dataesr/scientific-paragraphs-categorization huggingface dataset:
Mutations at the interface render ADAR1-dsRBD3 monomeric In order to investigate the importance of the ADAR1 dimeric status for its cellular functions, we sought to design point mutations at the interface of ADAR1-dsRBD3s that would disrupt the domain interactions and result in a monomeric ADAR1. For this, we have based our strategy on the structural comparison of ADAR1-dsRBD3 with an archetypal monomeric dsRBD, i. e. the Xlrbpa-dsRBD2 (pdb code 1di2)  [36] , a dsRBD we already used in our previous study to design a chimeric ADAR1-dsRBD3/Xlrbpa-dsRBD2 that retained the nuclear localization properties of ADAR1  [7]. We thus first superimposed our ADAR1-dsRBD3 structure with that of Xlrbpa-dsRBD2 and derived the corresponding structure-based sequence alignment (Supplementary Figure  S4 ). Based on these structure and sequence alignments, and considering the main interactions observed at the ADAR1-dsRBD3 interface (Figure  1C , D), we decided to create a mutated construct with four point-mutations, i.
0, 5% (w/v) PEG 4000, 30 mM CaCl2, and 230 mM KCl. Crystals were cryoprotected using 20% (v/v) glycerol before flash freezing. Data were collected on a single crystal at the micro focused PX2 beamline at the SOLEIL synchrotron (Saint-Aubin, France). Diffraction data were indexed and integrated using XDS  [69]  and processed using STARANISO  [78]  in order to take into account the moderate anisotropy of the dataset, that diffracted up to ~2. 8 Å along the best direction, but only up to ~3. 3 Å in the weakly diffracting ones. Phases were determined by molecular replacement using PHASER  [71] , and different combinations of models consisting of ADAR1-dsRBD3 monomer structure, double-stranded 13-nucleotides A-form RNA generated in COOT, and single-stranded 13-nucleotides adopting an A-form-like RNA structure.
Acknowledgments We acknowledge  SOLEIL  for provision of synchrotron radiation facilities and we would like to thank  William Shepard ,  Martin Savko  and  Serena Sirigu  for assistance in using beamline PX2. We also thank  Pierre Legrand  for assistance in preliminary data collection on PX1, and for the development of the XDSME package. We are immensely indebted to  Aurélien Thureau  for assistance in SEC-SAXS data collection at the  SWING beamline. We also acknowledge  Franck Brachet  for the access to the crystallography platform of the IBPC,  Alexandre Pozza  and  Françoise Bonneté  ( IBPC ) for assistance in SEC-MALLS data measurements and analysis, and  Sylviane Hoos  and  Patrick England  ( Molecular Biophysics Facility -PFBMI, Institut Pasteur ) for assistance in ITC data measurements.
We also thank  Alwine Hildebrandt  and  Tanja Rohr  for excellent technical assistance. Access to the biomolecular NMR platform of the IBPC, supported by the  CNRS , the  Labex DYNAMO  ( ANR-11-LABX-0011 ), the  Equipex CAC-SICE  ( ANR-11-EQPX-0008 ) and the  Conseil Régional d'Île-de-France  ( SESAME grant ) is acknowledged. PB and MFJ are supported by a joint  ANR-FWF   Grant  (Nos. ANR-16-CE91-0003  and  FWF-I2893 ). Part of this work was also supported by grant  F80-07  by the  Austrian Science Foundation.
7. 12; parameters: -B 4 -T 30) 119. T-DNA coverage and sequencing depth at each genome position were obtained using the SAMtools coverage and depth programs (1. 15. 1). Manta was run (1. 6. 0) 120 to detect SV. The second approach used the tool TDNAscan 121 to characterize the T-DNA insertions. This tool does not map reads on concatenated genomes but instead selects reads that map onto the T-DNA sequence (IR1) and aligns them onto the reference genome (IR2). Clustering IR2 provides the insertion site and a fasta of the inserted sequence; Analysis of the CIGAR code of the soft-clipped reads the orientation of the T-DNA at the insertion site (SupData_14B). To visualize the alignments at the insertion sites and into the T-DNA sequences, we used the Integrative Genomics Viewer (IGV) 122. Whole-genome Hi-Seq Illumina data were submitted to ArrayExpress (E-MTAB-13442).
