dataesr/scientific-paragraphs-categorization huggingface dataset:
IMPROVEMENTS TO THE PSS PROGRAM The development of PSSweb prompted improvements to the PSS program that are summarized here. We have included more alternatives for both multiple sequence alignment and structure superposition. Multiple sequence alignment can now be performed with the Clustal Omega program (3), and structure superposition with the Theseus program (4). Superposed structures can now be interactively visualized directly in the HTML report thanks to JSmol  (5). Finally, the format of the chain field in the Structure list has been enriched with first chain and multiple chains autodetection. W402 Nucleic Acids Research, 2016, Vol. 44, Web Server issue.
Structure list. A list of structures is needed as input. Each entry consists of a PDB file basename, an optional chain identifier and an optional user-defined family name. Autodetection of the first chain and of all chains of a PDB file is possible with 'first' (default) and '*', respectively. A blank chain identifier is entered as ' '. If the basename has the format of a PDB code, an attempt is made to download the file from the PDB server. It is also possible to upload custom PDB files or ZIP of PDB files. Only standard PDB ATOM records are treated by PSSweb. When an atom has alternate locations, only the first one is considered. Only the first model of nuclear magnetic resonance (NMR) PDB files is read. Position list. It is sometimes of interest to focus the analysis on a specific subset of amino acid positions.
The advantage is speed and no risk of error in the alignment, in particular at gap borders. If the numbering is not coherent, this method will give unreliable results and so proper checking should be performed when using PDB numbering. The Clustal Omega program (3) is a fast and scalable general multiple sequence alignment program, default options are employed. The sequence and sequence/structure alignment methods with MODELLER rely on the SALIGN (6-8) module of the MODELLER program  (9). Details on the parameters used with SALIGN are available in the PSS documentation. Sequence alignment methods are the recommended default choice. The sequence/structure method is recommended for more distantly related sequences. It is not possible to define a general criterion to decide which sequence alignment method will work best, experimentation is encouraged and generated alignments should be assessed for quality.
Superposition of structures Superposition of structures can be performed with the Theseus program (4) or with the MODELLER program  (9). Theseus uses a maximum likelihood approach, while MOD-ELLER uses an iterative least-square minimization algorithm. The reference structure for the superposition can be defined, otherwise the first structure is taken. Interactive 3D visualization of the superposed structures is possible thanks to a JSmol (5) viewer included in the results page. The rendering style and coloring mode can be modified and the individual structures can be displayed or hidden. Visualization with JSmol can be slow for large ensembles or large structures. It is also possible to download the result directory thanks to the ZIP file provided and to visualize the superposed structures offline with the PyMOL  (10)  and VMD  (11)  scripts included.
Clustering The structures can be subjected to a hierarchical clustering analysis, based on the Algorithm::Cluster Perl module, an interface to the C Clustering Library  (12). Structural variables taken into account are dihedral angles and can be controlled with the Position list. The circular distance between selected dihedral angles is the metric. Maximum linkage is used. A tree is calculated and the clusters are obtained by cutting the tree according to the chosen cluster radius option (default is 60 • ). The radius parameter corresponds to the minimum distance allowed between clusters. A dendrogram figure is prepared to visualize the clustering tree obtained, with the chosen cutting radius as a dotted line. To facilitate the setup of another PSSweb run with a definition of families corresponding to the identified clusters, a Structure list file with cluster numbers in the family column is provided.
CONCLUSION The PSSweb server (http://pssweb. org) for protein structural statistics was presented. PSSweb takes as input an ensemble of PDB files of protein structures, performs a multiple sequence alignment and proposes different optional functionalities: structure superposition, structural statistics in both Cartesian and dihedral coordinates and cluster analysis. The results are presented in an interactive HTML page, containing figures, tables, 3D visualization of superposed structures and access to raw data. PSSweb is based on the already published PSS program  (2). The web server interface greatly facilitates access to PSS for a wider audience by avoiding the installation step and the command-line usage requirement. Further improvements will be guided by user demands. The automated comparisons of protein structures performed by PSSweb are of the greatest interest in structural biology, homology modeling, ligand and protein design.
