dataesr/scientific-paragraphs-categorization huggingface dataset:
b One row of X: the mass spectrum of one studied sample. c One column of X: aligned mass peaks for an annotated compound. d One independent source is plotted against another. The distribution of samples can be seen in the space described by these two sources. e represents the contribution of metabolites to these sources (loadings of metabolites) Independence conditions detected by ICA involve both orthogonality (linear independence) and higherorder independence (e. g. , exponential, polynomial), while classical PCA only ensures orthogonality between components. Therefore ICA could potentially extract additional information from the dataset. Since non-targeted metabolomics data usually contain huge numbers of variables and only a few samples, certain techniques using nonlinear mapping could result in computational complexity and overfitting  [33].
However, tests of this algorithm on non-targeted datasets present several uncertainties: firstly, it is hard to decide on the types of priors for A and Γ in a non-targeted study since the dataset reflects the complexity of the study and has multiple manifolds; besides, the performance of the meanfield-based approach is doubtful if it cannot be compared with a full Monte Carlo sampling (too time-consuming); in addition, BIC maximization is usually impossible for high dimensional datasets with a reasonable amount of components. Therefore we developed a heuristic method based on the FastICA algorithm and hierarchical clustering. The method, named MetICA is based on the Icasso algorithm used in medical imaging studies  [39, 40]. We start with data pre-processing, including centering and dimension reduction, for which a classical PCA was used  [22].
The FastICA algorithm is run many times on the PCA score matrix with m different inputs, generating many estimated components. Close estimates give birth to a cluster. The reliability of the FastICA algorithm can be reflected by the quality of clustering. Moreover, as with any statistical method, it is necessary to analyze the statistical reliability (significance) of the components obtained. In fact, a relatively small sample size can easily induce estimation errors  [41]. Bootstrapping original datasets and examining the spread of the sources estimated might identify these uncertainties. Both reliability studies would help to decide the optimal number of components. In addition to the adaptation of the Icasso algorithm in non-targeted metabolomics, the novelty in the present study is the dual evaluation of algorithmic and statistical reliability for model validation.
Metabolomics data acquisition and pre-treatment Non-targeted metabolomics data were obtained from a DI-MS platform: a Bruker solariX Ion Cyclotron Resonance Fourier Transform Mass Spectrometer (ICR/FT-MS, Bruker Daltonics GmbH, Germany) equipped with a 12 Tesla superconducting magnet (Magnex Scientific Inc. , UK) and an APOLO II ESI source (BrukerDaltonics GmbH, Germany) in negative ionization mode. Mass spectra of each sample were acquired with a time domain of 4 mega words over a mass range of m/z 100 to 1000 (Fig. 1a ). The technique has ultrahigh resolution (R = 400 000 at m/z = 400) and high mass accuracy (0. 1 ppm). After de-adduction and charge state deconvolution, mass peaks were calibrated internally according to endogenous abundant metabolites in DataAnalysis 4. 1 (Bruker Daltonics GmbH, Germany) and extracted at a signal-to-noise ratio (S/N) of 4.
The peaks extracted were aligned within a 1 ppm window and generated a data matrix. Each row represents the intensity of one mass signal in each sample (Fig. 1b ). Masses found in less than 10 % of samples were not considered during further data analysis and other absent masses were set at zero intensity in the sample concerned. We applied the software Netcalc developed in-house to remove potential spectral noise and isotope peaks. This software also unambiguously annotates the elemental formula assigned to the aligned m/z based on a mass difference network  [42]. The annotation process is considered as an unsupervised filtration that reduces data size and reveals an underlying biochemical network structure inside the data set. Our ICA algorithm is applied on this filtered data matrix.
At the end of AF (sugar depleted), methanolic extracts of 45 samples were studied on the ICR/FT-MS platform with the method described in the section "Metabolomics data acquisition and pre-treatment". We randomized the order of strains for the fermentation experiment and for the non-targeted study. The resulting data matrix "Yeast-Experimental. txt" (Additional file 1) had n = 45 rows (samples) and p = 2700 columns (filtered mass signals). Prior knowledge about yeast strains according to the yeast producer, including basic genetic traits, fermentation behaviors and wine characteristics, will be used for component interpretation and method validation.
