dataesr/scientific-paragraphs-categorization huggingface dataset:
2005). Another issue to consider while screening environmental sequences is whether the infrequent sequences are indeed members of a highly diverse microbial "rare biosphere" or only represent sequencing artefacts. To address this question, tintinnid ciliates, a species-rich group that can be easily distinguished morphologically, were surveyed to assess the accuracy of 18S rRNA pyrosequencing of Mediterranean samples with different patterns of tintinnid diversity. The inferred number of typing units outnumbered tintinnid cells in the samples, which was found to be primarily dependent on the data treatment, suggesting that many undescribed environmental sequences might indeed be artefacts  (Bachy et al. 2013). The intention of this review is to critically evaluate the usefulness of methodological advances for studies of kinetoplastid diversity.
The scarcity of protist environmental data is a large obstacle for the perception of true eukaryotic diversity. An analysis of the SILVA SSU database of the eukaryotic phyla  (Pruesse et al. 2007)  showed that less than 5% of the 18S rRNA sequences originated from protists  (Pace 2009). A recent re-evaluation of environmental studies revealed that protists that were previously overlooked constitute the bulk of extant eukaryotic diversity  (Pawlowski et al. 2011). Metabarcoding has become a fundamental approach for diversity assessment in recent years. The possibility of revealing previously unknown microorganisms through metabarcoding and the potential of unveiling their physiology and ecology through metagenomics pose great opportunities and challenges to protistologists.
FP7 (316304) ,  Bioglobe  ( CZ. 1. 07/2. 3. 00/30. 0032 )(to JL),  Praemium Academiae  (to JL),  CAPES ,  MCT/CNPq ,  FAPERJ ,  FIOCRUZ VY  is supported by the  Moravskoslezský Kraj research initiative (MSK. DT1/2014 ,  00955/RRC/2015 ) and the  Czech Science Foundation  ( P506/13/24983S ), CMD-L receives fellowship from  CNPq  and  FAPERJ , LD was supported by a post-doctoral grant of the LABEX BCDiv, AK worked in the context of state budgetary topic 01201376804 of ZIN RAS. CMD-L, VY, PG and JL contributed equally to this work.
Space-based detectors for the study of extreme energy cosmic rays (EECR) are being prepared as a promising new method for detecting highest energy cosmic rays. A pioneering space device -the "tracking ultraviolet setup" (TUS) -is in the last stage of its construction and testing. The TUS detector will collect preliminary data on EECR in the conditions of a space environment, which will be extremely useful for planning the major JEM-EUSO detector operation.
At the same time, detectors on board the satellites encounter a variety of difficulties: 1. Observation of EAS from a distance approximately 10 times greater than ground based experiments requires higher sensitivity and resolution of the optical system, including the photon detector. Desirable resolution of one detector pixel should be equal to the diameter of the lateral electron distribution in a shower. For a satellite orbit height of 500 km, the angular resolution of the orbital detector should be 0. 4-2 mrad, which is at least an order of magnitude higher than that of the existing ground-based detectors (20 mrad). 2. Nighttime atmospheric noise in the UV wavelength band (300-400 nm) used for EAS detection varies on the path of the satellite. The data from the Universitetsky-Tatiana satellites  [9, 10]  gave a scale of such variations: from 3 • 10 7 -2 • 10 8 photons cm -2 sr -1 s -1 on moonless nights (lower values are above oceans, higher values are above aurora zones and cities) to 2•10 9 photons cm -2 sr -1 s -1 on nights of full moon.
The qualification testing of phototubes was done with the hardware and software experimental setup (test bench), used successfully by JINR group for PMTs test of ATLAS Tile Calorimeter at LHC. After testing, PMTs with similar gain were grouped in 16 tube clusters. Data from each tube in the cluster are digitized by one ADC and then analyzed and memorized by the cluster FPGA. Final detector triggering and memorizing of all data is done by TUS central FPGA. The information volume of one EAS data is ≈100 Kbytes. The expected volume of the EAS data from one day transmitted to the mission center is 250 Mbytes.
