dataesr/scientific-paragraphs-categorization huggingface dataset:
Theoretically, the energetic return on investment can be estimated using empirical thermodynamic, kinetic and physiological data in metabolic network models  20, 21. However, the empirical data required to parameterise such models are not available for the large diversity of organic compounds 22 , enzymes 23 and microorganisms found in soil. Here, we propose an experimental approach to estimate the energetic return on investment that microbial decomposers can acquire from the consumption of organic matter. This metric is determined as the ratio between the energy available in the organic matter and the decomposition activation energy of each molecular species contained in the organic matter. The energy available in the organic matter was measured as the heat of combustion with bomb calorimetry  24  and the activation energy was estimated using the nominal oxidation state of carbon (NOSC) of each of the molecular species  25.
This implies that the metabolic response is more likely to be related to a combination of the overall energy availability and the form of the energy available. Energy return on investment (ROI) of soluble organic matters. We therefore estimated the energetic return on investment that is potentially available to microbial decomposers when decomposing the different organic matters. This was done using the bomb calorimetry data as an estimate of the total net energy available in the substrate  24  and the NOSC data as an estimate of the activation energy required to acquire energy from the available molecular species  39. The rationale behind this approach is based on the fact that molecular species containing carbon atoms that are more reduced on average (i. e. have a lower average NOSC) tend to have higher oxidation activation energies 25, 40, 41 (i.
Heat dissipation (µW g -1 soil dry weight) was measured during 24 h. Heat dissipation data was chosen as a measurement of the microbial metabolic response because it gives a more complete and robust measurement of microbial activity than do CO 2 emissions 58. Heat dissipation measurements during the rst hour were discarded as the signal was affected by the disturbance of the experimental setup. The heat dissipation due to microbial metabolism of the added organic matter was determined by subtracting the heat dissipation in the H 2 OmQ treatment. Soil bacterial community analysis. The bacterial community structure and diversity were analysed after extraction of soil DNA, ampli cation and sequencing of the V3-V4 region of the 16S rDNA sequences. The initial bacterial communities in the six soil samples were analysed in triplicate.
After ampli cation, PCR products are checked in 2% agarose gel to determine the success of ampli cation and the relative intensity of bands. Samples were pooled in equal proportions based on their molecular weight and DNA concentrations. Pooled samples were puri ed using calibrated Ampure XP beads, and used to prepare Illumina DNA library. Sequencing was performed by MrDNA-Molecular Research (www. mrdnalab. com, Shallowater, TX, USA) on a MiSeq instrument following the manufacturer's guidelines. Sequence data were processed using MR DNA analysis pipeline. In summary, sequences were merged, depleted of barcodes, then sequences < 150bp and sequences with ambiguous base calls were removed. After denoising, operational taxonomic units (OTUs) were generated and chimeras were removed. OTUs were de ned by clustering at 3% divergence (97% similarity).
Final OTUs were taxonomically classi ed using BLASTn against a curated database derived from RDPII and NCBI (www. ncbi. nlm. nih. gov, http://rdp. cme. msu. edu). A total of 1602297 reads were thus obtained, and datasets were normalized at 19047 reads per samples, using R tools. Analysis of energy content of soluble organic matter. The total energy content of the soluble organic matter was measured by bomb calorimetry  24. Values for heat of combustion (ΔE) were converted into J mmol -1 C (using the mean of the TOC of the respective water extractable organic matter). There was insu cient sample to reliably measure the ΔE for the rural forest soil. We also measured the composition and diversity of molecular formulae in the soluble organic matter by ultra high resolution mass spectrometry. These data were then used to derive the NOSC of the molecular species contained in the different soluble organic matters 25.
The temperature of the source was kept at 200°C. Mass spectra were recorded over a mass range of m/z 50-1000 targeting a resolution of 0. 5-2M according to m/z. External calibration was always performed in extemporaneously before the sample analysis using the G24221A Tuning Mix calibration standard from Agilent Technologies (Santa Clara, CA), by setting a signal-to-noise ratio equal to 3, reaching accuracy values lower or equal to 700 ppb. The spectra were acquired with a time domain of 16 megawords and twenty scans for fty ms were accumulated for each mass spectrum. A control sample containing only the solvent mixture (water/methanol (50/50 v:v) was systematically analysed and the resulting spectrum was subtracted from the spectra of the subsequent sample analysed. Data processing was done using Compass Data Analysis 4.