FUNDING Centre National de la Recherche Scientifique (CNRS) ;  Institut National de la Santé et de la Recherche Médicale  ( IN-SERM );  Université de Strasbourg  and the  École Polytechnique ;  Agence Nationale de la Recherche  [ ANR-09-PIRI-0018-02-Puzzle-Fit ,  ANR-IAB-201-BIP:BIP ]. Funding for open access charge: CNRS; INSERM;  Université de Strasbourg  and the École Polytechnique.
To circumvent these problems,  [15]  noted that all parameters in the joint model can be estimated by fitting all the bivariate models, based on f ðy s ; y t Þ ¼ Z Z f ðy s jg s Þf ðy t jg t Þf ðg s ; g t Þdg s dg t for all m(m -1)/2 pairs (y s , y t ), 1 s < t m, resulting from the main multivariate model. Estimators for the main parameters are obtained by averaging over the results from fitting the m(m -1)/2 pairwise models. They then showed that the pseudo-likelihood theory can be used to derive the asymptotic distribution of these estimators, and used SAS procedures for mixed models  [14]  based on the Newton-Raphson algorithm to fit their models, following the approach in  [17]. In some multilevel studies, focus is not to directly model y, but a few number of latent variables which cannot be quantified directly (e.
Here, the authors sped up the EM algorithm by analytically integrating the random effects out of the likelihood function, avoiding to treat them as missing data. [60]  used EM based modeling to estimate the parameters of the multivariate linear mixed model under a SAS macro program encoded in IML. Although the EM algorithm is known to be slow, one of the biggest advantages of this method is that it is not computationally expensive, even with a large number of response variables. In this context, our contribution is the writing of the EM-based estimators in a more general form than those used in  [58, 59]  and  [60]. The expressions of the EM-based estimators used in this paper can easily perform any analysis in the framework of the multivariate multilevel data analysis using multivariate linear mixed-effects model.
Unlike mixed-effects models, GIMME allows for the structure of the connectivity maps to be unique across individuals  [66]. One can also use a nonparametric function f to handle the relationship between the components of y and the covariates  [67] [68] [69]. This strategy requires also to have sufficient data per subject, in the case of multivariate longitudinal data. Other estimation strategies implemented under softwares and discussed by  [70]  can perhaps be extended to the multivariate analysis case, when necessary. Let us finally point out that the software packages which can easily and accurately analyze (jointly) the data of multivariate multilevel type are extremely rare, and one arranges the data and manipulates packages primarily designed for fitting univariate models to handle their analysis.
The SabreR  [71]  package, under the R software  [72] , which has been devoted to jointly fitting up to three mixed-effects models, with random intercepts only, has been recently removed from the depot. These facts prove by themselves that the analysis of multivariate multilevel data in a single framework is a challenging task. Bayesian-based approaches can be implemented using packages like R2WinBUGS  [73]  under the R software, and are useful but very time consuming and require a good expertise from the user who can easily be discouraged.
