dataesr/scientific-paragraphs-categorization huggingface dataset:
Two-tailed Wald test were used for DE tests where the alternative hypotheses was that the absolute value of the log2 transform of the fold change ratio is greater than 2. Adjusted p-values were computed with the Benjamini and Hochberg method and considered significant when equal or below 0. 05. For unreplicated datasets, DE analysis used the DESeq package  67  with default parameters except that a "blind" value for the method parameter and a value "fit-only" for the sharingMode parameter were applied instead in the calls to the estimateDispersions function. As above, dispersions were fitted using the "parametric", by default, or the "local" method, in case estimates did not converge. Benjamini and Hochberg adjusted p-values were computed for the null hypothesis that the value of the log2 transform of the fold change ratio is equal to 0.
The latest version of our primary sRNA processing workflow was written into a Snakemake pipeline and is available on gitHub (https:// github. com/ Aucom te/ sRNAm ake). For the analysis of differential gene expression using mRNA-seq data, we applied the ARMOR pipeline  68  with the following parameter values specified in the config file: additional_salmon_index: "-k 31", additional_salmon_ quant: "--seqBias --gcBias". Note that whenever available in the sample metadata information, a 'experiment' term was included in the statistical model for sRNA loci and gene DE analysis in order to block for experimental batch effects.
Secondary bioinformatic analysis. Rice Nipponbare genomic data  24  including genome sequence, gene annotation and transcripts sequences were downloaded from the MSU website. MIRNA annotation was obtained from MirBase (v22)  69. Unless otherwise stated, pri-miRNAs loci considered in these analysis correspond to those reference rice sRNA loci annotations in the Plant small RNA gene annotations database  46  that are flagged as "MIRNA" ("type" field), that are also listed in miRBase ("source" field has value "miRBase21") and for which an overlapping experimental sRNA loci has been identified in all the datasets considered in this study. We performed Singular Enrichment Analysis for GO Slim Gene ontology terms in the "Rice MSU7. 0 nonTE gene" dataset on the AgriGO website (http:// bioin fo. cau.
edu. cn/ agriGO/-date: 10/09/2020) with the list of cisgene identifiers and default parameter values (Fisher statistical test, Yekutieli p-value correction). xisRNA cis-gene kinase annotation in Fig. 3  and Supplementary Table  S3  was based on the kinase classification of Lehti-Shiu and Shiu 9 and the rice OsRLCK gene family classification of Vij et al. 70. To build the rice and Arabidopsis kinase phylogenetic tree available in Supplementary Data S1, the hmmsearch program  71  was used to search for Hidden Markov Model (HMM) kinase profile (PF00560) into the rice MSU7 and Arabidopsis TAIR10 proteomes. Kinase domain sequences were aligned using MAFFT  72. This alignment was cleaned with TrimAl configured to remove sites with more than 80% of gaps  73. Based on this alignment, a phylogenetic tree was computed with the Fasttree method (approximately-maximum-likelihood method)  74.
Full-length blots are presented in Supplementary Fig. S15. Equivalent results were obtained in two other similar experiments. (B) Differential expression statistics of xisRNA loci found to be induced by the Asian Xoo PXO99 strain in public sRNA-seq datasets. The treatment comparisons (bottom labels) involve tissue samples inoculated with PXO99 versus a mock control at the indicated time point. The plot displays xisRNA loci with a log2(fold change) ≥ 1 and an BH adjusted p-values ≤ 0. 1 in at least one comparison.
Acknowledgements GR and AP-Q and were supported by a doctoral fellowships awarded by the  French Ministry of Research and Higher Education (Ministére de l'Enseignement Supérieur et de la Recherche)  and the  Erasmus Mundus Action 2 PANACEA, PRECIOSA program  of the European Community, respectively. JMJ was supported by an  NSF   Postdoctoral Fellowship in Biology  ( DBI-1306196 ). This work was funded by the  ANR  with a grant ( ANR-14-CE19-0009-01 ) to SC. The authors acknowledge the IRD itrop (https:// bioin fo. ird. fr/) HPC platform at  IRD Montpellier  for providing HPC resources. We thank  Dr. Blake Meyers  and  Dr. Bing Yang  for granting access to their sRNA deep sequencing dataset in the Plant MPSS databases. We are particularly grateful to our colleagues who shared seeds of rice genetic material used in this study:  Dr.
