dataesr/scientific-paragraphs-categorization huggingface dataset:
| Need to verify cryptic lineages and A. mackerrasae as species Another interesting pattern obtained by collectively analyzing the CO1 and CYTB data is that the sample mtDNA genome from A. mackerrasae  (Aghazadeh et al. , 2015)  is nested within samples that have been identified as A. cantonensis (Subclade I. 2, Figures  1  and 2 ). In particular, the A. mackerrasae sample is very closely related to and less than 1% divergent (p-distance) at the CO1 and CYTB (Table  2 ) from AP017672 (Figures  1  and 2 ), an unpublished GenBank entry that was part of the 50 Helminth Genomes Project (BioProject: PPRJEB493). AP017672 was identified as A. cantonensis from Taiwan with no other information given. Recently  Song, Yong, and Eamsobhana (2017)  compared the whole mtDNA of A. mackerrasae  (Aghazadeh, 2015)  to KT947978 (Thailand isolate of A.
Haplotype 7 is very closely related to the A. mackerrasae sample (differs by 2 of 852 bp; Subclade I. 2; Figure  1 ). CYTB Haplotype 7 was restricted to individuals that were in microsatellite Cluster 6 and no other haplotype was found in this cluster (n = 15;  Dusitsittipon et al. , 2017). Again, as there was 100% concordance between nuclear and mtDNA data, this is suggestive evidence in support of A. mackerrasae as a distinct species. Nevertheless, additional multilocus data are needed to confirm whether there are independent evolutionary units (i. e. , distinct species) within Clade I. For example, based on the collective analyses we conducted herein, we would predict that if A. mackerrasae is indeed a distinct species, then samples of morphologically identified A. mackerrasae from Australia would have a microsatellite profile that would cluster them with Cluster 6.
| Evolutionary epidemiological inferences: gaps in linking the phylogenetic data to the life history and morphology First, we highlight two caveats in linking the CYTB and CO1 subclades. (i) We are assuming that there is no effective recombination in the mtDNA. Mitochondrial recombination, which has been documented in a few animal species  (Ladoukakis & Zouros, 2017) , would preclude anchoring the two phylogenies. However, as noted TA B L E 2 Pairwise p-distance between the six mtDNA reference samples. CO1 is above the diagonal and CYTB is below highlight critical areas for future study. We only briefly summarize some key traits below. For a more extensive discussion of the life histories of these three species, we refer readers to an excellent review by  Spratt (2015). Angiostrongylus cantonensis, A.
(2017) , country locations, and host species. Affiliations were made from sample information (Tables  S1  and S2 ) from either the CO1 or CYTB haplotypes that fall within the respective clade. (L = laboratory-maintained, W = wild-collected, U = unknown origin. If no superscript is given, then it was wild-collected. ) background (e. g. , intermediate or definitive host species). Monte et al. (2012) also report greater larval output with infections of CO1 ac9. In  Lee et al. (2014) , morphology and pathogenicity were examined between two CO1 mtDNA isolates, Pingtung (P) and Hualien (H), identified as A. cantonensis from Taiwan. There is difficulty in interpreting their sequence data (see Table  S2 ). Nonetheless, they reported significantly lower recovery of adult worms from rats for the H isolate compared to the P isolate and smaller female body size in the H isolate.
malaysiensis in Thailand  (Spratt, 2015). In particular, a mixed infection of A. cantonensis and A. malaysiensis has been reported from rats in Bangkok, Thailand  (Bhaibulaya & Techasoponmani, 1972). These   S1  and S2 ). Thus, there are no mtDNA-based identifications of A. malaysiensis outside South-East Asia. In contrast, mtDNA-identified samples of A. cantonensis, Clade I, have not only been found throughout South-East Asia, but also in Japan, Brazil, and the continental United States. Subclades I. 1 and I. 3 have only been reported from Thailand. Subclade I. 4 is largely restricted to South-East Asia and Japan. The Hawai'i report (GenBank KP721454 in Figure  1 ) in this subclade is from a Thailand laboratory-maintained isolate by way of a Japan laboratorymaintained isolate (Table  S1 ).
