dataesr/scientific-paragraphs-categorization huggingface dataset:
Urinary EGF levels reflect renal EGF expression, and subsequent studies confirmed that low levels of urinary EGF excretion predict a poor renal outcome in individuals with DN and with other CKDs  (Betz et al. , 2016; Ju et al. , 2015). Hence, nonbiased transcriptomic approaches could be used to identify as-yetunknown prognostic biomarkers for therapeutic targets or to recruit high-risk individuals for clinical trials. Such transcriptomic datasets should be made freely available on databases such as Geodataset (http://www. ncbi. nlm. nih. gov/gds/) or Nephroseq (https://www. nephroseq. org), as this will enable researchers to select the model in which their pathway of interest is differentially activated in a similar manner to human disease. Such 'precision modeling' could improve the chances of translating findings made in rodent models to the clinic.
To resolve these slightly varying analyses, we used SplitsTree software to build a phylogenetic network derived from the ANI, MUMi and GGDC distance matrices. The results showed no obvious ambiguities, confirming that all three genome sequence-based methods give broadly consistent results (Fig. 2 ). The ANI, MUMi and GGDC methods are all based on whole-genome comparisons and have been shown to correlate well with the traditional standard method, DDH. Nonetheless, the RSSC can be divided into three to five species depending on how the genomic distances Fig. 2  Phylogenetic network derived from genomic distances. The distance matrices were generated with ANI, MUMi and GGDC methods and the combined tree was created with the SplitsTree4 software. The orange, blue and green cells represent strains clustered into species using the criteria specific to the ANI, MUMi and GGDC methods, respectively are calculated and the criteria used.
Proteomic diversity in the RSSC A total of 73 bacterial strains representing the four phylotypes were subjected to comparative proteomic analysis as a complementary method to the genome-based analyses presented above (Additional file 2). Protein mass spectra corresponding to each strain were obtained using MALDI-TOF and clustered using SPECLUST software  [37]. This generated a list of common peaks represented as inter-sample consensus m/z values. The best results were achieved using a "within peak match score (σ)" of 3 Da, as defined in the SPECLUST documentation. The consensus spectra matrix was translated to a binary matrix in which the absence/presence of a consensus peak in all strain profiles was represented as 0 or 1, respectively. This binary matrix was used to infer the phylogenetic relationships among the strains with the MALDI-TOF data and Bayesian analysis using MrBayes v3.
2. 2 software  [38]. The MALDI-TOF approach was previously used for bacterial identification  [39]. Mass fingerprinting is a simple, quick and reproducible method for bacterial identification through the generation of large spectral databases  [40]. Taxonomically, molecular typing using protein profiles has been useful for bacterial classification at the species and subspecies levels  [41, 42]  and at the strain level, depending on the type and class of bacterial group considered  [43]. In the present study, a combination of MALDI-TOF profiling, consensus mass peak lists, and Bayesian inference was used to cluster the 73 Ralstonia strains into three groups with strong branch support. The first cluster contained phylotypes I and III, whereas the second cluster contained phylotypes IIA and IIB, and the third cluster contained phylotype IV (Fig.
Phenotypic, genomic and proteomic data converge on a three-species model The taxonomic classification of R. solanacearum has changed repeatedly over the last 50 years, grouping strains with divergent ecological, geographical, genetic and phenotypic profiles, including many pathological 3 Tree derived from mass spectrometry analysis. Majority-rule consensus tree based on the presence/absence of a consensus MALDI-TOF peak list obtained using the MrBayes software. The probability values are indicated along the main branches. Red colored strains indicate the sequenced strains. Black lines delineate strain clusters variants. Safni et al. recently used DDH for a taxonomic revision of the RSSC, proposing the division of this species complex into three distinct species. We used modern genomic techniques to explicitly reveal the phylogenetic relationships between strains and support a revision of the current taxonomy of this species complex.
Genomics The complete and assembled genome sequence data used here are publicly available via the MicroScope web interface at www. genoscope. cns. fr/agc/microscope/home/. The Average Nucleotide Identity (ANIb) between genomes was calculated according to Konstantinidis and Tiedje  [30] , and the genomic distances were obtained after subtracting the ANIb values from 1. The Maximal Unique Matches index (MUMi) distances between genomes were calculated using the Perl script developed by Deloger et al. [29]  using MUMmer genome alignment software  [45]. The Genome-to-Genome Distance Calculator (GGDC) was used as previously described  [31]. The DDH values were derived from the GGDC distances using formula 2  [31]. A phylogenetic network derived from the distance matrices produced with all three methods was created using SplitsTree4 software  [46].
