dataesr/scientific-paragraphs-categorization huggingface dataset:
Compared with the assemblies generated by each assembler, a hybrid set of assemblies of superior contiguity and accuracy was obtained by CISA. Next, 102 hybrid contigs were given, resulting in a total genome size of 5,686,107 bp and an N 50 of 107,803 bp. The result-ing contigs were ordered by alignment to the reference genome of E. coli O26:H11 strain 11368 (10) using Mauve Contig Mover version 2. 3. 1  (11, 12). Functional annotation was carried out using the tools of the MicroScope platform  (13) , and the annotated genome was implemented in the public EscherichiaScope database (see https://www. genoscope. cns. fr/agc/microscope/home /index. php). The assembly contains 5,781 genomic objects, among which are 5,611 coding sequences, 10 rRNA genes, 92 tRNA genes, and 68 noncoding RNAs. The genome GϩC contents were relatively close to those of the reference genomes (50.
6%). A detailed report of the phylogenetic analysis of this draft sequence will be included in a future publication. Nucleotide sequence accession numbers. The draft genome sequence of the E. coli O26:H11 strain 21765 in this study has been deposited as a whole-genome shotgun project at the European Nucleotide Archive (http://www. ebi. ac. uk/ena/data/view) under the contig accession numbers CDLB01000001 to CDLB01000102.
The identification of the strain using API 1 20E and 32GN systems (Biomerieux, Marcy l'Etoile, France) was unambiguously Yersinia pseudotuberculosis. Bacterial identification of the strain was performed by 16S rRNA sequencing as previously described;  1  we obtained a 470 bp sequence, which was found to differ at only 1 nucleotide position from that of Y. pseudotuberculosis and Y. pestis (GenBank CP002956 and CP001048, respectively), consisting of 99. 8% similarity. We used protein profiling through the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (Bruker Autoflex I MALDI TOF mass spectrometer) to identify the strain, as described. 2 We obtained a spectrum with no reliable identification: scores were higher than 2 (up to 2. 22) for both Y. pseudotuberculosis and Y.
Statistical Analysis There was no previous clinical trial to compare the C. fistula extract emulsion with placebo. Therefore, this study was designed as a pilot study to calculate the suitable sample size for future precise randomized clinical trials. We set a sample size at n = 13. To ensure accuracy, all data obtained from three sets of independent experiments were entered into a data sheet twice, and were independently reviewed by experts in the field. All statistical calculations were then performed using Graphpad prism for Windows software (version 5). Paired sample t-test was used for assessing the variation between the active formulation and placebo. ANOVA (Analysis of variance) with Dunnett Multiple comparisons post-test was used to find the correlation between baseline values and the different time intervals values.
Also, we aim to compare our data (obtained from macro-emulsions) with those that will result from micro-emulsion and nano-emulsion-based experiments assessed in several age groups, including the elderly. Eventually, we intend to identify and purify the active ingredients present in the crude extracts of C. fistula's pods in order to unravel their molecular mechanisms and precisely determine their pharmaco-toxicological aspects, notably by employing our state-of-the art fluoro-carbon spectroscopy-derived tools and methods  [63].
Acknowledgments This work has been supported by the grant number:  074-0992-BM4-073  awarded by the  Higher education commission (HEC) of Pakistan. The authors are grateful to HEC, to  Mahmood Ahmad ,  Dean of the Pharmacy department, Islamia University of Bahawalpur, Pakistan , and to  Bouzid Menaa , expert in chemical sciences and nanobiotechnology,  Hymetec SA ,  Belgium.
For anti-phospho-IRS-1 (Ser 302 ), anti-phospho-p38 MAPK and anti-p38 MAPK antibody, the molecular weight and amino acid sequences were monitored in the SIGENAE database  [37]  to check for a good conservation of the antigen sequence. After washing, membranes were incubated with an IRDye Infrared secondary antibody (LI-COR Biosciences, Lincoln, NE, USA). Bands were visualized by Infrared Fluorescence using the Odyssey Imaging System (LI-COR Biosciences) and quantified by Odyssey Infrared imaging system software (Version 3. 0, LI-COR Biosciences).
