dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). Parametrization of the process-based SOC model to simulate the soil carbon dynamics was challenging as very few countries and regions have published long-term carbon datasets which can be used to verify model accuracy. Ogle et al. (2010)  demonstrated that uncertainty in simulating SOC accumulation was associated with model structure and that improvement will depend on model parameterization and the number of field measurements. Here, the availability of data from long term field experiments (LTE) for systematic reuse would be an important step forward, if their locations can represent spatial agro-climatic and soil quality variations  (Grosse et al. , 2020). The uncertainty in simulating SOC would be exacerbated in a very sandy soil. Additionally, the initialization of the carbon distribution between the different carbon pools was critical for the SOC model prediction which in general assumes that carbon stocks were near steady state.
Acknowledgments The present study was carried out in the context of the  SUSTAg  project, funded in the frame of the  ERA-NET FACCE SURPLUS  which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  652615. University of Bonn  has been funded by the  German Federal Ministry of Education and Research (BMBF)  under reference number  FKZ 031B0170B. C. M was also funded  BMBF  under reference number ( 031B0170A ). We are grateful to  Susanne Schulz  ( Sanny ) for her continuous support for conducting the simulations. We thank  Andreas Enders  and  Gunther Krauss  for their technical support.
It segments the per-base log2 fold change (log2-FC) using FPOP, a method designed to identify changepoints in the mean of a Gaussian signal  (28). Intuitively, the per-base log2-FC is a measure that scales with the intensity of the transcription differences at each genomic position between the two compared biological conditions. Expression differences are then statistically assessed for each region using the negative binomial generalized linear model of DESeq2  (29)  and the outputs can be visualized in IGV  (30). DiffSegR and competitor methods (Figure  1 ) were compared on two plant RNA-Seq datasets that were previously used in combination with molecular biology techniques to decipher the roles of the chloroplast ribonucleases PNPase and Mini-III  (31, 32). DiffSegR was the only method able to retrieve all the segments known to differentially accumulate outside of the annotated genic regions (i.
Overview of R implementation DiffSegR is implemented in the R statistical environment (www. R-project. org/) and can be found on GitHub (https://github. com/aLiehrmann/DiffSegR) with the installation procedure as well as a vignette with functional examples. All the simulations were performed with an Intel Core i7-10810U CPU @ 1. 10GHz, 16 Go of RAMs and 10 logical cores. On both chloroplast RNA-Seq datasets (see below), DiffSegR returns results in less than 2 minutes. In comparison, it takes less than 30 seconds for a standard differential gene expression (DGE) analysis. The identification of segment boundaries using changepoint detection analysis runs in less than a second on both datasets. The slowest step is the construction of the coverage profiles followed later by the segment count table using the featureCounts program and the BAMs files (Table  S1 ).
Data and read mapping The true positive rate (see below) of DiffSegR and competitors were evaluated on two RNA-Seq datasets comparing Arabidopsis thaliana control plants (col0) to mutants deficient in the PNPase and Mini-III chloroplast ribonucleases  (31, 35). We refer to these datasets as pnp1-1 and rnc3/4, respectively. In the rnc3/4 dataset both conditions contained two replicates with about 19. 5 million reads each while in the pnp1-1 dataset, both conditions contained two replicates with about 18. 6 million reads each. DiffSegR ability to work on a bacterial genome was evaluated using a RNA-Seq dataset comparing a Bacillus subtilis control strain (CCB375 strain) to a mutant deficient for the Rae1 ribonuclease (SSB1002 strain)  (36). We refer to this dataset as Δrae1. (37). This pipeline uses the STAR aligner  (38).
The BAM files corresponding to the aligned Bacillus subtilis RNA-Seq dataset were kindly provided by Ciarán Condon. The alignment was performed using the Bowtie aligner  (39). These alignments were then used for the downstream analyses. Because DiffSegR is the only evaluated method able to analyze stranded RNA-Seq reads, the BAM files were then split by strand in order to be used by the competing methods and the results for both strands were finally merged.
