dataesr/scientific-paragraphs-categorization huggingface dataset:
The functional classification tool from the Database for Annotation, Visualization and Integrated Discovery (DAVID version 6. 7) website (https://david. ncifcrf. gov/home. jsp) was employed to group proteins contained in our dataset based on functional similarity (Supplementary Data Table  S2 ). Functional analysis was also performed using the Panther Functional Classification System (http://pantherdb. org/) to evidence the most represented molecular functions (Fig. 2A ) and biological processes (Fig. 2B ). The functional analysis was completed using the "Set Distiller" module of GeneDecks (http://genecards. weizmann. ac. il/v3/index. php? path¼GeneDecks; Supplementary Data Table  S3 ). The data presented here consist in a list of intact and unmodified (by chemical treatment) endogenous low molecular weight biomolecules (o15 kDa) present in chicken sperm.
Acknowledgments This work was supported by the  French National Infrastructure of Research CRB  anim funded by " Investissements d'avenir ",  ANR-11-INBS-0003  and from the  French National Institute of Agronomic Research. The high resolution mass spectrometer was financed ( SMHART  project no. 3569 ) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm). Laura Soler  has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship (under Grant  agreement no. 267196 ).
(d) Determining length of season Lac Arlet water temperature was measured every half an hour throughout the study period using a data logger (HOBO Water Temperature Pro v2 Data Logger -U22-001). The logger was positioned 2 m from the shore, approximately half a meter below the water surface, attached to a large rock with nonperishable wire. For each year, the date of the onset of spring and the date of the presumed end of the amphibian active season was recorded. The onset of spring was defined as the first day of the year (00:00-23:59) with a mean water temperature above 18C, which remained so until the winter. The end of the active season was defined as the first day in the second half of the year when the mean water temperature dropped below 2012 þþ þþ þþþ þ 2013 þ þ þ þ þ þ 0 2014 þ þ þ þ þ þ 0 rstb.
(g) Statistical analysis All statistical analyses were carried out using the statistical software package R v. 3. 2. 3. All Bd DNA values (GE) were rounded to the nearest whole number and treated as count data. Any value of 0. 1-0. 9 was assigned a value of 1. Negative binomial regression models (function glm. nb from the R-package MASS) were used to look for any differences in the intensity of infection (GE values) between years and species. Likelihood ratio tests were used to assess the significance of predictor variables and of differences between factor levels within predictors. Where more than three factor levels remained significant, Tukey posthoc tests (function glht from the package multcomp) were applied to allow pairwise comparisons. Pearson Correlation tests were used to determine whether there was a correlation between the prevalence of infection in both B.
The sample A total of 12 sources have been previously classified as TDs and pre-TDs in the Chamaeleon I region. Several of these targets have been modeled in detail, mostly based on their Spitzer MIR spectra (e. g. Kim et al. 2009). Additionally, a number of studies have already explored Herschel data of these disks (e. g. Winston et al. 2012; Ribas et al. 2013; Rodgers-Lee et al. 2014). To analyze the feasibility of estimating disk masses using Herschel data of (pre-)TDs, we selected three different objects in Chamaeleon I: • SZ Cha, a pre-TD  (Kim et al. 2009 ), • CS Cha, a disk with a gap surrounding a binary system (binary separation of ∼ 3. 5 AU,  Nagel et al. 2012) , i. e. , a circumbinary disk  (Guenther et al. 2007; Nagel et al. 2012) , • T25, a TD with no known companion  (Kim et al.
1 to process the maps of the region. We adopted the standard map-making algorithms used in the Herschel Science Archive, i. e. , jscanam for PACS maps (a HIPE adaptation of the Scanamorphos software, Roussel 2013), and destriper for SPIRE maps. In the case of jscanam we remove turnarounds with speeds 50 % lower or higher than the nominal speed value, and we do not use the extended emission gain option for destriper, as recommended for point source photometry. We estimated PACS fluxes with the AnnularSkyAper-turePhotometry task in HIPE. We adopted aperture radii of 15 , 15 , and 22 for PACS 70, 100 and 160 bands, respectively. These apertures were specifically selected after inspecting growth curves of each target in each band. The background was estimated within annulus with inner and outer radii of 25 and 35.
