dataesr/scientific-paragraphs-categorization huggingface dataset:
Results An implementation of the ABC-SMC method (Algorithm 2) is available in the Python package ABC-SysBio  (Liepe et al. , 2010). Several kernels are already available in the ABC-SysBio. We used the component-wise Gaussian (cwG), the multivariate Gaussian (mvG) and the K-neighbors multivariate Gaussian (KmvG) for comparison. Assume a set of parameter vectors Θ Θ Θ = (θ 1 θ 1 θ 1 ,. , θ N θ N θ N ) with weights w w w = (w 1 ,. , w N ), where each parameter vector has d components θ i θ i θ i = (θ i 1 ,. , θ i d ). In the cwG kernel each component θ j of the parameter vector is perturbed independently according to a Gaussian distribution with mean θ j and variance: σ 2 j = N i=1 w i (θ i j -µ j ) 2 V -(V/V ) (33) where V = N i=1 w i and V = N i=1 w i 2 are the sum of the weights and the sum of the squares of the weights respectively and µ j = N i=1 w (i) θ i j is the weighted mean for the specific component.
In the mvG kernel, the correlations between the components of the parameter vector are taken into account. A particle θ j θ j θ j is perturbed using a multivariate normal distribution with mean θ j θ j θ j and covariance matrix: Σ = V V 2 -V = N i=1 w i (θ i θ i θ i -µ µ µ) T (θ i θ i θ i -µ µ µ) (34) where µ µ µ = N i=1 w i θ i θ i θ i is the weighted mean. The KmvG kernel, starts by selecting for each particle θ j θ j θ j the K-nearest neighbors. Then a multivariate normal distribution with mean θ j θ j θ j and covariance matrix computed by (34) but taking into account the K-nearest neighbors only, is used to draw a new particle. In ABC-SysBio, K is set by default to a quarter of the number of particles, and this value was used in the following studies. Further to the above described packages, featured by the ABC-SysBio framework, we have developed a Python package for the DPGMM and the Gibbs sampler described in Section 2.
While the proposed methodology was used with ABC-SMC its applicability is not limited to the specific framework. It can be easily extended to several bayesian inference algorithms, like MCMC, to design proposal distributions. Similarly, it could be used for other problems like model selection. In the ABC-SysBio package an ABC-SMC algorithm for model selection is available. Thus, the use of DPMs for model selection is straight forward. An interesting extension of the present work is the development of an ABC-SMC algorithm to estimate model parameters using single-cell experimental data. We envisage that the increased efficiency of DPMs in estimating multimodal distributions will be beneficial in studying cell-to-cell variability and infering multidimensional parameter distributions describing a population of cells that may be grouped according to their environmental conditions.
With this sequencing technique, hypervariable regions within the gene are amplified, sequenced, and clustered into operational taxonomic units (OTU). Taxonomic classification of representative sequences from each cluster is then aligned against a database of previously characterised 16S ribosomal DNA reference sequences to identify bacteria of interest. As alterations and changes in microbiomes have been associated with a range of diseases including obesity  [3] [4] [5] , Crohn's disease  [6]  or ankylosing spondylitis  [7] , it is integral that we analyse this data appropriately given the impact on human health and disease treatment outcomes  [8]. A number of statistical analysis tools have been proposed to examine differences between microbial communities as well as to identify features that are key to driving the differences.
Finally, the other critical issue we address in this study is high inter-subject variability  [4] , which is often reduced with an appropriate experimental repeated-measures design where each subject acts as its own control. Thus, microbial sampling is performed repeatedly on the same subjects over different habitats. While such experimental design has been widely adopted by community profiling studies such as the Human Microbiome Project (HMP,  [23, 24] ) to define a 'healthy' microbiome community by characterising different body sites in the same subjects, very few statistical methods have taken advantage of this design and accommodate inter-subject variability. We introduce mixMC, a multivariate analysis framework for 16S data to identify OTU features discriminating multiple groups of samples.