Detailed example. We place ourselves in the case of longitudinal data where we observe two response variables y 1 and y 2 which are respectively the weight (kg) and the size (cm) of infants according to the score (V 2 ) of the quality of their food as well as the quality score (V 1 ) of their mothers' food. Infants are n = 3 girls (sex = F) and boys (sex = M) who are monitored over time. The dataset is presented by Table  1. Suppose that the model at each of two dimensions has one random intercept by subject (infant) and one random slope by subject in the direction of the infant's age (in months). For example, considering an identifiability constraint covering the sex variable whose level F is the reference, the bivariate linear mixed model can be written as follows: y 1 ¼ ð1 V 1 sex ¼ MÞ |fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflffl} ¼X 1 b 1 þ Z 1 g 1 þ ε 1 y 2 ¼ ð1 V 2 sex ¼ MÞ |fflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflffl} ¼X 2 b 2 þ Z 2 g 2 þ ε 2 ð8Þ where, explicitly X 1 ¼ 1 0 B B B B B B B B B B B @ 1 C C C C C C C C C C C A ð9Þ Z 1 ¼ 1 0 0 0 0 0 0 0 1 4 0 0 1 6 0 0 0 0 0 0 0 0 1 0 0 0 1 7 0 0 0 0 1 10 0 0 0 0 0 0 1 9 0 B B B B B B B B B B B @ 1 C C C C C C C C C C C A ; Z 2 ¼ 1 0 0 0 0 0 0 0 1 4 0 0 1 6 0 0 0 0 0 0 0 0 1 0 0 0 1 7 0 0 0 0 1 0 0 1 16 0 0 0 0 0 0 0 0 1 9 In the present example we have dim(X 1 ) 6 ¼ dim(X 2 ) and dim(Z 1 ) 6 ¼ dim(Z 2 ) due to the presence of the NA (Not Available) within the values of the variable V 1.
Initialization and stopping criterion of the algorithm Various ways exist for obtaining starting values for bk , Γ, Ŝk ; for k ¼ 1; Á Á Á ; m. Taking inspiration from  [75]  and  [60] , we have separately fitted each dimension of the model by using the lme4 package  [76]  under the R software and have used marginal estimated parameters to initialize bk and Ŝk. We then keep the expected random effects g ¼ Ê½gjy to initialize Γ by 1 n À 1 X n i¼1 gi g> i ð28Þ The stopping criterion is related to the relative error of the components of θ as follows: max j y ðrÞ j À y ðrþ1Þ j y ðrþ1Þ j < tol ð29Þ where (r) is the iteration index and θ j the jth component of θ. tol = 10 -5 seems to work well in practice. Test of the significance of d Corðg 1 ; g 2 Þ After the calculation of Γ on dataset, we sometimes need to investigate if the correlation between marginal random effects is statistically significant, by testing H 0 : Cor(γ 1 , γ 2 ) = 0 against H 1 : Cor(γ 1 , γ 2 ) 6 ¼ 0.
Here, the CV ranges from 0. 08 to 0. 19, and is represented by the Fig  1  for more visibility. Given these CV values, it seems that none of the entries of Γ is particularly poorly estimated. Deep investigation on the estimates' accuracy. Here, we compute the Mean Square Error (MSE) of the EM-based estimators with N = 600,1000 and N = 3000 across n = 50, 60, 100 and 300 to investigate how both values of n and N affect the quality of the estimates. For each value of n and N, we simulate 1000 data sets on which we estimate the model parameters and compute the MSE of these estimates. Without surprise, Table  3  shows that the quality of estimates is clearly improved when both n and N grow. Estimations performed on dataset containing N = 3000 observations are more accurate than those performed with N = 600, observing the maximum value of the MSE in each case.
Applications on real data sets In this section we analyze two data sets by using the likelihood ratio test through the EM-based estimators presented above. The first dataset is of multivariate multilevel type and the second is, specifically, of longitudinal multivariate type. Application to education data in the Netherlands. The data used here are named 'bdf' under the package nlme  [13]  of the R software. These data contain N = 3776 Grade eight students (aged about eleven years) in n = 208 elementary schools in the Netherlands  [79]. These pupils were tested twice (with an interval of one year between grade seven and grade eight) on their proficiency in Dutch language and arithmetics, where the goal was to investigate which characteristics of schools can account for the differences in the effectiveness of schools with regard to pupils' progress in language and arithmetics.
Most of the previous analyses of this dataset were concerned with investigating how the language test score depends on the pupil's intelligence, his family's socio-economic status and on related class or school variables. By fitting two independent (separate) models,  [79]  found that variables in Table  4  have a significant effect on post-test scores (language post-test and arithmetic post-test). These variables are: socio-economic status, intelligence score, age, gender and nationality. They also found a The null hypothesis, H 0 , that the arithmetic post-test score and the language post-test score are independent, is rejected with a p-value of 1. 436 × 10 -7. This result justifies a joint analysis of post-scores conditionally on the covariates present in the model and is therefore a supplementary information obtained from the data.
