dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Here, we have developed a statistical model, implemented in the R package SparseDOSSA 2, for fitting and/or simulating microbial community profiles. These can comprise taxonomic abundances (i. e. relative abundances or counts) from shotgun metagenomic or amplicon sequencing; although not evaluated here, the model is in principle also appropriate for other microbial feature abundances (e. g. genes or pathways). The model can be fit to communities with different host-associated or environmental ecological structures, and it accurately captures their fundamental characteristics, including the distribution of abundances across community members and the diversities of microbial composition across populations. In addition, to support quantitative benchmarking of new methods for microbial community statistics and epidemiology, SparseDOSSA is able to reliably induce user-specified correlation structures involving feature-covariate or feature-feature associations in simulated ecologies.
This was demonstrated not only in silico, but by end-to-end reproduction of results paralleling those in an interventional mouse feeding study. The underlying generative model thus efficiently and effectively summarizes real microbial communities and recapitulates their latent structure in a manner that is both computationally efficient and statistically principled. The SparseDOSSA model assumes that the characteristics of a template (real) microbial community are well-captured by the distributions it includes for each component (individual features, feature-feature relationships, sparsity, etc. ) More specifically, this requires that 1) the non-zero component of absolute abundances is approximately log-normal, 2) that feature-feature association structure is sparse (as captured by the penalized estimation procedure), and 3) that intrinsic population substructure among samples are absent in the template dataset (i.
non-Westernized gut  [38] ), or the relationships between π j vs. mean log-abundance μ j across microbes (i. e. prevalence vs. abundance) are directly informative as to their neutral dispersal vs. selection  [39]. To some degree this is evident from the murine feeding example above, but most such applications remain to be demonstrated in broader "real-world" datasets. Relatedly, SparseDOSSA successfully reproduced reported dietary effects on the mouse gut microbiome  [24] , without assuming such differences a priori (Fig  5). By fitting our model on microbial observations of separate treatment groups and time points, we allowed Sparse-DOSSA to adapt to each subset independently, but without assumptions on the existence or magnitude of differences between them. The emergent reproduction of differentiation by diet in the resulting synthetic communities and features (Fig  5A ) exemplifies SparseDOSSA's utility in capturing environment-or treatment-specific dynamics of real-world microbial communities.
SparseDOSSA, in comparison, utilizes rigorous perturbation models to explicitly specify the marginal means of taxa as functions of chosen covariates. This a) enables much more flexible applications such as the inclusion of confounders or random effects (by incorporating them as covariates), and b) yields spiked-in datasets that are strictly compatible with the standard assumptions of (generalized) linear models. Alternatively, differentiation between simple binary (case-versus-control) contrasts could be achieved with our current model by training SparseDOSSA separately on the two corresponding population subsets, given that each was sufficiently large to serve as a template. Our modeling and simulation procedure for generating feature-feature correlations is, in turn, directly based off the feature-covariate model and comparatively more restrictive; we expect to explore more rigorous and flexible approaches in future work, since any one "correct" way to model ecological associations in absolute vs.
Generating synthetic microbial observations and simulating new features Given that our model is fully parametric, synthetic microbial observations, including (hidden) absolute abundances, normalized relative abundances, and sequencing counts, can be generated following the same specifications as described above. To provide model parameters, the user can adopt one of the pre-trained sets included with the software or use the SparseDOSSA 2 training procedure to estimate parameters from any microbial template dataset suited for their simulation case. Users may also be interested in generating "new" microbial features from the same ecological environment. For this, SparseDOSSA additionally models the per-feature parameters (π j , μ j , σ j 2 ) with a three-dimensional non-parametric distribution F.
Evaluation with real-world datasets For evaluation and comparison of microbiome simulation methods, we examined three realworld datasets with different host environments and disease statuses (S1 Table)  [2, 23]. We subset publicly available species level profiles from  [23]  (all healthy) to baseline time point stool (Stool) and posterior fornix (Vaginal) samples, and those from  [2]  to baseline time point IBD samples (IBD, including Crohn's disease and ulcerative colitis patients). We additionally removed samples with lower than ~3,000 reads mapped to identified taxa and features present in less than 3 samples. The datasets' dimensions (sample size, number of features), post processing and filtering, are included in S1 Table. A summary of all simulation evaluations performed in this work is included in S2 Table.