Differentially expressed regions (DERs) Differentially expressed regions (DERs) stand for the largest set of segments with a foldchange > 1. 5 (symmetrically < 2/3) and a false discovery proportion upper bound set to 5%. Both per-segment fold-change and p-value are estimated using DESeq2  (29). The post-hoc upper bound is obtained by controlling the joint error rate (JER) at significance level of 5% using the Simes family of thresholds implemented in the R package sanssouci  (40, 41). Unless specified, all methods were compared using these thresholds. All quality control of the DiffSegR results, including a PCA of counts, a dispersion-mean plot and an histogram of p-values are available in supplementary data for pnp1-1 (Figures  S1-S3 ), rnc3/4 (Figures  S4-6 ) and Δrae1 (Figures  S7-S9 ) datasets.
Benchmarking For the purpose of benchmarking DiffSegR against other methods in terms of true positive rate (see below), one or more parameters likely to change the number and/or the positions of the identified changepoints were adjusted. 1. The minimum depth threshold (minDepth) is common to derfinder RL and srnadiff. All contiguous positions with mean of coverages above this threshold are kept. For each method, on both datasets, one hundred minDepth values evenly spaced within the interval  [1, 6000]  were tested. The default minDepth value of derfinder RL and srnadiff are 5 and 10, respectively. 2. The minimum log2-FC threshold (minLogFC) is used by srnadiff to keep only contiguous positions with absolute normalized log2-FC above the threshold. For both methods, on both datasets, one hundred minLogFC values evenly spaced within the interval [0.
1,7] were tested. The default minLogFC value of srnadiff is 0. 5. 3. The emission threshold (emissionThreshold) is used by srnadiff to define the HMM states. For both methods, on both datasets, one hundred (emissionThreshold) values evenly spaced within the interval [0. 09, 0. 9] were tested. The default emissionThreshold value of srnadiff is 0. 1. For all these comparisons and on both datasets, the DiffSegR hyperparameter λ was kept to its default value, λ=2. In other analyses, all parameters from the different methods tested were set to their default values.
Comparisons on pnp1-1 and rnc3/4 labeled datasets For the comparisons on the pnp1-1 and rnc3/4 labeled dataset the error E was defined as the total number of labels which are not overlapped by at least one DER. A label is a genomic portion whose corresponding transcript has previously been validated by molecular biology techniques to be differentially accumulated in the mutant compared to WT. The genomic coordinates of the labels can be found in Table  S3 -S4. The true positive rate is given by ܴܶܲ ൌ ேିா ே where N is the total number of labels.
DiffSegR facilitates the visualization of DERs DiffSegR was applied to a RNA-Seq dataset comparing control plants (col0) to a mutant deficient in the PNPase chloroplast ribonuclease (pnp1-1), a major 3' processing enzyme  (35). When dealing with a gene dense genome like the plastome, annotating a DER using the nearest gene can lead to ambiguities. In this case, visualization of the DERs in a genome viewer, as exemplified for the psbB-psbT-psbN-psbH-petB-petD gene cluster (Figure  3 ), is often the simplest solution. In line with previous molecular studies, DiffSegR identifies 15 DERs, 8 on the forward and 7 on the reverse strand, respectively. For example, the overexpressed segment, in 5' of the psbB gene (DER 1 with genomic positions 72,233 to 72,395) matches an area previously shown to over accumulate RNA 5' ends in pnp1-1  (43)  and the segment 2 overlapping psbH and antisense to psbN (DER 2 with genomic positions 74,224 to 74,846) corresponds to various 400 to 700 nt long RNA isoforms previously characterized in WT or pnp1-1 mutants  (35, (44) (45) (46).
DiffSegR improves the search for DERs The ability of DiffSegR and competitor methods derfinder and srnadiff  (19, 22)  to identify DERs was evaluated on two RNA-Seq datasets generated for plants lacking the chloroplast ribonucleases PNPase (see above) and Mini-III (rnc3/4)  (31, 35). In comparison to control plants, these two mutants over accumulate RNA fragments that are mainly located outside of the annotated genic areas and the RNA-Seq data have been extensively validated using molecular techniques  (31, 32). These validations were used to define 23 labels (17 in pnp1-1 and 6 in rnc3/4) where a DER was expected to be found (list and coordinates of the labels in Table  S3 -S4). Using its default segmentation hyperparameters (λ=2) DiffSegR identified 434 and 25 DERs in the pnp1-1 and rnc3/4 datasets respectively (Tables S5-S6; Figures  S10-S30 ), including all the predefined labels (TPR = 1).
