dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014) , where EdgeR and DESeq2 are the most frequently used ones  (Lamarre et al. , 2018). In our study, differentially expressed transcripts (DET) were computed with DESeq2  (Love et al. , 2014)  package version 1. 20. 0 supplied by R software (version 3. 4. 4) (R Core Team, 2019) via a likelihood ratio test implemented in DESeq function. For all the analysis, we kept transcripts with FDR less than 0. 05 corrected via the Benjamini-Hochberg method  (Benjamini and Hochberg, 1995). Transformed counts by DESeq2 were visualized with a principal component analysis (PCA) [prcomp()] in R. PCA is a dimensionality reduction method that maximizes the variability explained by the newly formed dimensions. 1-3 dimensions known by principal components (PC) can be chosen to represent the data, where each PC is orthogonal to the other  (Abdi and Williams, 2010; Lever et al.
, 2017). To construct transcript co-expression network, we ran the cutreeDynamic function of the (WGCNA) R package (version 1. 63)  (Langfelder and Horvath, 2008)  on the matrix of normalized counts for the DETs identified with DESeq2. The matrix of normalized counts was computed with the varianceStabilizingTransformation function of the DESeq2 package. WGCNA applies PCA where the first PCs of each formed module are called eigengenes. Soft threshold (beta) that represents the exponential parameter for power law distribution was chosen based on a scale free topology criterion. Adjacency matrix was constructed and then transformed into a topological overlap matrix. Transcripts were hierarchically clustered using the flashClust function and clusters of transcripts having similar profiles, referred to as modules, were formed.
We assumed that a GO term is enriched in a certain module if z-score > 2. Transcription Factor Enrichment Analysis oPOSSUM 3. 0  (Kwon et al. , 2012) , a freely available web accessible software was used to identify enriched transcription factors binding sites (TFBSs) in the 5000 bp upstream and downstream sequence of the DETs. Default search parameters of the single site analysis were kept as they are originally set in the software. oPOSSUM performs an exact Fisher test which measures the probability of a non-random association between the co-expressed gene/transcript set and the TFBS of interest and calculate a F-score as equal to -ln(p-value). F-score is thus assessing the probability that at least one TFBS would be significantly associated with the observed transcript list. Besides, a z-score is calculated using the normal approximation to the binomial distribution to compare the rate of occurrence of a TFBS in the target set of genes/transcripts to the expected rate estimated from the pre-computed background set.
Functional Network Inference Protein-protein interaction (PPI) networks were generated using the freely available STRING database version 10. 5  (Franceschini et al. , 2013). Clustered TFs were inputted separately in STRING to generate background networks based on the predicted associations between these TFs (curated or experimentally determined interactions). Resulting TF-PPI networks were expanded into several layers (shells) via sequentially adding predicted associated proteins (nodes) until networks of five layers were generated as explained in Supplementary Figure  S6. During each step, GO terms with FDR < 10 -4 were assigned to major biological processes. The major biological processes were then normalized to the total number of identified GO terms in each network. To find out how similar is the processes' prediction to that of DETs, shared biological processes between TF-PPI network and DET were sorted out and then were normalized by the total number of shared GO terms.
Samples' Processing and Data Analysis Flow cytometry experiments were conducted using Fortessa X-20 equipped with four lasers and 16 fluorescent detectors. Markers of macrophages (CD11B, F4/80, CD206, and CD86) and neutrophils (Ly6g) were analyzed after immunostaining (antibodies are listed in Supplementary Table  S1 ) in order to estimate the proportion of each population. 100,000 of total events were acquired for each condition. Data were analyzed by DIVA Software (BD Biosciences). The percentage of each cell subtype was calculated after the multiple gating of the different fluorescent markers. The sorting of macrophage subtypes was performed as followed: CD11b+ and/or F4/80+ positive cells were gated and represented the total macrophage population. Within this cell population, the percentages of type 1 macrophages (M1) (CD206-/CD86+), type 2 macrophages (M2) (CD206+/CD86-), M1 + M2 (CD206+/CD86+) and negative M1 + M2 (CD206-/CD86-) macrophages population were figured out.