The other Hawai'i reports, CO1 haplotypes ac5b and ac5 (sister to Subclade I. 2; Figure  2 ), are also based on laboratory-maintained isolates in Japan or Thailand (Table  S2 ). As the Hawai'i isolates are not in the same subclades, it appears that there may be more than one type of Hawai'i laboratory isolate. The most globally distributed subclades are I. 2 and I. 5. Subclade I. 2 is interesting in that it contains the A. mackerrasae sample, which is the sole sample from Australia. Intriguingly, the larval samples from  Dalton et al. (2017)  (GenBank MF000735 and MF000736, Figure  2 ) that were reported from wildlife in the continental United States are closely related to A. mackerrasae (Figure  2 ). Thus, if Subclade I. 2 represents A. mackerrasae, then caution is advised in interpreting past global reports of A.
| SUMMARY Our study represents an initial attempt to consolidate the available mtDNA data to link the findings of past studies. Assimilating the available mtDNA has highlighted three critical factors that investigators need to be aware of or resolve in future studies on the evolutionary epidemiology of A. cantonensis. First, there are incongruences between species identifications and mtDNA variants. We provided a resolution to this problem above and Tables S1-S4 to aid future studies. Second, we presented strong evidence of a CYTB pseudogene. The primers we designed should enable avoidance of this pseudogene. Third, current mtDNA-nuclear concordant patterns strongly hint at the presence of several cryptic species within Clade I and Clade II  (Dusitsittipon et al. , 2017). More multilocus-based studies are needed to assess species delimitations and to test the validity of A.
cantonensis (GenBank AY295804; Carreno & Nadler, 2003)  or A. malaysiensis (GenBank EF514914; Fontanilla & Wade, 2008). To facilitate future studies, we provided TablesS5 and S6, which provide sample information linking 18S haplotypes and CO1 haplotypes as well as the alignment of the unique 18S haplotypes of A. malaysiensis and A. cantonensis in fasta format. We also note that the nuclear 18S clades of Rodpai et al. (2016)  are completely congruent with the two main diverged microsatellite clusters observed in Dusitsittipon et al. (2017) , thereby reinforcing the species delimitation of clades I and II. Of special note, because KT186242(Yong, Song, et al. ,   2015)  would now be classified as being from A. malaysiensis, then the genome sequence inYong, Eamsobhana, Lim, et al. (2015)  also needs to be reclassified as coming from A.
Up to four PCR products of different primer sets were pooled for each individual and separated by capillary electrophoresis on an AB3130xl Genetic Analyzer. Alleles were sized against the internal size standard GeneScan TM LIZ500 TM (Applied Biosystems) and scored using GeneMapper ® software Version 4. 0 (Applied Biosystems). The observed allele size of all genotyped individuals was decreased by 18bp in order to account for the M13(-21) universal sequence tag.
The program BOTTLENECK  (Piry et al. 1999 ) was used to detect recent genetic bottlenecks in the studied populations. Based on the observed number of alleles and the sample size of a population, the program computes the gene diversity expected under the assumption of mutation-drift equilibrium and compares it to Hardy-Weinberg gene diversity (H e ; Nei 1978) to establish whether there is a significant deficit of gene diversity resulting from a recent bottleneck. As recommended by the authors of the program, a Wilcoxon sign-rank test  (Luikart et al. 1998 ) was performed using 1000 bottleneck simulation replicates under the stepwise mutation model (SMM). FSTAT 2. 9. 3  (Goudet 1995)  was used to estimate genetic differentiation among populations by F ST. We also measured R ST , an analogue of F ST specific for microsatellite data, employing a stepwise mutation model (SMM, Slatkin 1995); R ST was measured using the software SPAGeDi 1.
Characteristics of 15 microsatellite markers developed in Ruta lamarmorae. Shown for each marker are the GenBank accession number, forward and reverse sequences of the primer pair, repeat motif, and size of the expected PCR product (bp). Primers in bold were used for genotyping; locus RL9 was excluded from statistical analyses as all genotyped individuals of R. corsica and R. lamarmorae were heterozygotes for the same two alleles.
This paper presents two novel statistical analyses of multiblock data using the R language. It is designed for data organized in (K +1) blocks (i. e. , tables) consisting of a block of response variables to be explained by a large number of explanatory variables which are divided into K meaningful blocks. All the variables -explanatory and dependentare measured on the same individuals. Two multiblock methods both useful in practice are included, namely multiblock partial least squares regression and multiblock principal component analysis with instrumental variables. The proposed new methods are included within the ade4 package widely used thanks to its great variety of multivariate methods. These methods are available on the one hand for statisticians and on the other hand for users from various fields in the sense that all the values derived from the multiblock processing are available.