Unsupervised multivariate analysis. PCA variants, such as Principal Coordinate Analysis (PCoA,  [17] ) allows for dimension reduction of the data and visualisation of diversity patterns in microbiome studies. PCoA is commonly applied to non Euclidian sample-wise dissimilarity matrices (e. g. Bray-Curtis  [18] ) or phylogenetic distances between sets of taxa in a phylogenetic tree (weighted or unweighted Unifrac distance,  [19, 20] ). Alternatively, and to avoid spurious results arising from compositional data PCA can be applied on log ratio compositional data using either CLR transformation, or Isometric Log Ratio transformation (ILR,  [32] , described in S1 Text). In mixMC we applied PCA on ILR transformed data using customised R scripts from the robCompositions package  [33]. Multilevel variance decomposition.
Parameters and performance evaluation. The number of features to select per component must be specified in sPLS-DA and is usually optimised using cross-validation. In this study we used 10-fold cross-validation repeated 100 times. For varying features selected by sPLS-DA the classification error rate resulting from the cross-validation process was then recorded and the lowest error rate indicated the optimal number of features to select on each component. This procedure concurrently indicated the optimal number of components for the sPLS-DA model. Once those parameters chosen, the final sPLS-DA model was run on the entire data set to obtain the final list of discriminative OTUs for each component. Graphical and numerical outputs. We further characterised each selected OTU by calculating its median normalised count in each body site.
In this plot the background colour indicates the body sites where the OTU is most abundant, the node size represents the median OTU count in that body site and the node colour indicates a negative (black) or positive (yellow) weight from the sPLS-DA regression coefficient. Other insightful outputs include sample representation where each individual is projected onto the sPLS-DA components, the list of OTU features selected on each component, the cross-validation error rate per component and the number of features contributing to each body site for each component. The multilevel sPLS-DA framework is implemented in the R package mixOmics [41] using multilevel decomposition  [35]. The cladogram was generated using the GraPhlAn Python code  [40]. R codes and tutorials are available on our website www.
Case studies HMP case studies. We analysed subsets of the NIH HMP16S data downloaded from http://hmpdacc. org/HMQCP/all/ for the V1-3 variable region. The original data contained 43 146 OTU counts for 2 911 samples measured from 18 different body sites. We focused on the first visit of each healthy individual and further divided the data into two data subsets. For both data sets a preliminary exploratory PCoA confirmed that there was no confounding covariate effect due to run center or gender (see  S1 Fig). Most diverse body sites dataset. Understanding microbial community diversity across body habitats is fundamental to study the human microbiome. In their extensive HMP data statistical analysis, Li et al. quantified intra-sample diversity using the Shannon index. Based on their results we chose the three most diverse habitats according to all genera-based and OTU-based taxonomic units  [46] , namely Subgingival plaque (Oral), Antecubital fossa (Skin) and Stool sampled from 54 unique healthy individuals for a total of 162 samples.
The prefiltered dataset included 1 674 OTU counts (S1 Table ). Oral body sites dataset. While many published analyses have focused on the main microbial habitats (gut, oral cavity, skin and vagina from the  [24, 47] ), little has been done to comprehensively characterise multiple sites within a single habitat. In this data set we solely considered samples from oral cavity, which has been found to be as diverse as the stool microbiome  [46]. The nine oral sites were Attached Keratinising Gingiva, Buccal Mucosa, Hard Palate, Palatine Tonsils, Saliva, Subgingival Plaque, Supragingival Plaque, Throat and Tongue Dorsum. After prefiltering, the data included 1 562 OTU for 73 unique healthy individuals and a total of 657 sample (S1 Table ). Koren dataset. Koren and colleagues examined the link between oral, gut and plaque microbial communities in patients with atherosclerosis and controls  [25].
We compared our HMP most diverse results to the healthy individuals from this dataset. This study contained partially repeated measures from multiple sites including 15 unique patients samples from saliva and stool, and 13 unique patients only sampled from arterial plaque samples. The data were downloaded from the QIITA database (http://qiita. microbio. me/study/description/349) and included 5 138 OTU. After prefiltering, the data included 973 OTU for 43 samples.