By contrast, srnadiff and derfinder RL identified 16 and 4 labels out of 17 in pnp1-1 and 4 and 0 labels out of 6 in rnc3/4 (Table  2 ). After adjusting the per-base log2-FC threshold, only srnadiff was also able to reach a TPR of 1 (Figure  S31 -S34). As expected, standard differential gene expression (DGE) analysis, which relies on known gene annotations and is considered as a routine research tool (3), was unable to identify labels located outside of these annotations, therefore resulting in an TPR of 0. Because the the large number of DERs found by DiffSegR could suggest it has a high FPR, we evaluated and compared it to classical DGE analysis (47) using a RNA-Seq dataset containing 10 replicates of the nitrogen deficiency condition. Any DER identified between subsamples of the replicates was therefore considered a false positive.
DiffSegR better captures the differential landscape Because derfinder RL and srnadiff use a two-or three-level segmentation model they are susceptible to merge in a single DER various contiguous segments having different log2-FC. As a consequence, distinct DER events stemming from distinct RNA maturation processes could be wrongly associated together (Note S4). In contrast, DiffsegR segments the mean of the per-base log2-FC without making any assumption on the number of levels. It should therefore be able to distinguish between contiguous DER events, leading to shorter DER than the other methods. We therefore compared the length distribution of DERs identified by DiffSegR, srnadiff and derfinder RL. In agreement with our expectation, the DERs identified by DiffSegR are on average smaller than those identified by its competitors in both the pnp1-1 and rnc3/4 datasets (Figure  5 ).
We therefore compared the length distribution of not-DERs identified by DiffSegR, srnadiff and derfinder RL in both pnp1-1 and rnc3/4 datasets. Figure  5   S39 ). In addition, DiffSegR returned significantly up-regulated DERs overlapping 60 other genes (Table  S7  and S8 ). A striking feature was however the overrepresentation of very short DERs. The five most abundant ones were indeed 4 (6. 5%), 6 (6. 4%), 5 (5. 9%), 2 (5. 6%) and 8 (5. 4%) nt long while the five most abundant ones in the pnp1-1 dataset were 55 (1. 7%), 73 (1. 7%), 83 (1. 1%), 204 (1. 1%), 56 (0. 8%) nt long.
DiffSegR accurately captures the differential landscape DiffSegR finds all the extended 3' and 5' ends of transcripts, as well as accumulated antisense RNA, in RNA-Seq labeled datasets pnp1-1 and rnc3/4. These labels were previously verified through molecular techniques, and DiffSegR was able to identify them with its default settings, while none of the competitors tested were able to do so. However, the use of the same dataset twice in DiffSegR (and its competitors), a procedure so-called double dipping, first for segmentation and then for differential analysis may result in an inflated false positive rate  (57) (58) (59). We therefore verified that the FPR of DiffSegR is similar to standard DGE analysis using a blank experiment (Figure  4 ). A possible explanation to the observed robustness is the fact that DiffSegR uses different aspects of the data in its two steps: while the segmentation uses the per-base log2-FC, the DEA relies on normalized counts, persegment log2-FC, and dispersion.
The last three parameters are estimated by DESeq2. We are therefore confident that the numerous DERs identified outside of the predefined labels in the two chloroplastic RNA-Seq datasets represent bona fide DERs. For example, 387 out of the 434 DERs identified in the pnp1-1 RNA-Seq experiment did not overlap labels. While an exhaustive molecular validation of these 387 segments is beyond the scope of this study, numerous evidences suggest they are accurate. Specifically, DiffSegR identifies 72 DERs overlapping all the 25 plastid introns in the PNPase mutant, a feature previously shown to reflect a lack of intron degradation following splicing in the mutant  (45). Neither srnadiff nor derfinder RL were able to capture this feature entirely. Another example suggesting that DiffSegR does not over-segment the differential transcription profile is displayed for genomic area 51,012-52,156 in Figure  5.
Symmetrically, the remaining tracks correspond to the same data on the reverse strand. DiffSegR finds 8 up-regulated DERs on the forward strand (IDs 1 to 8), 5 up-regulated DERs on the reverse strand (IDs 9 to 11, 14 and 15), and 2 down-regulated DERs on the reverse strand (IDs 12 and 13). Table  1  provides a summary of the molecular validations published for the DERs identified in the psbB gene cluster through DiffSegR analysis. The bedGraph and gff3 files used to generate the tracks and the xml file used to load them in IGV were created using the exportResults function of the DiffSegR R package. The session was loaded in IGV 2. 12. 3 for Linux. The eCDFs of FPRs from DiffSegR (solid curves) and DGE (dashed curves) methods are compared by re-sampling two groups from 10 biological replicates of the same nitrogen deficiency condition in the IDEAs dataset.
