dataesr/scientific-paragraphs-categorization huggingface dataset:
This effect of EDTA could be explained, on one hand, by the necessity of Mg 2+ for catalytic activity of ACs. Indeed, Mg 2+ which binds to the active Fig. 6. cAMP is produced in Sf9wt cells after toxin treatment. Sf9wt cells were incubated 20 min in complete media only (-) or media supplemented with (+) 100 µM forskolin (Fors. ), 1 mg/l Cry1Ca toxin (Tox. ), 5 mM EDTA or 5 mM EGTA. After incubation, cytosolic HCl extract were performed and cAMP amount measured using an immunoassay kit. cAMP fold increase was evaluated compared to non-stimulated cells (see Materials and Methods). Data are presented as the mean±s. e. of three independent experiments done in duplicate. Student's t-tests were used for comparisons among groups. ***P<0. 001; n. s. , not significant compared to the condition with forskolin only.
Two pictures per toxin concentration were used to count both stained and unstained cells (300-400 total cells). Cell viability was calculated by the formula: (stained cells/total cells)×100. Data were then treated on the SigmaPlot 10. 0 software (Systat Software) and typical dose-response sigmoid curves were obtained with non-linear estimation. Cry1Ca lethal doses giving 50% (LD 50 ) or 80% (LD 80 ) dead cells were directly deduced from the curves. Resistance ratio (RR) due to treatments were obtained by the formula: LD 50 (Sf9+treatment) / LD 50 (Sf9 in free media).
Microarray data analysis Data analysis was performed using the Bioconductor suite of statistical packages and limmaGUI  (Gentleman et al. , 2004; Wettenhall and Smyth, 2004). Specific expression intensity was obtained by subtracting the background intensity from the foreground intensity for each non-flag spot (all flagged spots were eliminated). The expression data were normalised by within-array normalization with the 'loess method' and between-array normalization using the 'quantile method'  (Yang et al. , 2003). The linear model for the series of arrays and empirical Bayes method was then applied for assessing differential expression  (Smyth, 2004). The false discovery rate of the P-value for multiple tests was controlled using the Benjamini-Hochberg method. Differentially expressed genes were selected if the absolute value of log2-fold-change was greater than 1 and adjusted P-value below 0.
Bacteriocins inhibited closely related species  [57]. For example, lacticin 3147 and nisin are lantibiotics that exhibited an antibiotic activity against Gram-positive bacteria, notably Meticillin-resistant Staphylococcus aureus (MRSA) or Vancomycin-resistant Enterococcus  [58]. Nisin is the most famous lantibiotic, largely used as food preservative because of its activity against Listeria monocytogenes, MRSA and Streptococcus pneumoniae  [59]. Nisin is also used in veterinary medicine under the name Wipe Out ® for the prevention of dairy mastitis  [57]. The synergistic effects of lantibiotics with antibiotics have also been demonstrated in vitro  [60]. According to the Antimicrobial Peptide database, 2,478 antibacterial peptides are already known  [61]. All medical fields combined, 60 peptides have obtained the FDA approval and 140 are in clinical trials, mainly for oncological and metabolic diseases  [62].
Discovering antimicrobial effects from already known compounds Some antibiotics were discovered years before they were used (fidaxomycine, daptomycin, or linezolid). Several million of chemical compounds are known in chemical databases and could provide a potential source of antibiotics  [32]. Moy et al. have tested the activity against E. faecalis of more than 6,000 chemical compounds and 1,136 natural products in the Caenorhabditis elegans animal model, discovering 16 molecules increasing the survival of animals  [77]. The bottleneck remains the selection and the high throughput testing of these molecules.
4. 4. Genome mining Secondary metabolites are encoded by Biosynthetic Gene Cluster (BGCs). Thousands of prokaryotic genomes are available in sequence databases. These data have generated thousands of BGCs that potentially encode unknown molecules  [82, 83]. Despite the fact that many of them do not have any antimicrobial activity, little is known about them. Walsh et al. [83, 85]. For instance, lichenicidin is a bacteriocin synthesized by Bacillus licheniformis that was discovered using the mode-of-action genome mining approach. The authors screened the databases for LanM genes, which are involved in the biosynthesis of lantibiotics  [86]. The identification of putative bacteriocins encoded by BGC from the genome sequence is possible using bioinformatic algorithms  [87]. Bacteriocins are easily found using bioinformatic tools compared to NRPS or PKS.