Centromere movement quantification Using the instantaneous centromere displacements (dx, dy, dz) exported from Imaris, we reconstructed the complete individual trajectories (x,y,z positions of each centromere as a function of time). Trajectories whose duration was less than 15 sec. were considered too short for meaningful analysis and were excluded from the dataset. Trajectories located along the boundary of the image domain were also excluded from further analysis to minimize potential artifacts due to boundary effects. Finally, a sample drift correction was systematically applied by removing from all centromere trajectories in the same acquisition the average trajectory of the somatic centromeres tracked in that acquisition. Several measures were calculated on each track (SupData_17). Descriptors related to instant displacements included the instant speed, its average along the track, and the instant turning angle.
Acknowledgments We wish to thank  Chloé Girard ,  Christine Mézard ,  Eric Jenczewski ,  Rajeev Kumar , and  Raphaël Mercier  for critical reading of the manuscript and  Nadia Bessoltane  for her help in whole genome sequencing analyses. We also thank  Yuki Sakamoto  and  Sachihiro Matsunaga  for sharing the CRWN1::YFP and CRWN2::YFP lines,  Yoshihisa Oda  for the SUN1::GFP and SUN2::GFP lines and  Arp Schnittger  for the REC8::RFP line. This work has benefited from the support of the  Institut Jean-Pierre Bourgin 's  Plant Observatory technological  platforms. This research was funded by  ANR  ( COPATT ANR-20-CE12-0006  and  MeioMove   ANR-21-CE12-0042 ). The  Institut Jean-Pierre Bourgin  benefits from the support of  Saclay Plant Sciences  ( ANR-17-EUR-0007 ).
Results analysis and discussions In (  20 ), (  22 ) and (  24 ), 𝑓 * 𝑥 refers to the buckling limit and 𝑛 stands for the 𝑛th-order buckling mode. In (  20 ), (  22 ) and (  24 ), the amplitudes 𝐶 1 and 𝐶 2 of the trigonometric functions are not defined so any value would satisfy the conditions, and they could be positive and negative, yielding Scenario 2 and Scenario 3 in Figs. 2(a )-2(c). Here, 𝐶 1 and 𝐶 2 are chosen as 1 for simplicity. However, we can still use  (20) ,  (22)  and  (24)  along with  (16)  to characterize the buckling mode shapes of different orders, as shown in Figs. 3,  5  and 7. Here, BBCM and SD-FEM denote the results of the proposed Body-frame Beam Constraint Model and solid-mechanics-based finite element method (SD-FEM). SD-FEM is a very common module in most commercial FEM-based software frameworks, such as ANSYS, COMSOL and Abaqus.
After several washing steps, the staining was scanned using the ChipCytometry microscope. For the next panel of antibodies, each position was again photo-bleached for 20s, and the background was re-adjusted. After the completion of all antibody panels, images were adjusted by finding the ideal combination between the contrast and the background in the ZellExplorer App of the ChipCytometry instrument. Signal intensity was analyzed using the Image J Fiji (https://imagej. net/software/fiji/) script from the GitHub repository (https://github. com/ SebastianJarosch/ChipCytometry-Image-Processing). The intensity data obtained from Image J was converted to the FCS file format for quantitative analysis using writeFCS in MATLAB (https://de. mathworks. com/matlabcentral/fileexchange/42603-writefcs-fname-data-text-other) or fcswrite in python (https://github.
Sequences with less than 300 and more than 600 nucleotides and paired reads with an expected error >3 were excluded from the analysis. The remaining reads were trimmed by 5 nucleotides on each end to avoid GC bias and nonrandom base composition. The presence of chimeras was tested using UCHIME  48. Clustering of zero-radius operational taxonomic units (zOTUs) was done at 97% sequence similarity. Taxonomic binning was assigned at an 80% confidence level using the RDP classifier  49  and compared to the SILVA ribosomal RNA gene database project  50. Analysis was performed using the R-package Rhea  51. In brief, rarefaction curves were used to assess sequencing depth and eliminate lowquality reads. zOTU counts were normalized, and percentage relative abundance was computed. Beta-diversity analysis was used to determine the diversity between groups based on generalized UniFrac distances.