Statistical analysis The results of western blot (N=4) and gene expression (N=6) are expressed as means + s. e. m. and were analyzed using one-way ANOVA, followed by a Tukey test. In cases where data were nonparametric or not homoscedastic, data transformations, such as logarithms, square roots and reciprocals, were used to meet ANOVA criteria. Normality was assessed using the Shaprio-Wilk test, while homoscedasticity was determined using Levene's test. For all statistical analyses, the level of significance was set at P˂0. 05.
Acknowledgments W. Dai  gratefully acknowledges the financial assistance provided by the  China Scholarship Council  ( CSC, File No. 2011633111 ) for his doctoral fellowship. We thank  K. Dias ,  V. Véron ,  A. Herman  and  M. Cluzeaud  for technical assistance in the laboratory. We also acknowledge the technical staff of the  INRA experimental fish farm at Saint Pee sur Nivelle (P. Aguirre  and  Y. Mercier ) for fish rearing.
Protein was precipitated with TCA (12. 5% final), and total protein concentration was measured using a Bio-Rad protein assay. Samples were read using a scintillation counter, and data were expressed as the number of counts per minute per microgram of protein and normalized to the 35 S-Met/Cys ACSF used for incubation. Western blotting. For Western blotting, metabolically labeled hippocampal homogenates were taken before TCA precipitation and boiled in Laemmli sample buffer, resolved on SDS polyacrylamide gels (Bio-Rad), transferred to nitrocellulose, and incubated overnight in primary antibodies for p-ERK1/2 (Thr202/Tyr204; Cell Signaling Technology), ERK1/2 (Cell Signaling Technology), and ␤-actin (Abcam). Blots were incubated with their respective fluorophore-conjugated secondary antibodies and imaged on an Odyssey imaging system (LiCor Biosciences).
Research Council studentship (S. A. B. ),  Medical Research Council  Grants  G0700967 (P. C. K. )  and  MR/K014137/1  (D. J. A. W. ), the  Patrick Wild Centre , the  RS MacDonald Charitable Trust , a  National Alliance for Research on Schizophrenia and Depression Young Investigator  grant from the  Brain and Behavior Research Foundation (L. S. W. ),andFrance-BioImagingGrantANR-10-INSB-04(U. V. N. ). WethankourcolleaguesinthePatrickWildCentrefor theirconstructiveinputduringthecourseofthisstudy.
Species and Protected Area data We base our analysis on the species listed on IUCN's Red List of Threatened Species  [31]. As part of their assessment of global biodiversity, IUCN has developed species distribution maps for thousands of species of vertebrates and plants. Species distribution in this dataset is delineated by the known extent of occurrence, along with expert knowledge of habitat preferences and habitat suitability. The polygons depict the species distribution range, without implying that a species occurs everywhere within that polygon. Even though the IUCN spatial data does not give a precise description of species occurrence, it is the best available global dataset on where species are likely to occur. From the IUCN dataset we include all African species in our analyses that have spatial data and are categorized as extant, probably extant or possibly extant.
This resulted in 6527 species from all IUCN listed taxonomic classes, including mammals, birds, reptiles, amphibians, fish, corals and a small number of insects, snails, ferns and flowing plants. Information on African PAs was derived from the World Database of Protected Areas  [32]. Since 1981 UNEP-WCMC, through its Protected Areas Programme, has been compiling and sharing global information on PAs, which range from strictly conserved National Parks to Community Reserves with human presence. From the entire WDPA dataset we selected all PAs for African countries, including Madagascar, for which PA boundaries are available. We included all PA management categories, but omitted very small parks with an area smaller than 100 ha and the PAs for which the park boundary location was unknown (represented by square polygons in WDPA).
The SAI for a PA was calculated as the sum of the image counts for each species that occurs in the PA based on the intersection between the IUCN Red List range data and the WDPA PA location. All species which have a range overlapping with a PA location were included; even if the area of intersection was very small. Per PA the SAI value is normalized to 0 to 1, following a simple nontransformative min-max normalization, making the results easier to present. For this transformation we subtracted the minimal value of SAI for all PAs in Africa from the SAI of that PA, and divided this by the range of SAI values for all African PAs.