IRS spectra of transitional disks We retrieved low resolution IRS spectra from the Cornell Atlas of Spitzer/IRS Sources (CASSIS) database  (Lebouteiller et al. 2011)  for the (pre-)TDs disks in our study. CASSIS provides optimally extracted IRS spectra, and is well suited for our purposes. For these spectra, we first separated the optimal zones of the IRS spectra (7 to 14 µm and 20. 5 to 35 µm for the first order, < 20. 5 µm for second one), and rejected bad pixels (e. g. negative or NaN values). As a compromise between estimating monochromatic fluxes required for model fitting (see Sect. 3) while reducing the impact of possible artifacts in the spectra, we chose to bin them in ten equally spaced wavelengths throughout the spectra coverage, and estimate the fluxes for each of them as the mean value of ten pixels centered around each corresponding wavelength.
MODELING We aimed at modeling the selected targets and quantifying the impact of adding photometric Herschel data to this process. For this reason, we used two different datasets for each object. The first dataset comprises the available data from 2MASS, IRAC1/IRAC2, and the binned IRS spectra. The second dataset also includes the Herschel photometry. We used the MCFOST software  (Pinte et al. 2006 (Pinte et al. , 2009 ) version 2. 19 to model these disks. MCFOST is a Monte Carlo-based raytracing code which generates synthetic SEDs and images of circumstellar disks. First, it produces temperature and density maps of the disk using the provided stellar and disk parameters. In this case, we used 10 7 photons in this step (enough to produce smooth and well-sampled temperature maps of the disks).
Fitting results without Herschel data We first use the dataset without Herschel data to explore which parameters can be constrained with NIR/MIR information. The posterior distributions for the three targets are shown in Fig. 2 , and the obtained values in Table  3. MCMCs also allow to study degeneracies between different parameters by plotting the chains in different 2-D projections. The degeneracies are very similar in all cases, as revealed by the cornerplots in the Appendix (Figs. A1 to A3 ). The following conclusions can be drawn from the obtained posterior distributions for the model parameters by analyzing 5-95 % confidence intervals (Fig. 3 ): • The inner radius (Rin) can be constrained within 5-20 AU for all three targets, corresponding to relative uncertainties between 80-120 %. This is likely due to the fact that most of the NIR/MIR emission arises in the exposed wall, hence probing the location of this parameter.
• NIR/MIR photometry allows to calculate the scale height (H100) with uncertainties within 10 AUs. This is expected, since different scale heights modify the amount of stellar flux intercepted by the disk, changing the emission from the inner disk • The dust mass (M dust ) and the rest of the geometrical parameters (Rout, h, and p) show little or no constraint at all with NIR/MIR data alone. • Special attention should be paid to the two-peak distributions obtained for amax. Any observation at a given wavelength λ is only sensible to emission from grains of size a ∼ λ  (Draine 2006 ). If we allow amax to take small enough values (< 10 µm), it could produce substantial changes in the SED and therefore play a role in the modeling, which may explain the double peaked posterior distributions. Although much larger grains are generally expected in disks, this dataset is not enough to resolve this effect if we allow amax to take small enough values.