Alpha diversity within species was calculated based on species richness and Shannon effective number of species. EvolView was used to visualize phylogenetic trees  52. Shallow shotgun metagenomics. Shallow shotgun metagenomic sequencing was performed on a NovaSeq machine (1G raw data with paired-end 150bp) using gDNA isolated from murine colonic content. After demultiplexing the data, adapters were trimmed from the raw sequencing reads using Trim Galore (https://github. com/FelixKrueger/TrimGalore). Reads were (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted August 17, 2023. ; https://doi. org/10. 1101/2023. 07. 27. 549844 doi: bioRxiv preprint 35 subsequently processed with the KneadData quality control pipeline to remove low-quality read bases and murine contamination (https://bitbucket.
org/biobakery/kneaddata/wiki/Home). Taxonomic profiling was performed using the MetaPhlAn3. 0. 13 classifier 53 , which unambiguously classifies metagenomic reads to taxonomies based on a database of cladespecific marker genes derived from 17,000 microbial genomes (~13,500 bacterial and archaeal, ~3,500 viral, and ~110 eukaryotic). Species-level assignments were extracted, and data was centered log ratio scaled.
Data availability. The demultiplexed reads for all 16S amplicon sequencing datasets from patient cohorts included in this manuscript are either publicly available or can be made available upon request. Raw data from HSCT and Enable cohorts have been deposited to the NCBI Sequence Read Archive [http://www. ncbi. nlm. nih. gov/sra] under the accession No. PRJNA565903 and No. PRJNA701859, respectively. Data from the UC cohort have been deposited to EMBL-EBI BioStudies database under the accession No. S-EPMC7599849. Software used to analyze the data are either freely or commercially available. Source code data are available from the corresponding author on request. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 4  (which was not certified by peer review) is the author/funder.
ACKNOWLEDGMENTS We thanks  Alain Vincent ,  Filipe Pinto-Teixeira  and  Cédric Maurange  for comments on the manuscript. We acknowledge the contribution of  SFR Biosciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL ): Arthro-tool facility. We thanks the the IGFL facility platform. This work was funded by the  Atip-Avenir program ,  FRM  (# AJE20170537445 ) and AFM (# 21999 ) to J. E.
Following the RANO 41 criteria, the clinical endpoint of PFS was de ned as the time in months from the initial treatment to either the date of progression or censoring. Informed consent was obtained from all patients. The institutional ethics committee (Comité d'Éthique du CHRU de Nancy) approved the study, on 26 August 2020. The trial was registered at ClinicalTrials. gov (NCT04469244) and complied with the principles of the Helsinki declaration.
Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we employ convolutional neural networks (CNN) to estimate the emission fluxes from a plume in a pseudo XCO 2 image. Our dataset used to train and test such methods includes pseudo images based on simulations of hourly XCO 2 , NO 2 and wind fields near various power plants in Eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state of the art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method.
This simplification of the problem reduces the need for a complex encoder. In addition to NO 2 and XCO 2 fields, winds are also used to assist in the XCO 2 plume contour prediction, although experiments show that the addition of this data has very little influence on the predictions. Finally, the U-net models were designed to make predictions beyond the geographical region of their training data. Specifically, the model that learns to predict the mask of the Boxberg PP plume from an image centred at Boxberg PP is trained on a dataset excluding the images centred at Boxberg.
Description of the preprocessing layers The preprocessing layers consist of a six steps sequence, as presented in Fig. 4. The purpose of steps 1-2-3 is to extend the initial database, thereby enhancing the model's ability to generalise to unseen data. A pair of input/output data for training is constituted in this way: 1. a target CO 2 plume corresponding to a PP at a time t is chosen randomly. A background must be constructed to form the XCO 2 image with this plume. This background is chosen randomly and therefore does not necessarily correspond (geographically and temporally) to the chosen target plume. It may correspond to another PP as well as to another time. Furthermore, in SMARTCARB, the background is partitioned into multiple segments. In our case, the background is a random number b ∼ U (-3.