PCA-denoising PCA is done by a singular value decomposition (SVD) of the centered data matrix X : The denoised matrix X d is obtained by X d ¼ X Ã K , where K is the k first PCs of loading matrix, obtained from the prcomp function in the script MetICA_fastICA. R (Additional file 1). Working on X d preserves 90 % of the relevant information and reduces the potential noise given by 10 % of variance.
FastICA algorithm The functions ica. R. def ('deflation' method) and ica. R. par ('parallel' method) from the R package fastICA, version 1. 2-0 (http://cran. r-project. org/web/packages/fastICA/ index. html), were applied to the denoised matrix X d (Fig. 2   The number of ICs is set to be the same as the number of PCs chosen for denoising. The hyperbolic logcosh function is fixed for negentropy approximation as a good general purpose contrast function  [34]. The script MetICA_fastICA. R contains two methods of extracting more than one IC: ica. R. def ('deflation' or one at a time) and ica. R. par ('parallel'). 'Deflation' avoids potential local minima  [45] , while 'parallel' has the power to minimize mutual information between sources  [46]. Therefore each method is responsible for half of the runs.
Production of simulated data To confirm the power of the MetICA algorithm, a simulated data SX was generated to mimic the real non-targeted metabolomics data. The visual illustration of this process is in (Additional file 2: Figure  S1 ) and the function used was in MetICA_simulated_generator. R (Additional file 1). From the centered yeast metabolic footprinting data X , a multivariate Gaussian background noise N was created to have the same covariance as X. In parallel, we performed a simple PCA and used non-Gaussian PCs (measured by kurtosis) to reconstruct a matrix, RX. The simulated SX is the sum of I N and RX, wherein I is a real number controlling the level of noise. The simulated data for I = 0. 1 was stored in Yeast-Simulated. txt (Additional file 1).
Diagnostics of simulated and experimental data The FastICA algorithm is based on the maximization of negentropy, an exact measure of non-Gaussianity. It is equivalent to the minimization of mutual information, or searching independent components  [34]. The algorithm only works when the dataset is derived from non-Gaussian sources and thus contains non-Gaussian features. Therefore we measured the non-Gaussianity of each mass using kurtosis (Additional file 2: Figure  S3 ). The distribution of kurtosis for the experimental data showed a significant amount of super-Gaussian (kurtosis >1) and sub-Gaussian (kurtosis <-1) variables, while the background matrix N mainly contained Gaussian variables (kurtosis between -1 and 1). The simulated matrix SX contained a large number of super-Gaussian variables, knowing that two super-Gaussian PCs (PC11, kurtosis=1.
Our strategy started with the visualization of all the estimated sources (from different algorithm inputs) after projection onto a 2D space. A reliable projection should preserve the distance between estimated sources and hierarchical clusters should only contain neighboring points. According to our tests, Curvilinear Component Analysis (CCA, Matlab SOM Toolbox 2. 0,  [49] ) outperformed multidimensional scaling (MDS,  [48] ) and the Self-Organizing Map (SOM,  [50] ) for this purpose. In fact, CCA preserved the distance better and gave more explicit visual separations between clusters. In order to examine the HCA results in the 2D space, the executable program MetICA_CCA. exe (Additional file 1) assigned randomly different colors to the sources belonging to different clusters. We could monitor cluster splitting by increasing the number of clusters (Additional file 2: Figure  S2 ) until we obtained compact, well-separated clusters (Fig.
3a-c , minimal partitions necessary for different level of noise). Apart from visual monitoring, we applied a quality measure to decide the optimal number of partitions. The index is simply the ratio between the average within-clusters distance and the betweenclusters distance (Additional file 2: Figure  S2 ). The smaller the index is, the more compact and better separated the clusters seem to be on the 2D space. At the beginning this index decreases as a function of the number of clusters. From a certain point, it tends to be stable or increases, meaning that adding another cluster does not much improve the data modeling. The decision regarding the optimal number of clusters via this index is consistent with visual monitoring (Fig. 3a-c ). After the optimal number of clusters was chosen, centrotypes of clusters were verified by comparing to components used for data generation (PC11 and PC15).