Statistics In order to determine the number of mice per group, a power analysis was performed with G * Power (version 3. 1. 9. 2)  (Faul et al. , 2007)  with the following conditions: One-way ANOVA parameters with alpha = 0. 05, beta = 0. 2, and effect size = 0. 7 with three groups (0 min, 45 min, 24 h). Statistical analysis were performed with One-way Anova (Tukey's multicomparison test and Kruskal-Wallis nonparametric test), student t-test, two-way anova tests and spearman correlation analysis were performed with Graphpad Prism (version 7. 0a) (GraphPad Software, La Jolla, CA, United States) 2.
Experimental Design A methodical issue to assess the sham effect relies on the complexity of the analysis of OMICs dataset. These latter are very sensitive to the statistical power of the study, the experimental design and the accuracy of the analytical pipeline of OMIC studies. The experimental design of dynamic transcriptomic studies is largely affected by factors as the precision of measures, ethical rules, expenses' constraints and statistical power. The precision of measures is, however, highly dependent on the biological variability of the studied system and the experimental error introduced by the surgery effect or the RNA-seq protocol  (Conesa et al. , 2016). Another type of errors originates from static comparisons between "pretreatment" and "post-treatment" samples which can neither correctly recapitulate the dynamic of a pathology nor enable the characterization of the molecular cascade ruling the modification of gene/transcripts expression  (Kim et al.
Weighted Gene Co-expression Network Analysis First, we looked for co-varying transcript signatures and aimed to predict their associated biological processes. Weighted gene co-expression network analysis (WGCNA) is an unsupervised analysis that aims to construct modules (clusters) of highly correlated transcripts according to the similarity in their expression profiles  (Langfelder and Horvath, 2008). WGCNA was performed on the1209 DETs. Prior to analysis, we checked for outliers' samples to exclude, but none was detected as displayed in Supplementary Figure  S2A. The soft threshold was set to 30 based on the scale free topology criterion (Supplementary Figures  S2B, C ). Transcripts were hierarchically clustered (Figure  2A ) and nine clusters of transcripts with similar profiles were formed.
List of transcripts assigned to each module is available in Supplementary Table  S3. For a better understanding, a color code was assigned for each module of transcripts. The size of these modules ranged from 27 to 210 transcripts and 300 (24. 8% of DETs) were not assigned to any module, colored in gray. As examples of the different profiles obtained in these modules, heatmaps representing the expression profiles of the transcripts assigned to the magenta (M[1]), red (M[4]) and blue (M[7]) modules were displayed in Figure  2B , where remaining modules' expression profiles are displayed in Supplementary Figure  S2D. WGCNA summarizes the distribution of transcripts expression via PCA and the first PC is called "eigengene. " Eigengene values of transcripts modules were calculated for each sample and eigengene means were plotted for each module over time in order to summarize the average variation in transcript expression (Figure  2C ).
Transcription Factor Enrichment Analysis and Gene Network Inference Transcriptomic signature could be considered as the outcome of a response to a stimulus carried by molecular pathways. Retroanalysis could thus be used to delve into the molecular history of the biological system. We started assessing which transcription factors could have likely regulated DETs and transcripts' modules. Over-represented transcription factor binding site (TFBS) in the promoter sets of the DETs using oPUSSUM software was calculated by the mean of z-scores and fisher exact test scores. Z-score measures the change in the TF motifs of the target set compared to the background set, whereas the fisher score assesses whether the genes associated with the TF is greater than what would be expected by chance  (Kwon et al.
TF-PPI networks were built on an increasing number of proteins from the initial layer to the fifth layer (+40N) (Supplementary Figure  S7 ) which, however, depending on the count of initial proteins represented different growth of the network (Figure  4B ). We considered that a useful network added enough information (neighbors) to predict the mechanism upstream TFs (receptor, signalization pathways). In the third step, GO terms predicted for each TF-PPI network (simulated GO) were retrieved, filtered (FDR < 10 -4 ) and compared with GO terms of DETs (experimentally derived GO) to determine the shared GO terms between simulated data and experimental data. First, we figured out the sensitivity of the TF-PPI network built by measuring the proportion of all DETs-predicted GO terms shared in the different TF-PPI networks.
