dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic composition Taxonomic composition of viromes is determined by sequence similarity between virome reads and complete known viral genomes, and can be displayed as either raw number of hits or number of hits normalized by genome length  [32]. Virome composition can now be visually compared in two ways: (i) merging multiple compositions on the same Krona chart  [33]  and (ii) an in-house developed interactive heatmap, which allows a more hierarchical view. As an example of the latter, a taxonomic heatmap was generated for the 16 datasets from the human gut (Figure  1 ). This heatmap allows the user to quickly visualize that these datasets only exhibited similarities with bacteriophages, in accordance with the results presented in Minot et al. (  [49] , Figure  two c ). Even when the same bacteriophage groups are found in the different datasets, their proportion differ between each virome: Myoviridae constitute between 11 and 42% of each virome, Podoviridae 2 -35%, Siphoviridae 24 -55% and Microviridae 0 -31%.
k-mer frequency bias A recurrent observation in analyses of virome data is that the majority of reads has no similarity to any known viral sequence  [6] , as can be noted for human gut viromes (top of Figure  1 ). Therefore, methods that consider viromes in their entirety rather than only the small fraction affiliated with known sequences are of particular interest. Analysis of k-mer nucleotide frequency bias is such a method and was proved to distinguish viromes from different biomes. This analysis, now available in Metavir, was here applied to the 16 human gut datasets using 4-mer nucleotides (tetranucleotides) and a non-metric multidimensional scaling (Figure  2 ). Results are again similar to those obtained in Minot et al. (  [49] , Figure  five A ): even though viral communities seem to be affected by diet (X, H, L), the different samples from each subject (X1, H1, H2, L1, L2 and L3) are gathered indicating that each individual contained a unique virome.
To gain a better view of the diversity in each sample and of the relationships between samples, Metavir 2 now offers the opportunity to compute phylogenetic trees that include reads from other viromes. As an example, we Viral species are classified according to the NCBI taxonomy, and taxonomic groups can be folded or unfolded with a mouse click. Columns have been re-ordered through mouse drag and drop to gather datasets from each subject. Samples are named according to the diet (X: ad-lib diet, H: high fat/low fiber diet, L: low fat/high fiber) of 6 subjects (X, L1, L2, L3, H1, H2) and to the day of the sample collection after the beginning of the experiment (d1, d2, d7 and d8). conducted such an analysis on the Picovirinae, a subfamily of Podoviridae (Maximum-likelihood tree computed with FastTree, with default parameters).
Individual viral genome recruitment plots Besides the analysis of single reads through BLAST or phylogenetic tools, plots of metagenomic sequences recruited by reference genomes of interest can give a sense of how well this genome is represented in a metagenome (see for example  [53] ). Indeed, visualizing a chosen genome and the distribution of its associated reads is useful to determine which genes of a known virus are found in an environmental dataset and the similarity level between reference and virome sequences. Recruitment plots can be generated in Metavir, and here again, several datasets can be included in a single plot in order to compare the gene conservation of a virus in different samples. As an example, this tool was here used to further study Lactococcus phage 1706, one of the most abundant phages in the 16 datasets from the human gut.
As this phage has been isolated from bacteria involved in milk fermentation and not directly from gut microbes, its actual presence in human gut samples is questionable. The plot of virome reads recruited by Lactococcus phage 1706 shows that Figure  2  Comparison of the 16 unassembled human gut viromes and the assembled dataset based on their tetranucleotide compositions. The NMDS was generated from the pairwise distances computed from the tetranucleotide frequency bias. Each virome is named according to the subject (H1, H2, L1, L2, L3) and day of sampling (day 1, 2, 7 or 8). Samples taken from the same individual are highlighted in shades of blue, yellow and red. Highlighted in green are both the control dataset (X in Figure  1 ) and the assembled virome described in  [16]. most characterized genes (coding for the main functions of the genome, i.
e. replication and structure module, highlighted in red on the plot) are retrieved whereas most of the unknown genes (in blue) are not (Figure  4 ). This suggests that even though phage 1706 is the nearest neighbor of abundant human gut phage(s) in the current state of the reference databases, these gut phages do not have a gene content entirely similar to phage 1706. Furthermore, a gene cassette made of two putative tail proteins and two other structural proteins known to be major players of phage-host specificity in phage 1706 is scarcely retrieved in these datasets (  [54] ; black frame on Figure  four ). Thus, it is very likely that the phages retrieved in the human gut viromes, even though similar to this Lactococcus phage, infect an alternative host. This example illustrates how recruitment plots help in further understanding the genomic content of environmental viruses and their genomic relatedness with known viruses.
