dataesr/scientific-paragraphs-categorization huggingface dataset:
DAC and NRS laboratory was supported by  NIH  award  AI056034. BS is funded by  Wellcome Trust  grants ( 92383/Z/10/Z  and  095831 ). Work in DZ's laboratory was supported by the  Israel Sciene Foundation. PF and JL were supported by the  Grant Agency of the Czech Republic  ( P305/11/2179 ) and the  Praemium Academiae  award to JL, who is a Fellow of the  Canadian Institute for Advanced Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Trees were constructed by multiple sequence alignment followed by trimming of the N-and C-termini (the EM1 sequence is truncated), and reconstructions by Mr Bayes and PhyML. Statistical support is shown for all nodes as Mr Bayes/PhyML posterior probabilities/bootstraps, respectively. The two Phytomonas isolates analysed here are colored in red, and the N. gruberi sequence, included as an outgroup, is in gray. Note that branch lengths indicate that EM1 and HART1 are of similar divergence as T. brucei brucei versus T. congolense or L. infantum versus L. mexicana. EM1 is more closely related to P. serpens than to HART1 based on this dataset. doi:10. 1371/journal. pgen. 1004007. g001.
We assume a two-component model atmosphere of water and a solar mix of hydrogen/helium gas, motivated by the fact that water is the most abundant icy volatile for solar abundance ratios. Clouds are modeled as a gray, optically thick opacity source below a given altitude. See Figure  3  for the retrieval results. For this model, the data constrain the cloud top pressure to less than 10 -2 mbar for a mixing ratio with mean molecular mass equal to solar and less than 10 -1 mbar for a water-dominated composition (both at 3 σ confidence). At the temperatures and pressures expected in the atmosphere of GJ 1214b, equilibrium condensates of ZnS and KCl can form in the observable part of the atmosphere. While these species could provide the necessary opacity, they are predicted to form at much higher pressures (deeper than 10 mbar for a 50x solar metallicity model)  16  , requiring that clouds be lofted high from their base altitude to explain our measured spectrum.
Clouds generally do not have constant opacity at all wavelengths, so further progress in this area can be made by obtaining high-precision data with broad spectral coverage. Another avenue forward is to focus on measuring exoplanet emission and reflection spectra during secondary eclipse, because the optical depth of clouds viewed at nearnormal incidence is lower than that for the slant geometry observed during transit  24. Fortunately, the next generation of large ground-based telescopes and the James Webb Space Telescope will have the capabilities to make these kinds of measurements, bringing us within reach of characterizing potentially habitable worlds beyond our Solar System. The transmission spectrum of GJ 1214b. a, Transmission spectrum measurements from our data (black points) and previous work (gray points)  [7] [8] [9] [10] [11]  , compared to theoretical models (lines).
Observations We observed 15 transits of the super-Earth exoplanet GJ 1214b with the Wide Field Camera 3 (WFC3) instrument on the Hubble Space Telescope (HST) between UT 27 September 2012 and 20 August 2013. Each transit observation (or visit) consisted of four 96-minute HST orbits of time series spectroscopy, with 45-minute gaps in data collection in each orbit due to Earth occultation. We employed the G141 grism, which covers the wavelength range 1. 1 to 1. 7 µm. The spectra were binned at resolution R ≡ λ/∆λ ∼ 70. To optimize the efficiency of the observations, we used spatial scan mode, which moves the spectrum perpendicular to the dispersion direction during the exposure. Spatial scanning enables longer exposures for bright targets that would otherwise saturate, such as GJ 1214. We used a 0.
1 s using the mode SPARS10, NSAMP=15, and scanned successively forward and backward. These approaches yielded 67 and 75 spectra per visit with duty cycles of 58% and 76%, respectively. One transit observation (UT 12 April 2013) was unsuccessful because the Fine Guidance Sensors failed to acquire the guide stars. We do not use data from this observation in our analysis. We also exclude data from the transit observations on UT 4 August 2013 and UT 12 August 2013, which showed evidence for a starspot crossing. Our final analysis therefore used 12 transit observations.