In conclusion, we propose an analytical pipeline for dynamic transcriptomic dataset which can be divided into two parts. First is the use of co-variance analysis of transcript expression leading to transcript clustering, combination of best transcripts modules prior to performing GO predictions. This method was efficient in discriminating modification of cell subtypes in the tissue like the one caused by immune cell infiltration or differentiation. Second, we used a retro-analysis strategy which starts with the prediction of the most potent TFs response elements from the all DETs and is followed by the simulation of TFs-based PPI network to predict the major biological processes upstream the TFs induction. We explained a way to test and select the most specific and sensitive predicted networks by comparing the GO terms predicted from simulated data (TFs) with the experimentally derived GO terms (from DETs).
By means of FACS analysis and ELISA assay measuring the level of circulating cytokines, we validated the main hypothesis raised from the predictions done from the transcriptomic dataset. Altogether, our results suggest that (i) Il-6 was induced by the surgery stress and likely initiated the tissue/cell responses and (ii) the surgery stress induced the recruitment of neutrophils and monocytes and the differentiation of hybrid M1/M2 macrophages as well. Since it was shown that Il-6 plays a major role in the neutrophils' trafficking to the inflammation site  (Kaplanski, 2003; Fielding et al. , 2008) , it is likely that Il-6 is activating the immune response in the sham hearts. Finally, this study demonstrates that the so-called "Sham" condition must be performed with a similar timing than the experimental conditions in order to be able to assess the surgerybased effects and discriminate it from the specific experimental effect.
ACKNOWLEDGMENTS We thank  Dr. Laurent Modolo  for his valuable advices in the statistical analysis used in the manuscript,  Mrs. Catherine Rey ,  Severine Croze , and  Mr. Abdel Goumaidi  of the ProfileXpert/Viroscan3D genomic platform for mRNA sequencing. We also thank  BioSyL Federation  and  Ecofect LabEx  ( ANR-11-LABX-0048 ) for inspiring scientific events.
FUNDING SB's Ph. D. was funded by the  National Council for Scientific Research and Lebanese University  fellowship ( CNRS-L) program , the  Eiffel Scholarship Program of Excellence of Campus France  and the  Fondation Leducq (Mitocardia network : grant number  16CVD04  to MO). ZH was funded by the  Association de Specialisation et d'orientation Scientifique. This work was supported by the  IHU OpéRa  ( ANR-10-IBHU-004 ) within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR) , by  INSERM  through the "young researcher funding" granted to GB, and by the  Cedar program of Campus France  ( 2017-2018 ).
In the Upper Rhine Graben, several innovative projects based on enhanced geothermal system (EGS) technology exploit local deep-fractured geothermal reservoirs. The principle underlying this technology consists of increasing the hydraulic performances of the natural fractures using different stimulation methods in order to circulate the natural brine at commercial flow rates. For this purpose, knowledge of the in situ stress state is of central importance to predict the response of the rock mass to different stimulation programs. Here, we propose a characterization of the in situ stress state from the analysis of ultrasonic borehole imager (UBI) data acquired at different key moments of the reservoir development using a specific image correlation technique. This unique dataset has been obtained from the open-hole sections of the two deep wells (GRT-1 and GRT-2, ∼ 2500 m) at the geothermal site of Rittershoffen, France.
We based our analysis on the geometry of breakouts and drilling-induced tension fractures (DITFs). A transitional stress regime between strike-slip and normal faulting consistent with the neighboring site of Soultz-sous-Forêts is evident. The time-lapse dataset enables us to analyze both in time and space the evolution of the structures over 2 years after drilling. The image correlation approach developed for time-lapse UBI images shows that breakouts extend along the borehole with time and widen (i. e. , angular opening between the edges of the breakouts) but do not deepen (i. e. , increase in the maximal radius of the breakouts). The breakout widening is explained by wellbore thermal equilibration. A significant stress rotation at depth is evident. It is shown to be controlled by a major fault zone and not by the sediment-basement interface.