Seventy-two hours post inoculation, O. D. 600 measurements were taken from each culture. Two to four biological replicates were conducted per strain, per treatment (+/-NO 3 -). To determine if nitrate respiration contributed to anaerobic growth, O. D. 600 data were compared between + and -NO 3 -treatments for each strain and depicted as a ratio. A ratio above 1 indicates that the strain grew better anaerobically when provided with NO 3 -. A ratio of 1 or below indicates that the addition of NO 3 -did not enhance anaerobic growth, and that the strain did not respire with NO 3 -under the conditions tested. Additionally, all cultures were visually assessed (± bubbles) over the course of 96 h for production of dinitrogen gas, the end product of complete denitrification  [1, 48].
Analysis of denitrification genes Presence or absence of homologs involved in denitrification were determined in all sequenced strains (Table  1 ) using the MicroScope web interface and BLAST [49] to look for loci identified in the GMI1000 strain: narG (RSp0974); nosZ (RSp1368); aniA (RSp1503); and norB (RSp1505). Identity values were computed with the R package seqinr [50] after aligning amino-acid sequences with MUSCLE [51].
The suspensions were vortexed twice for 10 s and centrifuged at 20,000 g for 10 min at RT. The supernatants were transferred and aliquoted into new tubes and stored at -20 °C until further analysis. One microliter of the bacterial extracts was mixed with 1 μL of a saturated solution of α-cyano-4-hydroxycinnamic acid (Sigma-Aldrich), which was used as a matrix. The resulting sample/matrix mixture was deposited onto a stainless plate, dried at room temperature, and introduced into the MALDI-TOF MS instrument for analysis. The mass spectra profiles were obtained using a bench-top Microflex™ MALDI-TOF from Bruker Daltonics, including the Flex Control and Flex Analysis v3. 3 software, at the Bacteriology Division of the CHU of St. Pierre, La Réunion. All spectra were obtained in linear positive-ion mode with an m/z range of 2000-20,000 Da.
Each spectrum was calculated as the sum of 320 accumulated laser shots obtained after a spiral trajectory of the laser. For each sample, two bacterial extracts were obtained and measured in duplicate, and all the spectra were calibrated using a standard preparation of Escherichia coli DH5α, according to Bruker Daltonics. All bacterial spectra were analyzed using FlexAnalysis software (Bruker Daltonics) to generate peak lists for each strain, and only peaks with a relative intensity greater than 2 % were considered for cluster analysis (Additional file 3). The peak lists were exported to a CSV file, exported to single files using a custom macro and loaded onto the SPECLUST web-service (http://co. bmc. lu. se/speclust/) to obtain a consensus peak list for all strains considered. For the consensus peaks, a peak match score (σ) width of ±3 Da was considered.
Phylogenetic reconstruction using the MALDI-TOF data The consensus peak list was formatted into a sequential Nexus binary file and loaded into MrBayes 3. 2. 2 software (http://mrbayes. sourceforge. net/). Phylogeny was obtained through Bayesian inference using the restriction data type (two states: absence or presence of a peptide denoted by a 0 or a 1, respectively), assuming that the frequencies of the two possible states had a Dirichlet (1. 00, 1. 00) prior. Bayesian analysis was performed in two runs using 8 Markov chains and 3,000,000 generations. The potential scale reduction factor implemented in MrBayes 3. 2. 2 was used as a convergence diagnostic. A majority-rule consensus tree (50 %) was obtained after discarding 25 % of the initial trees (burn-in = 0. 25) generated before the stabilization of the log likelihood values of the data plotted against the number of generations.
Acknowledgments We gratefully acknowledge  Boris Vitnazer  for providing some of the IIB-1 genomes and  Carolee Bull  for valuable discussions. This research was supported through  INRA, CIRAD , the  University of Wisconsin-Madison College of Agricultural and Life Sciences  and by the e. b. s federative structure of the  Université de la Réunion  through the  MALDIV grant   EBSFIN13-01. The authors would like to thank  Aurélie Lajus  and  Claudine Médigue  ( Genoscope, France ) for technical support and  A. Michault  from  CHU de La Réunion Groupe Hospitalier Sud Réunion  for generously providing the  MALDI-TOF-MS instrument. Borja Sánchez  received funding from the  INRA  and a  European Union AgreenSkills postdoctoral fellowship  (grant agreement  n°267196 ).