Data were transformed to ensure normality and homogeneity of variances where necessary. Non-parametric tests were carried out with the ARTool package 62 (version 0. 10. 7) when transformations did not result in normality. Differences between groups were determined when relevant by pairwise comparisons of the least-square means using adjusted P-values (Tukey -implementated in the "emmeans" library 63 version 1. 5. 0). The correlations between the total heat dissipation and the energy return on investment index of each soluble organic matter respectively for each soil have been tested and ploted using the function "ggscatter" (present in the "ggpubr" library version 0. 4. 0) 64. Taxonomic diversity indices were calculated with the R script Rhea 65. The richness was calculated by enumerating the number of variables with a value different from zero.
The effective Simpson diversity index was calculated as the inverse of the Simpson diversity index, as follows: Simpson effective = 1 / (Σ p i 2 ) (5) Where p i is the proportion of the ith variable abundance to total abundance in a given sample. The effective Simpson diversity index represents the number of equally abundant variable that would give the same value for the Simpson index  66. The effective Simpson index has the advantage of been linear compared to the Simpson index  66. Bacterial composition heatmaps were generated using the plotMRheatmap function in the package "metagenomeSeq" 67 (version 1. 30. 0). The relationships between heat dissipation pro les, molecular formulae composition in organic matter and microbial community composition in soil were assessed using Mantel tests on the respective distance matrix with the package vegan 68 (version 2.
Acknowledgments This study was supported by the  Agence Nationale de la Recherche  ( ANR ECOVILLE ;  ANR-14-CE22-0021 ) and by  Formas  project ( 22836000  and  OPTUS 22551000 ). Financial support from the  National FT-ICR network  ( FR3624CNRS ) for conducting the research was also gratefully acknowledged. The authors would also like to thank  D. Billoux ,  V. Pouteau ,  J. Kikuchi  and  J. Fiedler  for assistance with laboratory work. We also thank  Q. -L. Fu  for updating of the TRFu algorithms.
RNA-seq data analysis The program FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) was employed to determine the base quality of the raw reads (in FASTQ format) and we removed the paired-end reads containing more than 5% ambiguous residues (Ns) and those containing >10% bases with a Phred quality score of 10. Then, the raw reads were trimmed using Trimmomatic, version 0. 32  [6]. After cleaning and quality reassessment with FastQC, approximately 31. 6-56. 8 million high-quality reads of 90-bp length remained in each sample. The highquality reads were mapped to the sesame reference genome v1. 0 (htt p://ocri-genomics. org/Sinbase/login. htm)  [69]  using the STAR software  [12] , allowing no more than one mis-match in the alignment. Approximately, 88. 9-97. 4% of the clean reads were uniquely mapped to the reference genome, with 94.
Time-serie clustering of gene expression profiles Because of the high number of collecting time points, a lot of information related to the gene expression changes could be masked in a traditional clustering method such as principal component analysis. Therefore, we implemented the tsne reduction scatter plot, which is a non-linear dimensionality reduction method for embedding high dimensional data in a low-dimensional space  [63]. The analysis was performed with the R software (http://www. r-project. org) using the 'tsne' package v. 0. 1-3 (https://cran. r-project. org/web/packages/tsne /tsne. pdf).
Enrichment analysis of TF family and cis-regulatory motifs The sesame transcription factor (TF) database composed of 58 TF families and 2326 members was downloaded from the PlantTFDB v4. 0 (http://planttfdb. cbi. pku. edu. cn/,  [32] ). Overrepresentation analysis of TF families among the DEGs in response to waterlogging/drainage treatments was performed using the cumulative hypergeometric distribution (P ≤ 0. 05) computed with the 'phyper' function (https://stat. ethz. ch/R-manual/R-devel/library/stats/html/Hypergeometric. html) in the R software (http://www. r-project. org) with a Bonferroni correction of the P values  [7]. The next step consisted in detecting the enriched cis-regulatory motifs within the promoters of the regulated genes by these enriched TF families. First, all the sesame TF binding motifs were downloaded from the JASPAR website (http://jaspar.
