dataesr/scientific-paragraphs-categorization huggingface dataset:
We also analyzed two real data sets. A single-cell data set on mouse embryos was used to illustrate the performance of the methods on trees. Comparison with a clustering obtained in a previous study on these data  [43]  showed that the proposed methodology can integrate several trees while preserving the biological meaning of the data. A TCGA breast cancer data set was also used to illustrate the process on network data. It emphasized two groups of breast cancer subtypes that are consistent with the literature. It also allowed to create two consensus networks that highlight differences in the protein interactions in these two groups. In both simulations and real data application, the procedure was shown to be an efficient and useful tool for the user to identify groups of data that are relevant to integrate.
The use of different methods like self-organising maps  [50]  or bayesian hierarchical clustering  [51]  might lead to different results, especially as the MFA is an exploratory method. The unsupervised and exploratory aspects are particularly visible in the TCGA-tree application, where the consensus ARI was still quite low despite being an improvment compared to each individual tree. MFA is an unsupervised descriptive and exploratory method, and is therefore not dedicated to supervised analyses implicitly searching for differences among factors. However, such supervised analyses may be done in the framework of MFA using for example the multiblock redundancy analysis  [52] , extending the redundancy analysis, which is a supervised version of PCA  [53]  to a multiblock context. The process was applied here on real-life datasets where the separation between objects was clear, and where there was an informative signal in the omics datasets.
Availability of data and materials The simulated datasets analysed during the current study are available at https:// github. com/ AudreH/ intTr eeNet. TCGA BRCA methylation, mirna, protein and gene expression datasets were downloaded using the curatedTCGAData R-package and can alternatively be accessed through their portal at https:// portal. gdc. cancer. gov/. The single-cell datasets analysed during the current study are available using the R-package mouseGastrulationData on Bioconductor https:// bioco nduct or. org/ packa ges/ relea se/ data/ exper iment/ html/ Mouse Gastr ulati onData. html.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al. String v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):607-13. https:// doi. org/ 10. 1093/ nar/ gky11 31.
For taxonomy analysis, features were assigned to operational taxonomic units (OTUs) with a 99% threshold of pairwise identity to the Greengenes reference database 13_8  [24]. Unprocessed sequencing data are deposited in the Genome Sequence Archive (GSA) in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number CRA005149, publicly accessible at http://bigd. big. ac. cn/gsa.
After end repair and ligation of adapters, mRNA libraries were amplified by PCR and validated using BioAnalyser, according to the manufacturer's recommendations. The purified library was then subjected to sequencing using an Illumina NextSeq500 at the Georgia Genomics and Bioinformatics Core (https://dna. uga. edu/, Athens, GA, USA). Sequencing data obtained were demultiplexed and quality filtered using fastq_quality_filter command (http://hannonlab. cshl. edu/fastx_toolkit/) with -q28 -p 80 in order to only keep sequences with at least 80% of bases having a minimum quality score of 28. Nest, SortMeRNA version 2. 1 (https://bioinfo. lifl. fr/ RNA/sortmerna/) was used in order to remove rRNA sequences using the following reference databases: silvaarc-16 s-id95. fasta, silva-arc-23 s-id98. fasta, silva-bac-16 s-id90.
fasta, silva-bac-23 s-id98. fasta, silva-euk-18 s-id95. fasta, silva-euk-28 s-id98. fasta, rfam-5 s-database-id98. fasta, and rfam-5. 8 s-database-id98. fasta. The obtained quality-filtered and rRNA-filtered sequence file was uploaded to Galaxy Europe https://usegalaxy. eu/ and used to run HUMAnN2 program in order to profile for the abundance of microbial pathways and gene families  [26]. A transcript table with relative abundance was generated for each sample and compared using either principal coordinate analysis of the Bray-Curtis distance or volcano plot created using R software. Unprocessed sequencing data are deposited in the Genome Sequence Archive (GSA) in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number CRA005148, publicly accessible at http://bigd.