The results are presented in Table  7 , where the estimated fixed effects and their significance generally remain the same. The estimated covariance matrix, related to results contained in Table  7  of random effects is Application to malaria immune response data in Benin. The data come from a study which was conducted in nine villages (Avamé centre, Gbédjougo, Houngo, Anavié, Dohinoko, Gbétaga, Tori Cada Centre, Zébè and Zoungoudo) of Tori Bossito area (Southern Benin), where P. falciparum is the most common species in the study area (95%)  [80]  from June 2007 to January 2010. The aim of this study was to evaluate the determinants of malaria incidence in the first months of life of child in Benin. Details of the follow-up procedures have been published elsewhere  [81]. Data description. Mothers (n = 620) were enrolled at delivery and their newborns were actively followed-up during the first year of life.
Every three months, venous blood was sampled to quantify the level of antibody against malaria promised candidate vaccine antigens. The environmental risk of exposure to malaria was modeled for each child, derived from a statistical predictive model based on climatic, entomological parameters, and characteristics of children's immediate surroundings as reported by  [83]. Concerning the antibody quantification, two recombinant P. falciparum antigens were used to perform IgG subclass (IgG1 and IgG3) antibody quantification by Enzyme-Linked Immuno-Sorbent Assay (ELISA) standard methods developed for evaluating malaria vaccines by the African Malaria Network Trust (AMANET [www. amanet148trust. org]). Protocol was described in detail  [84]. Data analysis. For our analysis, we use some of the data and we rename the proteins used in the study described above, for confidentiality reasons (some important findings are yet to be published).
Thus, the proteins we use here, are named A1, A2, B and C, and are related to the antigens IgG1 and IgG3 as mentioned above in the description of the study. A1 and A2 are different domains of the same protein A, and C and D are two different proteins. Information contained in the multivariate longitudinal dataset of malaria are described in the Table  8 , where Y denotes a protein which is one of the following: IgG1 A1; IgG3 A1; IgG1 A2; IgG3 A2; IgG1 B; IgG3 B; IgG1 C; IgG3 C ð43Þ The aim of the analysis of these data is to evaluate the effect of the malaria infection on the child's immune (against malaria). Since the antigens which characterize the child's immune status interact together in the human body, we analyze the characteristics of the joint distribution of these antigens, conditional on the malaria infection and other factors of interest.
Based on these works, Further theoretical investigations may be done to properly find out the asymptotic distribution of the likelihood ratio statistic in the case of this bivariate correlation test. Finally, we have illustrated the usefulness of the test on two different real-life data. The first dataset, which is of multivariate multilevel type, concerns the effects of school and classroom characteristics on pupils' progress in Dutch language and arithmetics, where the scores in language and arithmetics are the two response variables which have been considered. Our method has yielded results that are consistent both with information in existing publications and with a conceptual understanding of the phenomenon. On this dataset, we have highlighted a joint effect between the scores in arithmetics and language within schools in the Netherlands.
The second dataset, which is of longitudinal multivariate type, concerns a study of the effect of the malaria infection on the child's immune response in Benin. By jointly analyzing all the pairs of protein profiles of interest, we have plotted a hierarchical cluster tree of these proteins, using the bivariate correlation test. Information contained in this hierarchical cluster tree is consistent with the biological literature related to this issue. The model as it is written is easily extendable to more dimensions despite a sparsity problem in choosing the parameterization of the covariance matrix or the precision matrix. Probably we could use this two-dimensional dependence test to structure a larger covariance matrix. The bivariate correlation test can help to construct iteratively, using a stepwise procedure, a parsimonious joint model containing all the components of y.