Analyzing assembled datasets using the new contig section Even though unassembled viromes proved to be useful for a better characterization of environmental viral communities, long genomic fragments generated through the assembly of metagenomic datasets are usually more informative. Indeed, complete ORFs predicted out of such contig sequences (i) are more often similar to known viruses than short reads  [55] , (ii) provide more robust phylogenies than using reads representing only a portion of a gene, and (iii) are more appropriate than short random reads in determining the gene content and genetic diversity of a viral community  [56]. Moreover, analysis of the genomic content and architecture can provide decisive insights into virus classification and evolution of viral groups  [20]. A new section dedicated to the annotation and navigation within sets of contigs has therefore been implemented in Metavir.
When assembled viromes, i. e. sets of contigs, are uploaded by users, ORFs are predicted  [41]  and then annotated using sequence similarity results against viral genomes and protein domains. In addition to the general taxonomic composition, contig maps and annotations can be displayed for every contig. As datasets can consist of tens of thousands contigs, users can choose to visualize contigs (i) longer than a defined threshold, (ii) predicted as circular or linear, (iii) affiliated to a particular viral family, and/or (iv) possessing a particular gene. Finally, tools available for read analysis were specifically adapted to assembled datasets: taxonomic compositions are computed using either gene or contig affiliation, phylogenies are generated using predicted ORFs and genetic diversity is computed using either predicted ORFs or domain conservation.
Conclusion This new release of Metavir provides a wide range of tools to analyze either raw or assembled viral metagenomes in a comprehensive way. As virome projects now regularly encompass multiple samples and as more and more viromes are being published, a special effort was made towards virome comparison. Two new large scale methods were implemented and all existing Metavir tools were modified so that they can be used to compare datasets. Furthermore, a new section has been specifically developed to handle sets of large genomic contigs. As these datasets can be large and as all individual sequences can be of interest, we paid special attention to the interface, with filtering panels and network visualization. Selected contigs can then be analyzed in detail by comparing their automatic annotations in terms of gene content and genomic maps.
Figure4Recruitment plot of Lactococcus phage 1706 for 6 human gut datasets. Only the 6 viromes with more than 200 reads recruited by this genome were included, i. e. reads having their best BLAST hit with this genome. Stacked histograms represent the total number of reads similar to each 500-nt long genome part, with a different color for each virome. Each gene is plotted as a rectangle on the genome map of the Lactococcus phage 1706 at the bottom, with hypothetical proteins in blue, and characterized genes in red. A black frame highlights the three genes involved in host specificity.
Figure5Automatic annotation of contig 187_43 (complete name: 1470_2012_5M_iter1 _k63_scaffold187_43. 0). On top, a dynamic map displays the predicted genes colored by affiliation (green for genes affiliated to Refseq Virus, yellow when only PFAM affiliation is available, and red for uncharacterized genes). The associated gene table (below) displays for each gene the accession number and annotation of the most similar gene in RefseqVirus and in PFAM (when available).
Back Close Full using the Adaptive Mapping Functions (AMF) from  Gegout et al. (2011). Simulations were performed for different configurations: variations in the reflectometer height, mask effects due to terrain, satellite network geometry. This article is composed by three main parts following the logical structure of the Fig. 2. The first part presents the datasets used for initiating simulations, the second one concerns the methodologies for the determination of the reflection points while the last one deals with the simulator performances and simulation results.