ACKNOWLEDGMENTS We thank the referee  Simon Casassus  for his review and constructive comments, which helped improving and focusing the paper. This publication has been possible thanks to funding from the  National Science Foundation. The data processing and analysis presented in this paper have made extensive use of the following open source software programs:  Python, IPython, NumPy, SciPy , and matplotlib. This work has also made a significant use of Topcat (http:// www. star. bristol. ac. uk/~mbt/topcat/Taylor 2005). We are grateful to the developers of these softwares for their contributions. This work is based on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
Classifications and multivariate analyses Once the discriminant parameters are selected, a multivariate analysis needs to be chosen for the problem, here a binary classification blazar/non-blazar classification. The construction of a classification follows two steps. During the first one, a fraction of the signal and the background samples, called the training sample, is used to construct a final discriminant parameter, called ζ , maximising the signal/background differences. The second step makes use of the remaining part of the samples, called the test sample, to derive the performance, i. e. the rate of blazar identification ε Sig and the rate of false identification ε Bkg , both as a function of ζ. The TMVA package  [7]  proposes different multivariate analyses methods to tackle this kind of problem such as random forests, neural networks, support vectors machine or boosted decision trees (BDT).
Introduction Most of the time the cell is vibrant with chemical activity. Dormancythe condition of the spore or the seedis a state that lies in limbo, not dead but not quite alive. Life has to go forward. It begins with the chemical transformations which make the root of metabolism. At the ground level of life, we find dynamic transformations that extract compounds from the environment, process them into the cell's building blocks and energy supply, while discarding what is toxic or cannot be used. Continuously improved by genomic studies, a comprehensive descriptive knowledge of the matching pathways is available in textbooks and databases that provide access to all what we would like to ask about metabolism (perhaps excluding what we failed to notice). This provides us with a description of the basic chemical transformations that continuously unfold in cells.
Beside some examples of reactions involving 8-oxoG, the MetaCyc database lists a small fraction of the consequences of OH • reactivity (alteration of nucleic acids via production of 8-oxo-guanine, 2-hydroxy-adenine (isoguanine) or 5hydroxy-cytosine, but also 5-hydroxyuridine). All produce metabolites that must be exported or degraded by the cell. The pathways of nucleotide degradation should provide a rich set of novel activities. The cell protects itself first by preventing 8-oxoG assimilation into nucleic acids, via degradation of 8-oxo-(d)GTP into 8-oxo-(d)GMP [using the MutT enzyme, a member of the Nudix hydrolase family  (Hamm et al. , 2016) ]. Enzymes of this family will cope with situations (in particular alkylation of nucleotides) when a nucleotide analogue must be prevented to cross the genome sequence quality control, by hydrolysing pyrophosphate from the triphosphate group of the nucleoside triphosphate analogue.
, 2016). Catabolism of aromatics is particularly significant in Pseudomonadales that, beside carbon cycles, use a variety of enzymes to degrade heterocycles. In general, their authentic substrates have not yet been deciphered (see  (Belda et al. , 2016) , and the corresponding regularly updated knowledge base at https://www. genoscope. cns. fr/agc/microscope/mage/viewer. php? O_id=2834). Future metabolomic studies should help identify some of the pathways that evolved to cope with unavoidable accumulation of modified nucleic acid degradation products. The subsequent fate of urate is remarkably diverse. Some of it is illustrated in Fig. 2. Remembering that annotations in databases are often unreliable  (Percudani et al. , 2013) , it is essential to try and follow the chemical constraints in the postulated pathways and reappraise current annotations.
Yet it might generate oxamate in a S-ureidoglycine degradation pathway: ureidoglycine could not be tested in the assays if the protein precluding identification of this activity if it was indeed present  (Li et al. , 2011). Subsequently, oxamate would release oxalate and ammonium, as observed for oxamate amidohydrolase HpxW in Klebsiella michiganensis  (Hicks and Ealick, 2016). To substantiate this view, I note that, using the PHYLOPROFILE software in B. subtilis shows that protein YwrD is very similar to the Klebsiella HpxW counterpart and that it coevolves with most of the enzymes involved in the xanthine degradation pathway (Table  S2 ). Remarkably, periplasmic degradation of purines is functionally associated in E. coli with MsrPQ, another set of molybdoenzymesconsistent with their co-evolution  (Medigue et al.