Evaluation scripts used are publicly available at https://github. com/biobakery/sparsedossa_paper. To evaluate the performance of individual methods, we randomly partitioned each dataset (Stool, Vaginal, IBD) into halves for five iterations. For each partitioning, we fit the parameterization/simulation methods (DM, metaSPARSim, and SparseDOSSA 2) on one half of the data (training). We then simulated synthetic microbial observations with the same sample size using the fitted results. Lastly, we compared the synthetic observations with the other half of the partitioned data (testing) in terms of both overall dissimilarity with PERMANOVA  [25]  and per-feature distribution differences (methods detailed below). Within each partitioning, this simulation was also performed five times for each method.
That is, each method was used to randomly simulate five synthetic datasets for comparison with the testing half. The partitioning procedure allows us to evaluate method performance without a model overfitting effect. The DM was fitted using R package "dirmult" and metaSPARSim was fitted using the implementation referred to in its publication  [18]. For metaSPARSim fitting, the percent not zero filter for features was set to 0 instead of the default 0. 2. In our evaluation this led to an observed performance increase (thus a favorable assessment), likely due to the existence of many highly zero inflated microbial species. To evaluate overall dissimilarity between the original and synthetic samples, for each partitioning we combined the testing half of the original samples with the simulated datasets (five for each fitted method).
We calculated the sample-to-sample Bray-Curtis dissimilarity matrix D on the combined dataset. The univariate PERMANOVA model, D~I{Sample is simulated} was fitted. The corresponding R 2 statistic quantifies the percentage of variability between samples attributable to the difference between original "real-world" as compared to simulated samples. Smaller R 2 statistics indicate less difference, and better performance of the simulation method. For each method, a total of 25 PERMANOVA evaluations (5 original dataset partitioning × 5 simulation) was performed for each real-world dataset. Lastly, we additionally evaluated the R 2 between the training and testing halves of a dataset for each partitioning; this yields an estimation of minimum achievable R 2 's for each dataset. R 2 's between methods are compared with Wilcoxon rank sum tests (S3 Table ).
To evaluate the difference between distributions of individual features in the original and synthetic datasets, we simply combined the synthetic datasets generated across all partitioning and simulation repetitions. An individual feature was compared for its relative abundance distribution between the original real-world data and combined synthetic samples. This was quantified with the Kolmogorov-Smirnov (K-S) summary statistic, which is defined as the largest absolute difference between the empirical cumulative distribution functions of the real-world and synthetic abundances. Smaller K-S statistics indicate better approximation of the targeted real-world distributions with the simulation method. To compare overall per-feature performance between models, the paired K-S summary statistics between SparseDOSSA 2 and alternative models (DM and metaSPARSim) were compared using Wilcoxon signed rank tests (matched by features, S4 Table ).
For simulation of feature-feature associations, we first set the correlation between feature pairs in SparseDOSSA 2 to zero (i. e. , O = I where I is the identity matrix). This ensures that feature absolute abundances are independent in the "null" dataset (Fig 3C left panel, bottom right), whereas spurious correlation still exists in relative abundances due to compositionality. Two random pairs (four features) in the top ten most abundant features were selected for nonzero feature-feature association spike-in. As specified in Methods above, we simulated two independent normal synthetic hidden metadata variables, one for each feature pair to be associated. For the first feature pair, they were spiked in both abundance and prevalence with the same effect (β = β abundance = β prevalence ) at varying sizes, for positive association.
Benchmarking Since "true" associations with prescribed effect sizes are known for SparseDOSSA synthetic datasets, they can be used for benchmarking microbiome analysis methods as well as for power analysis of microbiome study designs. For benchmarking analysis (Fig  4A ), we again selected the top 5% (16 total) most prevalent features in the Stool dataset to perform abundance spike-in, such that the selected features had the highest effective sample size. A total of 200 microbial profiles were simulated to be associated with a balanced binary metadata (100 cases, 100 controls). We varied effect sizes with half spiked features at β abundance = (0, 0. 5, 1. 2) and the other half with β abundance = (0, -0. 5, -1, -2), correspondingly (in the effect size 0 case no spike-in was performed and microbial profiles are generated independently of metadata).