Respective median sizes are equal to 211 and 455 nt for DiffSegR and srnadiff (p-value < 2. 2*10 -16 , Mann-Whitney U test) in pnp1-1. In rnc3/4 respective median lengths are equal to 15 and 97 nt (p-value = 0. 0362, Mann-Whitney U test) (Figure 5. A). An identical trend can be observed between DiffSegR and derfinder RL. In pnp1-1 respective median sizes are equal to 220 and 826 nt (p-value < 2. 2*10 -16 , Mann-Whitney U test). In rnc3/4, derfinder fails to detect DERs, accounting for the absence of overlapping DERs between DiffSegR and derfinder RL in this particular dataset (Figure 5. B).
(A & B) In both datasets, DiffSegR not-DERs are on average longer than srnadiff not-DERs and derfinder RL not-DERs in both datasets. Additionally, DiffSegR DERs are on average smaller compared to srnadiff DERs and derfinder RL DERs (Mann-Whitney U test). (C) Comparison of DiffSegR, derfinder RL, and srnadiff analyses for the trnV gene and the 3' ends of atpE, located on the reverse strand of the chloroplast genome. The tracks are defined as depicted in Figure 3, and further enhanced by incorporating the results from the derfinder RL and srnadiff analysis. DiffSegR identifies 6 up-regulated DERs (IDs 1 to 6). derfinder RL fails to detect any DERs within this region. Lastly, srnadiff discovers a singular DER (ID 7).
DiffSegR and derfinder RL. In pnp1-1 respective median sizes are equal to 833 and 80 nt (pvalue < 2. 2*10 -16 , Mann-Whitney U test). In the rnc3/4 dataset, respective median lengths are equal to 327 and 122 nt (p-value < 2. 2*10 -16 , Mann-Whitney U test) (Figure5. B). We conclude that both srnadiff and derfinder RL over-segment regions that are not differentially expressed in comparison to DiffSegR.
FUNDING This work was supported by the  Agence Nationale de la Recherche  through the grant  ANR-20-CE20-0004  JOAQUIN to BC. The IDEAS experiment was funded by an  ATIGE grant  from  Génopole. AL was supported by a PhD fellowship from the  French ministère de  l'enseignement supérieur et de la recherche. The IPS2 benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Data availability • Raw sequences for the rnc3/4 dataset have been retrieved from the BioProject database with the number PRJNA268035. • Raw sequences for the pnp1-1 dataset have been retrieved from the SRA database with the number SRA046998. • Raw sequences for the nitrogen deficiency condition from IDEAs dataset have been retrieved from the SRA database with the number XXXXXXX • Raw sequences for the Δrae1 dataset can be accessed from the GEO database with the number GSE93894.
DiffSegR and derfinder RL. In pnp1-1 respective median sizes are equal to 833 and 80 nt (pvalue < 2. 2*10 -16 , Mann-Whitney U test). In the rnc3/4 dataset, respective median lengths are equal to 327 and 122 nt (p-value < 2. 2*10 -16 , Mann-Whitney U test) (Figure5. B). We conclude that both srnadiff and derfinder RL over-segment regions that are not differentially expressed in comparison to DiffSegR. DiffSegR can be used on sparser genomes Sparsity refers to the fraction of a genomic region with a null RNA-Seq coverage and is known to cause artifacts in statistical analyses (49). Because the two plant chloroplasts RNA- Seq datasets previously used have a low sparsity ranging from 0. 42 to 0. 57 we tested DiffSegR on a Bacillus subtilis RNA-Seq dataset previously used to decipher the role of the Rae1 ribonuclease (36) and whose sparsity ranged from 0.
Acknowledgements: We thank G-stats team for the fruitful discussions. This work was supported by the  ERC  grant # 786854 G- Statistics from the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  and by the  French government  through the  3IA Côte d'Azur Investments   ANR-19-P3IA-0002  managed by the  National Research Agency.
