dataesr/scientific-paragraphs-categorization huggingface dataset:
based on the wavelet analysis of EC data at KR-Crk. For KR-Crk data in the FLUXNET-CH 4 database, WTD under drainage was provided as 0 cm; thus, the model predicted more saturated conditions at the surface than the actual conditions, resulting in higher contributions by ebullition. Based on the site-scale validation, iPEACE estimates were consistent with production, ebullition, or diffusive flux observations at two sites, but inconsistent with observations from four sites. A comprehensive validation of estimated transport fluxes is challenging at the site scale owing to limited sites with both EC data and process studies available at the same location (Table  1 ). Furthermore, no study has in-situ measured the three transport fluxes simultaneously, resulting in uncertainties in how transport fluxes by process studies are consistent with CH 4 emissions measured with EC towers.
the forcing variables. The current analysis aims to shed light on the relative importance of processes involved in CH 4 production, transport, and oxidation across 25 freshwater wetland sites in temperate, boreal, and Arctic regions. The observed data included in the FLUXNET-CH 4 database were used to constrain a process-based model which has a similar structure used in previous modeling studies (Riley et al. , 2011; Walter & Heimann, 2000; Wania et al. , 2010).
To overcome these difficulties and to be able to detect particles in a completely automated way, we developed an image processing algorithm based on a convolutional network  (Chen et al. , 2017). This approach brings an advantage over traditional image processing techniques by alleviating the need of updating the algorithm parameterization for each novel image. However, like all supervised learning algorithms, convolutional networks require a dataset of annotated experimental images to be trained, referred to as training dataset or ground truth in the following. To construct the images of the ground truth, it is necessary to identify manually the objects that have to be detected, here the positions and sizes of the particles. The annotation is a time consuming task, which renders the use of supervised algorithms difficult in a lot of problems related to physical applications.
In addition, the annotation itself is error prone due to the inevitable loss of attention of the operator in charge of annotating the images. The main originality of our approach is that we entirely trained the network on a series of synthetic images generated with morphological models  (Figliuzzi, 2019; Figliuzzi et al. , 2021; Jeulin, 2021; Stoyan et al. , 2013) , commonly used to simulate micro-structures in materials engineering  (Bortolussi et al. , 2018; Figliuzzi et al. , 2016) , rather than on images of real experiments. With our approach of artificial synthesis of the ground truth, the positions and sizes of the particles are known by construction, which allows us to get rid of the difficulty of obtaining a reliable learning database 1. The lack of annotated data has long been identified as a critical issue that prevents the use of state-of-the-art supervised algorithms in many image processing problems.
In particular, annotating images obtained during physical experiments is often expensive, which triggers interest in alternative methods where ground truth images are generated in a synthetic manner. The development of such methods is increasingly being studied in the literature  (Jahanian et al. , 2021; Nagy et al. , 2022; Ravuri and Vinyals, 2019). In  Besnier et al. (2020) , a generative adversarial network is for instance used to generate a dataset of images similar to those of ImageNet. These generated images are then used to train a classification network. In  Baradad et al. (2021) , the authors investigate image generation models that produce images from simple random processes. These generated images are subsequently used as training data for a visual representation learner. The outline of the article is as follows.
Generation of synthetic training images As discussed in the introduction, a major difficulty associated with the use of convolutional networks is that these architectures require a significant amount of annotated data to be trained. The manual annotation is a time-consuming task, especially when the number of particles to annotate in each image is in the order of thousands. To train the neural network, we rely on a dataset of synthetic images constructed with a morphological model. The use of synthetic images enables us to obtain training images along with a ground truth without the need of annotating a subset of the experimental images manually. The difficulty associated with the use of synthetic images is that these images must be highly similar to the experimental images to ensure good generalization properties of the trained neural network architecture.
Evaluation dataset To allow for a quantitative evaluation of the results and to study the generalization of the algorithm trained on synthesized images to actual experimental images, we manually annotated 5 images obtained experimentally by labeling all the suspension particles by a disk. Each particle becomes therefore characterized by the center and the radius of the disk. Although 5 represents a relatively small amount of images, these images are very large and therefore contain a significant number of particles, as shown in Tab. 2. Hence, the detection results are tested against a large number of particles, which ensures their statistical validity. In addition, we specifically selected in the experimental dataset a poor quality image where a significant illumination gradient occurs. This image, displayed in Fig.
