dataesr/scientific-paragraphs-categorization huggingface dataset:
We used this approach to design serotype-specific primers aiming to match all possible DENV strains circulating worldwide, by considering 2,056 available GenBank DENV sequences  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). This is the greatest difference compared to other previously published RT-LAMP assay designs in which primer design focused on the conserved 3' UTR, NS1 or C-prM regions but detailed limited information about the DENV sequences used to develop the primers. As the LAMP primers were designed from different clusters of each DENV serotype obtained after PCA and phylogenetic analyses, the individual LAMP amplicons locate to several regions across the DENV genome conserved in these clusters (Fig  1 ). This allows an overall detection of DENV variability surpassing any other molecular amplification assay.
For the latter assays the analytical sensitivities determined were 10 to 100 plaque-forming units (PFU)/mL and 10 RNA molecules detected respectively. Our RT-LAMP assay for DENV1 showed a limit of detection as per probit analysis of 102 PFU/mL with a confidence interval from 20 to 7. 8x10 3 PFU/mL (data non-shown). The assays developed were serotype-specific, and no cross-detection of other flaviviruses was observed. Surprisingly, 2 viral preparations tested-KDH0010A (DENV3) and VIMFH4 (DENV4)-were also found positive for DENV1. Subsequent analysis by serotype-specific nested PCR  [20]  confirmed the presence of DENV1 RNA probably due to contamination during RNA extraction or virus culture, and indicating that the DENV RT-LAMP assays had picked up the contamination correctly. EQA panels have been developed in order to evaluate the performance and reliability of current diagnostic methods in laboratories worldwide, by using different samples (both negative and positive samples, including different concentrations) that provide information about their specificity and sensitivity  [58, 59].
To conclude, we have shown a novel approach to designing LAMP primers that makes use of fast growing sequence databases. During the study time the number of complete DENV genome entries grew by 932 genomes deposited. To be able to cover all of the diversity documented, our approach devised 4 complicated mixes of oligonucleotides for the detection of the individual DENV1-4 serotypes. The DENV1 and DENV2 assays were validated with viral RNA extracted clinical samples and showed very good performance parameters. Finally the combination of PCA analysis and molecular detection assays design should also be considered for other molecular assay formats since the available sequence dataset of several pathogens has increased beyond what can be handled by traditional design based on simple alignments.
The presented segmentations correspond to the original image for a PSNR = 14 dB. Therefore, on the one hand, considered segmentations must be tied to the robustness of the detector. On the other hand, the scores must increase monotonously, with an increasing order as a function of the edge detector quality. Note that the matlab code of FoM, D k , S k and ∆ k measures are available at http://kermitimagetoolkit. net/library/code/. The matlab code of several other measures are available on MathWorks: https://fr. mathworks. com/matlabcentral/fileexchange/ 63326-objective-supervised-edge-detection-evaluation-by-varying-thresholds-of-the-thin-edges. Firstly, the segmented images tied to corrupted images with PSNR = 14 dB, representing the best edge quality map for 28 different measures, are presented in Figures  20-47.
Fermentation system Both the initial enrichment and subsequent bioreactor studies (R1, R2 and R3) were carried out in 1. 5 L bioreactors (without sterilization) with a working volume of 1 L and operated by a Qplus multi-bioreactor system (Sartorius Stedim, Bohemia NY). The bioreactors were stirred at 280 rpm. The temperature was controlled at 55 °C by a circulating Polystat water bath system (Cole-Parmer, Vernon Hills, IL). Software accompanying the Qplus bioreactor system was used to record data for pH, base addition, temperature and stirrer speed. The pH was monitored using an InPro3253/225/Pt100 pH probe (Mettler-Toledo, OH). The pH of the growth medium fed to the reactors R1, R2 and R3 was adjusted to 7. 5 at room temperature, which was sufficient to keep the reactor pH above 6. 0 for all residence times.