Contribution of Red List species to nature-based tourism We subsequently explored if species with different conservation statuses contribute differently to the total attractiveness for tourism of a PA. IUCN groups Red List species that occur in the wild by the following increasing ranks of conservation concern: Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN) and Critically Endangered (CE). Evaluated species lacking assessment data are labeled by IUCN as Data Deficient (DD), whereas non-evaluated species are null in the dataset. Using a non-parametric Kruskal-Wallis test, we tested for differences between the Red List status and the number of posted images (with H0: no differences in distribution of photo counts are observed among the included Red List status classes CR, EN, VU, NT and DD).
Species attractiveness and tourism potential Intersecting the selected IUCN Red List species range data with the location of the African PAs resulted in a list of 2473 species that are assumed to occur within these PAs. Most of these  species are mammals (513), followed by bird species (365), amphibians (439), fish (253), reptiles (129), coral species (269) and the remaining 236 species of jellyfish, snails and plants. The Lion is by far the highest scoring species by photo counts, followed by the African Elephant, Leopard and Cheetah (Table  2 ). The first more uncommon species is the Red Kite (note, this search was performed on 'Milvus milvus' and its common name 'Red Kite' combined) occurring in northern Africa. In Fig  2  we plotted the number of images found in Flickr against the image count rank per species.
Relating SAI, species richness and conservation strategies We explored what type of species show up most often according to our search criteria in Flickr and therefore contribute most to a high SAI at the PA level. We found that the number of photos posted is positively correlated with the extent of species range, but only to a small extent (rho 0. 41, or when 0 counts excluded rho 0. 29, for p = 0. 05). We also looked for associations between the IUCN Red List status and number of image posts. Based on a Kruskal-Wallis test, differences between the Red List status and the number of photos were found. The photo counts are heavily skewed towards zero, 1441 of the 2473 species were not found at all on Flickr. When excluding species for which no images were found (i. e. are not contributing the SAI), significantly more images were found for species categorized as Vulnerable and Near Threatened compared to the low scoring Data Deficient category (with p = 0.
05, R-square of approximately zero). However, Tsavo East, Addo Elephant, Maasai Mara, and Zambezi PA which rank within the top 20 in the SAI list, also appear in the top 20 of highest reported visitor numbers in our limited dataset of 69 PA (in places 6, 8, 9, 19 respectively). We are well aware that actual tourism levels in an area are affected by a range of other factors besides the presence of attractive species  [14]. For example, when we include minimal travel time from major cities (using the accessibility map by,  [34] ) and photo counts into the linear regression equation to explain the visitor number of the 69 PA, the R-square changes from approximately zero to 0. 23, with travel time being the only significant factor (p <0. 01). Meaning that actual attractiveness of park can be largely explained by its proximity to major cities (see also  [14] ).
Safety and security for travelers also strongly impact the actual tourist numbers in Africa  [35, 36]  and if PA level information was available, these data would likely further explain the actual PA visitor numbers. Not including these factors in our species-based SAI explains that some well visited parks such as Amboseli NP in Kenya do not rank high based on their SAI value (Amboseli is ranked 86, see http://andrewcottam. github. io/tourism_ potential_africa/). To explore the validity of the use of species photo-counts in our SAI, we also counted photos posted in Flickr for all non-African species included in the IUCN Red list. That global species count resulted in high scores for widely known charismatic and iconic species, see Fig 6. The lion, tiger, African bush elephant, leopard, cheetah, polar bear, panda and Asian elephants lead the list (see the complete Top 100 http://andrewcottam.
github. io/attractive_species_gallery/). This indicates a general validity of our explored method. Several earlier studies for South Africa found that these popular species are mostly attractive to international, first-time visitors to African PAs  [26, 27, 37]. These studies highlight that African visitors and experienced wildlife viewers were more interested in bird and plant diversity, scenery, and rarer, less easily-observed, or less high-profile mammals. The SAI of PA is calculated based on a summation of photo counts per species. We decided not to use any data transformations to calculate the SAI to 1) directly follow posted image counts as we could not justify any transformation choice, and 2) to keep 'outlier' effects. While smoothing data through transformations might reduce the impact of errors in photo counts, we are dealing with attractive species of which some have exponentially higher public attraction, i.
e. these species would be outliers in the dataset. Our results show that only 2% of the studied species contribute 72% of the contributed photos, largely influencing the highest scoring PA. In the online interface we show the different SAI results when applying different transformations (log, sqrt, ranks, classes) and summation of species counts per PA (see http:// andrewcottam. github. io/tourism_potential_africa/) The input data used for the development of our index are the best available large scale datasets, but have known limitations, accuracies and uncertainties. For example, for many species maps in the IUCN database, the extent of occurrence that is shown is considerably larger than its realized range (area of occupancy), that leads to errors of commission when we intersect IUCN species range data with WDPA PA polygons.