However, these conventional restoration methods usually rely on general assumptions, such as the nature and level of noise and spatial regularity, which hampers their effectiveness on the diversity of structures and level of degradation in microscopy images. Over the years, the literature on image restoration has evolved considerably due to deep learning and the rapid growth of convolutional neural networks (CNNs). These methods have the advantage of making assumptions based on image content, resulting in state-of-the-art performance in denoising  33, 34  and deblurring  14, 19, 22, [35] [36] [37] [38]  fluorescence microscopy images. However, these methods have two major drawbacks. First, these CNNs often require a training step based on a large ground truth dataset that is generally not available in microscopy.
Overview of DeepCristae DeepCristae aims to restore mitochondrial cristae in intermediate to low spatial resolution microscopy images (Fig. 1 ). DeepCristae mainly consists of a U-Net trained on a dedicated dataset built from real high-resolution 2D STED images (Fig. 1  and Supplementary Table  1 ) and using a novel training loss we specifically designed for cristae restoration (Methods, Eq. (  1 )). Although the term is not fully appropriate, for simplification we refer to this dataset as "synthetic" 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠. A pipeline for random patch sampling focusing on regions of mitochondria in the acquired data was also developed (Supplementary Fig. 1 ). DeepCristae image restoration network was implemented in Python (TensorFlow version 2. 11) and is freely available as an open-source software (see code availability).
DeepCristae quantitatively outperforms state-of-the-art algorithms on the synthetic dataset 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Our method was quantitatively compared to existing both conventional and deep learning algorithms for image restoration. It includes denoising methods (ND-SAFIR  23  , 2D median filter and Noise2Void  33  ), deconvolution algorithms (Richardson-Lucy  29, 30  and Wiener 31 ), and approaches combining both, such as SPITFIR(e)  32  , CARE  19  , RCAN  37  and SRResNet  35  (details are in Supplementary Note 2. 2. 2). All methods were applied to 9 test images of 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (Supplementary Note 1. 2. 1). Note that in this dataset, the ground truth images were obtained by deconvolving the high-resolution 2D STED images with a Richardson-Lucy (RL) algorithm to enhance mitochondria cristae. Iterative RL algorithm is suitable for signal-dependent noise removal.
Robustness and stability of DeepCristae. We have shown that DeepCristae performs well on 2D STED images and outperforms stateof-the-art algorithms. However, it is important to verify the reliability of DeepCristae more widely. DeepCristae has been trained on a dedicated dataset acquired with specific microscope settings and mitochondria properties (e. g. , fluorescence markers, width in pixels of the mitochondria in the images). Any change in these settings is expected to alter the quality of the restoration results. To evaluate the influence of changes in these parameters on the results, three experiments were performed. Our model was trained from the training images of 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 depicting mitochondria of width 15. 64 ± 4. 04 pixels on average. We started by studying the quality of the predictions as a function of the mitochondria width in pixels in the input images.
To that end, the 9 test images of 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 were rescaled 11 times in order to contain mitochondria of specific widths (in pixels) on average. It thus results on 11 test datasets on which our trained DeepCristae model was applied (Supplementary Fig. 2a, b ) and the metrics (Supplementary Note 2. 1) were computed. The evolution of the metrics as a function of average mitochondrial width shows that the closer you get to the training parameters (i. e. an average width of 15. 64 pixels), the better the quality of the restorations. In fact, if the mitochondria are too small, few cristae are restored and the mitochondria are thin. On the contrary, if the size is too large, DeepCristae tends to create artifacts looking like cristae patterns. Next, our model was trained on images obtained with specific parameters that mimic microscope settings.
4h-k ), we observe that the 10 trainings agree overall on the presence or absence of cristae but diverge in their intensity and their precise boundaries. In this experiment, all networks were initialized with the same weights, confirming that our patch sampling method is robust and leads to homogeneous learning. A second similar experiment was carried out. Ten trainings were performed from one dataset but with 10 different weights' initializations (Supplementary Fig. 2c-h ). This experiment indicates that the same dataset leads to homogeneous learning, meaning that the randomness of initialization does not play a key role in the learning process. Finally, to qualitatively assess the performance of DeepCristae on real data, we acquired 5 pairs of real 2D STED images. Each pair contains one low-resolution (LR) and one high-resolution (HR) STED image, acquired as quickly as possible (~30 s), to minimize the displacements and deformations of mitochondria between the two acquisitions (Supplementary Note 1.