A total of 500 random simulations were performed for each parameter combination. We applied existing differential abundance analysis methods to detect the spiked-in features in each simulation dataset  [29] [30] [31] , with individual method configurations as reported in our previous benchmarking analysis  [29]. We summarized the empirical power and FDR of a method in one simulation dataset, across the twenty random replicates for each parameter configuration, and reported the mean and standard error in Fig 4A.
Otherwise, researchers can adopt one of the pre-fitted templates that best approximate their target environment. • Researchers can then simulate many datasets (e. g. hundreds or thousands) with the fitted parameters, across a range of spike-in effect sizes and sample sizes. We note that the Sparse-DOSSA 2 effect size has straightforward quantitative interpretations: log fold changes for abundance spike-in, and log odds-ratios for prevalence spike-in (details on model in Association spike-in subsection of Methods). • This should be followed by conducting the hypothesis testing that is planned for the study on each of the simulated datasets. This allows for a measure of how often the "true" (spikedin) associations are detected for a given sample size (i. e. statistical power). Additionally, the researcher can investigate whether other methods of testing can deliver comparable results to simulation-based estimates from SparseDOSSA 2.
Murine diet microbiome analysis We applied SparseDOSSA 2 to the longitudinal diet dataset of the mouse gut microbiome in  [24] , to show that our method is capable of reproducing a complex study's findings. To recapitulate the longitudinal diet effect as reported in  [24] 's Fig 1A, we fitted SparseDOSSA 2 separately on 1) the control Chow diet samples at baseline, 2,3) Tuber diet samples at day 1 and day 5, separately, and 4,5) Meat diet samples at day 1 and day 5, separately. This approach allows SparseDOSSA 2 to independently fit subsets of the data, without assuming a priori the observed differences noted in  [24]. We then used SparseDOSSA 2 fitted results to simulate synthetic observations for each diet/timepoint combination, with five times the original sample size (to reduce variability due to random sampling).
D) Per-feature Kolmogorov-Smirnov summary statistics quantify that SparseDOSSA outperforms existing methods in simulating realistic feature-level relative abundance distributions. First, the similarity between the model-simulated feature abundance distribution versus that in the real-world dataset is quantified with K-S statistics. Then, the K-S statistics for SparseDOSSA and the other two models (DM and metaSPARSim) are plotted on the x-and y-axis, respectively (each point representing one feature, smaller K-S statistics represent better approximation). Lastly, the K-S statistics of SparseDOSSA versus other models are formally tested using Wilcoxon signed rank tests (p-values are significant and included in S4 Table).
Materials and methods Serotype C viruses have been archived at the FAO WRLFMD, The Pirbright Institute, UK, arising from samples collected between 1932 and 2004. Sequences of the VP1 coding region of serotype C FMDV were available for 100 isolates from public databases. An additional 49 VP1 sequences were either newly determined or obtained from collaborating organisations and submitted to GenBank (Supplementary Table  S1 ). VP1 coding sequences were amplified from nucleic acid extracted from cell cultured virus preparations, using reverse transcription -polymerase chain reaction (RT-PCR) with the primers and methods described in  Knowles et al. (2016). A final dataset of 149 serotype C sequences, encoding the VP1 capsid region (630 nucleotides), were aligned using MAFFT 7. 450  (Katoh and Standley 2013).
Recombination analyses performed in GARD  (Pond et al. 2006)  and LDhat  (Auton and McVean, 2007)  using both the VP1 and the full-capsid region sequences did not find evidence of recombination breakpoints. Maximum-likelihood (ML) tree topology reconstruction was pipelined using PhyML 3. 3  (Guindon et al. 2010 ) to obtain the initial BioNJ tree and optimise branch lengths and substitution rates, along with RAxML 8. 2. 12  (Stamatakis 2014)  to search for the ML topology and to bootstrap for 200 runs. A similarity matrix was generated by estimating raw evolutionary distances between pairwise sets of VP1 sequences in R 4. 0. 3 (R Core Team 2020) using the ape package  (Paradis and Schliep 2019). Phylogenies were plotted in R using the ggtree package  (Yu et al. 2017). The time-stamped phylogeny was obtained from BEAST 1.