Light curve fits We fit the spectroscopic light-curves with both the divide-white and model-ramp methods and determined the best-fit parameters and errors with a Markov chain Monte Carlo (MCMC) algorithm. We divided the light curves into 19 data sets (12 visits, with 2 data sets for 7 of the visits), separated by transit epoch and spatial scan direction, to account for a normalization offset between the forward-scanned and reverse-scanned light curves. We fit the data sets in each spectral channel with five 10 5 step MCMC chains, with 2. 5 × 10 4 burn-in steps removed from each chain. We tested for convergence using the Gelman-Rubin diagnostic. The results reported are from the five chains combined. We analyzed each spectral channel independently. The free parameters for the divide-white fit are a normalization constant C λes , a linear limb darkening parameter u λ , and the planet-to-star radius ratio R p /R s,λ,e.
Given that we do not see evidence for water absorption in the spectrum, any contribution from water in unocculted star spots must be below the level of precision in our data. As a check, we computed the transmission spectrum from three transits occurring over a timespan of just two weeks, during which time the spot coverage should be roughly constant. Even with just these three transits, we rule out a pure H 2 O atmosphere at > 5 σ confidence, which confirms that any noise introduced by unocculted spots does not change our conclusion. The derived limb darkening coeffients are shown in Extended Data Figure  6. We fit a single limb darkening coefficient to each channel and constrained the value to be the same for all the transits. Our limb darkening fits illustrate the importance of careful treatment of limb darkening for cool stars.
Acknowledgements This work is based on observations made with the  NASA/ESA  Hubble Space Telescope that were obtained at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. These observations are associated with program  GO-13021. Support for this work was provided by  NASA  through a grant from the  Space Telescope Science Institute , the  National Science Foundation  through a  Graduate Research Fellowship  (to L. K. ), the  Alfred P. Sloan Foundation  through a  Sloan Research Fellowship  (to J. L. B. ),  NASA  through a  Sagan Fellowship  (to J. -M. D. ), and the  European Research Council  (for D. H. under the European Community's  Seventh Framework Programme , FP7/2007-2013 Grant Agreement no.
INTRODUCTION Programmed ribosomal -1 frameshifting (PRF-1) has been recognized more than 25 years ago as a mode of translational control of specific genes, first in retroviruses  (1) (2) (3)  and later in bacterial genes  (4, 5). Since then the number of demonstrated or suspected cases, has greatly increased, generally through homology searches, or by taking advantage of the many sequenced genomes to look for genes containing potential frameshift signals  (6, 7). For example, the Recode database  (8)  has 245 entries for -1 frameshifting originating from eukaryotic viruses (192 cases), from transposable elements (31 cases, 6 bacterial and 25 eukaryotic), from bacteriophages (12 cases) and from chromosomal genes (10 cases). Overall, 219 entries come from eukaryotic genes. This may give the impression that -1 frameshifting is less common in prokaryotes, but it is not necessarily true.
Analysis of the ISFinder database  (9) , dedicated to bacterial transposable elements called insertion sequences (IS), showed that more than 500 IS elements very likely use -1 frameshifting to synthesize the proteins necessary for their mobility  (10). Another bioinformatics study carried out on 973 bacterial genomes revealed more than 5000 genes that probably use -1 frameshifting  (11). These genes can be grouped into a limited number of clusters most of which correspond to IS elements. Although, like their eukaryotic counterparts, most bacterial genes likely using programmed -1 frameshifting are found in mobile elements, such as IS transposons or bacteriophages  (12) , utilization of -1 frameshifting may not be limited to them. Another study revealed a set of 146 prokaryotic gene families with various potential programmed frameshifts, several of which were experimentally tested  (13, 14).
Most of these families correspond to non-mobile genes encoding proteins of known functions and proteins with conserved domains performing yet unknown functions. Sequences of genes from these clusters are available from GenTack database  (13). The execution of frameshifting at a significant level requires a relatively simple signal which is embedded within the coding part of certain mRNAs (Figure  1 ). The two components of this signal were revealed by the earlier stud- Codons in frame with the upstream initiation codon (frame 0) are separated with underscores; their position relative to the ribosomal E, P and A sites and their tRNAs, at the onset of frameshifting is indicated. ies on retroviruses  (1) (2) (3). The obligatory component is a short sequence of 7 nucleotides, X XX. Z ZZ. N, called the frameshift motif or 'slippery' motif, where XXX and ZZZ, are triplets of identical bases, and N is any nucleotide (underscoring separates codons in frame with the initiation codon, i.