Unsupervised analyses on Most Diverse body sites dataset We applied unsupervised analyses PCoA or PCA on ILR transformed data to visualise diversity patterns between microbial communities, then compared different types of normalisations (TSS-ILR, CSS) followed by a multilevel variance decomposition for repeated measures. A PCoA performed on the filtered OTU raw counts (with no normalisation) showed that the unweighted Unifrac distance could highlight diversity patterns between each body site better than weighted Unifrac (Fig  2 ). As this study focuses on the most diverse body sites, the presence or absence of microbial communities is expected to drive the differences between body sites more than the relative abundance usually highlighted by weighted Unifrac. Applying PCoA on the unfiltered count data led to similar interpretation (S2 Fig) , but we observed a lower amount of explained variance of the first and second coordinate as more 'noisy' OTU were present in the data (unweighted Unifrac: 11.
Supervised analysis on Most Diverse body sites dataset We applied multilevel sPLS-DA to identify a microbiome signature characterising each body site and compared the different normalisation strategies (TSS+CLR or CSS) in our multivariate method to DESeq2 and ZIG univariate methods. Impact of normalisation to identify discriminative features with sPLS-DA. The sPLS-DA classification performance was similar in both TSS+CLR or CSS normalised data. The lowest classification error rate was obtained for two components (0. 7% for TSS+CLR and 0. 3% for CSS, S4 Table ). Both normalisations consistently misclassified antecubical fossa on the first component but correctly classified the two other body sites, and the addition of the second component enabled a better classification of all body sites (Fig  4 ).
Analysis of the oral body site dataset with mixMC Buccal mucosa 0 5 Hard palate 2 (1) 0 Palatine Tonsils 1 (1) 0 0 5 (3) 0 2 (2) 4 (2) 6 (4) Tongue dorsum 30 (9) 0 0 100 (8) 0 37  (11)  60 (  13 ) 57  (12)  doi:10. 1371/journal. pone. 0160169. t001 buccal mucosa (component 2) and gingiva (component 3) than the CSS normalisation. The CSS normalisation also led to a substantial number of ties (equal median counts) when assessing the body site contribution of the selected OTU (not shown). Therefore, the detailed analysis that follows solely focuses on a multilevel sPLS-DA model with TSS+CLR normalisation. Body sites characterisation. We mainly focused on the first three sPLS-DA components for our interpretation ( Features contribution. Table  1  details the number of features contributing to each oral site per component.
Interestingly, component 3 discriminated subgingival plaque (Burkholderiaceae, Flavobacteriaceae, Gemellaceae, Micrococcaceae, Neisseriaceae, Prevotellaceae and Streptococcaceae) from supragingival plaque (Actinomycetaceae, Burkholderiaceae, Flavobacteriaceae, Fusobacteriaceae, Micrococcaceae, Neisseriaceae and Streptococcaceae) with some overlap between the families. The analysis of the Oral dataset using our mixMC framework identified relevant bacteria families characterising subtle differences in the oral environment as well as deciphering particular characteristics in each body site.
Comparison with the Koren data set To further validate the relevance of our multivariate method to discriminate and identify microbial features describing microbial communities, we applied our sPLS-DA to the study from Koren et al. [25]. Since the dataset only contained partially repeated measures from multiple sites (individual patients samples in plaque were not sampled in other body sites), we applied a non multilevel sPLS-DA on the TSS+CLR data, resulting in a selection of 30+100 OTU on two components (S7 Fig,  S1  File). We found that sPLS-DA was able to clearly and distinctly discriminate the three body sites saliva, plaque and stool. Component 1 best characterised stool identifying families of bacteria such as Lachnospiraceae, Ruminococcaceae and Bacteroides; similar to what was observed in the HMP dataset.
Component 2 best discriminated arterial plaque and saliva. Arterial plaque was characterised by families including Burkholderiaceae, Propionibacteriaceae, Pseudomonadaceae and Staphylococcaceae, which was consistent with what the authors reported to as the 'core microbiome' for arterial plaque samples. Our analysis also identified Alcaligenaceae, Enterobacteriaceae, Moraxellaceae and Comamonadaceae as bacterial families describing arterial plaque. Saliva was also characterised on component 2 by the same families of bacteria both reported by Koren et al. and our microbiome signature in the HMP data set. Our comparative analysis demonstrates that sPLS-DA not only produced reliable and consistent results across different sequencing platforms and datasets but was also able to identify key members of the microbial community characterising in particular saliva, plaque and stool.