Design of the simulator The simulator has been developed in the GNU R language, generally used for data treatment and statistical analysis. A user manual and a description of the R language can be found on the website http://www. r-project. org/. The main interest of such a language remains in that it is distributed under GNU GPL license which does R routines an open source program, available on various platforms (i. e. GNU/Linux, FreeBSD, NetBSD, OpenBSD, Mac OS and Windows). The simulator is composed by different blocks (Fig. 2 ): an input block which contains the different elements mandatory for the processing; a processing block where the user can choose which algorithm to be used, and an output block containing the different results of the simulation. As inputs, this simulator requires the receiver coordinates, the satellite ephemeris and a set of optional environmental parameters such as a DEM in order to take the possible masking of the terrestrial topography into account, as well as adaptive mapping functions to integrate atmospheric delays and bending effects.
GNSS orbit parameters The simulations are based on the determination of the positions of the specular reflection points, once the receiver and the satellites positions are known. Satellites coordinates can be obtained from the International GNSS Service (IGS) ephemeris final products which provide GNSS orbit and clock offset data with a temporal resolution of 15 min in the SP3 format for the past epochs, or derived from the Keplerian parameters (semi-major axis, inclination, and argument of perigee) to predict GNSS satellite positions. These products are available on the IGS website: http://igs. org/.
The YUIMA Project is an open source and collaborative effort aimed at developing the R package yuima for simulation and inference of stochastic differential equations. In the yuima package stochastic differential equations can be of very abstract type, multidimensional, driven by Wiener process or fractional Brownian motion with general Hurst parameter, with or without jumps specified as Lévy noise. The yuima package is intended to offer the basic infrastructure on which complex models and inference procedures can be built on. This paper explains the design of the yuima package and provides some examples of applications.
Introduction The plan of the YUIMA 1 Project is to construct the bases for a complete environment for 1 YUIMA is both the acronym for Yoshida-Uchida-Iacus-Masuda-Andothers but also the name of an important character in Buddhism religion (http://www. kyohaku. go. jp/eng/syuzou/meihin/kaiga/chuugoku/ item01. html) whose approach to problems fits this project well. simulation and inference for stochastic processes via an R (R Core Team 2013) package called yuima. The yuima package adopts the S4 system of classes and methods  (Chambers 1998). Under this framework, the yuima package also supplies various functions to carry out simulation and statistical analysis. Both categories of procedures may depend on each other. Statistical inference often requires a simulation technique as a subroutine, and a certain simulation method needs to fix a tuning parameter by applying a statistical methodology.
e. , expectation of an irregular functional of a stochastic process) provides precise approximation values instantaneously. The expansion formula, which has a long expression involving more than 900 terms of multiple integrals, is already coded in the yuima package for generic diffusion processes. The yuima package delivers up-to-date methods as a package onto the desk of the user working with simulation and/or statistics for stochastic differential equations. In the yuima package, stochastic differential equations can be of very abstract type, multidimensional, driven by Wiener process or fractional Brownian motion with general Hurst parameter, with or without jumps specified as Lévy noise. The yuima package is intended to offer the basic infrastructure on which complex models and inference procedures can be built on.
This paper explains the design of the yuima package and illustrates some examples of applications. The paper is organized as follows. Section 2 is an overview about the package. Section 3 describes the way in which models are specified in yuima. Section 4 describes how to simulate yuima models. Section 5 explains asymptotic expansion methods. Section 6 is a review of basic inference procedures. Finally, Section 7 gives additional details and the roadmap of the YUIMA Project. Although we assume that the reader of this paper has a basic knowledge of the R language, most of the examples are easy to understand if he/she knows stochastic differential equations intuitively or symbolically.
How to obtain the package The yuima package is hosted on R-Forge and the web page of the project is http://R-Forge. R-project. org/projects/yuima. The R-Forge page contains the latest development version, and stable version of the package are available through the Comprehenisve R Archive Network (CRAN) at http://CRAN. R-project. org/package=yuima. Development versions of the package are not supposed to be stable or functional, thus the occasional user should consider to install the stable version first.