RgpF domain in O. tauri is currently the only occurrence in eukaryotes, the species distribution being otherwise restricted to bacteria (Pfam: PF05045). Similarly, Wbx GT domain is found in bacterial gene clusters involved in synthesis of O-antigen, which in Shigella comprises repeated monomers of L-rhamnose, D-galacturonic acid and N-acetylgalactosamine residues  [45, 46]. One putative gene cluster (ostta19g00110-140) comprises a predicted NAD-dependent epimerase/dehydratase, glycoprotein-N-acetylgalactosamine 3-beta-galactosyltransferase, integral membrane galactosyltransferase and triose-phosphate transporter. Strikingly, the 3-beta-galactosyltransferase was associated with sequences from metazoans, while the adjacent genes appear to have homologues in the green lineage. A second putative gene cluster was a CAZy (carbohydrate-active enzymes database) family 92 GT containing a triose phosphate transporter domain (ostta19g00600) adjacent to CMP-N-acetylneuraminic acid hydrolase (ostta19g00610) of metazoan origin.
Transcription of OtV5 As almost all lines were R P at the time of RNA sequencing, we explored the transcriptomes in sequenced samples for evidence of OtV5 transcription (S1 Table ). In susceptible controls, a low number of read counts were assigned to few OtV5 genes (S4 Fig) ; in particular, OtV5_154c and OtV5_159, which correspond to highly transcribed viral specific genes (S3 Table ). As susceptible cells had not been exposed to OtV5, apparent transcription of viral genes were taken to be artefacts probably arising from mis-assigned sequencing sample indexes to highly expressed transcripts within the shared sequencing flow cell lane (all samples were multiplexed together). BLAST searches at neither nucleotide levels nor protein levels revealed similarities that would intimate horizontal transfer of these genes between host and virus.
Differential gene transcription analysis In order to have a balanced experimental design and to control for batch effects, four OtV5-resistant and four control lines that were from matching RNA extraction batches were selected for differential gene expression analysis (S1 Table ). Transcriptome read pairs (fragments) were aligned using TopHat2  [91]  (alignment parameters: -i 10 -I 4000, -G) to the annotated genome sequence of O. tauri RCC4221  [4]. The counts of fragments aligning to each gene was determined using the htseq-count function of HTSeq  [92]  with parameters (-t CDS -s no -m intersection-nonempty). Exploratory and differential gene expression analyses were performed on fragment count tables using the R package DESeq2  [93]. Hierarchical clustering of the distance between transcriptome profiles after correction for batch effects separated resistant and susceptible lines in two groups (S5 Fig).
Accession numbers Sequence data used in this study can be found in the GenBank data libraries. O. tauri RCC4221 chromosome sequences are under accession numbers CAID01000001. 2 to CAID01000020. 2 [4]  except for the updated chromosome 19 sequence (this study), which is available from http://wwwphp. obs-banyuls. fr/publications/data/2/. Transcriptome data is available from PRJNA344946. The updated genome sequence and annotation of OtV5  [24]  is under accession EU304328. 2. Gene models and annotations for O. tauri RCC4221 are also available from the Online Resource for Community Annotation of Eukaryotes (ORCAE) under Ostreococcus tauri V2  [95, 96].
Acknowledgments We thank  Marie-Line Escande ,  Leila Kerkache  and the microscopy platform of the  Oceanological Observatory of Banyuls  for their contribution of the electron microscopy analysis, the  GeT (Génome et Transciptome) core facility  for the transcriptomic and genomic sequencing,  Benoît Piégu  for assistance with annotation of transposable elements,  Rozenn Thomas  for preliminary observations made during her Ph. D. studies (2011,  Sorbonne Universités , Paris, in French), and the  Genophy (Evolutionary and environmental genomics of Phytoplankton ) team for useful discussion.
This work was supported by the  French Agence Nationale de la Recherche  grants  REVIREC  ( ANR-12-BSV7-0006-01 , coordinator NG) and  DECOVIR  ( ANR-12-BSV7-0009 , coordinator  Y. Desdevises ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Sequence data used in this study can be found in the GenBank data libraries. O. tauri RCC4221 chromosome sequences are under accession numbers CAID01000001. 2 to CAID01000020. 2 except for the updated chromosome 19 sequence (this study), which is available from http://wwwphp. obsbanyuls. fr/publications/data/2/. Transcriptome data is available from PRJNA344946. The updated genome sequence and annotation of OtV5 is under accession EU304328.