Multiblock methods preserve the original structure of the data and thus allows one to analyze the (K + 1) tables simultaneously. They can be used to select explanatory variables in the datasets (X 1 ,. , X K ), generally numerous and quasi-collinear, that are strongly related with the dependent variables in Y. After this selection step, multiblock methods can also be used to identify the complex links between explanatory and dependent tables both at the variable and block levels. Although methods designed for the analysis of (K +1) tables have been available for a few years, with a straightforward and single eigensolution, there are few published applications. The main reason for this lack of interest is probably the poor availability of free statistical software implementing these methods.
Multiblock partial least squares regression is only available in the free Multi-Block Toolbox (Van den Berg 2004) of the commercial software package MATLAB (The MathWorks, Inc. 2015). However, this method is designed for more complex multiblock data (K explanatory datasets to explain K dependent ones) and there is no proof of the convergence of the associated iterative algorithm. In R (R Core Team 2018), no methods are implemented to deal with (K + 1) tables and two unsatisfactory solutions can be envisaged. A first alternative is to ignore the multiblock structure of the explanatory variables so that a two-table technique can be applied. For instance, partial least squares regression (pls package;  Mevik and Wehrens 2007)  or redundancy analysis (pcaiv function in ade4;  Dray and Dufour 2007)  can be used to study the link between the merged explanatory dataset X and the dependent table Y.
On the other hand, the user may also use methods developed for more complex data structures, such as partial least squares path modeling (plspm package;  Sanchez, Trinchera, and Russolillo 2017). However, this method is not specifically designed for a (K + 1) structure and the iterative algorithm convergence is only practically encountered but no formal proofs have been provided  (Henseler 2010). We implemented new statistical and graphical functionalities to analyze multiblock (K + 1) data in the ade4 package for R. This package provided classes, methods and functions to handle and analyze multivariate datasets organized in one  (Dray and Dufour 2007) , two or K tables  (Dray et al. 2007 ). We propose additional tools for the statistical analysis of (K + 1) datasets with explanatory and modeling methods in ade4.
We implemented two multiblock methods that are based on the optimization of a criterion with a direct eigensolution. The first method is multiblock partial least squares regression (MBPLS) applied to the particular case of a single response dataset Y  (Wold 1984). The second one is multiblock principal component analysis with instrumental variables (MBPCAIV) also called multiblock redundancy analysis  (Bougeard, Qannari, and Rose 2011). We detail preliminary data manipulation, present the two selected multiblock methods and give advice to select the most relevant. We illustrate the main advantages provided by the methods and the overall descriptive graphical displays. Multiblock methods are also devoted to modeling purpose and we thus propose a crossvalidation procedure to select the optimal model dimension and diagnostic plots to describe its quality.
Data manipulation We consider a response dataset Y with M variables and K explanatory datasets X k with P k variables (k = 1,. , K). The merged dataset X is defined as X = [X 1 |. |X K ] and contains P = K k=1 P k explanatory variables. All these variables are measured on the same N individuals and are centered. Note that we added some restrictions concerning the number of individuals (N ≥ 6; defined from our experience in multiblock analyses), the explanatory variables (P k ≥ 2) for k = (1,. , K). The dependent block Y may contain a single variable (M ≥ 1). No missing values are allowed. The ade4 package provides the class 'ktab' that should be used to store the multiblock explanatory datasets (X k ). Variables from the same block must be contiguous. Different procedures can be used to create a 'ktab' object.
In the following example, we illustrate the use of the ktab. data. frame function. The data [Y|X 1 |X 2 ] are stored in the douds object available in ade4. The dataset Y contains 27 variables, the first explanatory table called "River" contains 4 variables and the second one called "Chemicals" has 7 variables. R> library("ade4") R> data("doubs", package = "ade4") R> Y <-doubs$fish R> X <-doubs$env R> blo <-c(4, 7) R> tab. names <-c("River", "Chemicals") R> ktabX <-ktab. data. frame(df = X, blocks = blo, tabnames = tab. names) We implemented new functions to manipulate easily multiblock data. For instance, ktabX[1, 1:5, 1:3] can be used to select data for the first five individuals and the first three variables of the first table. The dependent dataset Y should be analyzed by a one-table method providing an object of class 'dudi'.