The main objects and classes Before discussing the methods for simulation and inference for stochastic process solutions to stochastic differential equations, we give an overview of the main classes in the package. As mentioned there are different classes of objects defined in the yuima package and the main class 'yuima'. This class is composed of several slots. Figure  1  represents the different classes and their slots. The different slots do not need to be all present at the same time. • drift is an R expression which contains the drift specification. • diffusion is itself a list of 1 slot which describes the diffusion coefficient relative to first noise. • parameter which is a short name for "parameters" which is a list of objects. Figure  1 : The main classes in the yuima package. For example, in case one wants to simulate a stochastic process, only the slots model and sampling should be present, while the slot data will be filled by the simulator.
We discuss in detail the different objects separately in the next sections. The general idea of the yuima package is to separate into different subclass objects the statistical model, the data and the statistical methods. As will be explained with several examples, the user may give a mathematical description of the statistical model with setModel which prepares a 'yuima. model' object by filling the appropriate slots. If the aim is the simulation of the solution of the stochastic differential equation specified in the 'yuima. model' object then, using the method simulate, it is possible to obtain one trajectory of the process. As an output, a 'yuima' object (i. e. , a possibly incomplete, object of class 'yuima') is created which contains the original model specified in the 'yuima. model' object in the slot named model and two additional slots named data, for the simulated data, and sampling which contains the description of the simulation scheme used as well as other informations.
This means that, for example, a(t, X t , θ) = t• √ θX t will be passed as t * sqrt(x * theta), therefore, the order of the arguments is not relevant to R as well as its mathematical description as long as it is consistent through each specific section. Further, yuima is able to accept any user-specified notation for the state variable x (for X t ) and the time variable t and the remaining term of an R expression will be interpreted as parameter as will be explained in Section 3. 1. We are now able to give an overview of the main slots of the most important class of the yuima package. The 'yuima. model' class contains information about the SDE of interest. The constructor function setModel is used to give a mathematical description of the SDE. All functions in the package are assumed to get as much information as possible from the model instead of replicating the same code everywhere.
6); dimension reports the dimensions of the parameter space. It is a list of the same length of parameter with the same names; J. flag is for internal use only. As seen in the above, the parameter space is accurately described internally in a 'yuima' object because in inference for SDE's, estimators of different parameters have different properties. Usually, the rate of convergence for estimators in the diffusion coefficient are similar to the ones in the i. i. d. (independent and identically distributed) sampling while estimators of parameters in the drift coefficient are slower or, in some cases, not even consistent. The yuima package always tries to implement the best statistical inference for the given model under the observed sampling scheme.
R> str(mod2) Formal class 'yuima. model' [package "yuima"] with 16 slots. @ drift : expression((-theta * x)). @ diffusion :List of 1. $ : expression(1/(1 + x^gamma)). @ hurst : num 0. 5. @ jump. coeff : expression(). @ measure : list(). @ measure. type : chr(0). @ parameter :Formal class 'model. parameter'. @ all : chr [1:2] "theta" "gamma". @ common : chr(0). @ diffusion: chr "gamma". @ drift : chr "theta". @ jump : chr(0). @ measure : chr(0). @ state. variable : chr "x". @ jump. variable : chr(0). @ time. variable : chr "t". @ noise. number : num 1. @ equation. number: int 1. @ dimension : int [1:6] 2 0 1 1 0 0. @ solve. variable : chr "x". @ xinit : num 0. @ J. flag : logi FALSE In order to simulate the parametric model it is necessary to specify the values of the parameters θ and γ as shown in the next code chunk R> set.
Notice that in this way the only visible parameter for yuima is mu as rho and sig are inside the bodies of the functions f2 and f3. If we want to instruct yuima about these parameters, they should appear explicitly as arguments of the functions as shown in the following R code R> f2  <-function(t, x1, x2, x3, rho, sig)   <-matrix(c("f1(t, x1, x2, x3)  R> sabr. mod <-setModel(drift = c("0", "mu * g(t) * x3", "mu * x3"), + diffusion = diff. coef. matrix, state. variable = c("x1", "x2", "x3"), + solve. variable = c("x1", "x2", "x3")) R> str(sabr. mod@parameter) { + ret <-0 + if(x3 > 0) ret <-rho * sig * x3 + return(ret) + } R> f3 <-function(t, x1, x2, x3, rho, sig) { + ret <-0 + if(x3 > 0) ret <-sqrt(1 -rho^2) * sig * x3 + return(ret) + } R> diff. coef. matrix.