Discussion The results for the CAN neural network, the K-mean segmentation and the Otsu and adaptive thresholding algorithms for the segmentation metrics (precision P , recall R and average IoU between the particle and the detection mask) are reported in Fig. 7  and Tab. 2, where we also report the average distance D (in pixels) between the centers of the particles and the centroid of their corresponding detection masks. Overall, we can note that the quality of the segmentation obtained with the convolutional neural network significantly surpasses the performances of traditional approaches for all considered metrics. Among the images of the test dataset, we can notice a slightly lower performance of the algorithm for image #3: this is explained by the fact that this image is of lower quality than the other images, which makes its segmentation more difficult.
Funding This work was supported by  GDR EGRIN CNRS   3485  and by the  French government , through the  UCAJEDI Investments  in the Future project managed by the  National Research Agency (ANR)  under reference number  ANR-15-IDEX-01. The authors are grateful to the OPAL infrastructure and the  Université Côte d'Azur's Center for High-Performance Computing  for providing resources and support.
In situ cryo electron tomography reveals the use of the Z-site during elongation in MEF cells To first analyze the baseline ribosomal elongation situation in control MEF cells, we used in situ cryoelectron tomography. We obtained 28,644 ribosomal particles which altogether yielded a subtomogram average of the ribosome at ~6. 7 Å resolution (Fig. 2A, 2B ). To increase the number of particles and obtain a resolution benchmark for our setup, we pooled these control particles with the 4h ANS dataset (18,285 particles), leading to an improved resolution down to ~4. 5 Å in the best resolved part of the LSU (Fig. S2, S3 ). We next used classification to separate the distinct SSU rotation and tRNA occupation states of the 80S ribosomes from untreated cells. The resulting 9 main classes display clear neighboring ribosome densities indicating that they can be found on polysomes and correspond to eukaryotic 80S elongating complexes as previously reported (20-22) (Fig.
Low dose ANS stress is associated with increased abundances of Z-site bound states and apparition of a monosomal 80S complex To investigate the effect of low anisomycin stress on native ribosomal populations we performed the same analysis on datasets collected on ANS-stressed MEF cells (Fig. 2E, S3 ). We focused our analysis on the 4h timepoint where cells persist despite a first wave of ISR and RSR (Fig. 1B, C, E ) and our polysome profiling suggested an increase in isolated 80S. We compared this 4h stress timepoint with the 20min and 1h datasets used as indicative of earlier intermediate stress timepoints. All main populations described for the control cells were also found in polysomes in stressed cells. However, their relative abundance is affected by the ANS stress over time, indicating possible kinetics barriers introduced by the elongation stress (Fig.
We calculated distances between an 80S mRNA entry site and the closest exit site on other 80S in the proximity and the other way around. We selected the shortest distance for counts and used a 12nm cutoff to focus our analysis on distances compatible with neighbor particles on the same polysome (~7 nm). While most of the distances in the control dataset are spread across a wide peak centered around ~7 nm (Fig. 3A ), we observed that low dose ANS stress induces a continuous compression of these distances over time (Fig. 3A-D ). The corresponding plots display a narrow peak at ~4. 5 nm distance after 1h and 4h stress, compatible with mRNA length from entry to exit sites on collided disomes  (3, 12, 30). To visualize the ribosome populations with close neighbors, we used image classification with a mask on the positions for neighboring SSUs (Fig.
S6 ). We detected ribosomes with defined densities for contacting neighbors in all datasets, including the non-stressed cells, albeit in different amounts (Fig. 3E , S6-8): about ~4. 5% of 80S ribosomes in untreated cells -in good agreement with previous observations in vertebrate cells  (3, 8, 32) , followed by a ~2-fold increase to ~8. 5% at 20min stress, a peak at ~28% at 1h stress followed by a decrease to ~16% at 4h stress. These counts are given as indicative, as our image classification is limited by the presence of some false positive and negative in the collision classes precluding accurate quantification (ribosomes from these classes plotted back into their original tomograms do not fully result in perfect pairing as seen in Fig. S6-7 ). The decrease in collisions at 4h stress may be the results of negative feedback mechanisms reducing protein synthesis (Fig.