10. 4 (Suchard et al. 2018 ) using the uncorrelated relaxed clock model based on a log-normal distribution  (Drummond et al. 2006) , along with the GTR þ C4 model of nucleotide substitution and the Bayesian skygrid coalescent tree prior  (Gill et al. 2013). Discrete trait phylogeographic analysis  (Lemey et al. 2009 ) was further performed in BEAST to reconstruct FMDV serotype C movements between continents. Posterior estimates were obtained by running the Markov chain Monte Carlo for 200 million iterations, sampling every 20,000 states and setting the burn in at 10 per cent of the chain. The sequence alignment and the BEAST. xml files used to perform the analyses have been uploaded to Dryad (https://doi. org/10. 5061/dryad. v6wwpzgv6; last accessed 29 Jan 2021).
The published sequence of the Indian vaccine strain C/Bombay/IND/64 (GenBank Accession No: MK413704; Hemadri et al. 2003), isolated from a pig at the Aarey Milk colony, Mumbai, Maharashtra, is closely related to C 1 viruses circulating at that time in Europe (Figs. 2 and 3; Hemadri et al. 2003). However, another virus purporting to be C/Bombay/IND/64 (Indian Ref. No. IND/154/64) was received by the WRLFMD in 1971 (WRLFMD Ref. No. IND/4/71) and is completely unrelated, sitting close to the root of the ASIA topotype. A later vaccine strain, C/IND/51/79.
Acknowledgements The authors acknowledge the support of other members of the  OIE/FAO Reference Laboratory Network  for foot-andmouth disease and the contributions of  Aldo Dekker ,  Rossana Allende ,  Abraham Sangula  and  Graham Belsham  at the Annual meeting of the Network in 2015. The purpose of the Network is to make available accurate and timely data to support global surveillance and control of FMD (https:// www. foot-and-mouth. org /Ref-Lab-Network ;last accessed  29 Jan 2021 ).
Funding Work of the  OIE/FAO Reference Laboratory Network  for footand-mouth disease is supported by the  European Union  (via a contracted project granted from the  European Commission  for the control of Foot-and-Mouth Disease, EuFMD). The views expressed herein can in no way be taken to reflect the official opinion of the  European Union. Sequencing work at The  Pirbright Institute  was funded by the  Department for Environment, Food and Rural Affairs (Defra) ,  United Kingdom  [research grant  SE2944 ]. The Pirbright Institute receives grant-aided support from the  Biotechnology and Biological Sciences Research Council of the United Kingdom  [projects  BBS/E/I/00007035 ,  BBS/E/I/ 00007036 ].
Statistics Data were log-transformed (ln) to fit normal distribution before statistical analysis. Paired t-test was used for comparison between the non-Doxo and either of Doxo treatment dosages. p-values < 0. 05 were considered as statistically significant. All horizontal lines in the dot plots and bars represent median values. GraphPad Prism 8 software (La Jolla, CA, USA) was used for statistical analyses.
To further comprehend the dynamics proteins released from HC and ALL donor BM MSCs after Doxo exposure, the protein array data were used to create a protein-association network for upregulated and downregulated proteins (Figure  1E ). Despite HCs displaying a more diverse set of cytokines and chemokines affected by Doxo compared to ALL patients, in both investigated groups altered levels of CXCL12, IL-6 and GDF-15 appeared as primary PC niche factors for further evaluation. Of note, we did not detect any substantial levels of BM MSC derived APRIL and BAFF in HC or ALL donors prior or after in vitro Doxo exposure (Supplementary Figure  S2 ). The relative amounts of cellular CXCL12, IL-6 and GDF-15 mRNA and proteins in BM MSCs cultures were examined in HC and ALL donors. CXCL12 mRNA levels were significantly decreased following 0.
Simulation Setup The simulations are based on three realistic network topologies (see Table  1  and Figure  8 ) widely used in the field of optical network research  [25]. These three topologies are NSFNET (small size), GEANT (medium size), and CORONET (large size)  [26]. Each topology is represented by a graph G(V, E) that is implemented using Networkx  [27] , a python package for graph analysis. V is the number of nodes and E is the number of links. Note that for each topology, the number of nodes that have the wavelength conversion capability |V c | is selected randomly (uniform law) in [1; |V| 2 [. For each topology, a simulation was performed. Note that without loss generality, we assume that each link (i. e. , optical fiber) supports at most |Ω| wavelengths (|Ω| = 16). A simulation consists of performing the following process 5000 times: • Randomly select a node, which is taken as the source node of a multicast connection.