Moreover, values obtained at the 96-h, 216-h, and 274-h time points are presented Figure  S3  (bacterial density), Figure  S4  (beta diversity analysis of microbiota composition), Figure  S5  (alpha diversity analysis of microbiota composition), and Figure  S6  (expression of pro-inflammatory molecules). Significance was determined using one-way group ANOVA with Bonferroni's multiple comparisons test (GraphPad Prism software, version 6. 01). Differences were noted as significant *p ≤ 0. 05.
e. , untreated) microbiota cultures, all of which were generated from a single healthy subject. Samples were collected at 17 different time points (4 pre-treatments, 10 during treatment, 3 post-treatment), as outlined in Figure  S1. Emulsifying agents were added to the BRM medium prior to autoclaving at a concentration of 0. 1%, based on our previous studies in mice investigating CMC and P80  [5, 11, 27] , as well as estimated consumption in human  [12]. We first measured the impact of these compounds on microbiota density, as measured by qPCR using universal 16S primers. As detailed in the "Methods" section and presented in Fig. 1 , results are displayed as the impact of each compound relative to untreated microbiotas during the treatment and post-treatment period. This approach avoided reliance on one single time point and, rather, accounted for data from all the time points collected during a specific phase (treatment and post-treatment).
Most, but not all, emulsifiers impact microbiota composition We next investigated the impact of these food additives on microbiota composition via 16S rRNA gene sequencing. Data were first assessed via the use of principal coordinate analysis (PCoA) of the Jaccard matrix from all of the samples generated in the three independent experiments. Color coding based on time point (i. e. , irrespective of treatment) revealed a marked shift during the first 48 h, particularly along PC1, which accounted for 17% of the overall differences in the samples, after which time changes were relatively modest and primarily observed along PC2, which accounted for 7. 2% of the overall differences between the samples (Fig. 2a ). That similar temporal changes were seen when looking at untreated samples indicated that this pattern reflected stabilization of the fecal microbiota in the MBRA system, as previously reported  [19]  (Fig.
Specifically, we calculated both weighted UniFrac and Jaccard matrix distances, which, respectively, consider, or disregard, phylogenetic distance and relative abundance of microbiota members. These analyses, presented in Fig. 3 , revealed a significant impact of CMC, P80, maltodextrin, propylene glycol alginate, kappa carrageenan, gum arabic and xantham, guar, and locust bean gums on microbiota composition relative to control samples, as assessed by Jaccard distances (Fig. 3a, b ). A generally similar pattern was observed when analyzing this data with the weighted UniFrac metric, which yielded the previously observed impact of P80  [11] , but also found that all tested forms of carrageenan, guar gum, and locust bean gum significantly altered microbiota composition (Fig. 3c, d ). Interestingly, the limited impact of emulsifiers on the weighted UniFrac distance during the post-treatment phase (Fig.
Transcript were assigned to a function and a bacterial origin using the UniRef50 database, and principal coordinate analysis of the Bray-Curtis distance was used to globally assess each compound's impact on microbiota gene expression. Visualizing the resulting matrices by PCoA analysis revealed clear treatment-based clustering, indicating that most of the 20 food additives tested impact the metatranscriptome. To quantitate the extent of such global changes in gene expression, we calculated Bray-Curtis distances separating emulsifier-treated from untreated microbiota. This approach indicated statistically significant impacts on the transcriptome in response to P80, maltodextrin, propylene glycol alginate, kappa and lambda carrageenans, gum arabic, guar gum, DATEM, and glyceryl stearate (Fig.
Acknowledgements We thank  Robert Britton  ( Baylor College of Medicine ) and  Jennifer Auchtung  ( University of Nebraska-Lincoln ) for their help and guidance with the MBRA experiments. We thank  Anne Jamet  ( APHP, Paris, France ) for her expertise on 16S sequencing/Gram correlation and  Marie-Caroline Michalski  and  Cecile Vors  ( CARMEN, Université de Lyon 1, INSERM, INRA, INSA, Lyon, France ) for their expertise on emulsifying agents properties. We thank  Jean-Paul Motta  and  Nathalie Vergnolle  ( IRSD, Université de Toulouse, INSERM, INRA, ENVT ,  UPS ,  U1220 ,  CHU Purpan, CS60039, Toulouse, France ) for their feedback on this work.
