dataesr/scientific-paragraphs-categorization huggingface dataset:
The haplotype found downstream River Nora (PaAst3-03, San Claudio location) was in a separate clade supported by a bootstrap value of 65, containing the haplotype JN639014 found in Estonia and Wales; the haplotype JN639014 found in Hammond Harbor in Oregon and Devils Lake in Wisconsin; and the New Zealand haplotype AY955393 (North Island). The haplotypes of New Zealand South Island were also separated in this tree, the haplotype from River Collins being monophyletic with the upstream and midstream Asturian samples and other European references, and the Onomalutu River haplotype exhibiting an intermediate and less clear position in the middle of the two branches (Fig  5 ). Indeed the haplotypes obtained from Asturias water samples (blue circles in Fig  5 , GenBank accession numbers KU933000-KU933002) matched perfectly with the haplotypes of the individuals found from the same place.
All bioinformatic steps were done using the USEARCH package and following the UPARSE pipeline  (Edgar, 2013)  unless mentioned otherwise. Briefly, lowest quality tails were truncated from all reads and the paired reads merged using the -fastq_mergepairs command and yielding between 25 221 and 604 457 reads of average length > 400 nt per sample. Merged reads were then quality filtered, reads shorter than 350 nt discarded, and converted to FASTA. All FASTA files were concatenated for OTU clustering, with the following steps: dereplication at 100 % identity and keeping infor-mation on the weight of each unique sequence, sorting of unique sequences by decreasing weight and discarding of singletons (weight = 1), and clustering into OTUs with the -cluster_otus command using a maximum dissimilarity of 2 %.
The last steps includes chimera filtering  (Edgar, 2013). Finally, the merged reads from the different samples were mapped back onto the generated OTUs using VSEARCH v1. 1. 3 (Rognes et al. , 2015)  with at least 98 % identity to create the final OTU table. For taxonomic classification of the OTUs, they were submitted to https://www. arb-silva. de/ngs/ and classified using the SILVA SSU taxonomy and database release SSU 119. 1  (Quast et al. , 2013). Cyanobacterial OTUs that SILVA had only classified to family level were manually curated by BLASTN searches of these OTUs against NCBI's nucleotide collection. If a hit with > 95 % sequence identity was found, the genus of this classification was added to the OTU table. For all further use, relative OTU abundances in each sample were calculated.
Calculation of diversity measures. The Shannon and Simpson indices as measures of diversity were calculated separately for M1 and Nouméa lagoon waters using the diversity function in the R package vegan  (Oksanen et al. , 2015). (Braak, 1986). The variables were the logtransformed biogeochemical stocks and fluxes, the objects were the 34 dominating OTUs in terms of abundance (expressed in relative OTU abundance), and the sites were sampling time and origin of the samples. Non-metric multidimensional scaling (NMDS) was used to find the best display of differences in 16S-derived community structures. The full OTU table containing relative abundances of each OTU per sample, with samples as rows and species as columns, was used for unconstrained ordination with the R package vegan  (Oksanen et al.
95. Like this, we could consider clusters with non-overlapping ellipses as significantly different from each other at a significance level α = 0. 05. Permutational multivariate analysis of variance (PERMANOVA) was conducted using the adonis function in the R package vegan on the Bray-Curtis distance matrices described above. We tested the dependence of sample distances on the factors location (M1 or lagoon waters), depth (1 or 12 m), and time (separation of samples into three phases: days 2-8, 10-16, and 18-21).
At the same time in M1, Prochlorococcus inferred abundance reached a maximum of 37 000 cells mL -1 on day 6, corresponding to ∼ 16 000 cells mL -1 counted with flow cytometry (Figs. 2a  and 3f ). Variations in Synechococcus abundance correlated with changes in Chl concentrations, except for days 2-5 with high Synechococcus cell counts but low Chl concentrations  (Figs. 1b, 2a). N 2 -fixing cyanobacteria were generally 1 to 2 orders of magnitude less abundant than Prochlorococcus and Synechococcus and were in M1 most prevalent on days 2-4, owing mainly to Trichodesmium with ∼ 5000 cells mL -1 (Fig. 3f ). After the DIP fertilization, Trichodesmium 16S tags dropped quickly by a factor of 10, leaving Calothrix as the most abundant diazotroph until day 12. Important to mention, the SILVA database used for 16S classification lists all Richelia intracellularis 16S se-quences as descendants of the Calothrix node.