Flow Cytometry and Integrin a6 Cell Sorting GSCs were collected, rinsed and blocked with FACS buffer (0. 5% BSA, 2 mM EDTA dissolved in PBS; pH 7. 2) for 15 min at room temperature. Cells were stained with unconjugated anti-integrin a6 antibodies (1/100; clone NKI-GoH3; Millipore, Burlington, MA, USA) for 15 min at room temperature. Bound antibodies were then revealed by speciesspecific Alexa Fluor conjugated secondary antibodies (Invitrogen, Waltham, MA, USA). As DGCs cultures do not express integrin a6  [32] , they were used as negative controls. The stained cells were acquired on FACS Canto (BD Biosciences, Franklin Lakes, NJ, USA) using FACS Diva software (Becton Dickinson, Franklin Lakes, NJ, USA). Data analysis and median fluorescence intensity (MFI) calculation were performed using the FlowJo software (Tree Star Inc.
Western Blot Proteins were extracted in reducing condition with 0. 3% CHAPs (Sigma-Aldrich) lysis buffer supplemented with protease inhibitors (Complete and PhosSTOP from Roche). Antibodies against b-actin (1/5000; clone AC-15; Sigma-Aldrich), integrin a6 (1/500; clone HPA012696; Novus Biologicals, Littleton, CO, USA), and p-ERK1/2 (1/2000; clone #4370; Cell Signalling Technology, Danvers, MA, USA) were used. Densitometric analysis was carried out using Multi Gauge software (FujiFilm Corporation, Tokyo, Japan).
In-Vitro Extreme Limiting Dilution Assay (ELDA) To obtain single cell suspension, GSCs were mechanically dissociated and filtered with a 40 µm cell strainer (BD Biosciences). Cells were seeded into a 96-well plate at 1, 5, 10, 20 cells per well, with at least 10 replicates for each condition. The exact number of cells seeded was determined after 24 h in a bright field inverted microscope. Cells were then irradiated following a fractionated schedule. After 14 days the number of gliomaspheres/well (size cut-off: 90 µm) was scored. Stem cell frequency in each samples was calculated using the Extreme Limiting Dilution Analysis web tool (http://bioinf. wehi. edu. au/software/elda, accessed on 12 July 2017)  [39].
After qPCR quantification, sequencing was carried out using 2 × 100 cycles (paired-end reads of 100 nucleotides each) on an Illumina NovaSeq 6000 system (S1 flow cells). Around 50 M paired-end reads were obtained per sample. Bcl2fastq was used to generate fastq files from raw sequencing data, where demultiplexing was performed according to indexes. The Institut Curie RNA-seq bioinformatics pipeline (available at https://github. com/ bioinfo-pf-curie/RNA-seq, accessed on 7 March 2021 (v0. 2. 4)) was used to process se-quencing reads. Briefly, potential rRNA contamination was cleaned by sequencing reads on the rRNA human complete reference (U13369. 1). Then, by using STAR (v2. 5. 3a) and the GENCODE annotation database (v19) with the following parameters (-outFilterType BySJout-outFilterMultimapNmax 20-alignSJoverhangMin 8-alignSJDBoverhangMin 1-outFilterMismatchNmax 999-outFilterMismatchNoverLmax 0.
04-alignIntronMin 20-align-IntronMax 1,000,000-alignMatesGapMax 1,000,000-outSAMprimaryFlag OneBestScore-outMultimapperOrder Random) the remaining reads were then aligned on the human reference genome (hg19). Finally, the raw gene counts tables were also generated by STAR, using the option -quantMode GeneCounts. Differential gene expression analysis on raw gene counts was performed using the DESeq2 suite of analysis tools, v1. 30. 0  [40]. The EnhancedVolcano R package was used to create volcano plots, by means of the fold change and adjusted p-values of the genes, obtained using the DESeq2 suite. The fold change of the genes was also used to create graphic representations of pathway enrichment, using the pathview R package. Gene set enrichment analysis (GSEA) was performed using the public Broad Institute software and its publicly available molecular signatures database (KEGG, HALLMARK, Gene Ontology) with raw gene counts data as input.