Gene ontology Gene ontology (GO) enrichment analysis was conducted in the software AgriGO v2. 0 (http://bioinfo. cau. edu. cn/agriGO/,  [62] ). A hypergeometric test with a significance threshold of 0. 05 after a Bonferroni correction was used to identify overrepresented GO categories. Similarly, the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were conducted using the Blast2GO software  [10].
Regulatory network construction To predict the regulatory network connecting the TFs to the transcriptional phases, we selected TFs within the DEGs that have annotated DNA binding motifs obtained from the JASPAR database as described above. Thereafter, the regulated genes belonging to each transcriptional phase were gathered together to test for overrepresentation of each DNA binding motif using the hypergeometric distribution (P ≤ 0. 05). According to the transcriptional phases in which a particular TF binding motif was enriched, a directional edge was drawn from the corresponding TF to that phase. The Cytoscape software v. 3. 6. 1  [56]  was employed to display the resulting networks.
Root anatomy experiment Root samples were collected along the taproot (top 2 cm) and stored in formalin-alcohol-glacial acetic acid (90:5:5, v/v/v) for 24 h. Dehydration, staining and transverse sections were processed following descriptions of Dossa et al. [13, 14]. Leica scanner Aperio CS2 (Leica Biosystems, Germany) was used for imaging of the root sections and image processing was performed with the Aperio Imagescope software (Leica Biosystems, Germany).
High-resolution gene expression pattern under time-course waterlogging/drainage We sequenced a high-resolution time series transcriptome data from root of two contrasting sesame genotypes (EG and R2G) at 11 consecutive time points within a 48 h period waterlogging/drainage treatments (Fig. 1a ). The tolerant genotype R2G exhibited low wilting signs throughout the experiment and rapidly recovered after drainage. On the contrary, the sensitive genotype EG was strongly wilted (Fig. 1b ). The root biomass of the stressed R2G plants was increased as compared to the non stressed plants while the opposite was observed in EG (Fig. 1c ). We further analyzed the anatomy of the taproot samples collected from plants after 4 h and 36 h waterlogging treatment. We observed that R2G presented a well-organized taproot structure after 4 h stress with very few intercellular spaces resulting from the collapse of the parenchyma cells after 36 h waterlogging (Fig.
Future works As a complement to our transcriptional regulatory network analysis, we generated dense time series leaf metabolome data. We expect that the integration of these two omics datasets will further inform on the biological processes underpinning each transcriptional phase in waterlogging/drainage responses in sesame. These datasets will also be of great importance for our ongoing genome-wide association study, which aims at unraveling the potential allelic variations and genes associated with variations in waterlogging tolerance using a large panel of sesame germplasm. Our predicted regulatory network highlighted several switches with unknown functions in plant waterlogging responses. Therefore, future experimentations aiming at elucidating the mechanisms involving these genes would significantly advance our understanding on plant responses to waterlogging.
Acknowledgments The study was supported by the  China Agriculture Research System  ( CARS-14 ), the  Agricultural Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences  ( CAAS-ASTIP-2013- OCRI) ,  Wuhan cutting-edge application technology  fund ( 2018020401 011303 ), and the  Fundamental Research Funds for Central Non-profit Scientific Institution  ( 1610172019004  and  Y2019XK15-02 ).
Groups identification To construct these groups, we suppose that there are information describing performance of the existing version i. e. , a database (referred here as ℒ  : ℒ) which contains, for each item, its context and the reward (conversion, number of clicks, survival,. ) produced on this "original" version. Thus, if the test to be carried out concerns the same type of reward and on a variation of the original version (referred here as the arm ), using this information to build groups can only be beneficial. To do that, a model can be learned using learning and validation sets extracted from ℒ. This model is then used it to predict the group a new item belongs to according to its context. A lot of supervised methods exist to produce such model. For instance, The decision trees-based algorithms like C4.