a correlation of r ¼ 0:895 between the random marginal intercepts, confirming a strong positive correlation between post-test scores in arithmetics and language. With a p-value of 8. 505 × 10 -5 , the likelihood ratio test indicates that the data are more compatible with the model incorporating random intercepts and random slopes at a time. Fixed effects seem very strong and do not significantly change between the independent and bivariate models. In contrast, a posterior distribution of random effects changes significantly between the independent model and the joint bivariate model. For example, we plot the joint distribution of random effects conditional on the data concerning School 47 in the education dataset under the independent model and the joint bivariate model. Fig 7a shows the joint posterior distribution of random intercepts under the independent model whereas Fig 7b presents the same posterior distribution under the joint bivariate model.
Background: Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse.
Results: In this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called "Minimum Information About a Plant Phenotyping Experiment", which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented. Conclusions: Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.
Phenotyping is a very wide and heterogeneous research field. It analyses both static quantities and dynamic processes. Sensitivity of the phenotypic observations to environmental conditions (in the sense of the genotypeby-environment interaction, G × E) requires scrupulous data handling for the acquired signal to be optimally preserved and persisted in databases to deliver most substantial scientific value. Meanwhile, differing amounts of metadata about experiment set-ups, lots of different trait names and their synonyms, and diverse rating scales are used (e. g. [2, 3] ), leading to ambiguity and inconsistency of phenotypic data description. Hence, both correct integration and interpretation of phenotyping experiments is hampered. Actions undertaken so far for phenotypic data have either been project-specific (DROPS  [4] ), platformspecific (PODD  [5, 6] ; Phenome FPPN  [7] ), or databasespecific (MaizeGDB  [8] , Triticeae Toolbox  [9] , Phenopsis DB  [10] , GnpIS-Ephesis  [11] ).
The lack of common standards of plant phenotyping experiments' description, both in terms of its content and the format, hampers the correct usage and re-usage of phenotypic data. A proper description of experimental metadata is a key to the correct interpretation of the outcome. In many research domains there have been initiatives aiming at provisioning of recommendations for the set of metadata needed to describe experimental results of particular biological assays. Most of them have resulted in a formulation of a "Minimum Information" or a similar "checklist" document, containing assay-specific recommendations. For example, the Genomic Standards Initiative formulated requirements for reporting sequences of nucleotides (MIxS  [12] ). The Microarray Gene Expression Database Group suggested the requirements for the description of transcriptomic data (MIAME/Plant  [13] ).
The Proteomics Standards Initiative published a corresponding set of recommendations for protein data (MIAPE  [14] ). Finally, the Metabolomics Standards Initiative provided rules concerning metabolomic observations (CIMR  [15] [16] [17] ) that were recently considered as a basis for more formal standardization by Rocca-Serra et al. [18]. These documents agree-in principle-on how to describe the experimental material and the treatments applied to it. A similar approach seems advisable to provide metadata recommendations for plant phenotypic data. As far as data formatting is concerned, for most data types the existing policies are database-specific. Formats that gained wider acceptance are MAGE-TAB  [19] , a text, tabular format required by the ArrayExpress database  [20] , storing gene expression data, and PRIDE XML or mzIdentML, required by the PRIDE database  [21]  for proteomics data.
The ISA-Tab format  [22]  has been developed to address descriptions for many types of experiments and assays. Its flexibility and focus on the experimental metadata, clearly separated from the data itself, make ISA-Tab a generic solution, now used by a number of research communities  [23] , with a potential to constitute a general experimental metadata description standard, also for phenotypes. In this paper, we report the measures taken to standardize the description of plant phenotypic data. We present solutions that are a concrete implementation of the opinions expressed recently by many partners of two European infrastructural projects, transPLANT (Transnational Infrastructure for Plant Genomic Science  [24] ) and EPPN (European Plant Phenotyping Network  [25] ) in  [1]. The solutions are generic and intended to systematize the way of describing all types of phenotypic data independently of the particular local requirements of a project or database, and thus aim for a better interoperability.