Analysis of XXXZZZN frequencies in protein coding sequences A customized perl script was used to count the occurrences of a pattern in all three possible reading frames (i. e. X XX. Z ZZ. N, XX X. ZZ Z. N and XXX. ZZZ. N) in both the real and randomized nrMEGs. Violin plots  (45) , generated with the vioplot package from the R software library (http://www. r-project. org), were used to visualize the occurrences of the 64 X XX. Z ZZ. N patterns. Z-scores were computed as follows: z-score = (xx mean )/x sd, where x is the frequency of occurrence of a pattern in the integrated genome, x mean is the mean of the distribution of the same pattern across 1000 randomized genomes and x sd is the standard deviation of the distribution of the same pattern across 1000 randomized genomes. Z-scores for the 64 XXXZZZN in all three frames are shown in Supplementary Table  S3  while all violin plots are available online at http:// lapti.
These clusters were enriched with additional homologous sequences from the genomes not included in the nrMEG in an attempt to obtain a better phylogenetic signal. The enrichment was carried out using a tblastn search against all bacterial sequences in the nr database as described previously  (11). The 'newly' obtained homologous sequences for each cluster were aligned by translating them into protein sequences, aligning these protein sequences and then back translating the aligned protein sequences to their corresponding nucleotide sequences. The coordinates of the conserved heptameric pattern for each cluster (which were recorded in the previous step) were recalculated to account for gaps introduced during alignment of additional sequences.
Search of potential stimulatory elements flanking the pattern Two types of potential frameshift stimulators were searched. The first type was an SD-like sequence (either GAGG, GGAG, AGGA, GGNGG or AGGKG, with K = [T,G]) located 6-17 nt before the second base of the motif; these sequences and the spacing interval were chosen because all were experimentally proved to be stimulatory  (32). A segment of 30 nt ending with the first base of the motif was scanned, using a script for Perl (version 5. 10. 1 from ActiveState), in all the sequences of each cluster for the above potential SD sequences. A cluster qualified as having a 'conserved SD' if at least 50% of its sequences had an SD. The second type of stimulator was an RNA structure downstream of the motif. A preliminary study of 271 IS3 family members (see Supplementary Figure  S2 ) led us to choose the following empirical rules: the structure is (i) a simple or branched hairpin of a length ranging from 17 to 140 nt, (ii) that starts 4-10 nt after the last base of the motif , (iii) with a G-C(or C-G) base-pair followed by at least three consecutive Watson-Crick or G-U or U-G base-pairs and (iv) has a G unfold@37 • C ≥ 7.
6 kcal. mol -  ; the G @37 • C value was determined using the default parameters of the RNAfold program from version 1. 8. 5 of the Vienna RNA package  (47). We limited our search to hairpin structures and and did not explore whether some of the structures could also form PKs at this preliminary stage. For each sequence of all clusters, a 197 nt segment starting at the fourth base after the motif was extracted and analysed with a custom Perl script. Each segment was first deleted from the 3 end one base at a time and down to 17 nt. Each set of nested deletions was passed to the RNAfold program and the potential structures were sorted out to retain those conforming to the rules. For a given cluster, structures were grouped in types according to hairpin size and distance from the motif. The frequency of each type of structure was calculated and only those present in at least 50% of the sequences of the cluster were retained; 53 of the 69 clusters had such a conserved structure.
nt -1 parameter. To assess that, a 197 nt segment starting 4 nt downstream of the frameshift motif was extracted for each of the 20 ISs as well as for one typical sequence of each of the 53 clusters with a conserved structure. For each 197 nt segment, a Perl script generated a subset of sequences by moving (1 nt at a time) a sliding window of the size of the corresponding conserved hairpin and passed it to RNAfold. The average G unfold. nt-1 ( G av. nt -  ) were calculated for each subset as well as the G. nt -1 which is the difference [( G hp. nt -  ) -( G av. nt -  )]. As expected, the 20 ISs possessing a stimulatory structure all display a higher than average G. nt -1 (Supplementary Table  S8 ).