Funding This work was supported by a Starting Grant from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. ERC-2018-StG-804135 ), a  Chaire d'Excellence from IdEx Universite´de Paris - ANR-18-IDEX-0001 , and an  Innovator Award  from the  Kenneth Rainin Foundation. E. V. is a recipient of an  Individual Fellowship Marie Sklodowska-Curie grant  from the  European Commission Research Executive Agency. A. T. G. is supported by  NIH  grants  DK099071  and  DK083890. Funders had no role in the design of the study and data collection, analysis, and interpretation, nor in manuscript writing.
MATLAB programming and computing platform to analyze data, develop algorithms, and build models Note: Yeast cells are grown (200 rpm, 30 C) prior to the experiment to reach the target OD600 of 0. 50 at the start of the experiment (see section before you begin). 1. Generate the pump profile. a. Download the codes to generate the pump profiles and see Ref. (Thiemicke et al. , 2019)  for the algorithm used to set up the pump profiles (codes: Jashnsaz_STARProtocols_2021/ Jashnsaz_et_al_dir06_GeneratePumpProfiles). b. In the above directory, follow steps 1 to 5 in README. txt, to use the MATLAB codes and excel files to set up and generate the desired pump profiles and load them to the syringe pump to be used to perturb cells according to time-varying profiles (Figure  1A ) during the time-lapse experiments.
The signal transduction network is modeled as a dynamic ordinary differential equations (ODEs) system where proteins regulate each other through biochemical reactions (Figure  2 ). a. Download the MATLAB codes at Jashnsaz_STARProtocols_2021/Jashnsaz_et_al_dir01_ Models to generate the models. Figures  2A-2E  outlines the implementation of the model3 in this code; the network topology diagram (Figure  2A ), the regulation matrices (Figure  2B ), the MATLAB implementation code (Figure  2C ), and the compiled dynamic ordinary differential equations (ODEs) system (Figure  2E ). Figures  2A-2E  provide the necessary details to use the codes to convert a specific network topology (Figure  2A ) to its corresponding ODE model (Figure  2E ). b. Use the MATLAB code visualize_models. m to import the models (Models.
mat that is generated by running get_models. m) and using the customized network_plot. m function to visualize the network diagrams for all the models (similar to Figure  2A ). Note: This framework is general to map arbitrary regulatory networks to their corresponding ODE models and it is implemented in MATLAB 2019a. The framework takes arbitrary number of regulatory nodes based on the proteins involved in the pathway (Figure  2A ). The framework allows the user to model network topologies with user-specified connections (by manually entering the elements of the regulation matrices) or to model all the possible regulation networks (by generating all the possible permutations of the elements of the regulation matrices) (Figures  2B  and 2C ). c. Specify protein states and the type of regulation functions.
iv. In this protocol, as a proof of principle, we will model the well-characterized stress sensing Hog1 MAPK signaling pathway in S. cerevisiae as a reduced 4-node topology. 9. Infer model parameters using signaling data (optimization algorithm). The optimization algorithm is provided in Figure  2F. We ran this algorithm over 21 iterations for the training conditions provided throughout the manuscript that resulted in fits converging within the standard deviation of the data. Each iteration consists of two Genetic Algorithm (GA, a MATLAB built-in function) calls each followed by the MATLAB fminsearch function (light blue boxes, Figure  2F ) through the following steps: a. Divide the timepoints of data. The first GA and its following fminsearch uses only a portion of the timepoints of each training dataset (25% selected randomly).
This reduces getting stuck in local minima of the parameters space. Then the second GA and its consequent fminsearch use all timepoints of the training data. b. Define the objective function. The objective function is defined as one half of the sum of squared residuals of the model fit to the data given in Equation  1. OBJ = X d n = 1 X N k = 1 ðx n ðt k Þ À O n ðt k ÞÞ 2 2s n ðt k Þ 2 ; (Equation  1 ) N is the number of timepoints (25% selected randomly for the 1 st GA, 100% for the 2 nd GA) in each dataset, d is the total number of datasets in the training data, X n (t) is the model, O n (t) is the data mean, and s n (t) is the standard error of the mean (experimental data) or additive noise (simulated data) for the pathway activation dynamics of dataset n. c. Sample parameter space. At the first 4 iterations (i%4) as well as at every 4 th iteration of the algorithm, the 1 st GA takes 200 parameter sets sampled uniformly in [À3,+3] in the logarithmic scale base 10, and returns a parameter set, that feeds into its following fminsearch.
