dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of other cytokines such as IL-1 or IFNγ would be of interest to refine our data and requires further studies. An important result of our study is that alterations of gut permeability and inflammation occurred in the presence of a very early and transient dysbiosis in the rat AIA model. This provides three important information: (i) intestinal alterations occur before joint involvement, (ii) dysbiosis occurs in parallel to intestinal permeability and inflammation, and (iii) dysbiosis is not necessary for the maintenance of the disease in the AIA rat model. These Fig. 5  Systemic bacterial translocation in AIA rats. Plasma LPS levels were measured in control and AIA rats at the preclinical (A), onset (B), and acute (C) stages. At day 4 (preclinical), 11 (onset), and 28 (acute) Q-RT-PCR analyses of TLR-4 (D,E,F) gene expression were performed.
Serum-soluble CD14 levels were measured at the preclinical (G), onset (H), and acute (I) stages. Values are expressed as means ± SEM (n = 14/15 per group). ***(p < 0. 0001). n. s. , not significant data indicated that dysbiosis could be required to induce changes in the intestinal barrier and could modulate the onset of arthritis. This was suggested by the dysbiosis in animal models of SpA such as the B27 transgenic rat model, having intestinal inflammation but in which arthritis is not systematically present  [31] , and in patients with SpA  [32]. Is intestinal dysbiosis the trigger for the observed intestinal alterations? This attractive hypothesis needs to be tested. Indeed, a recent publication in an animal model of rheumatoid arthritis showed that dysbiosis alone was not responsible for the increase in intestinal permeability and that dysbiosis did not occur in the early stage of the disease  [33].
Datasets In this study, we used three different datasets. The first included all types of amyloids with the known influence of repeats on aggregation propensity (Table  1 , extended version in Supplementary Materials, Table  S1 ). The initial dataset of amyloids was downloaded from AmyPro  (Varadi et al. , 2018). Then, we examined the amyloid sequences using RADAR  (Heger and Holm, 2000)  to extract amyloid proteins with repeats. If any repeats were detected in a protein, Google Scholar was searched with queries like "[ProteinName] amyloid repeats". We also checked queries like "Amyloid repeats" in order to find more examples for Table  1 , which were not included in AmyPro. No other computational work was performed on this dataset. The data were only treated as an overview of available information which may suggest potential influence of tandem repeats on protein aggregation, hinting at molecular mechanisms and interactions in play.
The second dataset (Table  2 , and its extended version in Supplementary Materials Table  S2 ) was collected on the basis of a few classical review publications specifically regarding functional amyloids (Otzen and  Riek, 2019 , Levkovich et al. , 2021). We extended these data by searching Google Scholar and including other instances. This provided us with the main dataset used for the quantitative study of functional amyloid proteins in bacteria. The third dataset included all known bacterial proteins, with no regard to their possible amyloid propensity, which may be unknown. It was downloaded from UniProt (UniProt Consortium, 2015) by applying filters "Taxonomy:Bacteria" and "Protein Existence:protein level", and gave 39 526 sequences (as of 5 Sep 2022). We discarded sequences with lengths above 50 amino acids (1387 sequences) and those causing utf-8 encoding problems (81 sequences).
Amino acid profile The amino acid profile was created for four datasets entire bacterial protein sequences (the reference data set from Uniprot) -BS, all units from the repeats found by RADAR for all bacterial sequences -BR, entire amyloid sequences from Table  2  -AS, all repeat units from the repeats found by RADAR for bacterial amyloid sequences -AR. For each dataset, we took each sequence and calculated the frequency of appearance of each amino acid f_aa. Then we calculated the average f_avg with respect to all sequences in the corresponding dataset. The statistical analysis of f_aa vectors was performed using the Mann-Whintey U test, which is nonparametric and rank based. It is frequently used as an alternative to a t-test for mean equality. It tests if two samples have different stochastic ordering  (McKnight and Najab, 2010).