Biogas measurement The biogas production rate was measured using a wet tip gas meter (http://wetti pgasm eter. com/meter s. php) filled with water acidified using either H 2 SO 4 or HCl until pH < 2. Data were manually recorded or recorded by a data logger (HOBO Pendant Event, 64 K, Onset Computer Corporation, Pocasset, MA) at each reactor feeding. To measure the concentration of CH 4 , CO 2 , H 2 and N 2 gas a 0. 1-0. 5 mL gas sample was taken from a gas-sampling port in the gas-out line between the reactor and the gas tip meter and analyzed with a model 310 Educational Gas Chromatograph (SRI Instruments, Torrance, CA) with a thermal conductivity detector using a 1. 8 m × 3 mm (6′ × 1/8″) S. S. HayeSep D packed column (SRI Instruments, Torrance, CA). Helium (20 mL/ min) was used as carrier gas for CH 4 , CO 2 and N 2 with the column at 40 °C.
Fractional carbohydrate solubilization, FCS, was calculated using the following equation, with carbohydrate on a soluble monomer equivalent basis: where R o denotes the mass ratio of carbohydrate per dry solids (g monomer equivalent/g dry solids), S o denotes the solid concentration (g/L), C denotes the concentration of carbohydrate in the reactor (g monomer equivalent/L), and V denotes the volume of the reactor. R o and C are calculated for either total carbohydrate, glucan, or xylan as indicated in the text. All measurements were conducted in duplicate. Steady-state solubilization data reported in Fig. 3  are based on averages of two or more samples taken after at least three residence times following a change in residence time as detailed in Additional file 4: Table  S3.
Read processing, OTU assignment and phylogenetic analysis Partial 16S rDNA sequencing reads were preprocessed, operational taxonomic units (OTUs) were selected and diversity metrics calculated using the methods and pipelines described in  [53]. Briefly, reads were trimmed of primers with Cutadapt (v. 1. 9. 1)  [54] , and then processed using a combination of UPARSE  [55]  and QIIME (v. 1. 9. 1)  [56]. Quality filtering of OTUs less 0. 0001% was performed in accordance with recommendations from  [57] , and counts were subsequently rarefied to the lowest sample OTU count, 74597. Diversity metrics were visualized with MacQIIME 1. 9. 1  [56]  and Emperor 0. 9. 51 plots  [58]. Data were deposited into the National Center for Biotechnology Information (NCBI) Sequence Read Archive under Bioproject PRJNA395747.
Partial 16S rDNA sequences of OTUs with greater than 1% average relative abundance were aligned with the most similar cultured species found in the non-redundant NCBI database (March 1, 2016) using MUSCLE 3. 8. 425 (10 iterations)  [59]  in Geneious 8. 1. 5 [60]. PhyML  [61]  was used to construct a maximum likelihood tree, with the following parameters: generalized time reversible (GTR) substitution model with four substitution rate categories under gamma distribution, with rates inferred from the data and with no invariable sites. The tree topology was determined using nearest neighbor interchange and optimization for topology/length/rate, the node support values were determined by bootstrapping (100 bootstraps).
Binning, sample matching, and functional analysis Automated binning programs-MaxBin2 (v2. 2. 1)  [65] , MetaBat (v0. 32. 4)  [66] , and MyCC (v1)  [67] -were evaluated with four metagenome samples prior to binning all metagenome samples. Effectiveness of binning was based on metrics provided by CheckM (v. 1. 0. 7)  [68]  including average % completeness of bins, average % contamination of bins, average % strain heterogeneity of bins, and the number of bins. Default parameters of each binning program were tested. Since the default minimum contig length of MetaBat was 2500 nucleotides, Max-Bin2 and MyCC were also tested with this parameter. The "superspecific" MetaBat parameter setting was also tested. Mean completeness and % of strain heterogeneity of MaxBin2, MyCC, and MetaBat were very similar (Additional file 5: Figure  S2 ).
Then predicted proteins were compared among every pair of bins from every metagenome using RAPSearch2 (v2. 22) with default parameters  [70]. Two bins were considered to be from the same organism if at least 70% of the protein identities were > 90%. The final output of MapBin was a list indicating the most similar bins from one sample to another along with their proportion of highly similar protein matches. MapBin is available at https ://sourc eforg e. net/proje cts/mapbi n/. Simulated datasets described in  [71]  were used to test the effectiveness of MapBin, which successfully mapped three 20× coverage bins against 80× coverage bins as well as 30 low and medium complexity bins to high-complexity bins. The genome mapping accuracy was 100% (i. e. , all mapped genomes were found to belong to the same organism).