Second, flow cytometry might miss some Syn or Pro, if cells are part of aggregates. This would matter if the fraction of Syn or Pro in aggregates changed between samples. To test for biases, we calculated the ratio of Syn to Pro for 16S-copy-numbernormalized 16S read counts (16S-norm reads) and for flow cytometry counts. In an error-free scenario, both should give the same ratios. With a systematic error source, e. g. , constant percentage of Syn in aggregates, the ratios would differ by a similar value in all samples. However, we observed varying ratio differences (Supplement Table  S1 ). In M1, Pro was either underestimated in flow cytometry or Syn underestimated in the 16S data, leading to higher Syn/Pro ratios in flow cytometry data than in 16S data for most samples. In the lagoon, this was the case until day 16, the samples after that showed the opposite.
). Our 16S-based diazotroph abundances compare reasonably well with those found by quantitative PCR of the nifH gene (Turk-Kubo et al. , 2015), if we assume that those 16S reads classified as Calothrix are indeed mostly Richelia (Het-1). The SILVA database positions Richelia below the Calothrix node, and the metatranscriptome data published in this issue show a substantial Richelia read fraction and only little Calothrix.
Acknowledgements. Funding for this research was provided by the  Agence Nationale de la Recherche  ( ANR starting  grant  VAHINE ANR-13-JS06-0002 ),  INSU-LEFE-CYBER program ,  GOPS ,  IRD , and  MIO. The participation of UP and WRH was supported by the  German-Israeli Research Foundation (GIF) , project number  1133-13. 8/2011 , and the MiSeq-based microbial community analysis by the  EU  project  MaCuMBA (Marine Microorganisms: Cultivation Methods for Improving their Biotechnological Applications ; grant agreement no. 311975 ) to  Wolfgang R. Hess. The authors thank the captain and crew of the  R/V Alis. We acknowledge the SEOH divers' service from the  IRD research center of Nouméa  ( E. Folcher ,  B. Bourgeois , and  A. Renaud ) and from the  Observatoire Océanologique de Villefranche-sur-Mer  (OOV,  J.
MISSION PRODUCTS The MERLIN mission will provide the following data products: Level 0: for each individual laser shot the rangegated backscatter signal data together with auxiliary data such as ranging information, laser pulse energy, time, wavelength in a chronological data sequence. Level 1a: Level 0 data with all calibrations computed and appended. Level 1b: integrated signal at echo for off-and onwavelengths, slant-path DAOD, shot-by-shot and averaged, geo-location and time. Level 2: column-weighted dry-air mixing ratios of methane (XCH4). Level 3: visualizations of level 2 data such as XCH4 maps. Aside from these data products, directly delivered by the MERLIN mission, level 4 data products are methane surface fluxes at various temporal and spatial scales, obtained through assimilation in transport model.
Level 4 data products will not be part of the official MERLIN mission but provided by scientists at various scientific research centers using lower level MERLIN data. In the context of the overall mission objective, additional data products can be derived from MERLIN. MERLIN secondary products can provide information about the surface (topography, estimates of the vegetation height and occasionally on the vertical structure, lidar retro-reflectance) and the atmosphere (cloud boundaries, including cloud base for small to moderate cloud optical thickness).
Three rinses in PBS were performed between all steps, except between saturation of non-specific protein binding sites and incubation with primary antibody. The corneal pieces were finally placed on glass slides, covered with Vectashield Mounting Medium (H-1000, Vector Laboratories) and gently flattened using a large glass coverslip retained immobilized by adhesive tape. Experiments were done at RT unless otherwise stated. In order to analyze morphologic changes after disruption of the tight junctions, other samples were incubated with a saturated solution of ethylenediaminetetraacetic acid (EDTA) in PBS without Ca 2+ or Mg 2+ for two and a half minutes at RT. Corneas were then immediately fixed in 0. 5% PFA for immunostaining as described above. For all labeling, the images were first acquired using an epifluorescence inverted microscope IX81 (Olympus, Tokyo, Japan) equipped with the Cell^P imaging software (Soft Imaging System GmbH, Munster, Germany).
Acknowledgements We would like to thank  Chantal Perrache (BiiGC)  for technical help,  Florian Dergandi  ( Laboratory of Anatomy, Faculty of Medicine, St-Etienne, France ) for tissue procurement, and  Andre Choo  ( Bioprocessing Technology Institute, Singapore ) for the kind gift of the Prdx-6/antibody Tag-2A12. This work was supported by the  University Jean Monnet (St-Etienne, France).
Since the rearrangement process is the basis of repertoire diversity, it is important to study its distribution quantitatively. With recent advances in high throughput sequencing, there is a growing body of data on repertoires for both T and B cells, in a variety of situations. Using large sequence datasets of rearranged, non-productive genes, the probability distribution of rearrangement events in human TCR beta chain and BCR heavy chains could be inferred using statistical methods, gaining important insights into the random processes underlying repertoire diversity  [1, 2]. However, these studies are based on a brute force approach, which enumerates every possible rearrangement scenario for each observed sequence. This is a very computationally costly procedure, which is unrealistic for very large datasets.