4. 4. Genome mining Secondary metabolites are encoded by Biosynthetic Gene Cluster (BGCs). Thousands of prokaryotic genomes are available in sequence databases. These data have generated thousands of BGCs that potentially encode unknown molecules  [82, 83]. Despite the fact that many of them do not have any antimicrobial activity, little is known about them. Walsh et al. Bacillus licheniformis that was discovered using the mode-of-action genome mining approach. The authors screened the databases for LanM genes, which are involved in the biosynthesis of lantibiotics  [86]. The identification of putative bacteriocins encoded by BGC from the genome sequence is possible using bioinformatic algorithms  [87]. Bacteriocins are easily found using bioinformatic tools compared to NRPS or PKS. Tools such as BAGEL, antiSMASH or PRISM are widely used for this purpose.
They rapidly expanded and now occupy a broader ecological range than their diploid progenitors  17. Notably, as frequently reported for polyploids  18, 19  , one A. arenosa tetraploid lineage successfully invaded a novel ruderal habitat, where they adopted a weedy life-cycle not shared by diploids or non-ruderal tetraploid A. arenosa  20, 21. This notwithstanding, the largely overlapping ecological niches and the recent divergence between the two A. arenosa lineages allows for cross-ploidy genomic comparisons  22. Using the most comprehensive diploid-autopolyploid dataset to date  22  , we characterize TE dynamics across diploid and tetraploid A. arenosa genomes. First, we assess how the relaxation of purifying selection impacted TE dynamics in autotetraploids. Second, we investigate whether a transposition burst was associated with the WGD event that all A.
Methods Data sources. Whole-genome sequencing data of A. arenosa individuals and paired-end alignments on the A. lyrata reference genome  22, 23  were obtained from Monnahan et al. 22  where the phylogenetic relationships between the different clades of diploids and tetraploids (Fig. 4a ) are described and analyzed. Identification of non-reference TE insertions. Split and discordant reads were extracted from individual alignments (2. 8-18. 1X, average 8. 4X) and mapped on a joint TE library assembled from the annotation of all TEs in the A. lyrata reference genome (87,477 TEs 26 ). Mapped reads were then soft-clipped and re-mapped to the reference genome to define putative non-reference TE insertion sites. These sites were intersected across individuals to identify those shared and supported in at least one individual by a minimum of three reads, including at least one upstream and one downstream.
45  (MOESM3), or indicated as super-family_new-followed by a unique identifier when previously unannotated (e. g. Mariner_new-617). Multiple TEs with the same common name were distinguished by a numbered suffix (e. g. ALLINE1_2. 1). Analyses of TE landscapes. Statistical analyses were performed using MATLAB (MathWorks, Inc. , Natick, Massachusetts, United States). Carrier frequency was calculated at each TE insertion site over all non-NA individuals for the site. In diploids, 95% of TE insertions were at carrier-frequencies (f) under 12. 2%. Under Hardy-Weinberg equilibrium, if f is below 12. 2%, it should reflect allelic frequencies (p) below 6. 3% (from the formula (1-p) 2 = 1-f). Thus, >97. 8% of diploid carriers are expected to be heterozygous, i. e. p~f/2. Similarly, in tetraploids at similar carrier-frequencies we should expect >96.
Acknowledgements P. B. would like to thank  Levi Yant  for discussions and access to unpublished data,  Vincent Castric  for communication of unpublished A. lyrata TE annotation, and  Pirita Paajanen  for the high-coverage diploid A. arenosa alignment. Work in the Colot lab is supported by the  Investissements d'Avenir ANR-10-LABX-54 MEMO LIFE ,  506 ANR-11-IDEX-0001-02   PSL* Research University. L. Q. acknowledges support from the  MOMENTUM program  of the  Centre National de la Recherche Scientifique. P. B. was the recipient of a postdoctoral fellowship (code  SPF20170938626 ) from the  Fondation pour la Recherche Médicale (FRM). Further support was through a  European Research Council   Consolidator grant  CoG  EVO-MEIO 681946  to K. B.