1D )  (7, 8). To assess the elongation states of the ribosomes in disomes, we further used image classification with a mask around tRNA sites and GTPase Activation Center (GAC). Our analysis reveals that collisions visualized in low ANS treated cells are more diverse than anticipated from previous work on purified disomes  (30). We distinguish two classes of stalled ribosomes: a major decoding-like state and a PRE+ like state. We also separate two classes of collided ribosomes: a major rotated-2+ state and another unrotated class, mostly populated by unrotated decoding like ribosomes (Fig. S6 ). The most abundant disome arrangement in our data displays an overall geometry that is in very good agreement with the recently reported cryoEM structure of XBP1u stalled human disomes (Fig. 3F )  (30).
Non-functional t-RNA bound 60S particles accumulate under persistent collision stress Polysome profiling in MEF cells revealed the accumulation of free 60S under persistent collision stress (Fig. 1E ). To investigate the possible reasons for their accumulation we analyzed 60S particles in our in situ cryo-ET datasets. A similar pattern was observed at all stress timepoints (20 min, 1h, 4h) and we focused our detailed analysis on the 4h dataset. We detected about 1500 and 2700 60S particles, respectively in the control and the 4h low ANS stress datasets. In the untreated cells, the major population detected (~64%) corresponds to a complex of the 60S particle bound by a density interacting with RPL23 displaying a shape and position compatible with eIF6, as well as an extra factor at the GAC, and a density probably corresponding to EBP1 at the exit tunnel (Fig.
To analyze the RQC response to the collision stress, we further classified the 60S populations of each dataset. We identified a sub class displaying densities typical for NEMF and listerin in size, shape and location  (19)  and accounting for ~10% of the total 60S populations in both the untreated and 4h ANS datasets. In the 4h ANS dataset, we detected additional tRNA bound populations possibly also bound by NEMF alone (Fig. S10 ). These observations confirm that under persistent collision stress, listerin is limiting in resolving the post-splitting 60S complexes containing a tRNA and a nascent chain  (18, 19) , leading to the accumulation of these non-functional 60S complexes in the cytosol. In addition, our data indicates that under persistent collision stress, late LSU maturation intermediates, generating subunit joining competent 60S complexes upon eIF6 release, may become limiting in the cell, thereby possibly contributing to the observed overall decrease in protein synthesis (Fig.
1D ). Further studies will be required to investigate how this pool is regulated under collision stress. Low dose ANS stress induces different perturbations of translation initiation over time. Cell stress has also been shown to impacts translation initiation, including via the ISR. Phosphorylation of eIF2α induces the formation of a stable inactive complex between phosphorylated eIF2-GDP and the recycling factor eIF2B present in limiting amounts, inhibiting canonical initiation and contributing to a decrease in protein synthesis  (36) (37) (38). We used subtomogram averaging and classification to analyze possible molecular differences between the small subunit complexes in untreated cells and under low dose ANS collision stress. In all datasets, we detected 40S and 43S complexes (Fig. 5A-F ).
As these populations are much less abundant than 80S populations, quantifications are indicative, and we focused our analysis on the main differences between normal and stress conditions. While the proportion of 40S complexes remain relatively similar across conditions (Fig. 1E ), we note apparition of new classes of particles under stress, with important shifts in the proportion of those complexes between timepoints. In the control cells, the largest population we observe is a 40S subunit which seems bound to eIF1 and eIF1A (Fig. 5A ). At 20 min low dose ANS stress, we observed the apparition of a 40S complex with densities resembling: eIF1, eIF1A as well as eIF2 (but not eIF3) and a P-site tRNA (Fig. 5B ). This complex is only observed in our 20 min stress dataset, when a fraction of eIF2α is phosphorylated (Fig.