Yet the non-productive rearrangements remain and are part of the sequence dataset. Since these sequences have no effect on the cell in which they reside, they have not been selected. Studying their statistics is thus equivalent to studying the generation process itself, with no selection. While the model specifies the distribution over rearrangement scenarios, the data consists of recombined sequences, denoted by s, which can be the result of different scenarios r. The recombination events are hidden variables, and the likelihood of a sequence is the sum of the probabilities of all scenarios leading to it, P (s) = r→s P rearr (r). The likelihood of the sequence dataset cannot easily be maximised with respect to the model parameters. To overcome this problem, the Expectation-Maximisation (EM) algorithm can be used to maximise the likelihood in an iterative manner.
In each iteration, new model parameters θ are chosen to increase the likelihood until the maximum is obtained. In the expectation step, the log-likelihood of hidden rearrangements is averaged over their posterior distribution under the current model θ, to form Q(θ |θ) = n a=1 r P (r|s (a) , θ) log P arran (r|θ ), where the sum on a runs over the n sequences (s (1) , s (2) ,. , s (n) ) in the dataset. The maximisation step consists of maximising Q(θ |θ) over θ to obtain the new parameter set. Because of the simple factorised form of Eq. 1, this second step is equivalent to calculating the frequency of each rearrangement event under the posterior P (r|s (a) , θ) = P (r, s (a) |θ)/P (s (a) |θ). For example, P (V, J) is updated according to: P (V, J) = 1 n n a=1 r:V,J P (r|s (a) , θ), (2) where the sum on r : V, J runs over scenarios with gene choices V and J.
A. Implementation The method was implemented in C++ (std11) as a command line tool. OpenMP was used for parallelization. The main pipeline has two parts, the alignment module and the inference module. The input for the entire pipeline is a FASTA or plain text file with unique recombined and non-productive nucleotide sequences, and FASTA files with the genomic templates for the different V, J germline segments (as well as D in the case of heavy or beta chains). Genomic files can be obtained from genomic databases such as IMGT  [20]. The alignment code was written to read IMGT FASTA files and extract gene names. Between the alignment and inference procedures, alignments below a certain threshold are discarded to improve performance. The threshold can be tuned for different data sets. Sequences that do not align well to at least one known gene of each type are completely eliminated as a quality control.
B. Application to alpha chain data The algorithm was applied to human TCR alpha chain sequences from  [21]. The data consist of around 80,000 non-productive sequences, each 151bp long, covering both the V and J segments. Sequences were aligned to lists of genomic sequences from the IMGT online database  [20] , and then given as input to the inference algorithm. The model converged rapidly as can be seen by the quick saturation of the likelihood (Fig. 3a ). The entropy of the rearrangement process quantifies the diversity of possible scenarios. It was calculated using Eq. 10 and found to be 32 bits (Fig. 3b , top line). This entropy can be partitioned into contributions from each of the rearrangement events -segment choice, insertions and deletions (bottom line). The largest contribution to the entropy comes from the insertions, followed closely by gene choice.
This program has been pursued in  [22]  at tree level in QCD and in SCET  [23]. In this work we improve on the previous LCSR work  [2]  by • computation of the full twist-4 (and partial twist-5) 2-particle DAs contribution to the FF (appendix B -available a downloadable Mathematica notebook), • determination of the DAs G v,a ⊥ (twist-5), in the asymptotic limit, filling a gap in the literature (appendix B), • discussing the impact of the EOM on uncertainty correlations (section 2. 2), including the aspect of scheme-dependence (appendix A. 5), • explicit verification of the EOM at tree level (appendices A. 3,A. 4) , for the asymptotic 2-particle DAs including O(m s )-corrections. • verification of the compatibility of the composite operator renormalisation with the EOM (appendix A. 5. 1), • discussion of non-resonant background for vector meson final states (section 2.
red, LCSR points in blue, the gray solid band shows the combined 3-parameter fit and the red dashed band the 2-parameter lattice fit from Ref. [11]. In the lower left plot T 1 > T 2 for q 2 > 0 GeV 2 above. We would like to add that the fits are valid under the premise that the LCSR and lattice QCD data points and their uncertainties, including correlations, are valid as well as the zexpansion being a reasonable model function. There is no evidence against the latter, as we have found that adding higher orders in the z expansion and using different parameterisations does not change matters. This is an aspect that could change in the future with more precise FF determination from LCSR or lattice QCD. Overall the agreement is good. For the central values, we find a χ 2 of 7. 0 for B → K * , 10.