The TUS EECR trigger operates in 2 stages: in the first stage, pixels with a signal A-times larger than noise RMS are selected; in the second stage, events with B neighbour pixels having signal ≥ A and line up in space and time, are selected. The final trigger  begins recording data from all pixels. Numbers A and B will be controlled from the mission center in a compromise between trigger rate, limited by the upper volume of information to be transmitted per day, and the TUS detector energy threshold.
Present data on background effects in measuring EECR from space One of the TUS mission tasks is the measurement of the atmosphere UV background radiation capable of imitating the useful events of EECR. In preparation of TUS operation, the experimental data on atmospheric radiation obtained in orbital measurements by Universitetsky-Tatiana (Tatiana-1) and Universitetsky-Tatiana-2 (Tatiana-1) satellites are used. It is worthwhile to present Tatiana-1 and Tatiana-2 data in connection with the TUS prototype, where the background effects will prevail. Experimental results of "Tatianas" UV average intensity are presented in Fig. 6. The data in this figure are presented for the moonless nights of winter 2009-2010, when borders in latitude of the Earth night side were 30 • S -60 • N. Intensity J of the atmosphere glow varies in a wide range from 3 • 10 7 ph/cm 2 s sr to 2 • 10 8 ph/cm 2 s sr.
Taking into account these data on the atmosphere glow, the exposure of TUS detector with FOV of 9 • was estimated  [12]. Efficiency of EECR event selection is close to 100% for E > 300 EeV and those events will be collected with exposure of 12000 km 2 sr yr in 3 years of in-orbit operation. Events with lower energies will be detected with less efficiency (exposure). For steep energy spectrum of EECR after the GZK limit (integral spectrum exponent ≈ 4 for energies E > 50 EeV), the statistics with minimum threshold energy (∼ 70 EeV) will be determined by real exposure at the darkest regions of the Earth: above the Pacific Ocean, deserts and part of Siberia (Fig. 6 ). With such limited exposure, the TUS detector will not make a breakthrough in the problem of EECR origin. Its aim is to check the EAS fluorescence detector performance in a space environment.
In orbital flight, the TUS detector will operate above some more intense sources of background glow: aurora lights, city lights, some unknown sporadic lights. Experimental results of Tatiana-1 Fig. 7  Photon number distribution of UV flashes  [13]. satellites showed that these higher intensity glow sources are not large in size of atmosphere area and do not greatly affect the exposure of the detector. Other sources of background in orbital EECR measurements are short UV flashes (duration of 1-100 ms), the origin of which is related to electrical discharges in the atmosphere. The last achievement in studying this background is data from Tatiana-2 satellite. The UV-detector of this satellite operated in conditions close to the orbital EECR detector, measuring the temporal structure of flashes in the atmosphere area of thousands of square km in nadir direction.
The duration of EECR flashes is much shorter than that of atmospheric flashes (0. 1 ms for EECR flash and 1 ms for atmospheric dim flashes) but still the imitation of EECR events by early stage atmospheric flashes is possible. The TUS operation will give important data on the background effect of atmospheric flashes. Operation of the TUS orbital detector will be an important stage of EECR study by a new method of fluorescent tracking of EAS from space.
This gave rise to the famous Paracelsus challenge awarding 1,000 dollars to the group that would successfully change the fold of one protein by changing no more than half the sequence. We then expect to import, from the analysis of the structures by the FOD method, pertinent rules that will be used as input in the MIR algorithm, which will hopefully decrease the number of false positive positions predicted by the MIR method. This is why we will focus on the early steps of the folding process. Besides, this goal allows escaping from the controversy about the conservation of the folding nucleus among the proteins of a given fold, with an expected high conservation on one hand  [14, 37]  or its absence on the other hand  [38]. Two protein domain datasets have been explored in this analysis. Actually, immunoglobulin-like (Ig) fold is not a protein family, or super family, in strict evolutionary terms, but is one of ten proposed "superfolds" adopted by unrelated sequences  [39].