Algorithm 2 CTREE algorithm Require: Apply CTREE for both of the new partitions Output: An hierarchical partitioning This regression is based on method described in  [22]  using the test statistic T which is derived from  [40]. Appendix B gives more details about this method. This function is used during the A/B test (Step 2). As the step 1 is done offline, it does not delay the computational time of Ctree-Ucb. • 𝜖 ∈]0, 1[ • A dataset ACM Trans. Interact. Intell. Syst. , Vol. , No. , Article. Publication date: 2019. Fig. 4  shows an example of obtained regression tree.
MATERIALS AND EXPERIMENTS SETTING To evaluate the performance of Ctree-Ucb, we compare it to existing A/B methods: a globalbased bandit (Ucb), the Lin-Ucb and Kernel-Ucb bandits, as well as a uniform-based algorithm which chooses the variations alternatively. The main criteria for this evaluation are the cumulative and average regret. The experiments are carried out over three data sets. The first one is from the movieLens movie database (small MovieLens,  [19] ) while the second and third sets come from the company AB Tasty and correspond to an e-merchant A/B tests. ) and their associated rates (from 0 to 500) given by film reviewers. To simulate an A/B test using this data, we define: • movies as items: There are 9125 movies in the original database • film reviewers as the variations: denoted by , , ,  and : the 5 reviewers.
AB Tasty datasets. These A/B tests are comparisons of a variation of a web-page with its original state (referred here as  1 and  2 , respectively). These tests were performed in 2018 on AB Tasty's servers for several e-merchant clients. It consisted in using a static allocation with an equal distribution of visitors between the two pages  1 and  2. For each test, visitors who navigate on the tested web page (i. e. , a potential customer) are identified by an ID: the first time one visits the web page, a new visitor description (see Tab. 2) and its associated ID are generated. This description may vary depending on the data available to the user. Then a variation ( or ) is allocated to it. A cookie memorizes the description, the ID and the allocated version. Each time they come back on the tested page, the same variation is shown.
A/B test simulation. The principle of simulation is to apply each algorithm on data sets and to compare their obtained cumulative regrets. We compare the results with a non-linear regression (CTREE) model that learns from all the data. Thus when assigning an item to a variation, regret is evaluated as the difference between the maximum prediction (between all possible variations) and the prediction of the chosen one. This assessment can be seen as a difference between conditional averages and is based on the theoretical definition presented in section 2. For Ctree-Ucb method, the offline step (see Section 5. 3. 3) consisting to learn a conditional regression tree is firstly done. Then each item is submitted to this tree in order to determinate the group it belongs to. Finally, the item is submitted to the classical UCB algorithm associated to this group.
A/B test (Dynamic allocation ). On MovieLens dataset, the last movies (with less ratings) of the dataset gets generally the same ratings for all reviewers. That is due to replacement of missing values by the average values. This can explain, for some movies, a regret equal to zero whatever the variation chosen. So find the best variation with dynamic allocation is thus difficult (see section 3. 1. 2). A such situation may decrease the performance of observed bandits and the uniform algorithm gets equal performance in a such situation. The results in terms of cumulative regret are little impacted by the value of the  parameter, and its impact on results are treated on the next experiments. So, for sake of readability, in Tab. 4, we report only the classical value of the accepted risk of error, i.
A/B test (Dynamic allocation ). On Config. 30,70 , all results provided by Ctree-Ucb (in bold) are the best. On AB tasty dataset 1, the Tabs 5 gives the cumulative regret according to the different parameters ( and   ). With all configuration the  parameter doesn't modify the tree structure. However, the challenge in step 1 is to avoid overfitting (to many groups as in Config. 100,100 ,  _ :  _1 in Tabs. 5) or underfittig ( to less group as in Config. 30,70 ,  _ :  _2 in Tabs. 5). In fact, too few groups lead to performance similar to that of a non-contextual strategy. On the other hand, too many groups slow down the exploration period, but Ctree-Ucb results remain more effective than the Ucb Kernel-Ucb and uniform. In this test, variation A was the best for all, therefore (see  Fig 16)  all bandit algorithms have a logarithmic cumulative regret It also appears that the exploration period would be stop early (after 2000 visitors) with our method than a frequentist approach (uniform) and consequently save money to the user.