C th is the theoretical covariance matrix that includes in particular the correlated FF uncertainties  19. In the case of Belle, we use the data up to q 2 = 8 GeV 2 or 12 GeV 2 and take the full covariance matrix provided in Ref. [89]. The BaBar dataset consists of a single bin in the low-q 2 region from 0 to 8 GeV 2 and the correlation between the charged and neutral decay is not provided. For B → ρ ν we obtain |V ub | B→ρ ν Belle, q 2 <8GeV 2 = (3. 36 ± 0. 17 ± 0. 34) × 10 -3 , (42) |V ub | B→ρ ν Belle, q 2 <12GeV 2 = (3. 25 ± 0. 14 ± 0. 34) × 10 -3 , (43) |V ub | B→ρ ν BaBar, q 2 <8GeV 2 = (2. 52 ± 0. 42 ± 0. 56) × 10 -3 , (44) and those from B + → ω + ν we get |V ub | B→ω ν Belle, q 2 <7GeV 2 = (2. 49 ± 0. 34 ± 0. 32) × 10 -3 , (45) 2. 0 2. 5 3. 0 3. 5 4. 0 4. 5 B→ρl ν LCSR + Belle, q 2 < 8 GeV 2 B→ρl ν LCSR + Belle, q 2 < 12 GeV 2 B→ρl ν LCSR + BaBar, q 2 < 8 GeV 2 B→ωlν LCSR + Belle, q 2 < 7 GeV 2 B→ωlν LCSR + BaBar, q 2 < 8 GeV 2 B→ωlν LCSR + BaBar, q 2 < 12 GeV 2  from exclusive  [80]  and inclusive channels  [89]  and indirect determinations from fits of the unitarity triangle  [93, 94].
In the BaBar and Belle analysis the S-wave has not been subtracted systematically. Hence if the precision below the 10%-level is to be reached then the experimental analyses have to perform an angular analysis 20  in order to subtract the S-wave. Leaving aside the inclusive determinations we conclude that our |V ub |-values from the combined Belle and BaBar analysis are somewhat lower but surely consistent with B → π ν determinations as well as the global CKM fits. The values of |V ub | which are considerably lower than the average come with large experimental uncertainties and are consistent at the level of one standard deviation. The uncertainty is rather large and an updated analysis with the full BaBar data set will be more telling.
Acknowledgments We thank  Wolfgang Altmannshofer ,  Frederik Beaujean ,  Martin Beneke ,  Christoph Bobeth ,  Peter Boyle ,  Vladimir Braun ,  Gerhard Buchalla ,  Jérôme Charles ,  Gilberto Colangelo ,  Greig Cowan ,  Luigi Del Debbio ,  Jochen Dingfelder ,  Thorsten Feldmann ,  Gudrun Hiller ,  Lars Hofer ,  Fred Jegerlehner ,  Alex Khodjamirian ,  Andreas Kronfeld ,  Vera Luth ,  Kim Maltman ,  Stefan Meinel ,  Mikolai Misiak ,  Matthias Neubert ,  Nils Offen ,  Steve Playfer ,  Christoph Schwanda ,  Peter Stoffer ,  Javier Virto ,  Yuming Wang , and  Matthew Wingate  for useful discussions. R. Z. is particularly grateful to  Patricia Ball  for collaboration on B → V form factor computations in the past. The research of D. S. is supported by the  DFG cluster of excellence "Origin and Structure of the Universe".
, 1998; Nevitt et al. , 2004; Ni et al. , 2009). ScYap5 is responsible for the activation of the high iron stress response  (Li et al. , 2008; Pimentel et al. , 2012). ScYap7, the ohnolog of ScYap5, was recently found to be a repressor of the nitric oxide oxidase encoding gene YHB1  (Merhej et al. , 2015)  and ScYap8 is involved in the response to arsenic  (Bobrowicz et al. , 1997; Amaral et al. , 2013; Kumar et al. , 2015). C. glabrata has 7 Yap members, which slightly differ from the S. cerevisiae family (Supplementary File S1). Orthologues of Yap1 (CAGL0H04631g), Yap2 (CAGL0F03069g, named CgYap1 thereafter), Yap5 (CAGL0K08756g, named CgYap5 thereafter), and Yap7 (CAGL0F01265g, named CgYap7 thereafter) are present, but Yap8 is absent. Two versions of Yap3 (CAGL0K02585g and CAGL0M10087g, named CgYap3a and CgYap3b thereafter) and only one ortholog for the Yap4 and Yap6 pair (CAGL0M08800g, named CgYap4/6 thereafter) are found.