We first present in this paper the geological and geodynamical context of the Rittershoffen geothermal site (France). We describe the borehole imaging data acquired in the GRT-1 and GRT-2 wells at the Rittershoffen geothermal project. We then proceed to a brief review of the methods used for ultrasonic borehole imager (UBI) analyses with their underlying assumptions. We applied the methodology proposed by  Schmitt et al. (2012)  and  Zoback et al. (2003)  in order to assess the stress state at this site. To analyze the three successive images of the wellbore acquired up to 2 years after drilling completion, we developed an image processing method for the UBI data to compare in time the geometry of breakouts. We deduce from this study the evolution of breakouts with time and evaluate its impact on our in situ stress state assessment.
, 2017). As a result, the in-jectivity was improved fivefold due to this thermal, chemical and hydraulic (TCH) stimulation program. Two other borehole imaging programs were conducted in December 2013 shortly after stimulation of the well and significantly later in June 2015. The amplitude and travel time (or radius) images used in the analysis are respectively shown in Fig. 2e  and f for the logging program in 2013 and in Fig. 2h  and i for the logging program in 2015. This time-lapse UBI dataset, whose characteristics are summarized in Table  1 , provides the essential information for the present study as it enables us to identify evidence of irreversible deformation and failure (natural and induced fractures, breakouts, fault zones, damage zones, etc. ) along the borehole wall. Vidal et al.
3b ). Wood grain textures are especially encountered below 2431 m MD. The quality of log data from GRT-2 is generally lower than for GRT-1. This is due to the deviation of GRT-2 that makes wireline logging more difficult. The 2014 log of GRT-2 suffers from stick-slip artifacts on its entire length. The effects of the alternating compression and stretching on the images Solid Earth, 10, 1155-1180, 2019 www. solid-earth. net/10/1155/2019/ highlighted in Fig. 3c  are particularly significant and possibly lead to errors in the recording of the fractures. The 2015 log in GRT-2 does not show any sign of stick-slip but presents an erroneous borehole radius record, leading to an incorrect borehole geometry assessment (Fig. 3d ). Despite these difficulties, the images collected in the GRT-1 borehole are of excellent quality.
Given the difficulty of measuring breakouts as discussed previously (i. e. , artifacts affecting the images, disputable positions of the breakout edges), a confidence ranking has been established for each breakout. This confidence level is presented in Fig. 2k. From the geometry of the breakouts, we compute the break-out widths which are obtained from the breakout edge azimuths. The deepest point of the breakout is used to determine the enlargement radius. In some situations, signal loss issues prevent the determination of the enlargement radius, as shown in Fig. 5  for the image of GRT-1 acquired in 2015. The measured width (black dots; in degrees) and enlargement radius (red dots; mm) are determined from the GRT-1 dataset acquired in 2012 and presented in Fig. 2l. Drilling-induced tension fractures (DITFs) are also identified from the GRT-1 borehole images using the same procedure as for the breakout determination.
Analyses of temporal borehole failure evolution The characterization of the stress tensor derived from the analysis of borehole failures typically relies on a single borehole image dataset. From this snapshot in time, stresses are estimated, while information on the evolution of breakout shape in time is not available. Interestingly, for the ECOGI project, the acquisition of three successive image logs allows us to study this evolution. Here, the time evolution of breakouts, referred to as breakout development, is analyzed to characterize the time evolution of the borehole failure. A common hypothesis concerning borehole breakout evolution is that breakout width remains stable and is controlled by the stress state around the well at the initial rupture time. Progressive failure is assumed, however, to lead to breakout deepening until a stable profile is reached  (Zoback et al.