, 2009). Briefly, the significant enrichment of underlying KEGG, GO, and Reactome curated pathways was determined from the hypergeometric distribution and corrected for multiple comparisons using Broad Institute Molecular Signatures Database v4. 0 (http:// www. broadinstitute. org/gsea/msigdb/). Gene sets with a false discovery rate p-value<0. 05 were considered significant. Genes that were up-or down-regulated were identified by selecting genes with logarithmic fold-change ratio (PPARa -/-/PPARa +/+ ) > 1. 3 (up-regulated genes) or < À1. 3 (downregulated genes). The raw data have been deposited in NCBI Gene Expression Omnibus and made accessible through the GEO database (accession number: GSE39669 and GSE39670).
The computation of the sample size is based on the assumption that there would be no missing data (i. e. , all mice will produce data). The data in all figure panels reflect multiple experiments performed on different days using pups (n = 6 with triplicates) derived from different litters. In this study, we used 6 mice per group (i. e. , biological replicates) for all experiments. Further, measurements and experiments were repeated in triplicates for each sample (i. e. , technical replication). Values were expressed as mean ± standard error of the mean (SEM). Statistical tests, including the two-tailed Mann-Whitney and two-way ANOVA with Bonferroni post-hoc analysis, were performed using GraphPad Prism software (version 5. 00). p-values<0. 05 were considered significant.
Acknowledgements We thank  Beatrice Desvergne ,  Federica Gilardi ,  Sven Pettersson , and  Andrew Nguan Soon Tan  for discussions;  Michae ¨l Baruchet ,  Corinne Tallichet-Blanc , and  Melanie Schuh  for technical assistance; and the  Mouse Clinic Institute in Illkirch, France , for the gift of Nr3c1 -/-mice. We also thank  Pierre Chambon  and  Daniel Metzger  for providing GR-mutated liver samples. Gene expression profiling was performed in collaboration with the  Lausanne Genomic Technology Facility. We thank  Nesibe Peker  from the  Singapore Institute for Clinical Sciences  for their technical assistance in measuring the oxygen consumption rate.
The observatories and data sets 2. 1 The IceCube Neutrino Telescope IceCube is a cubic-kilometer neutrino detector embedded in the ice at the geographic South Pole  [1]  between depths of 1450 m and 2450 m. Different IceCube data sets are considered in this work. The first is a set of cascades (ν e and ν charged-current, and all flavour neutral-current interactions) that have been detected in a search for high-energy events where the interaction occurs within the detector  [2]. These 39 cascades, which are part of the HESE ("High-Energy Starting Events") set (deposited energy range: ∼30-2000 TeV) are derived from data taken between May 2010 and May 2014 and are hereby called "high-energy cascades". A second set of events referred to as "high-energy tracks" (energy above ∼70 TeV) consists of the 7 tracks (ν charge current interactions) in the HESE sample  [2]  that have energies and directions which make them more likely to be of extraterrestrial origin than the other track events in that sample and 9 muon tracks found in a search of a diffuse up-going ν flux  [3].
e. , the bias and accuracy performance indices  (Ross, 1996). These data were gathered from ComBase database. Results showed that the model performed well with published responses of E. coli O157:H7 in broth, although a poorer agreement of the model was obtained from the correspondent responses of E. coli O157:H7 in food. It can be concluded that the use of (µ max ) A is an adequate alternative in the development of predictive models to describe the (µ max ) VC and a potential alternative to traditional viable plate count. A further step in surface colony image monitoring with a light microscope was presented by  Koutsoumanis and Lianou (2013). This research evaluated image series obtained at short time intervals, sequentially compiled and transformed into a video. The aim of this study was to assess the heterogeneity in the growth dynamics of micro-colonies emerging from single cells of S.
e. , lag, exponential and deceleration or stationary phase  (Kabanova et al. , 2012 (Kabanova et al. , , 2013)). Maximum growth rate showed a dependency on the inoculum level, i. e. , low inoculum levels led to higher values than those obtained from high inoculum levels. Although methods based on calorimetry allowed to study bacterial colonies non-invasively, the estimated growth dynamics gave information of the overall population. Thus, in  Kabanova et al. (2012)  the dimensions of the colonies were measured with a microscope Zeiss Axiovert 200 M coupled with AxioCam MRc5 camera and Zeiss AxioVs40 V45. 0. 0 software. Colonies up to 10 4 CFU/mL showed a lenticular shape. This morphology was related to the agar gel structure and the size of the pores in which colonies grow. Once the colony grew over the pore size, the bonds between chains of the gel agar were broken, triggering a lenticular colony formation.