Distribution of frameshift motifs in IS elements From the above experimental study, we concluded that a majority of heptamers (and nearly half of the tetramers) were capable of eliciting -1 frameshifting at substantial levels (at least twice the background level). To determine the range of motifs used in genes utilizing frameshifting for their expression, we carried out an analysis of IS mobile genetics elements known, or suspected, to use this mode of translational control. We focused on the members of the IS1 and IS3 families available in the ISFinder database in October 2012  (9). As shown in Figure  5 , both tetramers and heptamers are found, but with a marked preference for heptamers (87 against 403). Among the five tetramers, the three most shift-prone motifs, A AA. G and U UU. [U,C], predominate and the less efficient A AA.
ucc. ie/heptameric patterns clusters/. We anticipated that X XX. Z ZZ. N patterns characterized as shift-prone in our assays would be underrepresented due to selection pressure. Therefore, we expected to find negative correlation between z-scores and observed frameshifting efficiencies (in the absence of a stimulator) for these patterns. Surprisingly, no significant anticorrela-r= -0. 020 tion was found between the two measures (r = -0. 020, P = 0. 874; Figure  7A ). Previously, underrepresentation of one shift prone pattern (A AA. A AA. G) was found to be more pronounced in highly expressed genes than in lowly expressed genes  (6). Therefore, the distribution of X XX. Z ZZ. N motifs was analysed among 253 genes predicted as highly expressed in E. coli K12 (HEG database; http://genomes. urv.
While the nucleotides immediately flanking a given motif can modulate frameshift level  (19, 20, (36) (37) (38) , they probably cannot turn an inefficient motif into a highly efficient one or vice versa, as suggested by one analysis in E. coli [see  Table 3 in (36) ]. That the above rules likely apply to other eukaryotic organisms is supported by studies on yeast and plant viruses  (51, (59) (60) (61). Several observations suggest that the E. coli rules are probably valid for many other bacterial species. A survey of the GtRNAdb database [(62); http://gtrnadb. ucsc. edu/] in June 2013 indicates that out of 431 different bacterial species, 168 possess only one type of lys-tRNA, with a 3 UUU 5 anticodon, like E. coli  (41). In these species, covering all the major bacterial phyla, the A AA. R and V VV.
A AA. R motifs should be as shift-prone as in E. coli. Interestingly, the same two types of motifs are highly prevalent (74. 5%) among non-redundant IS elements from the IS1 and IS3 families present in the ISFinder database (Figure  5 ). Furthermore, among the 134 species present in the GtRNAdb database and in which IS3 family transposable elements are found (ISFinder database, October 2012), 83 contain both types of lys-tRNA ( 3 UUU 5 and 3 UUC 5 anticodons). ISs with an A AA. G or V VV. A AA. G motif are present in 46 of these 83 species. Thus, presence of a lys-tRNA with a 3 UUC 5 anticodon, which should pair perfectly with the AAG codon and thus reduce frameshifting  (41) , does not preclude the use of A AA. G or V VV. A AA. G frameshift motifs in IS elements from many bacterial species.
Assuming an average number of 3130 protein-coding genes per genome, this gives a frequency of candidates of about 0. 03%. Sequence comparison showed that 47 clusters contained genes from IS mobile genetic elements. Interestingly, a substantial proportion of them (22 clusters) may use programmed transcriptional realignment rather than translational -1 frameshifting (12 clusters) and in 9 clusters, both types of recoding may operate. The analysis with the Gene-Tack program of 1106 microbial genomes carried out by Antonov et al. (13, 14)  eventually revealed 4730 genes, potentially using frameshifting (in the +1 or -1 direction) or transcriptional realignment, which were grouped into 146 clusters. IS transposable elements genes are found in a minority of clusters, 17. Thus, other categories of genes of various functions predominate.