1B ). The affinity of eIF2-GDP for the methionine tRNA was shown to be 150nM vs. 9nM for eIF2-GTP  (39). We may thus be observing eIF2-GDP bound in this 40S complex, which could hamper the following steps of initiation until eIF2α phosphorylation level decreases (Fig. 1B ). Later at 4h ANS, after the first phosphorylation peak of eIF2α, our dataset displays 2 distinct populations of 40S complexes (Fig. 5C ). The first one seems bound by eIF1, eIF1A and a tRNA. The second one carries a tRNA in a different position and an additional factor. Structural comparison rather exclude that this factor could be ABCE1 or TSR1, while eIF5B and a de-acylated tRNA bound to this complex appear more likely (Fig. S11, 5C )  (40, 41). The major 43S complex observed is similar in all datasets, displaying densities resembling eIF1, eIF1A, eIF2a, eIF2b, eIF2g, eIF3, eIF4A1 and a P site t-RNA (Fig.
S12 ), indicating that the loading of those 43S complexes on mRNA may be inhibited. A subclass of the 4h stress 43S complexes (551 particles) displays an extra density compatible in size and shape with eIF4G (Fig. 5E )  (44). The 4F complex is necessary for scanning and this class may represent 43S particles that remained closer to the cap or result from an inhibition of eIF4E. To test whether stress conditions affect the spatial distribution of the observed small subunit complexes, we visualized the different populations of 40Ss in their original tomograms and noticed that the 43S complexes seem to form small clusters especially in the 4h stress dataset (Fig. S13 ). Plotting the percentage of particles with 2 or more neighbors within a given distance confirmed a tendency to clump of the 43S particles in the 4h ANS dataset (and to a lesser extent in the 20min ANS dataset, Fig.
Acknowledgments We thank  Sofia Ramalho ,  Joyce van Loenhout  and  Anjani Parag  for help with preliminary experiments. We are grateful to  Stuart C. Howes  and  Menno Bergmeijer  for cryoEM support as well as to  Mariska Gröllers Mulderij  for support with cell culture. We thank  Ramanujan Hegde  and  Stefan Pfeffer  for insightful discussions. The work was supported by the  European Research Council  under the European Union's  Horizon 2020 Program  ( ERC Consolidator Grant Agreement   724425 -BENDER ) and the  Nederlandse Organisatie voor Wetenschappelijke Onderzoek  ( Vici 724. 016. 001  to FF and  Veni 212. 152  to JF). A. d. G was supported by the  US National Institutes of Health (NIH)  grant  GM133598.
We therefore sequenced the population corresponding to the last cycle before their respective extinction, but no mutation was detected at frequency of 5% or higher, with the exception of one fixed mutation in population P5 (see supplementary Data 2 and 3). to be characteristic of evolved planktonic populations. To test whether fusA mutations could be present at very low frequency in planktonic populations, glycerol stocks of evolved planktonic populations were grown overnight and concentrated 10 times before being plated on 2x amikacin MIC plates. Among the clones growing on these plates, we could indeed detect a high diversity of mutations in fusA, even in the early evolution cycles (supplementary Fig. 7  and supplementary Data 6). This confirms the presence of fusA mutants at very low frequencies at early cycles, which were not selected and maintained in further selection cycles.
All fusA mutations were exclusively located in domains IV and V of the FusA protein. This bias towards these two domains was also observed in other evolution experiments of E. coli in the presence of aminoglycosides  55, 56, 58, 59  (see supplementary note and supplementary Fig. 13 ). Surprisingly, convergent fusA mutations detected in both planktonic and biofilm populations of A. baumannii and P. aeruginosa treated by stepwise increasing concentrations of tobramycin  44  did not display any apparent domain bias, suggesting that the functioning of this FusA factor might differ between species. Interestingly, the alignment of all the E. coli fusA sequences in nrprot database of NCBI (2143 sequences) identified 11 fusA sequences with the A678V mutation and 6 sequences with the P610S mutation (supplementary Data 7), therefore suggesting that the selection of E.
1 ). For biofilms, generation time was calculated by calculating equation (1) n1 = log 2 (CFUstep6)-log 2 (CFUstep5) and equation (2) n2 = log 2 (CFUstep2)-log 2 (C-FUstep6). For planktonic populations, because of two steps where 1/100-fold dilution was performed, generation time was calculated by calculating equation (3) n1 = log 2 (CFUstep6)-log 2 (CFUstep5/100) and equation (4) n2 = log 2 (CFUstep2)log 2 (CFUstep6/100). Therefore, for each cycle the total number of generation times was n1 + n2. Calculated generation times were then cumulated over the cycles (supplementary Data 1). At the end of the evolution cycles populations have been propagated for ~77, ~200 and ~36 generations respectively for biofilms treated with 5xMIC, 80xMIC and non-treated, and for ~324, ~212 (after 3 cycles since then bacteria were fully eradicated) and ~250 generations respectively for planktonic populations treated with 5xMIC, 80xMIC and non-treated (supplementary Data 1).