However, dimension reduction multivariate approaches provide intuitive plots and numerical outputs for a better understanding of the discriminative ability of the OTU features identified. Our study aligns well with recent studies that investigated the link between gut and oral microbial communities  [25, 50]. Franzosa et al. showed identified a subset of abundant oral microbes surviving transit to the gut that were linked with disease markers of atherosclerosis such as cholesterol  [50]. From our detailed analyses, we reached similar conclusions identifying bacteria such as Fusobacterium, Propionibacterium, Veillonella in both the oral body sites from both HMP data sets (including plaque, tongue and gingiva) and stool microbiomes as underlined by Koren et al. [25]. Our comparative study with the Koren data set demonstrated that sPLS-DA was able to identify a microbiome signature consistent across different individual cohorts and sequencing platforms.
Acknowledgments KALC was supported in part by the  Australian Cancer Research Foundation (ACRF)  for the  Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute  and the  National Health and Medical Research Council (NHMRC)   Career Development fellowship  ( APP1087415 ). FB was supported by the  Agence Nationale de la Recherche (ANR)  for the  SYNTHACS  project ( ANR-10-BTBR-05-02 ). The authors would like to thank  Christian Cherveaux  ( Danone Nutricia Research ) for fruitful discussions in the early stages of the project.
Funding: KALC was supported in part by the  Australian Cancer Research Foundation (ACRF)  for the  Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute  and the  National Health and Medical Research Council (NHMRC)   Career Development fellowship  ( APP1087415 ). FB was supported by the  Agence Nationale de la Recherche (ANR)  for the  SYNTHACS  project ( ANR-10-BTBR-05-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors confirm that there is no competing interest or financial disclosure to  Danone Nutricia Research.
Data from the Human Microbiome Project are available from http:// hmpdacc. org/HMQCP/all/ The processed data analysed in this study are available from our website www. mixOmics. org/mixMC Data from the Koren study were downloaded from the Qiita database http://qiita. microbio. me/study/description/349 The processed data analysed in this study are available from our website www. mixOmics. org/mixMC.
Availability of supporting data The data sets supporting the results of this article are available from the NIH Human Microbiome Project http://hmpdacc. org/HMQCP/all/ in raw data format, and in processed format on our website www. mixOmics. org/mixMC. R functions are available on our mixOmics package  [41, 52]. R scripts and a full tutorial to reproduce the results from the proposed framework are also available on our website.
1. So DPD is a baseband signal processing technique that corrects impairments of RF PAs. These impairments cause outof-band emissions, spectral regrowth and in-band distortion of Wideband signals with a high peak-to-average ratio. LTE/4G transmitters, are particularly susceptible to these unwanted effects. The Error Vector Magnitude (EVM) and Peak-to-Average Power Ratio (PAPR) metrics are measured to evaluate the power efficiency and the linearity of an amplification system (figure. 1). As shown in Figure  4 , DPD improves the adjacent channel power ratio (ACPR) caused by impairments already discussed. MATLAB PLATFORM To simplify the implementation of the platform developed and to solve various problems that may be encountered, we have chosen to divide the implementation into three dependent stages or steps The first Matlab platform is developed in Matlab/Simulink to implement an adaptive DPD algorithm.
The next part consists on deriving DPD coefficients using matrix inverse algorithm by reversing the roles of x and y in equation (  1 ) as the DPD is the inverse nonlinear function. So we can derive DPD coefficients using this equation (  2 ): (2) Where dkm are DPD coefficients used for implementing equation (  2 ) in Matlab for a static DPD. The last part consists of extending the static DPD design to adaptive one based on Least Mean Square (LMS) algorithm in order to evaluate a functional validation of the amplification system and provide an estimation of the ACPR and the EVM. The LMS algorithm used an indirect learning architecture to implement an adaptive DPD with no matrix inverse. Figure  4. Shows an example of curve's Output spectrum using the platform Matlab/Simulink simulation for a system with K=M=5.