Geographical separation between the training and test datasets We consider a strict geographical separation between the PPs of the training/validation and those of the test dataset. For example, to train a model to predict emissions from Boxberg plumes, we consider a training dataset consisting of images centred at all other available PPs except Boxberg. In this way, the model predicting Boxberg emissions never had access to the Boxberg emissions inventory. During the training phase, to avoid overfitting or underfitting, a separate validation dataset is used to follow the model's performance on new data. In our case, the validation dataset is chosen as unseen data from the same geographical area as the training dataset. Three distinct models are created and trained to predict emissions from three specific PPs.
Application: inversion of three power plants and model interpretation In this section, we study the performance of a trained CNN under the conditions exposed in section 4. Specifically, we analyse the potential of CNNs in the inversion of three different PPs: firstly, the model is evaluated on the Lippendorf PP in section 5. 1. The training dataset is composed of {Berlin, Jänschwalde, Schwarze Pumpe, Boxberg, Turow, Patnow, Opole, Dolna Odra}. Furthermore, we investigate how the assimilation of NO 2 and segmentation fields affects the CNNs; secondly, in section 5. 2, the model is investigated on the Turow PP, and is trained on {Berlin, Jänschwalde, Schwarze Pumpe, Boxberg, Patnow, Lippendorf, Opole, Dolna Odra}; finally, the model is evaluated on the Boxberg PP in section 5. 3. The training dataset includes {Berlin, Jänschwalde, Schwarze Pumpe, Turow, Patnow, Lippendorf, Opole, Dolna Odra}.
In a separate experiment, the CSF method was utilised on a reduced dataset (compared with that used to study the CNN results). Among other changes, this dataset considers only the CO 2 M overpass, or excludes the plume overlaps processed by the CNN. In this experiment, the exact emission profile used to simulate the COSMO-GHG fields of SMARTCARB was used to compute the effective wind. This methodology resulted in a median absolute error around 4 Mt. yr -1 , thereby indicating that the effective wind estimation significantly contributes to the errors associated with the CSF method. The CNN results are reliable, as the majority of errors are concentrated below 10 Mt. yr -1 or 50 %, with very few exceeding 100 %. This indicates that the models provide trustworthy estimates, with a relatively small margin of error.
Precisely, the segmentation model does not discriminate between plume pixels with high amplitude and those with low amplitude. Consequently, if the NO 2 images are of poor quality due to the presence of numerous alternative NO 2 plumes, the segmentation model will struggle to distinguish the main plume accurately. Taking into account these segmentation fields, which do not distinguish the background plumes in the test dataset, results in a degradation of the predictions. Regarding the benefits brought by the integration of NO 2 fields, their essential contribution is their facilitation of the plume segmentation, rather than a direct enablement of inversion based on the NO 2 levels alone. Indeed, as stated in section 4. 2. 1, the NO 2 plumes are not scaled like CO 2 plumes. To further investigate this, it is possible to verify this hypothesis by modifying the scaling of the plume in the NO 2 fields.
Performance evaluation In this section, we study the performance of three models with input similar to those of section 5. 1. 1, with this time a focus on a low-emission PP: Turow. Turow emissions range between 4 and 14 Mt. yr -1 , similar to PPs like Dolna Odra or Opole. This implies that most Turow CO 2 plumes are hidden under the background. All three models considered are trained on a dataset consisting of {Berlin, Jänschwalde, Schwarze Pumpe, Boxberg, Lippendorf, Patnow, Opole, Dolna Odra}. In a similar manner to section 5. 1. 1, KDE plots are presented in Fig. 8  to compare the true emission rates with the predictions of the four different approaches using relative and absolute metrics and corresponding statistics are summarised in table  3.
This implies that when applying the model that assimilates the segmented XCO 2 fields (with the assistance of the NO 2 fields), there is a potential loss of information on the NO 2 fields compared to using the model directly assimilating the NO 2 fields. The observed phenomenon could be a result of potential overfitting when employing the segmentation model, as it is trained on the same dataset as the inversion model. Consequently, the segmentation predictions on the training dataset are typically superior to those on the test dataset. This discrepancy can lead to an over-reliance on the segmentation fields, subsequently causing overfitting.