, 2011; Postollec et al. , 2011; O'Sullivan et al. , 2013). PCR targets a specific sequence such as the 16S rRNA gene in conjunction with genera/species specific primers. The 16S rRNA gene is universal amongst bacteria and large databases exist for specific species of most food related bacteria. They have been used to determine the presence and quantity of undesirable bacteria such as C. tyrobutyricum and certain Lactobacillus communities in cheese. However, the issue with these methods of bacterial detection is the lack of information we obtain relating to bacterial colony location and distribution within dairy foods. For these reasons, this review focuses on microscopy methods allowing for the visualization of bacterial colony location and distribution.
The addition of MRS-PI agar constituted time zero for the experiment. After the agar solidified, the chamber was placed in the automated microscope stage, and a random spot was chosen in each well as the starting position. Subsequently, a total of 49 positions were recorded in a 7 × 7 grid, which was programmed into the software. The pre-programmed grid was used to avoid user bias when selecting appropriate spots in the specimen. Furthermore, the 49 positions enabled us to obtain more valid information about the heterogeneity of the specimen. The recording of all 49 positions were then repeated throughout the experiment. Brightfield images and PI fluorescent images were captured using MetaMorph 7. 0 software package (Molecular Devices Inc. , Silicon Valley, CA, USA). The chamber was left on the microscope stage at 23 • C during the entire experiment.
This photodiode offered a low background noise (10 pA) with a wide dynamic range and provided a spectral response from 320 to 730 nm with the peak sensitivity at 560 nm. The active area for this photodiode is 1. 3 × 1. 3 mm 2 , providing a photo sensitivity of 0. 19 A/W for the incoming laser from the CDM sensor head on which a custom built active components driven I-V converter and preamp were installed at a preamp section. A custom built micro controller unit (MCU) (AVR128, Atmel) was used as a data acquisition unit. Through use of the MCU's internal 10 bit A/D conversion, all signals from the photodiode circuit and CDM were captured and transferred simultaneously to the PC by means of serial communication. All sequences were controlled by a custom built graphic user interface (GUI) at the PC, which was developed using Microsoft Visual Studio 2008 and both were analyzed and visualized in MATLAB.
SPECKLE ANALYSIS To provide quantitative speckle analysis, a MATLAB code was constructed utilizing an image processing toolbox V2. 6  (Vliet, 2014). A radial Gaussian blur was applied to the raw image to apply a low pass filter that eliminates the objects while keeps the slow change in the background, and the blurred image was subtracted from the raw image to remove the background noise. The subtracted image was binarized with locally adaptive threshold using approximation of gradient of background intensity to eliminate the external lighting and local CMOS sensor sensitivity characteristics effect on the image. Then, each speckle was segmented, grouped, and labeled. Number of pixels (size of the speckle) and feret diameter  (Merkus, 2009)  of each segmented group was computed, and analyzed.
MICROCALORIMETRY A 24 channel isothermal batch microcalorimeter TAM III Thermal Activity Monitor (TA Instruments, New Castle, DE, USA) was used for the monitoring of the growth of thermophilic starter St. thermophilus ST12. Data acquisition and analysis was carried out using TAM Assistant Program (v 0. 9. 1012. 40, SciTech Software AB, Thermometric AB).
9. 1012. 40, SciTech Software AB, Thermometric AB, Järfälla, Sweden) and Microsoft Excel. The recording of the heat evolution was taking place with the frequency 1 s -1. The time-points were resampled at equidistant intervals of 15 min. The value P max is characterizing the maximum aggregate rate of heat production (see Figure  1C ). After reaching the maximum, the decrease of heat production rate starts. The time point where P max is measured is a convenient time for the end of the exponential growth phase(s). The change of the number of viable cells over time was calculated using the equation  (Stulova et al. , 2013) : N = Q YQ (3) where N (CFU mL -1 h -1 ) is the number of bacteria grown during the selected time interval, Q (J mL -1 h -1 ) is the heat produced during the same time interval, and Y Q (J CFU -1 ) is the experimentally determined heat yield coefficient (Y Q , J CFU -1 ).