Data availability Whole-genome resequencing data of Monnahan et al. 22  are deposited in the Sequence Read Archive with the primary accession code PRJNA484107 and PRJNA472485 for RNAseq data. Data supporting the findings of this work are available within the paper and its Supplementary Information files. A reporting summary for this Article is available as a Supplementary Information file. The datasets generated and analyzed during the current study are available from the corresponding authors upon request. The source data underlying Figs. 1b-h, 2a-e, 2g, 3a, 3c, 4b-e, and 5e, and Supplementary Figs. 1 2 3 4 5 are provided as a Source Data file.
Code availability The SPLITREADER  25  code is freely available at https://github. com/LeanQ/ SPLITREADER, Bowtie2 v2. 3. 2 at https://sourceforge. net/projects/bowtie-bio/files/ bowtie2/2. 3. 5. 1/, Picard Tools at https://broadinstitute. github. io/picard/, SAMtools V1. 2. 1 at http://www. htslib. org/download/, and MATLAB version 9. 3. 0 (R2017b) from the MathWorks Inc. https://www. mathworks. com/.
For statistical analysis, the responses were firstly submitted to oneway ANOVA, evaluated in relation to the coefficient of determination (R 2 ) and F-value at 95% of probability, using the software Statistica 12. 0 (Statsoft, USA). The effects analysis was performed, and terms considered not significant at the 5% probability level were eliminated from the model and incorporated into the residue. The regions of interest of the response surfaces generated were studied, in order to determine the optimal process condition.
94 M thiocyanate solution with 500 µL of 0. 072 M Fe 2+ solution. The thiocyanate/ferrous solution (30 µL) was added to the methanol/1-butanol mixture, vortexed and incubated at room temperature for 20 min. Samples absorbance values were registered at 510 nm using a spectrophotometer UV mini-1240 (Shimadzu, Kyoto, Japan). Hydroperoxide content was determined by a standard curve with known concentrations of cumene hydroperoxide. The results were expressed as mg cumene hydroperoxide equivalent. kg -  oil. Analysis of variance (ANOVA) was performed using the SAS 9. 3 application and, when applicable, the data were compared by Tukey test at significantly different at p ≤ 0. 5.
Flow cytometry is used frequently to collect quantitative information about specific cell in specific regions such as damage to  DNA or membrane. This information can provide important data on cell stability when exposed to different reagents  (Georgakoudi et al. , 2004). In this study flow cytometry was used to evaluate the membrane integrity of Saccharomyces cerevisiae exposed for 4 h to green propolis dried extracts at different concentrations (0, 50, 100, and 200 mg. kg -  ). The percentage of cells with the intact membrane observed for the control was 98. 9% ± 1. 1. For the green propolis dried extracts exposed cell, values were 99. 5% ± 0. 5; 98. 6% ± 1. 0; 98. 5% ± 1. 2 for the concentrations of 50, 100, and 200 mg. kg -  , respectively. Green propolis dried extracts did not change the cell membrane in an expressive way, being this a good indicator of the innocuity of these extracts, since the integrity of the membrane is directly linked to cell viability and its normal functions  (Grégori et al.
Acknowledgements Funding: This work was supported by  São Paulo Research Foundation -FAPESP  (grant  2014/18227-4 , and grant  2015/50437-1 ) and it was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil -CAPES  ( Finance Code 001 ). Authors thanks the  Natucentro Indústria e Apiários Centro Oeste Ltda  for samples and  Dr. Thalita R. Augusto-Obara  ( University of Sao Paulo ) for her help in the lab analysis.
Having defined these Emax, we determined (i) the delay in onset of each Emax, (ii) the time of occurrence of these Emax (Tmax RR , Tmax CTemp , and Tmax ChEinh ), (iii) the pattern of decrease in these Emax that may be described by a peak or a plateau of effect, and (iv) the delay returning to normal values  [21]. Results are expressed as mean ± SEM (standard error of the mean). Three groups of six animals were used. Groups were compared using a two-way analysis of variance for repeated measurements using Prism version 5. 0, GraphPad Software (San Diego, CA, USA). For each parameter, a significant treatment multiplied by time interaction was found (p < 0. 001); this analysis was followed by multiple pairwise comparisons, the global risk being fixed at p < 0. 05. All tests were two-tailed.