IV. MATLAB/CADENCE PLATFORM This second Matlab/Cadence platform was developed to estimate with more accuracy ACPR, EVM and the analog part power consumption. So, the design of the analog part of the amplification system was done on Cadence using Verilog A language and modifying models of the PA, mixers, VCO, ADC and DAC available in Cadence library. This platform shown in Figure  5. Is based on a Matlab/Cadence cosimulation using couplers and saving the DPD algorithm already developed and presented in section III for the Matlab platform. Figure  6. Shows an example of simulation results of co-simulation using the AMS designer as Cadence simulator. In fact, the high level system concept is often specified in the early stages of design work. This process is well supported by both concept engineering and system-level simulation tools such as MATLAB/Simulink.
Co-simulation using AMS designer and MATLAB/Simulink combines the best of systemlevel simulation with lower-level analog and RF simulation. Simulink provides large libraries of DSP algorithms for generating complicated signals and post processing while AMS supports transient and envelope analysis of RF and communication circuits, such as mixers and oscillators both at the transistor and behavioral levels. Therefore, to run the cosimulation, Cadence recommends using specific software versions and some libraries and blocks must be installed for the two tools such as Couplers (13).
V. CADENCE ULTIMATE PLATFORM The PA, in the previous platform, modeled in Verilog A does not include the AM/PM distortion, therefore, it was necessary to add a filter after the PA. Furthermore, the power consumption of the analog part does not be accurate estimated. So, we implement the third Cadence Platform with the RF analog part in Transistor Level and Digital part in VHDL to obtain accurate estimation of power consumption of digital part (DPD algorithm). But, by using the AMS designer simulator, Harmonic Balance simulation cannot be established to visualize spectrums and deduce ACPR, EVM and DPD parameters. As a solution, we develop Post processing in Matlab using Cadence simulation results. In fact, we need also to improve PA model by including memory effects and the recursive adaptive DPD algorithm in VHDL.
VI. CONCLUSION This paper demonstrated workflow for implementation of three mixed signal platforms developed for the study of the www. ijacsa. thesai. org accuracy/complexity trade-off of Digital Predistortion (DPD) algorithms and optimization of the power consumption for LTE base station applications. The project goal acheived was providing libraries in order to compare various PA models with various DPDs algorithms and be able to estimate with precision the performances and the power consumption of the whole amplification system. We used Co-simulation: MATLAB was used to model digital part, and Cadence used to model and design the RF analog part of the amplification system.
, 2013)  and pea  (Alves-Carvalho et al. , 2015). In this study, we introduced the development of a resource, the Vigna unguiculata Gene Expression Atlas obtained from the de novo assembly of RNA-seq data from different plant tissues. This manuscript introduces how we identified transcript sequence and Unigene dataset in black-eyed pea that were annotated, and quantified to understand the molecular mechanisms occurring within the black-eyed pea tissues with a specific focus on pod and seed development. Finally, using data generated in this study, we investigated if drought-associated miRNAs could be implicated in the seed desiccation mechanisms.
From these 27 libraries, we obtained more than one billion high quality paired-end reads using Illumina HiSeq2500 analysers (Table  1 ). Pre-processing of reads including removal of adapter sequences and low quality reads was performed. Then, Trinity package was used to de novo assemble the reads following the three tools: Inchworm, Chrysalis and Butterfly as described in  Grabherr et al. (2013). In total 1 240 950 901 paired-end reads were mapped and used for transcriptome assembly, which resulted in 154 852 contigs. These contigs were filtered to remove redundant or highly similar contigs based on two The Black-eyed pea gene expression atlas 319 criteria: first we removed the low abundance transcripts displaying abundance less than one FPKM (fragments per kilobase per transcript per million mapped reads), then, we grouped transcript sequences displaying sequence similarities higher than 95% using CD-HIT-EST algorithm.