Both the WDPA and IUCN Red List of Threatened Species are neither perfect or complete datasets, however both are the most comprehensive and scientifically rigorous information about the of state and distribution of species  [38]  and PAs  [39]. Additionally our analysis aggregates large numbers of photos and species, which reduces the impact of misclassified Flickr images or incorrect range data. Even though the use of social media to capture social preferences to assess a cultural ES is promising, issues about data quality, validation, and representativeness remain. The work presented in this paper on the use of online-posted photos to quantify species contributions to nature-based tourism could be seen as a first step in this. The index could be improved by more precise selection of images to able to also include species of LC typical of particular natural habitats and hence exclude the generalist and urban LC species.
However, this is possible only if a precise structural knowledge of the complex formation and that of each protein partner are available. Most of the structural information obtained on efflux pumps from the RND family came from the study of the AcrAB-TolC pump from E. coli  [9, [24] [25] [26] [27] [28] [29]  (with 43 deposited structures for AcrB in the Protein Data Bank (PDB)), even though the structures of the three isolated proteins from the MexAB-OprM pump from Pseudomonas aeruginosa were solved  [30] [31] [32] [33] [34] [35] , as well as the heavy-metal efflux pump CusBA-CusC from E. coli  [36] [37] [38]. In addition we can find in the PDB the structures of isolated members of efflux pumps from other bacterial species, like the OMFs MtrE (Neisseria gonorrhoeae)  [39] , CmeC (Campylobacter jejuni)  [40]  and VceC (Vibrio cholerae)  [41] , the MFP ZneB (heavy-metal efflux adaptor from Cupriavidus metallidurans)  [42] , and the RNDs MtrD (Neisseria gonorrhoeae)  [43]  and ZneA (Cupriavidus metallidurans)  [44].
Residues lining the interior of the α-coiled-coil domain play different roles in both proteins, by not linking the same helices within the trimer and by creating different repartition of charges inside the tunnel, which can be important for the selection of the expulsed molecules and the efflux efficiency. As for TolC, OprM interacts with two other partners to form the complete efflux system via its α-coiled-coil domain. The interacting zone is supposed to be the tips of the helices forming this domain, even if some mutagenesis data does not favor this hypothesis, giving some grey zones about the opening mechanism. OprM is able to function with a large panel of pumps and the MFP's hairpin seems to be determinant in the selectivity of the assembly partners. At present, the functional oligomeric association of the complete efflux system RND-MFP-OMF is likely to be 3:6:3, and the MFP seems to pre-dimerize to interact with the others upon acidic pH.
H. E. S. S. Observations We report on a deep, 210-hours H. E. S. S. exposure, targeted at the region of the Tarantula nebula -corresponding to 30 Doradus (30 Dor) -the largest starforming region in the Local Group of galaxies. We reconstructed γ-ray showers with an image-fitting analysis  (18)  and cross-checked with a multivariate analysis based on image parameterization  (19, 20) , with consistent results. In both analyses, a cut on the uncertainty of the reconstructed γ-ray direction indicated an angular resolution of ≈ 0. 05 •. Fig. 1a  shows an optical image of the LMC overlaid with TeV gamma-ray point-source significance contours. In this dataset, 613 γ rays with a statistical significance of 33 σ are detected from the PWN N 157B. A likelihood fit of a model of two γ-ray sources to the on-source and background sky maps establishes the detection of a second source at an angular distance of 9 (corresponding to 130 pc at a distance of 50 kpc) from N 157B.