6 m. Failure did not occur in 2012, while breakouts are visible in 2013 and 2015. When superposing the 2013-2015 borehole sections, no change in breakout shape is highlighted for the west limb although a slight widening is visible on the east limb. Possible deepening of the east limb is occulted by signal loss issues. The borehole section computed at 2126. 2 m shows, in contrast, no modification of the breakout shape from 2012 to 2015 in GRT-1. The development of borehole failures also depends on the lithology. Breakout extension (longitudinal failure development) is quite common in the Buntsandstein, while it is very limited in the basement granites, which is highlighted in Fig. 8. The evolution occurs exclusively between the 2012 and 2013 datasets, while no longitudinal extension occurs during 2013 and 2015.
Evolution of breakout geometry with time Our analysis of the evolution of the breakout geometry with time proves a development of breakouts along well GRT-1 during the first year after drilling (Fig. 8 ). Indeed, we highlighted the fact that sections without breakouts in 2012, 4 d after drilling, present characteristic breakouts in 2013 and 2015, 1 year and 2. 5 years after drilling, respectively. We also observe numerous length increases in the existing 2012 breakouts with time, in particular in the Buntsandstein. The difficulty is to link this evolution with time to a specific process: the time-dependant rheology of the rock (i. e. , creep) or the effects of one of the stimulations (thermal, chemical or hydraulic). Moreover, the 2012 data were acquired at a period during which the thermal perturbations due to the drilling operations were still present.
The data they are compared with were collected in 2013 or 2015 after hydraulic, thermal and chemical stimulations of the well. As a result, the observed changes could have taken place during the thermal equilibrium of the borehole after drilling or during the simulation operations, i. e. , directly after drilling or later. The conclusion from our time evolution analysis of the breakout geometry contradicts the usual assumption that breakouts deepen (i. e. , an increase in the maximum radius measured in the borehole cross sections) but do not widen (i. e. , an opening between the edges of the breakouts) with time  (Zoback et al. , 2003). However, the statistical approach  applied in our study along the open hole of well GRT-1 must be interpreted with caution. Even if we propose a systematic analysis of a time-evolutive dataset, signal loss artifacts prevent an accurate measurement of borehole radius at some depths.
This locally limits our ability to reliably estimate the depth of the breakout, i. e. , the extension of the breakout in the radial direction. Given this limitation, we do not completely exclude the possibility that breakouts could have deepened with time. Our breakout width evaluation is also affected by uncertainty: deviation from the nominal cylindrical geometry of the borehole adds complexity to the measurements made considering the disputable positions of breakout edges. Meanwhile, we mitigated this difficulty by proposing a systematic analysis of all datasets to ensure a more consistent measurement and by attributing an uncertainty level to these values. Our study is thus more conclusive concerning this geometric parameter given that measured changes exceed our uncertainty level. The widening observed in our dataset can be explained by the process of thermal stress dissipation.
Evolution of breakout geometry with depth The development of breakouts depends on the rock rheology and subsequently on the lithology. For our dataset, breakouts are more numerous and extended in the sedimentary cover than in the granitic basement (Fig. 2 ). Moreover, their development is more pronounced in the sedimentary cover when they develop with time vertically along the well (Fig. 8 ). Both observations are consistent with the fact that the sediments have on average a lower strength compared to the granitic rocks  (Evans et al. , 2009; Heap et al. , 2019; Kushnir et al. , 2018) ; i. e. , conditions are closer to failure in the sediments. Another important aspect of the variation of breakout geometry with depth is the evolution of their mean orientation. From the combined measure of the azimuth of the maximum radial extension of the breakouts (BOs) and the azimuth of drilling-induced tensile fractures (DITFs), we analyze in Fig.
11  the evolution with depth of the orientation of the max-imum principal stress S H. The measurements are repeated for the images acquired in GRT-1 in 2012 and in 2015. The consistency in orientation between our data and the 2012 and 2015 datasets (the 2013 dataset was not oriented) builds confidence in the reliability of these indicators. Figure  11  suggests that the orientation measured in the granitic layers below 2420 m in Rittershoffen is consistent with the measurements carried out in the basement of Soultzsous-Forêts  (Valley and Evans, 2007b)  and tends to reach regional orientation. The red line in Fig. 11  is a moving average of the orientation data. It is computed over a 20 m window in depth. The measurement is carried out only if 50 individual measurements or more are present in the averaging window.