Only mutations at higher frequency than 5% are detected by BreSeq. No chromosomal rearrangements or movement of insertion elements were detected as selected by the applied treatment. The Muller Plots were inferred and produced using the lollipop tool version 0. 9. 0 (https://github. com/cdeitrick/Lolipop) as well as the ggmuller R package 0. 5. 3  104. Competition assay for fusA and sbmA mutants in the presence or absence of AMK. Competition assay for fusA and sbmA mutants were performed according to a previously published method  10  , with some modifications. Briefly, LF82_mars, LF82_GFP, and evolved strains (fusA and sbmA mutants) tagged with mars were grown overnight in LB at 37 °C. All overnight cultures were diluted to OD 600 of 2. 0. Then, LF82_mars and mars-tagged evolved strains were mixed with LF82_GFP at a 1:1 ratio.
The mixture of LF82_mars and LF82_GFP was used as a reference competition. Before starting competition assays, each mixture was verified to be in 1:1 ratio by MACSQuant VYB flow cytometer (Miltenyi Biotec, Bergisch Gladbach, Germany). Each mixture was diluted 300-fold with PBS in a microtube. Then, 200 μL of each diluted sample was inoculated in a 96-well plate for the flow cytometry analysis. At least 10,000 events per sample were acquired. An abundance of cells gated as 'redpositive' or 'green-positive' depended on competition combinations. The gating strategy is explained in Supplementary Fig. 14. The software used for analysis was MACSQuantify™ (Miltenyi Biotec). The experiments (i) and (ii) were performed using 3 biological replicates in each competition. The relative fitness of each evolved strain was calculated as follows equation (  6 ): relative fitness ¼ Competition assay without AMK treatment.
This experiment was performed using 9 biological replicates for each strain. Bioinformatic analysis of FusA sequences. All E. coli FusA proteins contained in the nrprot database of NCBI (version 2019-04-09) were identified by BLASTp (blast+ version 2. 2. 31  105  ) and only hits with an e-value lower than 1 × 10 -10 and sequence identity >70% were kept. The resulting 2143 sequences were aligned using mafft version 7. 407  106  with G-INS-I option and the alignment was used to compute entropy in each structural domain of the protein using the HIV sequence database website tool (https://www. hiv. lanl. gov/content/sequence/ENTROPY/ entropy. html). The same alignment was also screened to identify mutations relatively to our reference sequence from LF82 as in ref. 50. Growth capacity. An overnight culture of each strain was diluted to OD 600 of 0.
05 in fresh LB medium. Two hundred-μL aliquots were inoculated in a 96-well plate. The plates were then incubated in a TECAN Infinite M200 Pro spectrophotometer (Männedorf, Switzerland) for 20 h at 37 °C with shaking of 2 mm amplitude. The absorbance of each culture at 600 nm was measured every 15 min. Growth curve of each strain was measured using 3 biological replicates. The R package GrowthCurver 107 was used to infer the growing capacities for all strains based on the growth curves. Penetration of antibiotics through biofilms. Penetration of antibiotics through biofilms were determined according to a previous report  108  with slight modifications. In brief, the E. coli LF82 cultures were grown overnight in LB at 37 °C with shaking and then diluted in the same medium to an optical density of 0.
Acknowledgements We thank  Dr. Olaya Rendueles ,  Dr David Lebeaux  and  Dr Rebecca Stevick  for critical reading of the manuscript. We are grateful to  Dr. Olaya Rendueles  for the initial help with the analysis of the mutations. This work was supported by the  French National Research Agency (ANR) , project  EvolTolAB  ( ANR-18-CE13-0010 ), by the  French government 's  Investissement d'Avenir Program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n°ANR-10-LABX-62-IBEID) and by the  Fondation pour la Recherche Médicale  (grant  DEQ20180339185 ). S. T. -R was supported by the  French National Research Agency (ANR) , project  EvolTolAB  ( ANR-18-CE13-0010 ).