For instance, dudi. pca can be used to apply principal component analysis. R> dudiY <-dudi. pca(Y, center = TRUE, scale = TRUE, scannf = FALSE) Note that the transformation selected for the dependent variables in the call of the dudi. pca function (centering and scaling in this example) will also be applied in the subsequent multiblock analysis. In the rest of the paper, we consider a real dataset (chickenk in ade4) to illustrate the use of multiblock methods. This example concerns the overall risk factors for losses in broiler chickens (Y) described by (M = 4) variables (the first-week mortality rate, the mortality rate during the rest of the rearing, the mortality rate during the transport to the slaughterhouse and the condemnation rate at slaughterhouse). The (P = 20) explanatory variables are organized in (K = 4) thematic blocks related to the successive production stages of broiler chickens: the farm structure (X 1 , 5 variables, "FarmStructure"), the flock characteristics at placement (X 2 , 4 variables, "OnFarmHistory"), the flock characteristics during the rearing period (X 3 , 6 variables, "FlockCharacteristics") and the transport-lairage conditions, slaughterhouse and inspection features (X 4 , 5 variables, "CatchingTranspSlaught").
All these variables are measured on (N = 351) broiler chicken flocks. See ?chickenk for further details and  Lupo et al. (2009)  for a complete description. R> data("chickenk", package = "ade4") R> losses <-chickenk[[1]] R> dudiY <-dudi. pca(losses, center = TRUE, scale = TRUE, scannf = FALSE) R> ktabX <-ktab. list. df(chickenk[2:5]) Several questions can be associated to this epidemiological dataset: 1. Are there some relationships between losses in broiler chickens Y = (y 1 ,. , y Q ) and variables measured at the different production stages X = (x 1 ,. , x P )? 2. Do the chicken flocks (N ) have the same features in terms of their production stage description (X) in relation with losses (Y)? 3. Are there significant links between all the variables describing the production stages X = (x 1 ,.
Multiblock methods We implemented multiblock partial least squares regression (MBPLS) applied to the particular case of a single response dataset Y  (Wold 1984)  and multiblock principal component analysis with instrumental variables (MBPCAIV;  Bougeard et al. 2011). In comparison with MBP-CAIV, the method MBPLS is less sensitive to multicollinearity within explanatory blocks. For the case where only one block of variables is explained, Westerhuis, Kourti, and Mac-Gregor (1998) among others, showed that the solution obtained from the iterative algorithm, originally devoted to K datasets to be explained with K other ones, is equivalent to the solution obtained from a standard PLS regression of Y and the merged dataset X. The method MBPCAIV considers the multiblock structure of data and leads to a model with a better fitting ability than MBPLS but it is unstable when explanatory blocks contain quasi-collinear variables.
For MBPLS, the quantity maximized is equal to k VAR(X k DYv (1) ) = VAR(X DYv (1) ). In this case, the constraints w (1) k I P k = 1 and w (1)  I P = 1 are added so that the first order solution is also given by λ (1) = VAR(Y DXw (1) ) = VAR(Y Dt (1) ). To obtain the second order solution, for MBPCAIV and MBPLS, the variables in the datasets (X 1 ,. , X K ) are deflated by a regression onto the first global component t (1). The same maximization is then performed but the original datasets are replaced by the residuals obtained in the deflation step. Subsequent components are obtained by reiterating this process. The reader can consult  Bougeard et al. (2011)  for more details. With ade4, MBPCAIV is obtained by: R> res <-mbpcaiv(dudiY, ktabX, scale = TRUE, option = "uniform", + scannf = FALSE, nf = 5) The MBPLS is performed by replacing the call to mbpcaiv by a call to the mbpls function.
The first two arguments refer to the dependent and the explanatory datasets. Variable scaling of the explanatory dataset can be set by the scale argument (default is TRUE). The scaling of the dependent dataset has been previously defined in the first call to dudi. pca. The argument option defines the block weighting. The "none" option corresponds to no block weighting, "uniform" corresponds to the case where the merged explanatory dataset X (resp. Y) has a total variance equal to one and each of the K explanatory blocks to 1/K  (Westerhuis and Coenegracht 1997). The argument scannf allows to display the scree plot of eigenvalues to help with the choice of the number of the latent variables to be interpreted (default is TRUE). The optional nf argument indicates the number of selected dimensions (defaults is 2).