, 2020). (B-H) (B) A table of diverse kinetic inputs reaching different final concentrations result in 54 distinct simulated signaling datasets. (I) Red, blue, yellow, and green colors indicate each dataset belonging to traindata, testdata1, testdata2, and testdata3, respectively, to be used to train the model and to test model predictions (Figures  4  and 5 ). Synthetic pathway activation data simulated from the true model upon diverse kinetic stimulations (inserts) as step (t 0 , C), root ( ffiffi t p , D), linear (t 1 , E), quadratic (t 2 , F), quints (t 5 , G), and heptic (t 7 , H) functions over time each to final concentrations of 0. 050, 0. 10, 0. 20, 0. 30, 0. 50, 0. 70 M. All kinetic stimulations start at 0 min, all except steps reach their final concentrations at 25 min and then keep constant from 25 min to 50 min (insets).
Note: The extensive analysis under different training datasets conditions is performed in Ref. (Jashnsaz et al. , 2020). 14. Here, we used two conditions that include the same amount of training data that are generated upon step cell stimulations (Figures  4A-4C ) in comparison to diverse time-varying cell stimulation inputs (Figures  4D-4F ). 15. This task was repeated upon multiple independent training processes each using independent noise in simulated data and randomized optimization initialization. Beside reproducibility, this ensures that the final results are independent of the noise in the data and initial parameter guesses. 16. Under each training condition, each model's fit convergence is confirmed to maximize the likelihood of observing the training data and the fit errors are evaluated.
Under both conditions  (Figures 4A and 4D) , the model fits the simulated data equally well (Figures  4B  and 4E ). The fit errors, defined as the mean absolute difference of the fit and their corresponding synthetic data over time, is within one standard deviation of the experimental noise considered for the data (Figures  4C  and 4F ). CRITICAL: The question is whether different training conditions that result in models with indistinguishable fit errors lead to different predictions. To answer this question, next we evaluated the model predictions from each training condition (see next section, steps 17-18).
Problem 6 The fitting issues for different models (step 9). The optimization algorithm was validated using the ''true model'' (Figure  2A ) that has 20 unknown parameters and for the range of the training data up to 306 timepoints (6 datasets each with 51 timepoints). Under these conditions, we considered different values for the following parameters and the ranges in the Genetic Algorithm (Figure  2F ) and they did not have significant impact on the results: the GA Initial Population size (50 to 500 with increments of 50), the GA number of generations (5 to 95 with increments 5), number of time-points to drop for the 1 st GA call (0%-95% with increments of 5%), the absolute value of the models parameters range in the logarithmic base 10 space (1 to 6 with increments of 1). One of the above parameters was sampled in the specified range each time by setting the remaining parameters to values similar in the Figure  2F.
Figure2. Continued L = ðl 1 ; l 2 ;. Þ arranged such that odd entries correspond to catalytic rate constants and even entries correspond to the MM constants. (F) Optimization algorithm to parametrize the model using signaling data. The algorithm consists of a customized Genetic Algorithm (GA) and the MATLAB built-in fminsearch function. The algorithm takes a model and a set of training data and returns a set of best parameters (L*) that maximizes the likelihood of observing the training data. This task is done by minimizing the objective function (blue boxes) defined as the sum of squared residuals between the model and data over the time-traces of all the training datasets. Refer to section Optimization algorithm in step 2 for details. (G) The HOG pathway is modeled as a 4-node network of SLN1 and SHO1 branches each stimulated by the input, both regulating Pbs2, that in turn regulates Hog1 terminal kinase (see step 2 for details).