Furthermore, we are not aware of any temporal systematization of data collection in toxicological studies. To address this major limitation, we developed the concept of toxicodynetics which allows researchers to describe the time-course of effects in animals as well as humans  [19]. In contrast with data collected in humans, we observed a highly reproducible respiratory toxicity of a non-lethal dose of diethyl paraoxon in rats  [11, 13, 16, 27]. We extended the reproducibility of respiratory toxicity of the same dose of diethyl paraoxon in mice  [8]. In the present study, we assessed the respiratory toxicity of two compounds in which the lone difference was dissimilar alkoxy groups, namely methyl or ethyl. Noteworthy, both compounds exhibit the same magnitude of log P values  [19]. Furthermore, in the present study for DMPO and a previous study for DEPO  [11] , MLDs were rather similar, 0.
Metagenome-Wide Association Studies (MWAS) The reads obtained by metagenetic high-throughput sequencing were grouped into clusters (based on their similarities) and assigned taxonomically thanks to the Greengenes database v13. 8 (https://greengenes. lbl. gov). Thus, for each mouse, we got a list of taxa and associated abundance. The totality of the taxa and their relative abundance in each mouse allowed calculating an alphadiversity score and the microbial diversity at the individual level. The anti-glycan antibody data were compared with microbial diversity (and abundance) for each individual, to set associations and identify anti-glycan antibodies that potentially correlate with a specific taxon.
Statistics In PGA studies all glycans were printed in 6 replicates, and the binding results were expressed in relative fluorescence units (RFU) as the median and interquartile range (IQR, 25-75th). In the case of the ELISA, samples were determined by triplicate and levels of anti-αGal antibodies were expressed in relative units of Optical Density (OD) at 492 nm, as Mean ± SD. GraphPad Prism statics software (GraphPad Software Inc. , San Diego, CA, USA) was used for analysis and data graphing. To study microbial diversity in stool samples, Chao1 Diversity estimator was used with ANOVA post-tests and expressed as median (IQR, 25-75th). Spearman rank test was used to study the anti-glycan antibodies vs. microbiota interaction. The prediction of functional profiles from metagenomic linked to natural antiglycan antibodies for GalT-KO mice was made using the Tax4Fun R package.
Characterization of Gut Microbiota of GalT-KO Mice The profile of gut microbial population was assessed by metagenetic high-throughput sequencing of GalT-KO mice fecal samples. Firstly, the weight of animals (n = 11) was followed for 7 months. The GalT-KO mice under examination showed a normal progression, achieving 80% of the total weight during month 2 and month 3 of life. At month 5, the majority of mice achieved their maximum weight, which remained almost constant until the end of the study (Supplementary Figure  1 ). Bacterial DNA was extracted from fecal samples (feces), and Ion Torrent sequencing was performed by Gènes Diffusion (Institut Pasteur de Lille, France) to identify diversity in gut microbial population among the examined animals. High throughput sequencing data were deposited in the Sequence Read Archive (SRA) at NCBI (https://www.
FUNDING We thank  CERCA Program/Generalitat de Catalunya  for their institutional support. This work was supported by  Fondo de Investigaciones Sanitarias(FIS)  (grant  PI13/01098  to RM and  PI15/00181  to CC) from  Carlos III Health Institute ,  Spanish Ministry of Health. DB-G was benefited of a post-doctoral research position funded by the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under the Grant Agreement 603049 (TRANSLINK). Work of  NK, NS , and NB was supported by grant # 14-50-00131  of the  Russian Science Foundation.
Context. There are a number of methods that identify stellar sub-structure in star forming regions, but these do not quantify the degree of association of individual stars -something which is required if we are to better understand the mechanisms and physical processes that dictate structure. Aims. To present the new novel statistical clustering tool "INDICATE" which assesses and quantifies the degree of spatial clustering of each object in a dataset, discuss its applications as a tracer of morphological stellar features in star forming regions, and to look for these features in the Carina Nebula (NGC 3372). Methods. We employ a nearest neighbour approach to quantitatively compare the spatial distribution in the local neighbourhood of an object with that expected in an evenly spaced uniform (i.