The rank of the analysis is given by the element rank. A comprehensive summary of the first dimensions is provided by the summary function. For each dimension, the eigenvalues, inertia percentage and cumulated inertia percentage are given. The percentage and the cumulated percentage of the inertia of each dataset, X, Y and (X 1 ,. , X K ), explained by the global latent variables are also provided (VarY and VarYcum) for the dependent dataset:.
Multiblock principal component analysis with instrumental variables Class: multiblock mbpcaiv Call: mbpcaiv(dudiY = dudiY, ktabX = ktabX, scale = TRUE, option = "uniform", scannf = FALSE, nf = 5) Graphical tools to represent the outputs of the analysis are provided in the new adegraphics package  (Dray and Siberchicot 2018). The main results are provided by the plot method of the 'multiblock' class. By default, the first two global latent variables t (element lX) are used but higher order representations can be selected using the arguments xax (defaults to 1) and yax (defaults to 2). R> library("adegraphics") R> plotmbpcaiv <-plot(res, xax = 1, yax = 2) Results are illustrated in Figure  2. The first plot (top right corner) depicts the similarities between individuals (i. e. , the 351 chicken flocks).
Selection of the optimal number of latent variables Multiblock methods can also be used for predictive purposes and the first step is to select the optimal model dimension. The global latent variables can be expressed as linear combinations of X, i. e. , t (h) = Xw * (h) and the dependent dataset Y can be split up into the same latent variables such as Y = h l=1 t (l) c (l) + Y (h) , Y (h) being the residual matrix of the model based on h components. This leads to the final model Y = X h l=1 w * (l) c (l) + Y (h) for all the dimensions h = (1,. , H). The optimal model is obtained by selecting the number of latent variables with a two-fold cross-validation  (Stone 1974 ). The dataset is split in a calibration and a validation sets, this procedure being repeated several times. Among all the models corresponding to the various values of h, the optimal model is retained, as a compromise between a good fitting ability (minimization of the root mean square error of calibration RMSE (h) C ) and a good prediction ability (minimization of the root mean square error of validation RMSE (h) V ).
Lastly, the block importance index (bip) is proposed to assess the contributions of the blocks (X 1 ,. , X K ) in the modeling process. It is computed from the coefficients (a (h) 2 1 ,. , a (h) 2 K ) which measure the link between Y and (X 1 ,. , X K ). If the optimal model contains several components, the cumulated block importance index (bipc) is based on the weighted average of the bip indexes, taking as weights the relative importance of each dimension λ (h). Both these quantities are expressed as percentages. A block is considered to be significantly associated with the dependent dataset if the 95% confidence interval does not contain the threshold value 1/K, K being the number of blocks. All the details are given in  Bougeard et al. (2011). We implemented new classes 'randboot  ' and 'krandboot' and methods (print, plot)  to manage the outputs of bootstrap simulations.
Conclusion and perspectives We provide new tools to improve the handling and the statistical analysis of multiblock (K + 1) datasets in the ade4 package for R. These methods preserve the original structure of the data and provide an adapted framework that combines tools from factorial analysis and regression methods. Traditional graphical outputs are completed by cross-validation and bootstrap procedures to select and interpret the optimal model. The framework of multiblock methods provide several tools and could be enriched by considering hierarchical-structured design of individuals frequently met in biological surveys (e. g. , individuals partitioned in groups corresponding to different treatments). Multiblock methods can also be directly adapted to the explanation of several dependent datasets (Y 1 ,.
The data are provided as TOA reflectance in 109 × 109 km L1C tiles in the Universal Transverse Mercator (UTM) map projection (ESA (European Space Agency), 2015). Adjacent L1C tiles in the same UTM zone from the same swath overlap by about 10 km, and adjacent tiles from the same MSI swath may be defined in different UTM zones  (Roy et al. , 2016c). The L1C cloud mask product is not currently reliable. Consequently, in this study the cloud mask and per-pixel quality information generated by the Sentinel-2 radiative transfer atmospheric correction code SEN2COR (Müller-Wilm, 2016) was used. Landsat-8 has a 15°field-of-view and senses a 185 km swath. The data are defined in 185 × 185 km images in the World-wide Reference System (WRS-2) path (ground track parallel) and row (latitude parallel) coordinates  (Arvidson et al.