Statistical reliability of MetICA on experimental data MetICA revealed the convergence of FastICA on nontargeted metabolomics data. However, some of the convergences observed might only haven been due to a few particular samples. Therefore it is important to evaluate the statistical significance of each centrotype obtained. However, as an unsupervised method, ICA could not be validated via prediction error since no target information could be used. Once again, as an optimization-based component analysis, cross-validation (CV) methods widely used in PCA validation  [51]  are inappropriate or too time-consuming. In fact, to start each CV run, datasets must be divided into two groups and the whole MetICA procedure has to be run on one of them (training subset). Accordingly it is necessary to validate the convergence for each CV run.
Therefore we instead applied a sophisticated bootstrapping validation. Bootstrapping means random sampling with replacement. In general, bootstrapping is considered as a slight modification of the dataset without changing its size. Bootstrapping validation is widely used for model selection in Machine Learning problems  [52] [53] [54] , especially when strict mathematical formulations are not available. In our case, the statistical significance of MetICA components was barely evaluated mathematically. Therefore we tried to find a score that described the stability of MetICA components subjected to bootstrapping. It was expected that components distorted by particular samples would be very sensitive to these slight modifications, while statistically significant components were expected to remain stable.
The validation was implemented in the script MetICA_bootstrap. R (Additional file 1) for yeast exo-metabolome data as follows: from the original X (45 * 2700) we generated B = 100 bootstrapped data: X 1 , X 2 … X B by replacing 5 rows of X each time. Then, we fixed the algorithm input, the demixing matrix W 0 and ran FastICA once on 50 bootstrapped datasets with 'parallel' extraction and the other 50 with 'deflation' extraction. We extracted from each bootstrapped dataset k estimated sources (S b b1 , S b b2 … S b b1k ) to ensure R 2 > 90 % and we did likewise in each FastICA run for the original data (to ensure R 2 > 90 %). The 13 centrotypes OC 1 , OC 2 … OC 13 from the original dataset were compared with these k estimated sources. The most correlated source b S ba′ was considered to be aligned to centrotype OC a.
The absolute Spearman's correlation coefficient ρ a between OC a and b S ba′ was the score of OC a for the particular bootstrapped data. The higher the score was, the closer the estimated source was to the centrotype. The sum of scores H ¼ P ρ a from all the bootstrapped data was our final similarity score for centrotype OC a. It measured how similar MetICA centrotypes were to estimated sources of bootstrapped data, in other words, the stability of centrotypes after bootstrapping. The math input is as follows: H ¼ X B b¼1 max j¼1…k ρ OC a; S bj À Á The H score implies the statistical reliability of centrotypes given a fixed demixing matrix W 0. However, such a score might depend on the FastICA input. Therefore the scoring is repeated with fixed bootstrapped datasets but 50 randomized W 0. Finally, for each centrotype, we obtained a distribution of H.
We used the median b H of the distribution as an exact score of the centrotype. The dispersity shows how trustworthy the score estimate is. Our empirical experiment showed that the distribution was quite weakly dispersed (Fig. 6 , the results on the other datasets are similar). The visual illustration of the whole scoring process is in (Additional file 2: Figure  S4 ). The centrotype scoring leads to another possibility for deciding on the number of clusters. After the number of clusters was determined, we could evaluate the b H of each centrotype after which we obtained a score distribution of all the centrotypes for the particular number of clusters. Therefore we could monitor the b H for all the centrotypes as a function of the number of clusters (Fig. 5 ) and select the optimal number based on the amount of centrotypes containing a higher b H.
Comparison to other ICA algorithms The performance of MetICA was compared to other ICA algorithms (Table  1 ) using another non-targeted ICR/FT-MS-based metabolomics dataset (published data  [57] ). The data matrix counted initially 18591 signals measured in 51 urine samples from doped athletes, clean athletes and volunteers (non-athletes). For the purpose of filtering and formula annotation, such high data dimension was more efficiently handled by our in-house developed software Netcalc compared to other standard approaches, such as ChemoSpec (http://cran. r-project. org/web/packages/ChemoSpec/index. html) and MetaboAnalyst (http://www. metaboanalyst. ca/). The reduced data matrix Doping. txt (Additional file 1) with 9279 mass signals remained were analyzed directly with MetICA, as well as two FastICA algorithms in R ('Parallel' and 'Deflation').