Remaining pairwise comparisons were imported into Cytoscape (v. 3. 4. 0). Bins connected by less than three edges were separated, and only networks containing bins from more than three metagenomes were further analyzed. Genetic potential was evaluated with the remaining bins using genetic markers of interest including carbohydrate-active enzymes (CAZymes), markers with KEGG orthology (KOs) for xylose metabolism, and protein family (pfams) markers for syntrophy and methanogenesis, which were derived from  [72]  and MetaCyc. org  [73]  (Additional file 7: Table  S4 ). Organisms were considered to encode a genetic marker if the majority of matching bins contained the marker. Taxonomy was assigned using CheckM  [68] , and manually updated if the average nucleotide identity % calculated with both BLAST+ and MUMmer using the JSpecies webserver were greater than 95%  [74].
Acknowledging these factors, the first-order rate constant we observed for carbohydrate solubilization (0. 53 day -1 ) is larger, and 3. 3-day RT shorter than most reports in the literature for methanogenic digestion of lignocellulosic feedstocks  [9, 10]. Anaerobic microbiomes fed mid-season switchgrass reached steady-state at each of the four RTs examined as indicated by biogas production and composition, pH, organic acid levels, and fractional carbohydrate solubilization. As compared to the intermittent feeding strategy used in our studies, data interpretation is somewhat more straight forward for reactors fed in a fully continuous manner. However, fully continuous feeding becomes infeasible at high solid concentration, theoretical frameworks for considering intermittently fed reactors are available, and such frameworks suggest that differences between fully continuous and intermittent feeding are not large as long as the fraction of culture volume replaced per feeding is relatively small  [92].
Acknowledgements We thank the  Vermont Technical College anaerobic digester team  for providing samples for the initial inocula and  Marvin Hall  and  Kay DiMarco  from the  Pennsylvania State University  for partially preparing the feedstock for this study. We thank  Sean J. L. Murphy  and  Robert S. Worthen  for help in reactor maintenance. Chris Schadt (Bioscience Division ,  Oak Ridge National Laboratory ) provided instructional support for building a phylogenetic tree in Geneious and  Michael Robeson  ( Interstitial Genomics, LLC ) provided scripts and consultation for 16S amplicon data processing and analysis. Metagenomic data were generated and annotated by the  US Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the US Department of Energy  under Contract no.
Foraging analysis and spatial usage Spatial and statistical analyses were performed using R 3. 2. 1 using the "stats" package (R Development Core Team 2015) and "adehabitatLT" package  (Calenge 2006 , Calenge et al. 2009). From the GPS recorded data, foraging trips were reconstructed and data were rediscretized to have one location each 40 min. Some of the trips were largely incomplete (return journey not initiated; n = 15 corresponding to the 15 min intervals) because of battery limitations and were removed from the analysis. For each complete (n = 40) and incomplete (return journey initiated; n = 7) foraging trip, we computed the following foraging indices: maximum distance to the colony (Dmax, km), average movement speed (MS, km h -1 ) and daily distance covered (Dday, km d -1 ). For each complete trip, we calculated the additional following metrics: total distance travelled (Dtotal, km) and trip duration (T, h).
The extent of overlap between male and female home ranges was estimated using Bhattacharyya's affinity (BA), which ranges from 0 (no overlap) to 1 (complete overlap). Using these metrics we performed a randomization procedure to test the null hypothesis that there was no difference in the spatial distribution of males and females at the population level  (Breed et al. 2006 ). The sex of each bird was randomly assigned using the observed sex ratio in our data set and the overlap metric between males and females was calculated for 25%, 50%, 75% and 95% kernels. We performed 1000 randomizations of our dataset from which the probability of accepting the null hypothesis was calculated as the proportion of random overlaps that were smaller than the observed overlap. Since we were testing only if the observed overlap was smaller than random overlap, we considered this as a one-tailed test.
5 ´ 12. 5 km resolution) from the Institut Français de Recherche pour l'Exploitation de la Mer (Ifremer, ftp://ftp. ifremer. fr/ifremer/cersat/products/gridded/psi-concentration/data/antarctic). We also used bathymetry data (ocean depth at one-minute horizontal spatial resolution) obtained from NOAA's ETOPO (https://sos. noaa. gov/datasets/etopo1-topography-and-bathymetry/) as an additional habitat variable. Daily sea ice concentration and depth values were extracted for each foraging location (therefore excluding the commuting part of the trips at sea) on each track using bilinear interpolation from the native ice and depth grids using "raster" package in R  (Hijmans 2018). Since snow petrels are highly associated with the sea ice region (as defined by the region within >15% sea ice concentration isocline,  Cavalieri et al.