The second dataset, the flavodoxin (Flav) fold, which also occurs via a nucleation mechanism  [41] , contains 38 domains of divergent sequences with a mean pair sequence identity of 6. 9%. One of the reasons of this choice is because it belongs to the five most common observed folds, with a broad range of unrelated proteins with different functions  [42]. Its folding process goes through intermediate states, either on or off pathway, therefore the mechanism is not shared among all members of this fold  [43]. The fact the transition state ensemble of some of the proteins sharing this fold is diffuse (an expanded form of the folded state)  [44]  may not, a priori, necessarily lead to the conservation of the highly interacting residues. Besides, since some of the residues with high Phi values are not hydrophobic  [45] , this fold is worth investigating the conservation of the key residues.
Material and Methods Datasets One of the most frequent folds is the immunoglobulin beta-sandwich. Among this large group of proteins, there is no shared function, and sequence similarities can go down to very low values although there is a common structural core  [46]. Proteins used in a former analysis  [30]  were taken as the current data set and we added four immunoglobulin domains with experimental data related to their folding nucleus. A multiple sequence alignment was produced for the immunoglobulin fold, by adding their sequences to the former one, according to the pairwise best-hit structural superimpositions generated using DALI  [47]. The multiple sequence alignment of flavodoxin domains was derived from the superimposition of their structures computed by MUSTANG  [48]. The list of domains with short characteristics is given in Table  1  of the paper by Prudhomme and Chomilier  [31]  for the immunoglobulin-like fold, and in Table  1  of this paper for the flavodoxin fold.
The data set includes 94 single-segment domains: 56 immunoglobulin-like and 38 flavodoxins, whose limits have been retrieved from the CATH database  [49]. Mean length of the domains is 108 (standard deviation 19) for the immunoglobulin-like fold and 171 (standard deviation 39) for the flavodoxin fold. Precision on the limits of the domains could affect the FOD assignment if some significant length, typically one sheet (at least 5 amino acids) oriented outside of the globule, is added or removed. This is not a major concern in our study. Concerning MIR, there are some side effects that decrease the reliability of the prediction at both N and C terminals, but apart the ends where the results must be taken with care, the precision can be estimated at ±1 amino acids in the bulk of the sequence.
doi:10. 1371/journal. pone. 0125098. g004 replacement, from a finite population of size N containing exactly K successes. The density of this distribution, with parameters K, N-K and n, is given by: PðX ¼ kÞ ¼ K k ! N À K n À k ! N n ! ð3Þ for k = 0,. , n In this paper, N represents the total number of amino acids of the given protein dataset (Iglike or Flav fold), n is the total size of the given FOD class (i. e. the total number of amino acids which are considered to be drawn without replacement from the population), K is the total number of MIR or LIR in the given amino acid population, of which k are part of the sample (i. e. belong to the given FOD class). In other words, k represents the size of the IR/ FOD intersection. The hypergeometric distribution gives the probability that the given FOD class shares exactly k residues with the given IR status by chance, and reciprocally.
A p-value has been computed for each IR/FOD relevant combination of each fold dataset. It gives the probability that an IR/FOD intersection of size greater than or equal to the observed one would occur by chance. The distribution parameters and the corresponding p-values are given in Table  3  (fold-level analysis) and S1 Table (domain-level analysis), and the theoretical p-values as a function of the IR/FOD intersection size parameter are plotted in Fig 6. The fact that the computed size of the intersection is far above the random threshold (Fig  6 ) assesses the ability of joint MIR and FOD methods to decipher a reduced set of residues typical of the core and/or the nucleus of a fold. Let us also notice that residues both predicted as least interacting (LIR) and assigned as hydrophilic (H-1 or H-2) constitute events with a probability of occurrence significantly higher than a random drawn.
We compared the residues selected by the two methods (IR and FOD) to those experimentally identified as part of the folding nucleus in four proteins with Ig fold on one hand and attributed to the core of two proteins of Flav fold on the other hand (see below for details on these protein domains and experimental results). The sensitivity and specificity of both methods are presented in Table  4. The combination of MIR and FOD methods result in a simultaneous decrease in sensitivity and increase in specificity. For each of these domains, the specificity of the H+2/MIR method reaches at least 0. 95, indicating that at most 5% of the non nucleus residues are predicted as part of the nucleus or core by the intersection of our methods. In order to better evidence the effect of the combination of MIR and FOD methods on the number of key residues one can consider as a signature of a fold, we present in Fig  7  the percentage of residues both MIR and H+2 in the multiple sequence alignment of the two datasets.