1. 2). • Figure  21d  shows a case where the cumulative regret grows almost linearly throughout the A/B test. For this group, the variation chosen for exploration require time or is not the best for all visitors. There are different reasons may explain this: the gap between variations' average is really small, learning from the original page did not correctly identify all possible groups existing in the test dataset, or the characteristics used are not sufficient to give a reliable average for this group. Time response. When the user requires a fast response time (for example for an A/B test that chooses between two web pages, for each visitor the algorithm must give a choice in less than a half millisecond) The step one of Ctree-Ucb is computed before the ab test (offline), the user has the time to compute it.
The user identifies "personas": i. e. a type of visitor. This makes it easier to imagine variations. • Conditional inference trees manage categorical data well and this is what is most common on the web in practice. These characteristics can be provided by the visitor (device, browser, etc. ) or supplemented by external services (such as Data Management Platform) that provides "marketing" information (focus, gender, age category, etc. ). Unlike comparative methods, it's not required to transform categorical data into binary one. • Creating the tree on data from the original variation is reassuring for the user. There is no feeling of risk since it is the original variation, already in production, that is used. The variation (feared, for fear of losing money) will only be evaluated in a bandit context.
The good performance of Ctree-Ucb at identifying groups with different original variation suggests a correlation between the distribution of variations. In practice, most of the times, the changes provided by a variation are limited. So we can create groups on variation A and assume that they are similar on variation B. Our experiment on partial data (first 30%) or on the all dataset shows that the groups are persistent over the time. Moreover, groups identification can help guide the user on the composition of the test itself. If the test was irrelevant for a group, another test can be done, more specific.
30,702543 visitors are dedicated to Step 1 (learning step) and 5934 visitors are tested (Step 2). On Config. 100,100 steps 1 and 2 have 8477 visitors. There is 10 groups identifed by the first Step. Number of past visits before see the tested page has the strongest correlation with the purchase values. However, purchase value can increase or decrease according to the visitor's user agent or language. Influence of segmentation's parameters on the cumulative regret and average with AB Tasty dataset 1 ( = 1).
Total time calculation of each algorithm (Config. 100,100 ) on A/B Tasty dataset 2 Our experiences have indicated that Ctree-Ucb responds to different A/B test issues. Our results includes continuous or categorical characteristics, continuous reward and a number of possible variations higher than two (A/B/C/. ). It shows the performance of Ctree-Ucb for different types of tests. Ctree-Ucb requires 3 parameters : Conf. ,   ,  and. Our experiences conclude that: • A partial dataset (Conf 30,70 ) for step one is sufficient to obtain results comparable to the total dataset (Conf 100,100 ). • By considering different original variation   , the results of Ctree-Ucb may be different. However, Ctree-Ucb remain good compared to result of Lin-Ucb, Kernel-Ucb and Ucb.
Conf 30,70 : ℒ = 30% of  0 ,  / = ∑︀  {70% of   } • Conf 100,100 : ℒ =  0 ,  / = ∑︀    5. 3. 4 Experiment configurations. Tab. 3 summarizes all the parameters and their different potential values. Data set Algorithms Parameters MovieLens Ucb, Lin-Ucb, Kernel-Ucb 𝛼 ∈{0, 0. 25, 0. 5, 1, 1. 5, 2, 2. 5} 𝑉 𝐴 ∈ {𝑉 𝑖 } AB Tasty dataset 1 AB Tasty dataset 2 Ctree-Ucb Config. ∈ {Conf 30,70 , Conf 100,100 } 𝜖 ∈ {0. 01, 0. 05, 0. 1} 𝛼 ∈ {0, 0. 25, 0. 5, 1, 1. 5, 2, 2. 5}.
They have been done on a Intel® Core™ i5-8250U CPU 8-processors running at 1. 60GHz with 7. 5 GB of RAM on Ubuntu 17. 10, 64 bits. All materials (including data, conditional tree regression framework CTREE [23]  and Ctree-Ucb) are available at: https://github. com/R-workshop-strasbourg/bandit4abtest.