Statistical analysis Isotopic niche of the two sexes was established using the metric SIBER (Stable Isotope Bayesian Ellipses), which is based on a Bayesian framework that confers a robust comparison to be made among data sets concerning different sample sizes  (Jackson et al. 2011). The area of the standard ellipse (SEA C , an ellipse having a 40% probability of containing a subsequently sampled datum) was used to compare female and male isotopic values and their overlap in relation to the total niche width (i. e. both sexes combined), and a Bayesian estimate of the standard ellipse and its area (SEA B ) was used to test whether females' isotopic niche is narrower than males' isotopic niche  (Jackson et al. 2011 ). The standard. ellipse and convexhull functions were used to calculate these metrics from SIBER implemented in the package 'SIAR'  (Parnell et al.
We also used the residuals to correct the trophic component associate with d 13 C by regressing these values upon d 15 N signatures in feathers (male: F 1,36 =1. 945, P=0. 172, r=0. 226; female: F 1,45 =5. 863, P=0. 020, r=0. 340). Foraging probability was modelled using a binomial generalized additive mixed model (GAMM) in the 'gamm4' package in R  (Wood et al. 2017). This allowed for the possibility of nonlinear responses to environmental covariates, which we expected. The response variable was the tracking location, which was coded as 1 for a foraging location and as 0 for a commuting location, and explanatory variables were sea ice concentration and bathymetry. Because interactions between the variable sex and environmental covariates would be difficult to interpret in complex nonlinear models, separate models were developed for male and female birds.
Models included sea ice concentration and bathymetry as fixed factors, and bird identity as a random term to account for pseudoreplication issues. The smoothing parameter was chosen automatically using generalized cross-validation. To model spatial auto-correlation an isotropic thin plate spline was included, set up as a two dimensional smoother based on both x and y coordinates  (Cleasby et al. 2015). To ascertain whether collinearity between covariates may have occurred we examined the correlations between environmental variables using a Spearman correlation coefficient since covariates were not normally distributed. We assumed that a correlation of greater than r s 0. 4 was problematic, but the correlation was below this threshold (r s = 0. 14). Foraging intensity was modelled using GAM in the 'gam' package in R  (Hastie & Tibshirani 1990).
2012). During the incubation and chick rearing period, they did not find evidence for habitat segregation and foraging areas largely overlapped. In the Adélie penguin (Pygoscelis adeliae) at Pointe Géologie, females foraged more intensively in areas of higher sea ice concentration than males during the guard stage, and there was spatial segregation between sexes with females foraging further from the colony than males  (Widmann et al. 2015). Using a multiyear comprehensive dataset,  Paiva et al. (2017)  found that sexual segregation in foraging areas and foraging habitats of Cory's shearwaters (Calonectris borealis) varied between years, with greater sexual (habitat and spatial) segregation during years when sea surface temperatures were higher and chlorophyll a concentrations were lower, presumably corresponding to lower food availability.
Acknowledgements This preprint has been peer-reviewed and recommended by  Peer Community In Ecology  (https://dx. doi. org/10. 24072/pci. ecology. 100025). The authors thank  G. Brodin  for his help in designing GPS loggers and extracting some GPS data,  J. Vasseur  who helped with deploying and recovering some GPS loggers and collecting tissue samples. The authors are grateful to the analytical Plateformes of the  LIENSs laboratory  for the access to their analytical facilities, and in particular to  M. Brault-Favrou  and  G. Guillou  for running stable isotope analyses. The work was supported financially and logistically by the  French Polar Institute (IPEV program  No. 109 ,  PI H. Weimerskirch ), the  French Southern Territories administration (TAAF). This study is a contribution to program SENSEI funded by the  BNP Paribas Foundation.
Study Design and Population The experimental data are hospital-based. We have analyzed data recorded in the atria of 8 patients with persistent or chronic AF, chosen without any prior explicit exclusion criteria. These patients were enrolled to undergo radio frequency ablation between 2010 and 2012, in the international cardiac electrophysiology service of public hospital CHU Haut-Lévêque in Pessac, France. All patients gave written informed consent to the investigation of data from the intervention. A protocol for clinic research was approved by the institutional Clinical Research and Ethics Committee. For this specific investigation of the data, the authors accessed fully anonymized and de-identified data. As representative of the results obtained with our set of patients, we report in this manuscript the results of a detailed wavelet-based multifractal analysis of five long time series specially recorded in one of the 8 patients with chronic AF.
