) accounting for at least 0. 005% of all sequences  28. Taxonomic affiliation was performed with both RDP Classifier  29  and Blastn+ 30 against the SILVA 132 pintail 100 database  31.
Data set was rarefied and alpha diversity indexes (observed OTU, Chao1, Shannon and InvSimpson) were calculated and further plotted using the R "phyloseq" package  32. Principal coordinate analysis (PCoA) were performed on weighted unifrac dissimilarity matrices calculated using the R package phyloseq. Using non-parametric permutation-based multivariate analysis of variance (PerMANOVA, function adonis in R package "vegan") on unifrac and weighted unifrac-based distance matrices, we tested for significant differences in community structure. Assumptions of the adonis test were verified using the betadisper function in the R package vegan, which tests the multivariate homogeneity of group dispersions. Pairwise comparisons were performed on weighted unifrac distance matrix using pairwise. adonis function from "vegan" package and Bonferroni p-value correction (significance for p < 0.
05). Figures were prepared with R and the package ggplot2. Differentially abundant taxa in bacterial populations depending on various factors were identified using the linear discriminant analysis (LDA) effect size (LEfSe)  33. Relative abundances of all features were first compared by using the nonparametric Kruskal-Wallis rank sum test (significance for p < 0. 05), and each statistically significant feature was further subjected to effect size estimation using LDA.
conclusion In this contribution, original data on Salmonella prevalence and associated microbial ecology in a pig slaughterhouse were collected. The high prevalence of the monophasic variant of Typhimurium and the dominance of the Moraxellaceae family along the slaughtering chain were highlighted. Additional 16s rDNA analyses of microbial ecology should be performed over years in this slaughterhouse and also extend to a larger number of pig slaughterhouses in order to confirm such results. Complementary experiments should also be conducted to assess the existence of potential specific interactions between Salmonella and resident bacteria identified here (as inhibition with Enhydrobacter or positive interactions with Acinetobacter for instance). Interestingly, C&D procedures did not lead to the selection of Salmonella with higher resistance toward antibiotics and biocides but markedly impacted the composition of bacterial populations associated to slaughterhouse surfaces.
From these observations, it seems essential to study antimicrobial resistance dynamics in industries by integrating the whole bacterial community using global approach as shotgun metagenomics. Indeed, resident flora (as Psychrobacter here) constitutes the best illustration of bacterial adaptation in a given area and could participate to the dissemination of antimicrobial resistance throughout interactions with pathogens. Overall, data collected here are important for the construction of a realistic vision of Salmonella ecology and AMR emergence in food industries with a focus on the potential role of C&D procedures.
Sorption calorimetry measurements A double twin isothermal microcalorimeter was used to study the thermodynamics of the water vapor sorption. 29 The instrument involves a two-chamber calorimetric cell, with a sorption chamber that contains the dry sample and a vaporization chamber in which liquid water is injected to start the sorption experiment. The chambers are connected by a tube. Water vaporizes in the vaporization chamber and diffuses through the tube to the sorption chamber where it is absorbed by the sample. The calorimetric cell is inserted into a double twin isothermal microcalorimeter that separately measures thermal powers released or absorbed in the two chambers. The data for each experiment were recorded for ca. 14 days at T = 27 1C. The experimental setup can be considered a continuous titration of an initially dry sample with water vapor.
For density profiles and observables derived thereof data were gathered from 10 ns production runs of each of the five systems, amounting to a total of 50 ns. The repulsive pressure P was obtained from the shift Dm in the water chemical potential according to eqn (2). To determine m = m ex + m id , its excess and ideal contributions, m ex and m id = k B T ln(r w L 3 N A /M w ), were measured independently, where r w is the water density, L E 0. 0237 nm is the thermal wavelength of water at T = 300 K and N A is Avogadro's number. While m by definition is constant over the simulation volume in thermal equilibrium, m ex and m id are not. Due to the inhomogeneous water distribution perpendicular to the membrane surface, m ex (z) and m id (z) via r w (z) are functions of the perpendicular coordinate, z.
Results and discussion TMAO and urea are commonly-studied co-solutes, due to their abilities to stabilize and destabilize, respectively, the native fold of proteins. 46 In phospholipid bilayer systems, TMAO has been shown to be repelled from the lipid headgroup region, while urea has a slight affinity for the bilayer surface. 23 Here, the effect of TMAO and urea as well as NaCl on the hydration of phospholipid multilayers is investigated. Two lipid species, one with saturated chains and one with partially unsaturated chains, dimyristoyl-phosphatidylcholine (DMPC) and palmitoyl-oleoylphosphatidylcholine (POPC), respectively, are studied. First, the experimental data obtained via sorption calorimetry and sorption balance are presented. This is followed by a description of the simulation results and their comparison with the experimental data.
Such enthalpy-driven exclusion of uncharged solutes from macromolecular surfaces was earlier found to be important also for the stabilization of proteins by osmolytes. 60. 3. 5 Influence of the simulation forcefields. It has previously been suggested that the Berger lipid forcefield exhibits unrealistically high affinity for Na + ions. 55  Indirect comparison to experimental data by means of the salt-dependent head group order parameter suggests that the CHARMM36 lipid forcefield reproduces the salt adsorption more realistically. 55  In order to evaluate the robustness of the present simulation results with respect to the choice of the forcefields, we performed additional simulations employing the CHARMM36 forcefield for the lipids and NaCl in combination with the TIP3P water model. Fig. 7A  shows a comparison of the NaCl density profiles at the POPC interface for the Berger-based versus the CHARMM36based forcefield combinations.
Acknowledgements E. Schneck  acknowledges support by the  Max Planck Society  and by the  German Research Foundation  ( DFG) via Emmy-Noether grant  ( SCHN 1396/1 ). The  DFG  is gratefully acknowledged also for financial support (R. R. N. and A. W. -K. ) via grant  SFB1078. E. Sparr  and Q. D. P. acknowledge support by the  Swedish Research Council (VR) , both through regular grants and the  Linnaeus Center of Excellence  '' Organizing molecular matter ''. We thank  Lars Wadso  ¨and  Vitaly Kocherbitov  for help with the sorption calorimetry measurements. We kindly acknowledge  Håkan Wennerstro  ¨m for fruitful discussions. Open Access funding provided by the  Max Planck Society.
METHOD DETAILS Fabrication of Silicon Master Mold Optical lithography was performed on silicon wafers and carried out using the cleanroom facilities of the London Centre for Nanotechnology. Silicon substrates were cleaned through repeated washes with acetone and isopropanol, and covered with Su-8 2050 (Microchem). The photoresist was spun with a spin-coater, according to the manufacturer's instructions, to achieve a uniform thickness of 50mm. The soft-bake step, required after spin-coating to evaporate the excess of solvent and to densify the resist, was performed by placing the wafers for 3 minutes on a hot-plate at 65 C and, sequentially, at 95 C for 8 minutes. The photoresist is exposed to a pattern of intense UV light through a photomask. The UV exposure time needed to crosslink the SU-8 was calculated by dividing the exposure energy (mJ/cm 2 ) indicated in the Su-8 data sheet by the light intensity of the Karl Suss MJB3 mask aligner (20 mJ/cm 2 ).