Other reactive intermediates In a study quite remarkable for its implications and that should belong to the library of all those interested in metabolism, the laboratory of Andrew Hanson recently summarized the role of spontaneous chemistry in the cell's context. There are so many possibilities, however, that the authors had to make a choice among their preferred molecules. They established a list of 30 top compounds subject to accidents, prominent in metabolomic studies  (Lerma-Ortiz et al. , 2016). In contrast to the MetaCyc list, which is essentially based on literature, the Chemical-Damage-MINE database repository of reactive compounds http://minedatabase. mcs. anl. gov/#/top30/S1 is presented in a chemically consistent manner. As a consequence of their potential to disrupt the cell's functions, we may expect that these reactive compounds will require agents (essentially enzymes, hence genes) able to remedy the effects of their reactivity, correcting errors in metabolism while redirecting unwanted compounds back to the core pathways.
Since the discovery of this first virophage in 2008, others have been described, albeit mostly from metagenomic datasets (Table  1 )  [17]. Six close relatives of virophages were isolated from distinct locations, in France, Texas, Brazil, and Tunisia, and from various sources, including water, soil, and a contact lens rinse fluid (Table  1 )  [17] [18] [19] [25] [26] [27] [28]. The other virophages are known only by their genome, as a result of screening several metagenome sequences communities in various environments (Table  1 ). All of them have similar features among themselves, besides the difference in their size  (17-30 kbp) , their gene repertoire, and host viruses. ALM, Ace Lake Mavirus; bp, base pairs; DSLV1, Dishui Lake virophage; N/A, not available; OLV, Organic Lake virophage; QLV, Qinghai Lake virophage; YSLV, Yellowstone Lake virophage.
They contain 20-21 open reading frames (ORFs) that encode proteins with a size of 88-779 amino acids. Four genes are homologous to eukaryotic and bacteriophages genes, three to mimivirus genes and one to an archaeal virus gene. The remaining 13 ORFs are ORFans, as they do not have any detectable homologues in sequence databases. This mosaic gene content suggests involvement in lateral gene transfers between viruses  [17].
• Phaeocystis globosa virophage In 2013, analysis of metagenomes generated from Dutch coastal waters (southern North Atlantic and the North Sea) revealed the presence of a virophage-like genome. The assembly of Phaeocystis globosa virus strain PgV-16T (PgV-16T), a distantly related mimivirus, generated a 19,527-bp long scaffold in greater copy number than the PgV-16T virus corresponding to a virophage genome. Sixteen predicted ORFs were found in its genome but most of them had no significant matches in sequence databases. Three ORFs had homologs in mavirus, including an endonuclease, the predicted DNA primase and polymerase and one ORF had a homolog in the OLV virophage. The Phaeocystis globosa virophage may have lost structural genes, apart from a distant version of the major capsid protein (MCP) encoding gene  [44].
• Hybrid virophages Recently, Yutin et al. detected sequences of virophages corresponding to MCP from several metagenome datasets  [45]. Thus, 16 MCP from putative virophages were identified in six environmental metagenomes generated from freshwater sediment, marine water, wastewater, activated sludge, rumen gut, and a bioreactor  [45]. Phylogeny reconstruction showed that MCP from activated sludge and bioreactor metagenomes were clustered with Sputnik and Zamilon virophage MCP, whereas those from virophages of the Sargasso Sea metagenome were clustered with Organic Lake and Yellowstone Lake virophages. Sequences corresponding to mimiviruses were concurrently found, suggesting that this new family may parasitize these giant viruses. These findings also represented an additional case of detection of a virophage sequence from an animal, after the detection of 65 and 11 virophage-related sequences in human gut and animal-associated habitats, respectively  [30].
2%, and encodes 25 ORFs  [34]. QLV shares between seven and 11 homologous genes with YSLVs and OLV. Eleven ORFs were not found in other virophages, thus being specific to QLV. Phylogenetic analyses showed a closer evolutionary relationship between QLV, OLV, and YSLVs than with Sputnik and mavirus. In the same metagenome, Oh et al. identified sequences closely related to phycodnaviruses, suggesting that QLV are associated with these viruses  [34]. • Virophage capsid encoding sequences found in metagenomes Many virophage capsid homologs were detected in different metagenomic datasets generated from microbial communities sampled in lakes, rivers, Antarctic lakes, and freshwater lagoons, (Yellowstone, Lanier, Qinghai, Mendota, Trout, Erken, Vattern, Ekoln, Damariscotta, Spark, Amazon, Albufera).