Results In the first step, a literature search was carried out to find experimentally verified examples of amyloid proteins with tandem repeats. A set of instances was selected, for which the role of repeats in a sequence was studied in the context of protein amyloidogenicity. It was usually examined by means of protein mutants with a modified number of repeats. Unfortunately, not many such studies have been conducted so far. However, we have not found any study discarding possible influence of tandem repeats on protein aggregation propensity. In total, we found 15 amyloid proteins reported in this context (Table  1 ) in which the role of repeats in their amyloidogenicity was experimentally confirmed and characterized. This dataset was solely treated as an inspiration in search for a clue to possible molecular effects of tandem repeats in amyloids.
Next, we built the second dataset consisting of bacterial functional amyloids whose amyloid propensity was experimentally confirmed, even if little was known about the location of their amyloidogenic parts and no studies of potential tandem repeats were performed. As a result, we collected the dataset of 38 proteins from bacteria (Table  2 ) whose amyloid activity was experimentally determined. Among these 38 proteins, 5 were too short for repeat analysis (the length below 50 amino acids). They were phenol-soluble modulins and N-AgrD proteins. Therefore, we included 33 proteins in our analysis. The selected 33 proteins varied in the structure and function. Among them there were very long adhesive proteins (Aap, Bap, Esp, PAc) with lengths even up to 2397 amino acids, characterized by the appearance of the LPXTG motif responsible for membrane binding.
They often undergo cleavage, which may lead to the release or exposure of an amyloidogenic fragment of the protein. Other biofilm-related proteins included TasA, TapA, FapC, FapB, WapA, FadA, Mtp, SuhB. In the dataset, we also observed proteins with a prion-like character (microcin, Hpag) and those related to RNA and DNA binding (Hfq, Tuf, CarD). We found that only 8 of these 33 proteins did not have any repeats (27%). These included 4 proteins related to rodlet layer formation ChpGED and RdlB, spb, mtp, Hfq. We also did not detect PolyH repeats clearly visible in Hpn protein from Helicobacter Pylori due to the tool limitations, and hence did not include it in the following analyses. In total, we detected 69 different repeats present in 25 out of 33 amyloid proteins (73%). For the comparison, we analyzed 38 058 proteins from the third (reference) data set, which may also include some amyloid proteins, although their fraction should be negligible, or at least significantly lower.
The same repeat search was also performed there. Repeats were found in 67% of general bacterial proteins. This suggests that bacterial functional amyloids are slightly more likely to have repeats than other bacterial proteins. To infer the most typical role of such repeats, we first analyzed wellcharacterized amyloids, collected in Table  1. Table  1  contains a mixture of pathological and functional amyloids. Note that pathological amyloids are regarded as not designed by evolution to aggregate  (Shewmaker et al. , 2011). Nevertheless, even the accidental presence of the repeats in them could give a clue about the possible molecular effect of regular sequence patterns on the aggregation mechanism. The usefulness of this dataset, just as an inspiration, is further supported by the fact that the exact role of the pathological amyloids in the disease process is often unknown.
95 confidence level (pvalue = 0. 0002). To better understand the potential role of imperfection, we studied the case of a bacterial functional amyloid CsgA protein. It contains five imperfect repeats R1, R2, R3, R4 and R5, out of which R1, R3 and R5 are amyloidogenic. The structures of dimers from three mutant models were predicted (Alpha Fold 2): 1) CsgA only with five repetitions of R1, 2) CsgA only with five repetitions of R3, and 3) CsgA only with five repetitions of R2. Mutants only with R4 and R5 had poor quality models and were discarded. We analyzed the stability of generated 1), 2) and 3) models using CABSflex software to assess possible stability of fibrils created by CsgA with perfect repeats. Fig. 3  shows CABS energy as a function of RMSD for CsgA and its mutants. In general, the lower the CABS energy, the more stable the structure is.
Finally, we should mention well-studied cases of CsgA and CsgB  (Dueholm et al. , 2012) , which also belong to this group. At this point, it is worth noting that observation of an amyloidogenic regions within the repeat does not necessarily imply that all repeat units are amyloidogenic  (Wang et al. , 2008). In fact, only some of them may have the aggregation potential. With reference to these observations, we analyzed the physicochemical properties of the repeats. Although there are multiple amyloid hot spots predictors, they are predominantly trained on pathological sequences, as functional amyloids are severely underrepresented in hot spots databases. Furthermore functional amyloids show a distinctive amino acid profile (Ragonis-Bachar and Landau, 2020). Therefore amyloid predictors have problems with the identification of functional amyloids  (Szulc, 2021)  and their usage on a big scale in this study is debatable.