For example, if we want to specify the following fractional Ornstein-Uhlenbeck model dY t = 3Y t dt + dW H t we can proceed as follows R> mod4A <-setModel(drift = "3 * y", diffusion = 1, hurst = 0. 3, + solve. var = "y") R> mod4B <-setModel(drift = "3 * y", diffusion = 1, hurst = 0. 7, + solve. var = "y") R> set. seed(123) R> X1 <-simulate(mod4A, sampling = setSampling(n = 1000)) R> X2 <-simulate(mod4B, sampling = setSampling(n = 1000)) R> par(mfrow = c(2, 1), mar = c(2, 3, 1, 1)) R> plot(X1, main = "H = 0. 3") R> plot(X2, main = "H = 0. 7") and the two trajectories can be seen in Figure  5. In this case, the appropriate slot is now filled R> str(mod4A) Formal class 'yuima. model' [package "yuima"] with 16 slots. @ drift : expression((3 * y)). @ diffusion :List of 1. $ : expression(1). @ hurst : num 0.
R> str(samp) Formal class 'yuima. sampling' [package "yuima"] with 11 slots. @ Initial : num 0. @ Terminal : num 3. @ n : num 3000. @ delta : num 0. 001. @ grid :List of 1. $ : num [1:3001] 0 0. 001 0. 002 0. 003 0. 004 0. 005 0. 006 0. 007. @ random : logi FALSE. @ regular : logi TRUE. @ sdelta : num(0). @ sgrid : num(0). @ oindex : num(0). @ interpolation: chr "pt" As seen from the output, the sampling structure is quite rich and we will show how to specify some of the slots later on. We simulate this process by specifying the sampling argument in the simulate method R> set. seed(123) R> X2 <-simulate(mymod, sampling = samp) the sampling structure is recorded along with the data in the 'yuima' object X2 R> str(X2@sampling) Formal class 'yuima. sampling' [package "yuima"] with 11 slots. @ Initial : num 0.
q q q q qq q q q q q q q q q q q q qq q q qq qq q q q q q q q q q q q q q q q qq q q q q q q qq q q q q qq q q q q q q q q q q q qq qq q q q q q q q q q q q q q q q q q q q q q q q q q q qq q Figure 10: An example of subsampling used within the simulate command. Again one can look at the structure of the sampling structure. Subsampling can be used within the simulate function. What is usually done in simulation studies, is to simulate the process at very high frequency but then use data for estimation at a lower frequency (see Figure  10 ). This can be done in a single step in the following way: In the previous code we have simulated the process twice just to show the effect of the subsampling, but the reader should use only the line which outputs the simulation to Y. sub as seen in Figure  11.
In the present situation, each d i (g) is expressed as d i (g) = g(z)p i (z)φ(z; 0, v)dz, where p i is a polynomial and φ(z; 0, v) is the density of the normal distribution N (0, v) with v = Cov[ F (0) ]. Polynomials p i are given by the conditional expectation of multiple Wiener integrals. The expansion (7) holds uniformly in a class of functions g. As mentioned above, Monte-Carlo methods require a huge number of simulations to get the desired accuracy of the calculation of the expectation, while the asymptotic expansion of F (ε) gives very fast and accurate approximation by analytic formulation. The yuima package provides functions to construct the functional F (ε) and perform automatic asymptotic expansion based on the Malliavin calculus starting from a 'yuima' object. This asymptotic expansion approach to option pricing was proposed in early 1990's  (Yoshida 1992a; Takahashi 1999; Kunitomo and Takahashi 2001) , and several related papers are available today.