Four other ICA packages were tested on the PCA score matrix Xd (51 rows, 43 columns, ordered by variance explained): icapca in R  [58] , icamix in R (http://cran. r-project. org/web/packages/icamix/), kernel-ica toolbox version 1. 2 in Matlab with a Gaussian kernel  [59]  and mean field ICA toolbox in Matlab for Bayesian ICA described previously  [7]. If 'out of memory' problem occurred or the simulation failed to produce reasonable results, the corresponding package was applied only on first few columns of Xd (variance explained was reduced, Table 1  [1, 2] ). For all 7 ICA methods tested, 10 replicates were made with randomized algorithm inputs. We evaluated the shapes of extracted components Table 1  [3] [4] [5] ), the stability between simulation runs (Table 1  [6] ) and the reliability of components & model (Table 1  [7, 8] ).
We suggested two ways of deciding the optimal number of model components, namely the number of clusters: either by using a cluster quality index (algorithmic reliability), or through the bootstrap scores of all the centrotypes (statistical reliability). Seven ICA algorithms were compared based on  [1]  maximal percentage of variance the algorithm could handle (depending on the computer memory),  [2]  optimal number of components that the algorithm suggests,  [3]  kurtosis of the most super-Gaussian component  [4]  kurtosis of the most Gaussian component,  [5]  minimal kurtosis of components (the most sub-Gaussian when it is negative),  [6]  number of consistent components extracted in all 10 algorithms runs with an absolute Spearman's correlation between them higher than 0. 8 and on whether the algorithm suggests  [7]  model selection criteria  [8]  importance order of components The whole MetICA routine was tested on simulated data and several MS-based non-targeted metabolomics data, including low resolution MS datasets (an example is provided in Additional file 3).
Compared to other ICA methods, MetICA could efficiently decide a reasonable number of clusters based on algorithmic reliability. The bootstrap scores further validated this decision. For both high and low mass resolution and for any biological matrices, MetICA was able to handle more than 10 000 features and to sensitively select reliable models. Since our routine was based on a simple linear model, we could easily reconstruct the original dataset and calculate the fitting error. Therefore, our procedure could also be further used for dimension reduction before applying supervised statistical methods, or data denoising to remove undesirable signals (bias and instrumental noise)  [61]. All in all, it opens a door for extracting non-Gaussian information and non-linear independence from non-targeted metabolomics data.
It is based on the CSA Cloud Control Matrix (CCM) taxonomy of security controls  [13]  and is aimed to help CSCs understand the security coverage of specific cloud offerings in relation to popular security standards, control frameworks and regulations. The questionnaires answered by many CSPs are publicly available in the CSA Security, Trust and Assurance Registry (STAR)  [4]. We propose a methodology that uses the data extracted from STAR to evaluate the implementation of various controls provided by cloud solutions (see Subsection "The vulnerability parameter for a CSP"). Luna et al. introduced in  [18]  Cloud Security Level Agreements (SecLA) and proposed a methodology to benchmark SecLA of CSPs with respect to CSCs' requirements  [19]. Both CSP SecLA provisions and user requirements are expressed using a special data structure: Quantitative Policy Trees, allowing expressing controls with different granularity: CCM control areas, control groups, and controls (corresponding to CAIQ answers).
For example, the "Yes" answer to CAIQ Question RS06-01 "Are any of your data centers located in places which have a high probability/occurrence of high-impact environmental risks (floods, tornadoes, earthquakes, hurricanes, etc. )?" implies a negative outcome, which means the control is not implemented. In such cases we classify the "Yes" answer as Not Implemented and the "No" answer as Implemented. We analyzed 44 out of 70 CSPs from the mentioned registry providing answers to about 200 questions each. To be included in the selection a provider had to fill in the CAIQ questionnaire in the format provided by CSA (to enable automated processing). The responses of some of the big CSPs (e. g. Amazon, HP, Microsoft, RedHat, or SAP 1 ) who provided answers in other forms, though, were processed manually to ensure the consideration of the major players.