Objective. To characterize the current language that is used in describing and defining gout, its symptoms, and its treatment by reviewing recent publications in rheumatology and determining how word choice may, or may not, be reflective of recent scientific developments in gout specifically. Methods. This was a computational linguistics study, using collocations analyses and concordance analyses on a database of scientific literature related to gout. The final data set for analysis included 2,590 articles, all relating to gout and published between May 2003 and May 2013 and amounting to 12,101,036 tokens (sentence segments). Analysis was conducted by a team of linguists and social scientists. Results. Our primary finding is that current disease language in gout is marked by ambiguity and imprecision, as evidenced by numerous terms that have similar but distinct meanings, but are nevertheless used interchangeably, therefore blending the slight but significant distinctions between these words.
), country of origin, or publishing journal. The articles in the Open Access Collection are made available by the publishers under a license that allows mass collection and automatic text analysis, and were downloaded as XML files to allow the text to be easily extracted. The initial corpus was then augmented with a collection of 359 manually selected articles from the medi-cal literature that were downloaded as PDF files and converted to text. Each of the manually selected articles matched the same criteria as those from the Open Access subset (listed in Table  1 ), and were chosen randomly by members of the linguistic team to increase the size of the corpus, therefore providing a more robust data source for analysis. Because the corpus consists primarily of articles from the Open Access subset, there may be a concern that it fails to represent the literature in general.
Significance & Innovations A team of linguists and rheumatologists collaborated on a comprehensive, computational literature analysis spanning 10 years of scientific and medical literature relating to gout. This data-based study shows that language in gout is characterized by a lack of specificity and consistency. Clinical implications are discussed, and recommendations for an improved terminology in gout that would maximize patient adherence and outcomes are made. cates of a word of interest, or the words that co-occurred with the target word at a greater than chance frequency. To find collocates, we first retrieved all occurrences of the target word and then tabulated the frequencies of all of the words (e. g. , n 5 x, where "n" is the term and "x" is the number of times the word appears in the database) that occurred within a 10-word span to the left or the right of the target.
Further, the phrases serum urate and serum uric acid were evaluated. Clinically speaking, the majority of the substance in the serum following ionization is the former of these 2, serum urate. Data analysis showed more uses of the incorrect term, serum uric acid (n 5 2,862) than of the correct term, serum urate (n 5 2,400). The broad application of and intended meaning for the term gout presents other challenges. Many descriptive and qualifying terms that can help clarify and contextualize  the intended meaning of another term, including gouty arthritis, symptomatic hyperuricemia, intercritical gout, monosodium urate crystals, tophaceous gout, tophus, podagra, recurrent gout, acute gout, chronic gout, gouty arthropathy, and chronic gouty arthritis, are used. All words occurring as modifiers of gout at least 20 times in the database can be seen in Figure  2 , and the frequencies of many of the variations in describing gout are shown in Table  3.
Elimination (n 5 572), clearance (n 5 2,139), and excretion (n 5 2,837) are all high-frequency terms in the database, but with varying degrees of relation to gout. Beyond being used at the greatest frequency overall, the single most common use of excretion occurs in the phrase uric acid excretion (n 5 246), indicating a strong contextual relationship with gout. Although clearance is used at similar frequencies to excretion, the collocation in which it most commonly appears is creatinine clearance (n 5 435), suggesting a weaker contextual relationship with gout. Elimination is, like clearance, not strongly associated with gout. These data suggest, rather unambiguously, that to describe treatment effects, excretion is the term that is most accurate and contextually appropriate. Additional terms relating to therapeutic effect include dissolution and solubility limit.