6. The distributions of amino acid profiles, f_aa, for each dataset are in the forms violin plots. Additionally, we examined if the repeats from bacterial functional amyloids are enriched or depleted in certain amino acids in comparison to BR, BS and AS. Some repeats have a much higher content of a specified amino acid, which affects the general frequency analysis (Fig. 5  and Fig. 6 ). However, on average, bacteria and other invertebrates have long repeats with a high standard deviation of their lengths and a tendency for low complexity in their amino acid profiles  (Tørresen et al. , 2019) , as also shown by our results presented in Fig. 6. Therefore, their comparison is not straightforward and needs to be supported by statistical analysis. We performed the multiple Mann-Whitney U testing with Bonferroni   S3 ).
Data collection This article is based on data taken from two qualitative studies and focuses on secondary HIVST distribution among MSM, FSW, PWUD, and their respective social networks (PWUD were not interviewed in Mali). The studies conducted consisted of (i) individual face-to-face interviews to analyse the use and secondary distribution of HIVST by MSM, FSW, and PWUD who received the kits from peer educators and (ii) phone interviews to analyse the practices involved in HIVST kits distribution from the perspective of "secondary recipients" as well as the ability of those recipients to perform self-testing and to seek confirmation and care services in case of a reactive result. The face-to-face interviews with primary users were conducted between January-April of 2021. The phone interviews with secondary or tertiary users were conducted between June-August of that same year.
The interviews lasted between 40 min (phone interviews) to one hour (face-to-face interviews). Before the beginning of the interviews, basic data concerning the sociodemographic profile of participants were collected. The interviews were conducted in French and/or in national languages (Bambara in Mali, Wolof in Senegal) according to the preferences of the participants. All participants in Côte d'Ivoire preferred to be interviewed in French. The same researchers conducted the face-to-face and phone interviews (KBA in Côte d'Ivoire, CSC and KZO in Mali, SS in Senegal). Interviewers had a minimum of a master's degree in social sciences or public health and they were originally from the same country and racial group as interviewees; they were experienced in data collection with vulnerable key populations and were familiar with the groups and organizations or clinics where the interviews were conducted.
At this time, it remains unclear whether this strategy increases or reduces exposure to HIV. Condomless sex following a false negative result, or sex with a primary partner, might increase HIV-acquisition risk unless condomless sex would still have occurred in the absence of HIVST. In the latter case, HIVST would be a real risk reduction tool by preventing sex with individuals at risk of transmitting HIV. HIVST utilization by secondary users is effective. The results show that some of the initial concerns raised by stakeholders in these three countries at the beginning of the project were unfounded  [21]. Data taken from the phone interviews with participants who received the kits through the MSM and FSW channels demonstrate that the distributed kits are used. Although our study does not allow us to quantify the proportion of people who received an HIVST and did not use it, the literature on the subject suggests that they are few of them.
Inter-Station Coherence In all of the coherence calculations, we begin with a set of template seismograms d tkm that were created by  Bostock et al. (2012)  and which represent the signals generated by LFEs occurring at 130 locations on the plate interface (red crosses in Figure  2 ). The LFEs are recorded at a range of stations k and on three components m (east, north, and up). We compare the template seismograms at each station with 30 to 60-secondlong intervals of target seismic data (d dkm ). To assess whether a 30 or 60-second interval contains tremor coming from the same location as the template, we compute the interstation phase coherence at a range of frequencies f : Here dtkm (f ) and ddkm (f ) are the Fourier transforms of the template and target data at station k and component m, and we compare between stations k and l in each term.
Acknowledgments We thank  M. Bostock  for providing the LFE catalogue and waveforms and  J. Cassidy  for his help with missing data during the 2008 ETS. This study was funded by  NERC  standard grant  NE/P012507/1. We thank two anonymous reviewers and the editors for comments that improved the work. A geodetic matched-filter search for slow slip with application to the Mexico subduction zone. J. Geophys. Res. doi:.