(I) Red, blue, yellow, and green colors indicate each dataset belonging to traindata, testdata1, testdata2, and testdata3, respectively, to be used to train the model and to test model predictions (Figures4 and 5). Synthetic pathway activation data simulated from the true model upon diverse kinetic stimulations (inserts) as step (t 0 , C), root ( ffiffi t p , D), linear (t 1 , E), quadratic (t 2 , F), quints (t 5 , G), and heptic (t 7 , H) functions over time each to final concentrations of 0. 050, 0. 10, 0. 20, 0. 30, 0. 50, 0. 70 M. All kinetic stimulations start at 0 min, all except steps reach their final concentrations at 25 min and then keep constant from 25 min to 50 min (insets). Lines are means and shaded areas are the standard deviations of the means from five simulated biological replicates each containing 10 single cell trajectories (similar to A).
(Jashnsaz et al. , 2020)For each training condition, different sets of test datasets were to ensure unbiased testing conditions. Simulating data under numerous stimuli conditions of diverse temporal profile types and different final concentrations (Figure3B) allowed us to quantify how predictions depend on the choice of the test data in comparison to each training dataset. Our detailed analysis (FigureS4A-S4I in Ref. (Jashnsaz et al. , 2020)) indicated that test data that is temporally similar to the training data are usually predictable, while prediction errors increase for test data in conditions that are substantially different than those of the training data. Therefore, it is important to test the model predictions under new conditions that are different than the training data; not only in terms of their final concentrations, but also in terms of how the two (test versus training) stimulation profiles change over time.
ACKNOWLEDGMENTS G. N. is supported by  NIH ,  United States ,  DP2 GM11484901 ,  NIH R01GM115892 ,  NIH   R01GM140240 ,  Vanderbilt Deans Faculty Fellow award , and  Vanderbilt Startup Funds. Z. R. F. and B. M. are supported by  NIH ,  United States ,  R35 GM124747. Z. R. F. was also supported by the  Agence Nationale de la Recherche, France , and by  ANR-18-CE91-0002 , CyberCircuits. Z. R. F. is currently with the Center for Nonlinear Studies at  Los Alamos National Laboratory. The authors thank  Jason Hughes ,  Alexander Thiemicke ,  Benjamin Kesler ,  Rama Ali , and  Blythe Hospelhorn  for the discussion. This study used resources at the  Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville , TN ( NIH ,  United States ,  S10  Shared Instrumentation Grant  1S10OD023680-01  [Meiler]).
Data and code availability This study includes multiple datasets and codes as following: The published articles  (Johnson et al. , 2021; Kesler et al. , 2019; Munsky et al. , 2018; Neuert et al. , 2013; Thiemicke et al. , 2019)  include the image processing codes for cell segmentation and quantification that are used to analyze the Hog1 experimental data during this study. The codes are available at https://osf. io/kwbe6/.
9 Among the main recommendations formulated to improve the non-targeted approach is the development of robust userfriendly processing software. Likewise, AQUAREFthe French national reference laboratory for aquatic environment monitoring, which works in close concert with other European reference laboratoriespublished guidelines for HRMS untargeted analysis, for which SPIX could be a powerful tool. 10 e first part of this article discusses the notions of uncertainty and subjectivity related to untargeted analysis. The second part presents the general working principle of SPIX software. The third part is dedicated to the presentation of results obtained on real samples. It discusses the strengths and limitations of the software and its specificities compared with the few programs currently commercially available.
| NOTIONS OF UNCERTAINTY AND SUBJECTIVITY IN MODERN UNTARGETED ANALYTICAL APPROACHES, AND INTRODUCTION TO SPIX To illustrate the functionality of the SPIX software, it is necessary to address the notions of uncertainty and subjectivity that are fundamental in analytical chemistry. We propose to take an example in environmental chemistry. Consider a plant located on the bank of a river; it may be a treatment plant or, on the contrary, a source of pollution. The question is whether its presence significantly alters the composition of the water. The question seems simple enough, but providing a relevant answer is much less so. The conventional approach is to take water samples upstream and downstream of the plant, analyze them chemically and compare the results. This approach, while scientifically reasonable, nevertheless raises many questions at each step of the process.