Microarray analysis. Total RNA (50 ng per sample) for 12 RNA samples (4 replicates per condition) was amplified and fluorescently labelled to produce Cy5 or Cy3 cRNA using the Low Input Quick Amp Labeling two color Kit (Agilent, Santa Clara, USA) in the presence of spike-in two colors control as recommended by the manufacturer. After purification, 825 ng of labeled cRNA were hybridized onto G4845A Human GE 4 × 44 K v2 microarray (Agilent, USA) according to the manufacturer's instructions. The G4845A Human GE 4 × 44 K v2 microarray contains 27958 Entrez Gene RNAs sequences. Following hybridization, Agilent G2505 scanner (Agilent, USA) was used to scan microarrays and data were extracted using Feature Extraction software (Agilent, USA) using linear and Lowess normalization. Statistical analyses were done using R 2.
1 software (http:// www. r-project. org) after controlling the variance of genes. Data were analyzed with a Student's t test to identify differentially expressed genes and values were adjusted using the Benjamini-Hochberg correction at 1% aiming to eliminate false positives. Genes obtained were referred to as the "differentially expressed genes". miRNA expression analysis. The impact of epicatechin metabolites on the expression of miRNAs was analyzed using Human miRNA (V3) 8 × 15 K microarrays (Agilent, Santa Clara, USA) covering 866 mature miRNAs. MiRNAs (n = 12) were labeled using miRNA labeling and hybridization kit from Agilent technologies (Agilent, Santa Clara, USA) as recommended by the manufacturer. Briefly, 100 ng of each total RNA sample were treated with calf intestinal phosphatase for 30 min at 37 °C before denaturing the samples using pure DMSO at 100 °C for 5 min and rapid transfer in an ice water bath to prevent RNA reannealing.
RNA samples were labeled with pCp-Cy3 using T4 RNA ligase by incubation at 16 °C for 2 h. After purification with microBioSpin columns, labeled samples were hybridized to Agilent human miRNA microarrays, which contain probes for human microRNAs from the Sanger database. Hybridizations were performed for 24 h at 55 °C after which the microarrays were washed in GE Wash Buffer 1 and GE Wash Buffer 2 for 5 min. Following washing step, the microarrays were scanned with Aligent Microarray Scanner (Agilent, Santa Clara, CA, USA). The scanned images were analyzed using Feature Extraction Software (Agilent, Santa Clara, CA, USA). Genespring GX10 software (Agilent, Santa Clara, CA, USA) was used to quantify the signal and background intensity for each feature and to substantially normalize the data by the 75th percentile method.
The membranes were incubated with secondary fluorescent goat anti-rabbit and goat anti-mouse IgG (H + L) antibodies (1/20000) from Genetex (Euromedex, Mundolsheim, France). Proteins were visualized by Odyssey Li-Cor (Lincoln, Nebraska, USA) detection system and density of the signals was quantified using Odyssey software. Samples were normalized to β Actin housekeeping protein. The expression of β Actin was used as the control for protein loading. Docking metabolites/protein interaction analyses. Human p38α MAPK crystal structure 4F9Y  25  was retrieved from the RCSB Protein Data Bank (PDB). The retrieved structure of P38MAPK was preprocessed in Schrödinger maestro, which includes assignment of partial charges, addition of missing atoms and chain residues. Restrained energy minimization was performed by OPLS5 force field to obtain stable structure for docking.
The hybridization procedure was performed at 48 °C overnight (16-20 h) using an Illumina Hybridization oven. After hybridization, free DNA was washed away and the BeadChips were processed through a single nucleotide extension followed by fluorescent readout of the incorporated base using a Freedom EVO robot. Finally, the BeadChips were imaged using an Illumina iScan (Illumina, San Diego, CA, USA). Detection p-values were calculated to identify failed probes as per Illumina's recommendations. No arrays exceeded our quality threshold of >5% failed probes. Filtering of bad quality probes and normalization of raw methylation beta values was conducted using RnBeads package in R software  28. Probes with detection p-values higher than 0. 01, overlapping with SNPs at the last 3 bases in its sequence or containing missing values were excluded.