If the radiation fields are known, the magnetic field can be inferred from the combination of X-ray and γ-ray measurements. N 157B is likely associated with   (26). For N 132D, radio data is from  (33) , and X-ray limits are from  (34)  and from re-analysed Chandra data. Both leptonic (dashed lines) and hadronic (solid lines) models are shown. For further details on Fermi-LAT data and spectral modelling, see S1. 2 and S1. 3. the LH 99 star cluster  (21, 43, 44) , and therefore embedded in strong infrared radiation fields (see S1. 5). In this environment, the magnetic field in the PWN must be rather weak, not larger than 45 µG, in order to explain the multiwavelength data (Fig. 4 ). When considering the region from which the hard X-ray emission is coming, the total energy in the magnetic field is W B,tot = 1.
A. 2 Fermi-LAT analysis A search for GeV γ-rays from the sources detected by H. E. S. S. has been carried out using data from the space-born Fermi-LAT instrument. The LAT is a pairconversion telescope, operating in the energy range between ∼ 20 MeV and ∼ 300 GeV. A description of the instrument-and mission-related details is given in  (59). Based on the data from an 11 month observation period, the detection of diffuse emission from the Large Magellanic Cloud, best described by either a two-component Gaussian template or an HII template, has been reported  (14). Emission from individual point-like sources above this diffuse emission has not been detected in this dataset. To derive the γ-ray flux upper limits shown in Figure  4  in the main article, more than 5 years of spacecraft data (2008-08-04 -2013-10-21) have been analyzed using the Fermi Science Tools (FST) package with ver-sion v9r31p1 1.
5 eV cm -3 and ∼1. 5 eV cm -3. Note that these estimates assume that the Tarantula nebula and 30 Dor C are at the same projected distance. The derived magnetic fields for these radiation fields vary between 10µG and 18µG. The energy in electrons is 3. 8 × 10 48 erg to 2 × 10 49 erg for the different models. The X-ray data for 30 Dor C are from  (26)  and Fermi-LAT limits on the GeV γ-ray flux have been derived as discussed above. The modelling of the N 132D SED has been performed in a similar way to the modelling of 30 Dor C. Radio data are from  (33) , whereas for the upper limit on the X-ray synchrotron flux from N 132D we reanalyzed Chandra data, and based our estimates only on the (4. 5 -6. 3) keV band, which is poor in X-ray line emission (see below). The SNR shell is interacting with a nearby molecular cloud, which is also bright in infrared wavelengths.
A. 4 X-ray Analysis of 30 Dor C and N 132D The X-ray image of 30 Dor C shown in Fig. 1  in the main article was obtained from XMM-Newton observations (Observation IDs 113020201 and 104660301). Only the MOS1 and MOS2 detectors were used. The data reduction was carried out using the XMM-Newton software of the XMM-Newton Science Analysis Software (SAS, http://xmm. esac. esa. int/sas/). Calibrated event lists were produced for each exposure using the SAS emchain script. The periods affected by soft proton flaring were excluded  (62). To generate images in the energy band 0. 5 -8 keV, we created the quiescent particle background (QPB) images, the count images and model exposure maps for each observation and each instrument, using mos-spectra and mos back scale. The combined mosaic map was finally computed taking into account the different efficiencies of the two instruments.
Acknowledgements The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the IPNP.
ACKNOWLEDGMENTS. We thank  D. M. Weinreich ,  J. A. G. M. de Visser ,  T. Paixão ,  J. Polechová ,  T. Friedlander , and  A. E. Mayo  for reading and commenting on earlier versions of the manuscript and  B. Houchmandzadeh ,  O. Rivoire , and  M. Hemery  for discussions and suggestions on the Markov computation. Furthermore, we thank  F. J. Poelwijk  for sharing plasmid pCascade5 and pRD007 and  Y. Yokobayashi  for sharing plasmid pINV-110. We also thank the anonymous reviewers for remarks on the initial version of the manuscript. This work is part of the research program of the  Foundation for Fundamental Research on Matter , which is part of the  Netherlands Organization for Scientific Research (NWO). M. G. J. d. V. was (partially) funded by  NWO Earth and Life Sciences (ALW) , project  863.
Acknowledgments The present study was jointly funded by the  Agence Nationale de la Recherche sur le SIDA et les Hépatites virales (ANRS)  and the  European Union (FEDER). We would like to thank  Joaquim Marques  and  Rosângela Evangelista , field coordinators,  Mélina Bailleux  and  Carlos Wandescheer , coordinators of the cooperation program, the project team and all the participants in the study.