SeptiCyte RAPID is used in conjunction with clinical assessments, vital signs and laboratory findings as an aid to differentiate infection-positive (sepsis) from infection-negative systemic inflammation in patients suspected of sepsis  [18]. SeptiCyte RAPID generates a score (SeptiScore ® ) that falls within discrete Interpretation bands based on the increasing likelihood of infection-positive systemic inflammation. Analysis of public next-generation sequencing (NGS) datasets (e. g. ,  [19] ) suggested that SeptiCyte RAPID is able to discriminate between milder and more severe COVID-19 cases. Here, we report on a prospective study that sought to evaluate the performance of the SeptiCyte RAPID assay in patients with laboratory-confirmed COVID-19 with different degrees of severity, and analyze whether this assay could be used as a triaging tool for patients requiring hospitalization and potentially ICU care.
SeptiCyte RAPID testing of eligible patients was performed on qualified Biocartis Idylla instruments (Biocartis, Mechelen, Belgium) installed with SeptiCyte RAPID assay software. Residual EDTA blood samples from CBC were diluted within several hours of collection with phosphate-buffered saline (PBS; 1 vol blood to 2. 75 vol PBS), and 900 µL of each diluted sample was put into a SeptiCyte RAPID cartridge and automatically processed. The SeptiCyte RAPID test is completely self-contained within single use cartridges that are designed to run on the Biocartis Idylla system. The entire assay process from blood sample extraction to final test result is achieved on-cartridge, with fully integrated sample preparation followed by reverse transcription and quantitative real-time polymerase chain reaction (RT-qPCR) for the detection and quantification of changes in expression of the immune host response genes PLAC8 and PLA2G7.
Statistical Analyses Statistical analyses were performed using a combination of publicly available and custom scripts written in the R programming language  [22]. The pROC package in R  [23]  was used for AUC calculations, and some calculations were verified with JLABROC4 (http://www. rad. jhmi. edu/jeng/javarad/roc/JROCFITi. html; accessed 22 April 2022). Pearson's chi-squared test (stats package in R) was used to calculate p-values for categorical variables. Welch's two-sided t-test or one-way ANOVA (stats package in R) was used to calculate p-values for numeric variables. In the case of ANOVA, variables were assumed to be normally distributed. Summary tables were then generated using the gtsummary package in R. Combinatorial regression analysis, in which values of multiple clinical parameters (SeptiScore, CRP, D-dimer, lactate, monocytes, CD16-positive monocytes) were combined, was performed with a custom R script.
We note that SeptiCyte RAPID was initially validated for use in patients exhibiting ≥2 SIRS criteria and suspected of sepsis, with bacterial infection being the most common etiology  [18]. Optimal application of the test for discriminating COVID-19 severities, or for guiding the ICU admission decision for COVID-19 patients, could involve minor adjustment to the predefined assay cutoffs used by the SeptiCyte RAPID software. Testing of additional COVID-19 patients may provide further insight on this point. In comparing SeptiCyte and CRP, several points should be further emphasized. The study cohort was heavily treated with both corticosteroids (59/94 = 63%) and tocilizumab (24/94 = 26%), with some patients receiving both therapies (22/94 = 23%). It is wellknown that CRP is negatively modulated by both corticosteroids and tocilizumab  [34] [35] [36] [37] [38] [39].
In vivo capture of fluorescent liposomes in NOD mice Prediabetic female NOD mice were treated with 200 μl i. p. or 100 μl i. v. of NBD-PS-liposomes or NBD-PSIns(h)-liposomes. At 6 h and 24 h post-injection, splenocytes were obtained and labeled prior to FACS LSR Fortessa (BD Biosciences) acquisitionadditional information in Table  1  and Supplementary Table  1. Corresponding Fluorescence Minus One (FMO) staining was used as a control and FlowJo software (Tree Star) was used for the data analysis.
In vitro liposome capturing assessment by flow cytometry in NOD's splenocytes Splenocytes from female NOD mice were incubated with 1 mM NBD-PS-liposomes or NBD-PSIns(h)-liposomes overnight. Interleukin (IL)-10 + secreting cells were stained extracellularly with monoclonal antibodies and the APC Mouse IL-10 Secretion Assay Detection Kit (Miltenyi Biotech)additional information in Table  1  and Supplementary Table  1. Cells were acquired using FACS LSR Fortessa and FlowJo software was used for the data analysis. Analysis of Treg-cell subsets after liposome treatment in NOD. BDC2. 5 mice Female NOD. BDC2. 5 mice were treated with 100 μl i. v. or 50 μl i. d. of empty PS-liposomes or PS2. 5mi-liposomes and euthanized at day 4 or day 13 post-injection. Splenocytes were obtained and labeled extracellularly with several antibodies and intracellularly with Foxp3additional information in Table  1  and Supplementary Table  1.