However, if the absolute number of genes is considered, then IS elements prevail with a total number of 3317 genes. This probably reflects that ISs are prone to horizontal transfer and are often present in multiple copies in a genome. Assuming again an average number of 3130 protein-coding genes per genome, then the overall frequency of candidates found by Antonov et al. (14)  among 1106 genomes is about 0. 14%. One drawback of the disrupted-ORF approach is that it fails to detect cases where frameshifting would lead to a protein shorter than the product of normal translation. An example is provided by the dnaX gene family: while in E. coli and many bacteria frameshifting leads to a shorter protein, it results in a longer product in a more limited number of bacteria. The GeneTack analysis detected only the later cases in 17 genomes [see COF 239165634 in the GeneTack prokaryotic frameshift database;  (14) ].
10%. This value is close to that obtained by Antonov et al. (14) , therefore, suggesting achievement of a similar stringency by both searches. An internal validation of our method was provided by the fact that expected cases (dnaX gene, 7 IS elements and 2 bacteriophage genes; marked as [true] in Supplementary Tables S4 and S5 and in Figure  8 ) were found in the final set. Among the 59 remaining clusters, 1 is from IS elements, 3 are from prophages genes and 55 are from nonmobile cellular genes of known and unknown functions. In 57 of our 69 clusters, like in E. coli dnaX gene, but in contrast with ISs from the IS1 and IS3 families and all the 146 programmed frameshift clusters from the GeneTack database, frameshifting would lead to a product shorter than the protein resulting from normal translation (Figure  8B ).
(13, 14) , thus, demonstrating that the two approaches are complementary. The present study, in agreement with previous ones  (11) (12) (13) (14) , suggests that recoding in bacteria is mostly found (at least in terms of absolute number of candidates genes) in IS transposable elements and in a few bacteriophage genes. However, if numbers of gene clusters are considered, then it appears that non-mobile genes clusters predominate. Information about the function of these genes, as found in the Ecogene database  (67) , is shown in Supplementary Table  S5. Seventeen are of unknown function, four are predicted to be transcriptional regulators and the rest have different predicted functions. Once candidates have been found, a critical issue, as stressed in a recent review  (68) , is their functional validation.
(4,10)bution of Z ZZ. N and X XX. Z ZZ. N motifs in mobile elements from the IS1 and IS3 families. These two families were selected because biologically relevant -1 frameshifting was demonstrated in both(4,10). The sequences of the IS from these 2 families (63 entries for the IS1 family and 494 for the IS3 family), obtained from the ISFinder database (October 2012), were examined for the presence of potential frameshift signals (i. e. existence of 2 overlapping ORFs, with the second being in the -1 frame relative to the first and presence of a Z ZZ. N or X XX. Z ZZ. N motif in the overlap region; the Z ZZN motifs scored in panel B are those which are not part of an X XX. Z ZZ. N heptamer).
(4,10)bution of Z ZZ. N and X XX. Z ZZ. N motifs in mobile elements from the IS1 and IS3 families. These two families were selected because biologically relevant -1 frameshifting was demonstrated in both(4,10). The sequences of the IS from these 2 families (63 entries for the IS1 family and 494 for the IS3 family), obtained from the ISFinder database (October 2012), were examined for the presence of potential frameshift signals (i. e. existence of 2 overlapping ORFs, with the second being in the -1 frame relative to the first and presence of a Z ZZ. N or X XX. Z ZZ. N motif in the overlap region; the Z ZZN motifs scored in panel B are those which are not part of an X XX. Z ZZ. N heptamer). Occurence in the IS3 family A Motif (Z_ZZN) 0 0. 50 Relative PRF-1 frequency (in the IS3 context) 1 in the study; see Supplementary Table S1) were first combined in a single file set, comprising 169 302 se-quences, to form what we call a MEG.
Acknowledgement. Crown Copyright 2014 Introduction. Scrapie of sheep and goats belongs to transmissible spongiform encephalopathies (TSEs), which are fatal neurodegenerative diseases in animals and humans. As in sheep, the susceptibility of goats to scrapie is influenced by the prion protein (PrP) genotype of the host, but goat PrP polymorphisms have a different range of variations. Among the over 40 polymorphisms resulting in amino acid changes, at least five seem to be associated with TSE susceptibility so far. However, only limited data are known concerning the pathogenesis of scrapie in goats. Therefore the aim of our study is to characterize the diversity of natural scrapie in clinically scrapie suspect goats from a scrapie eradication program on Cyprus by using biochemical and immunohistochemical methods.