Experimental results We prototyped CARAM and run experiments the data in STAR [4] during the course of 2015. In this section we present the results of these experiments (see Additional file 2 -CARAM Experiments. xlsx for the detailed data). Figures  3  and 4  illustrate the vulnerability index ν i and the adjusted probability β i values for the 44 CSPs included in our analysis from STAR. These are calculated by the CARAM prototype as explained in Section "Risk levels computation". The differences between the vulnerability indices and therefore the adjusted probabilities of various CSPs in STAR are in the order of magnitude. For example, the lowest vulnerability index is 0. 011, which is the vulnerability index of a CSP called as Iguana (we call the CSPs in STAR database by using animal names to preserve their confidentiality).
We call any asset related to at least 10 % of the incident scenarios as highly exposed asset. Some assets are service-related and some are data assets. Finally, data assets can be personal or not personal. Based on these, we use the following five types of CSCs in our experiments: CSC 1 have all the assets in the ENISA's list. CSC 2 have all highly exposed assets. CSC 3 have all the data and service assets. CSC 4 have only data assets. CSC 5 have only personal data assets. CARAM computes the privacy, security and service impact values adjusted for these five classes of CSCs as depicted in Fig. 5. As clearly shown, the privacy impact of risk scenarios is somewhat higher than the service and security impacts. This may be explained by the fact that most of the risks directly or indirectly may impact an asset related to privacy.
Conclusion CARAM is a qualitative and relative risk assessment model for assisting CSCs to select a CSP that fits their risk profile the best. It is based on the existing frameworks such as ENISA, CAIQ and CNIL and complements them to provide the CSC with a practical tool. It is a risk assessment approach such that evaluation is carried out for a specific CSC, which means assessment for each CSP-CSC pair is for that pair and not generic. Moreover, the model can be easily adapted to assess further risks scenarios and/or security control frameworks, might they change in the future. These multiple features make CARAM unique with respect to the state of the art in risk assessment techniques and also among other works proposing cloud security metrics. We have implemented a Proof-of-Concept prototype as part of the Data Protection Impact Assessment tool developed in A4Cloud project  [28, 29].
Introduction Phylogenetic approaches have become a central component of various areas of the life sciences. A number of packages are available to handle and utilise phylogenetic trees (e. g. 'ape  ' Paradis et al. 2004) , in order to understand, for example, community assembly (e. g. 'picante  ' Kembel et al. 2010 , 'DAMOCLES  ' Pigot & Etienne 2015) , trait evolution (e. g. 'Coevol  ' Lartillot & Poujol 2011 , 'geiger' Pennell et al. 2014) , and diversification (e. g. 'BayesRate  ' Silvestro et al. 2011 , 'TreePar' Stadler 2011a , 'diversitree' Fitzjohn 2012 , 'DDD' Etienne et al. 2012 , 'geiger' Pennell et al. 2014 , 'BAMM' Rabosky et al. 2014). Here, we present and describe the R package RPANDA, which implements both model-free and model-based phylogenetic approaches that are not implemented in previous packages.
Description RPANDA is an R package (R Development Core Team 2014) than can be installed from the CRAN repository (http://cran. r-project. org). RPANDA relies on the R packages 'ape'  (Paradis et al. 2004 ), 'picante'  (Kembel et al. 2010) , 'phytools'  (Revell 2012) , 'deSolve'  (Soetaert et al. 2010) , and 'igraph'  (Csardi & Nepusz 2006). The main functions of the package are listed in 'Phyllostomidae' is the family-level phylogeny and 'Phyllostomidae_genera' is a list of 25 phylogenies corresponding to Phyllostomidae genera with more than one species.
Acknowledgement We thank members of  HM's group , as well as reviewers  D. S. Caetano  and  D. Silvestro  for comments on a previous version of the paper. The development of RPANDA is supported by grant  CHEX-ECOEVOBIO  from the  Agence Nationale de la Recherche  and grant  616419-PANDA  from the  European Research Council  awarded to HM.