Breg-cell subsets were determined as follows: CD19 + CD1d high CD5 + ; B1a, CD19 + CD5 + CD43 + ; B1b, CD19 + CD5 -CD43 + ; marginal zone (MZ), CD19 + CD21 high CD23 -. Data presented as mean ± SEM. c Percentage of IL-10-secreting cells from NBD-PSIns(h)-liposome-binding NBD + B-cell subsets (n = 3) and from non-liposome-binding NBD -B-cell subsets (n = 3). Data presented as mean ± SEM; no differences were found. increase their counts after i. v. PS2. 5mi-liposomes injection (Fig. 5b ). Exhausted CD4 + T-cells showed higher counts 4 days after PS2. 5mi-liposomes i. d. administration (and a tendency in i. v. treatment) (Fig. 5c ). Also, a significant increase in exhausted CD8 + T-cell counts was observed 4 days after i. v. treatment (Fig. 5d ). Both exhausted T-cell subsets decreased 13 days after injection.
Acknowledgments We are grateful to  Mr. Marco Fernandez  for his support in flow cytometry. This work was supported by  Ahead Therapeutics SL. , and the  Agency for Management of University and Research Grants (AGAUR) of the Catalan Government  (Fellowship to L. A. -F. ). This work has been supported by positive discussion through Consolidated Research Group # 2017 SGR 103 ,  AGAUR ,  Generalitat de Catalunya.
The identification of processes activated by specific microbes during microbiota colonization of plant roots has been hampered by technical constraints in metatranscriptomics. These include lack of reference genomes, high representation of host or microbial rRNA sequences in datasets, or difficulty to experimentally validate gene functions. Here, we recolonized germ-free Arabidopsis thaliana with a synthetic, yet representative root microbiota comprising 106 genome-sequenced bacterial and fungal isolates. We used multikingdom rRNA depletion, deep RNA-sequencing and read mapping against reference microbial genomes to analyse the in planta metatranscriptome of abundant colonizers. We identified over 3,000 microbial genes that were differentially regulated at the soil-root interface. Translation and energy production processes were consistently activated in planta, and their induction correlated with bacterial strains' abundance in roots.
49%/48% for bacteria; 100%/100% vs. 90%/86% for fungi, Fig. 2a, b ). In a second step, we aggregated bacterial and fungal read counts from both DNA and RNA datasets at class-, order-, family-, genus-, or species-level resolution. This revealed that RA profiles were largely consistent, irrespective of the method or the taxonomic resolution (Supplementary Fig. 2 ). The bacterial community was dominated by Xanthomonadales, Actinomycetales, Pseudomonadales, and Rhizobiales, whereas Hypocreales and Glomerallales prevailed in the fungal community (Fig. 2c, d ). Enrichment tests conducted for these abundant groups indicated that Pseudomonadales RA was significantly reduced in roots vs. matrix for both DNA-(DESeq2, P = 0. 018, log2 fold change (FC) = -2. 51) and RNAbased (DESeq2, P < 0. 001, log2FC = -3.
Extensive transcriptional reprogramming during root colonization at a strain-level resolution We performed differential expression analyses for individual strains (DeSeq2 39 ) to identify microbial genes induced upon host contact. We identified 3068 differentially expressed genes (DEGs) between root and matrix samples for bacteria (2007 up and 1061 down, Supplementary Data 2) and 94 DEGs for fungi (50 up and 44 down, Supplementary Data 3), which is consistent with the low RNA-Seq read coverage observed for these filamentous eukaryotes (Fig. 1b ) and earlier observation showing that bacterial commensals strongly inhibit fungal growth and prevent fungal dysbiosis in roots  12, 13. Fungal DEGs derived from core mycobiota members  40  did not display specific functional enrichment (topGO  41  , P > 0.