Performance evaluation The last studied PP is Boxberg which is characterised by high emissions and the presence of other PPs in the vicinity, which entails the presence of other high SNR plumes. We study the performance of three models, all trained on a dataset consisting of {Berlin, Jänschwalde, Schwarze Pumpe, Lippendorf, Turow, Patnow, Opole, Dolna Odra}. The three models differ in their inputs, mirroring the approach taken in sections 5. 1 and 5. 2. Figure  9  and table 4 present a comparison between the true emission rates and the corresponding predictions. In contrast to the inversion results obtained for Lippendorf and Turow PPs, there is a significant variation in performance among the three CNN models. A CNN only trained with XCO 2 , u and v winds demonstrates strong performance, comparable to those of the models estimating Lippendorf and Turow emissions in sections 5.
Overfitting investigation To understand the reasons behind the deviations between predictions and actual values, we conducted an analysis of the residuals in Fig. 10. This examination, which focuses on the disparities between predicted and true emissions, reveals a substantial underestimation of the actual Boxberg emissions by the model incorporating NO 2 or segmentation fields. This observation suggests an overfitting issue, since the majority of PPs used to train the model exhibit lower emission rates compared to Boxberg (6 out of 7: Schwarze Pumpe, Lippendorf, Turow, Patnow, Opole, and Dolna Odra). The low relative error observed on the training dataset of the models with NO 2 or segmentation fields, as depicted in Fig. 11 , further substantiates concerns regarding overfitting. An overfitting model tends to learn features that are overly tailored to the specifics of the training dataset.
Consequently, when presented with images from the test dataset with slightly different features, the model struggles to generate accurate predictions. Our hypothesis is that the model specifically overfits the low emissions PPs, which can be attributed to the information gained through the use of NO 2 or segmentation fields. When provided with new images, the model fails to recognise the new features and consequently yields predictions that align more closely with the training dataset, which predominantly consists of emissions from the low emissions PPs. The failure to recognise new features is due to the incorporation of NO 2 field as inputs. The model's ability to learn highly specific features is limited when no additional input is provided. Conversely, when the model incorporates the NO 2 field, it gains access to more information and can acquire more intricate features.
Consequently, the model's capacity to generalise worsens in the latter case. It is worth noting that some CNN models that we trained incorporating NO 2 or segmentation fields do not exhibit overfitting and provide highly accurate predictions. However, these models do not perform better on the validation dataset, rendering them unsuitable for selection. Next, we examine the performance of a new ensemble of models trained on a more balanced dataset, achieved by removing three out of the five low-emissions PP from the original dataset. The new dataset is composed of {Berlin, Jänschwalde, Lippendorf, Turow, Opole}, i. e. of two medium or high emissions PPs, two low emissions PPs and Berlin. Furthermore, the choice of the factor scaling the plumes during training (see section 4. 2. 1) varies depending on the ensemble member considered (see section 4.
2. 4). Specifically, the uniform distribution is defined with a minimum scaling factor of 0. 25 or 0. 5 and a maximum scaling factor of 2 or 3. Once more, we examine the outcomes for three models obtained from ensembling, as depicted in Fig. 12, and summarised in table  5. The three CNN models demonstrate very good performance, although the inclusion of NO 2 or segmentation fields still leads to a degradation in results, albeit to a lesser extent than in section 5. 3. 1. On the one hand, the median relative error of the CNN model trained on NO 2 and wind fields stands at approximately 20 %, while the median absolute error remains below with the XCO2 field and the winds as inputs on a dataset composed of {Berlin, Jänschwalde, Lippendorf, Turow, Opole}. Two of the models additionally assimilate the NO2 field or the predictions of the segmentation model.
The first and second models are built to invert the emissions from Lippendorf (see section 5. 1) and Boxberg plumes (see section 5. 3), respectively, whereas the two last models target Turow plumes (see section 5. 2). The first three models only use the XCO 2 field and the winds as inputs, whereas the fourth model considers an additional input, the NO 2 field. To apply the Integrated Gradient method, a random plume from the target PP is selected for each model. Columns of Fig. 13  represent the XCO 2 field, the corresponding XCO 2 plume and the Integrated Gradient between the model predictions and the inputs. In order to simplify the analysis, we choose the model that exhibits the best performance on the test dataset, rather than using the ensemble of models. It is worth noting that such a model yields similar performances to the ensemble of models.