Correspondence between the multiple alignment position and the PDB (Protein Data Bank) numbering of these residues is given in S2 Table. For a fibronectin type III domain (CATH code 1tenA00), four residues over 90 constitute the putative folding nucleus (in the following, residue numbers refer to their PDB numbering): I821, Y837, I860 and V871  [39]. Our recognition of this nucleus is L835, L873 and I874. If we accept a precision of the prediction of ±1 residues, we are partially consistent with these experimental results. Let us notice that I860 and V871 are MIRs and are both recognized as members of hydrophobic class H+1. Concerning I821, which is not recognized as MIR, it is nevertheless in the H+2 class. In a second fibronectin of type III, (CATH code 1k85A00)  [19] , the folding nucleus is composed of L578, V594, I611 and V624.
Positions 166, 252 and 266, predicted as not belonging to the folding nucleus, are actually in the experimental one. One must mention that they are all specific of the folding nucleus of the domain 1ttfA00, which has the peculiarity of having a nucleus that contains a double number of amino acids, compared to the three others. So the set of residues coding for the fold, resulting both from the combination of the methods, and from the use of a multiple alignment, has been decreased to five positions. If one now looks at the combined methods (residues both predicted as a MIR and assigned to the H+2 class) for the flavodoxin dataset represented in Fig 7B, there are three peaks at levels of conservation higher than 30%. Experimental Phi values have been published for two   3. The probability decreases from 1 down to 0 while k increases.
Positions where both ideal (following the 3D Gaussian distribution) and observed hydrophobic density values are significantly high (according to the HD profiles) are classified as highly hydrophobic (belonging to H+2 class of the FOD model). Intersection of MIR and H+2 positions, which is statistically significant, improves the prediction by decreasing their numbers to the level of  [3] [4] [5] % in each fold, (with standard deviation of 2. 5% and 1. 7%, respectively), instead of previous  [16] [17] [18] %. On the same data set, the mean number of LIRs following the FOD model (residues classified as highly hydrophilic, H-2) is at the level of 3%. %. This, together with standard deviation being close to 1. 7% in each fold, shows that LIR/H-2 distribution is more compact than that of MIR/H+2, however no correlation is observed between them.
In order to get rid of statistical fluctuations, one compares the NCN smoothed distribution of these two domains (available at http://sprouts. rpbs. univ-paris-diderot. fr/mir. html). Peak in the NCN distribution of PDB code 2LHC at L45 for GA disappears when mutated to Tyrosine in GB while the global shape of the distribution is maintained  [75, 76]. Combination of MIR and FOD methods reinforces the hypothesis of stability: in GA, two positions are both registered as MIR and H+2, L45 and I49, while in GB, there are three, at L7, F30 and F52. One may also ask the question of the effect of symmetry on the prediction of the key residues. Fold analysis has revealed a high prevalence of proteins presenting a structural symmetry  [77, 78]. Why does it happen so commonly? Following Longo et al. , it may be due to repeated instances of key folding nucleus.
It is important to notice that the two methods, using different methodologies based on different folding states of the protein domains (early step of the folding, simulated from amino acid sequences by MIR method, and final state as resolved in the 3D structure and compared to an ideal Gaussian function by the FOD method), are in agreement, which is proven by significant intersection of their key predicted residues for both immunoglobulin-like and flavodoxin folds. In most cases, sources of any discrepancy may be explained on the basis of protein's structural specificity  [83]. The performance of joint prediction improves the fitness of the putative folding nucleus determination by significantly decreasing the number of positions capable of forming high levels of hydrophobic interactions. Comparison with reliable experimental data leads to reasonable quality of the prediction, which can nevertheless be further improved.