Peak calling was performed with the bpeaks software  (Merhej et al. , 2014) , using both the Input DNA and the mock IP as references. For peak calling using the Input DNA as reference, the bpeaks parameters were T1 = 2, T2 = 2, T3 = 1. 5, T4 = 0. 7. For peak calling using the Mock IP as reference, the bpeaks parameters were T1 = 2, T2 = 2, T3 = 1. 5, and T4 = 0. Only the peaks which were found by the two analyses were kept for further processing. These peaks were then manually checked on a genome browser  (Thorvaldsdottir et al. , 2013)  to discard artifactual peaks (e. g. , peaks centered on a tRNA locus or perfectly overlapping a highly expressed ORF) which would have escaped the bpeaks filter (Supplementary Files S3, S4). The ChIP seq data can be downloaded from the GEO database (accession number: GSE77904).
, 2015). Two biologically independent experiments were performed for each condition, using dye switch. We used custom C. glabrata Agilent arrays in an 8 × 60 k format (array express accession number: A-MEXP-2402). After overnight hybridization and washing, the slides were scanned using a 2micron Agilent microarray scanner. The images were analyzed using the feature extraction software (Agilent technologies) and normalized using global LOESS  (Lemoine et al. , 2006). The mean of the biological replicates was calculated. A gene was considered as being differentially expressed if its mean absolute Log2(fold change) value was more than 0. 75 and if its expression variation was considered as being statistically significant using the LIMMA package with a cut-off p-value of 0. 02  (Ritchie et al.
Network Building The ChIP peaks were assigned to genes as described previously  (Merhej et al. , 2014). When a peak was located in a divergent promoter (i. e. , an intergenic region in between two divergent genes) the two genes were fused in one target in the network named "gene 1/gene 2, " unless we had transcriptome evidence supporting the regulation of one of the two genes. In this case, only the name of the regulated gene was kept. The network was represented using the igraph library (igraph. org, R programming language;  Csardi and Nepusz, 2006) , combining three different types of information (Supplementary Table  1 ). The ChIP parameter was used to define interactions (arrows) between the different Yap factors and their target promoters. The transcriptome parameter was used to color arrows depending on the directionality of the regulation (activation, repression, or no detected change).
Gene Ontology and Gene Set Enrichment GSEA were performed using the GSEA module of the Jexpress software suite with a cut-off FDR of 1%  (Subramanian et al. , 2005; Stavrum et al. , 2008). We used as input files transcriptome analyses of the responses of C. glabrata wild type cells to fluconazole  (Kuo et al. , 2010b) , sodium salt  (Roetzer et al. , 2008; Wapinski et al. , 2010) , heat shock  (Roetzer et al. , 2008; Wapinski et al. , 2010) , hydrogen peroxide  (Wapinski et al. , 2010; Roetzer et al. , 2011) , menadione  (Roetzer et al. , 2011) , glucose starvation  (Roetzer et al. , 2008) , sorbic acid  (Jandric et al. , 2013) , iron excess and iron starvation (this work, E-MTAB-4457), cadmium chloride and sodium selenite  (Thiebaut et al. , unpublished data).
Hence, we identified the genes for which expression was altered, directly or indirectly, in the absence of the corresponding transcription factor. To build a network from these three sources of information, we used a scoring system based on simple but meaningful logical rules (Supplementary Table  1 ). Briefly, a regulatory interaction was included in the network if it was detected by ChIP-seq. Then, the interactions and the edges were differently labeled depending on the transcriptome and TFBS data (Figure  1 ). As a consequence, the different interactions in the final network do not have the same value, depending on whether they were supported by one, two, or three experimental evidences (Supplementary Table  1 ). The final CgYap network contained 6 transcription factors and 255 regulatory interactions involving 214 promoters and 309 potential target genes (Figure  1 ).
Overlap between the CgYap Network and Stress Responses in C. glabrata We next used Gene Set Enrichment Analyses (GSEA;  Subramanian et al. , 2005)  to look for enrichments of our CgYap target sets in transcriptome analyses of C. glabrata wild type cells to various environmental stresses. The list and origin of the transcriptome data which were used can be found in the methods. As could have been expected from the results presented FIGURE 3 | Functional annotation of the CgYap network. Gene ontology analyses were performed on the target sets of the CgYap network. The main enriched categories are represented here by the colors of the corresponding targets (color code on the bottom left). "Oxido-reduction" corresponds to the GO categories "oxidation-reduction process," "response to oxidative stress," and "oxidoreductase activity.