Further study is required to ascertain the physical origin of the difference; (2) The different clustering properties between the NW and SE regions are also seen for OB stars and are even more pronounced; (3) There are no signatures of classical mass segregation present in the SE region -massive stars here are not spatially concentrated together above random; (4) Stellar concentrations are more frequent around massive stars than typical for the general population, particularly in the Tr14 cluster; (5) There is a relation between the concentration of OB stars and the concentration of (lower mass) stars around OB stars in the centrally concentrated Tr14 and Tr15, but no such relation exists in Tr16. We conclude this is due to the highly sub-structured nature of Tr16. Conclusions. INDICATE is a powerful new tool employing a novel approach to quantify the clustering tendencies of individual objects in a dataset within a user-defined parameter space.
These can then be used to characterise the distribution. That is the approach we will follow here. In this paper we present our new statistical clustering tool "INDICATE" (INdex to Define Inherent Clustering And TEndencies) that we are currently implementing in the SFM project. Hopkins & Skellam (1954)  established the Hopkins statistic to assess the global clustering tendency of a dataset by testing its spatial randomness through quantitative measurements of its uniformity. A single global value is calculated through a comparison of the mean k-nearest neighbour distances between objects within the dataset, and between points in the dataset and a similarly constructed uniform random sample. We propose instead to derive a similar index but for every point in the dataset individually. This paper is structured as follows.
General Tool Description INDICATE is a tool to quantify the degree of association of each point in a 2+D discrete dataset. It requires no a priori knowledge of -nor makes assumptions about -the sub-structure present in a dataset, since it is a local statistic. The separation of the spatial position of the j th point in the actual dataset with the N th nearest neighbour in an evenly spaced uniform (i. e. definitively nonclustered) control distribution is determined. The mean value of this separation, r, is then derived. Finally, INDICATE assigns an index, I, to every point in the actual dataset, which is simply the ratio of the actual number of real neighbours within this mean separation, r, and N, the nearest neighbour number. Since our tool fundamentally relies on properties linked to distance ratios in the real and uniform sample, it is itself region distance independent in principle at least (but see Appendix C).
Step I: Define the bounds of the dataset INDICATE is designed to be applicable to any desired Ndimensional parameter space. However, as outlined here, it assumes that all dimensions have the same scaling (e. g. J2000 sky coordinates should be converted to a local coordinate frame prior to beginning). The bounds of the dataset parameter space are defined from the density distribution, and the area occupied by the data, A, measured. The shape of the delimited area has a negligible effect on the tool (Appendix B), but for clarity we will use a rectangular parameter space for all our explanatory datasets in the description of the tool. In practice, the shape is related to the problem to be studied, and is defined by the user. The number density, n obs , of the dataset is determined using, n obs = N tot A (1) where N tot is the total number of points in the dataset.
Step II: Generate the control distribution An adaptive evenly spaced uniform point distribution which we designate the control distribution is generated. The control distribution is rectangular (regardless of dataset shape defined in Step I), populates the bounded parameter space and has the same number density as the dataset i. e. The mean Euclidean distance, r, of each point, j, in the dataset to its N th nearest neighbour in the control distribution is measured using: n con = n obs (2) r = N tot j=1 r j N tot (3) where, r j = (x j -x con N ) 2 + (y j -y con N ) 2 (4) and (x j , y j ) are the respective x and y axis coordinates of point j and (x con N , y con N ) are the respective x and y axis coordinates of the N th nearest neighbour in the control distribution.
2. 1. 4. Step IV: Calculate the Index, I The number of points, N r, closer than r to each point, j, in the dataset is counted (see Figure  1 ). The index of point j is then the ratio of the number of neighbours closer than r in the dataset with that expected by a non-clustered distribution i. e. I j,N = N r N ( 5 ) where N is the N th nearest neighbour number (e. g. if r is measured for the 5 th nearest neighbour, N = 5; 6 th nearest neighbour, N = 6. etc. ). The ratio I j,N is unitless and has a range of 0 ≤ I j,N ≤ N tot -1 N , such that the higher its value the more spatially clustered point j is. It is important to note the index is not a measure of local surface density -Eq. 5 describes the local spatial distribution of point j. Therefore although the index is proportional to the local point surface density of a dataset, it is possible for two datasets with significantly different densities to have identical index values if their points have the same spatial distribution.