02 A 00(5-206) Acetylxylan esterase Clostridium acetobutylicum 1ZMB 2. 61 A 01(2-248) Product of gene AT4G34215 Arabidopsis thaliana 2APJ 1. 6 A 00(17-260) H. pylori type II dehydroquinase Helictobacter pylori 2C4V 2. 5 A 00(1-158) Methylmalonyl-CoA mutase Propionibacterium freudenreichii subsp. shermanii 7REQ 2. 2 A 02(563-726) Res is the resolution in ångström for X-Ray structures, otherwise they provide from NMR. Limits in domain column are retrieved from CATH. Proteins are sorted according to their PDB code. doi:10. 1371/journal. pone. 0125098. t001 below 2 are called LIR (Least Interacting Residues), respectively. One example of the NCN distribution is given in Fig 2 for one protein of the Immunoglobulin-like fold, with CATH code 1svcP02.
The reviews and subsequent recommendations are focused on generating meaningful and rigorous data that can enable clear go/no-go decisions and facilitate longer term funding or partnering opportunities. The review process thereby acts to comment on viability, de-risking the process of proceeding on a development programme. To date TACT has held 10 review meeting and reviewed 29 program applications in several rare neuromuscular diseases: Of the 29 programs reviewed, 19 were from industry and 10 were from academia; 15 were for novel compounds and 14 were for repurposed drugs; 16 were small molecules and 13 were biologics; 14 were preclinical stage applications and 15 were clinical stage applications. 3 had received Orphan drug designation from European Medicines Agency and 3 from Food and Drug Administration.
The increasingly large number of industry applicants suggests that TACT may have a valued role in the field, including to pharma. Key to TACT's added value is also that its review is not conducted with the goal of making a funding decision or as a group funded by the prospective sponsor, but rather as an independent, centralized review service on drug development potential that can then be made accessible to many different advocacy groups and other funding organizations. This focus on de-risking the process of proceeding to trial and or ultimate registration is distinct from the role of individual scientific funding boards. The number of trials currently (January 2015) listed in www. clinicaltrials. gov for DMD (N = 143) and SMA (N = 116) indicates that the field has moved on during the 5 years since the establishment of TACT, though progress in drug approval remains disappointing.
Pierre Auger Observatory and Data Set The Pierre Auger Observatory (The Pierre Auger Collaboration 2004) consists of an array of 1660 water-Cherenkov detectors covering 3000 km 2 on a triangular grid of mostly 1. 5 km spacing, the surface detector (SD). It also has 4 sites with 27 telescopes overlooking the ar-ray to observe the fluorescence light emitted by the showers (The Pierre Auger Collaboration 2010a), which allows a calorimetric measurement of the shower energy deposited in the atmosphere and is thus particularly useful for the calibration of the SD energy reconstruction. In contrast to the surface detector, the fluorescense detector (FD) has a smaller duty cycle of 13%.
Modified Rayleigh Method When combining two different data sets covering different regions of the sky, such as the vertical and inclined samples considered here, a small difference in the energy crosscalibration of the samples could give rise to a difference in the measured fluxes in those regions, that could translate in a spurious large scale modulation. We will hence adopt a method that is essentially insensitive to these effects, studying the large scale distribution of the arrival directions by performing a classical Rayleigh analysis  (Linsley 1975 ) over both the right ascension and the azimuth angle distributions. The analysis is slightly generalized by weighting each event by a factor that takes into account small modulations in the exposure arising from the variations in the operating size of the array as a function of time, and for the effects of a small net tilt of the array surface (The Pierre Auger Collaboration 2012).
Conclusions We presented the results of an analysis of the large angular scale distribution of the arrival directions of the Pierre Auger Observatory data including for the first time inclined events with zenith angle between 60 • and 80 •. The inclusion of the inclined events not only provides an increase of about 30% in the number of events, but also leads to a larger fraction of the sky covered, up to 85%. We performed two Rayleigh analyses, in the right ascension and azimuth angles, that are sensitive to the right ascension and declination modulation of the flux, respectively. Two energy bins above the full efficiency for inclined events were analyzed: from 4 to 8 EeV and above 8 EeV. No significant departure from isotropy is observed in the distribution of events in the energy bin between 4 and 8 EeV.