Data availability Deep sequencing data was downloaded from SRA using the following NCBI BioProject accessions: 'MAI1' mRNA-seq dataset (PRJNA427491), 'Xoc' mRNA-seq dataset (PRJNA280380), PXO99 infection time course sRNA-seq dataset (PRJNA252424). The PXO99A sRNA-seq dataset was downloaded from the Plant MPSS databases (https:// mpss. danfo rthce nter. org/ web/ php/ pages/ libra ry_ info. php? SITE= rice_ sRNA& showA ll= true). The sequences generated in the course of this study were deposited to SRA under the following NCBI BioProject accessions: 'BAI3' sRNA-seq dataset (PRJNA552731), 'Diversity' sRNA-seq dataset (PRJNA552775), 'BAI3' mRNA-seq dataset (PRJNA679478).
Here, we performed an ensemble of six inversions using different combinations of observational constraints (surface vs. satellite, single vs. multiple species) and alternative prior estimates of 3-D OH distributions. With the ensemble results we aim to (1) identify key regions that contribute to the methane growth rate acceleration from 2010 to 2017 and (2) evaluate the consistency of results inferred from surface and satellite observations. Inversion methods and observational datasets are documented in Sect. 2. We report estimates of the global methane budget change from 2010 to 2017 in Sect. 3 and discuss regional attributions and sources of uncertainties in Sect. 4. Section 5 summarizes this work and provides some perspectives for future studies. 2 Data and methods.
Prior estimates of surface methane fluxes and OH fields We use prior estimates of climatological methane emissions from various sectors except for biomass burning. This choice is made to avoid prior assumptions about the interannual variations (IAVs) or trends in the surface emissions so that IAVs in the posterior fluxes are primarily driven by assimilated observations. The exception made for fire emissions is due to their non-Gaussian distribution and large variations across different seasons and years where the bottom-up esti-mates based on satellite-derived burned areas bring valuable prior information to guide the solution. The emission datasets from different sectors are listed in Table  S2 , and their spatial distributions are shown in Fig. S3. Note that soil deposition is treated as negative fluxes from the land to the atmosphere, and the emissions reported in this study are hence the net methane fluxes from the land to the atmosphere.
, 2019). Recent GOSAT inverse studies explored optimizing gridded annual anthropogenic methane emissions and associated trends, regional monthly wetland emissions, and global (or hemispheric) annual OH concentration with an analytical inversion scheme, where it is possible to compute the full posterior error covariance matrix  (Maasakkers et al. , 2019; Zhang et al. , 2021). The results suggest a strong negative error correlation between global anthropogenic emissions and methane lifetime (r = -0. 8), moderate correlations between wetland emissions and methane lifetime (r = -0. 4), and moderate correlations between the OH trend and wetland or anthropogenic emission trends (r = -0. 6) (Zhang et al. , 2021). Hence, assimilating GOSAT data alone, the inversion has limited information to separate the sources and the sinks.
4b ). Such difference is, to a large extent, related to a latitudinal-dependent difference between model states that fit surface data and that fit GOSAT data. Specifically, the posterior model states of S1 that fit surface observations show positive biases against GOSAT X CH 4 in the northern mid-and high latitudes but negative ones in the tropics (Fig. S5 ). Symmetrically, the posterior model states of S2 and S3, which fit GOSAT X CH 4 well, show negative biases in the northern mid-and high latitudes against surface observations (Fig. S7 ), while the biases turn positive gradually toward the tropics and the Southern Hemisphere (Fig. S16 ). However, no latitude-dependent biases are found between GOSAT-assimilated posterior model states (S2 and S3) against TCCON total column measurements, and the magnitude of remaining biases are in line with GOSAT data validation  (Parker et al.
, 2015). Yet S1 shows similar model bias structure against TCCON as compared to GOSAT X CH 4 (Fig. S7 ), suggesting discrepancies in the vertical distribution of methane concentrations between the model and the satellite retrievals. Such a bias pattern between model and surface or GOSAT data has been identified by previous inverse studies  (Alexe et al. , 2015; Turner et al. , 2015; Miller et al. , 2019; Maasakkers et al. , 2019) , which is likely related to biases in the model representation of the stratosphere. An empirical bias correction on the GOSAT data so that the assimilated model states also agree with surface observations are typically applied by some studies. Here, since we focus on the IAVs of the posterior fluxes where systematic biases do not impact such results, we did not apply an empirical bias correction to the GOSAT data.
Information content of observations on regional fluxes To assess the extent to which the surface and satellite observations can inform us about changes of methane fluxes in distinct regions, we conducted an information content analysis for a total of 18 regions (see Sect. 2. 2. 3). The regional mask following the convention of the Global Carbon Project  (Saunois et al. , 2019)  is shown in Fig. 8. Note that this analysis assumes all atmospheric methane changes result from surface flux changes and hence does not account for potential contributions from changes in OH or other sink processes. The results suggest that in most cases GOSAT data provide more constraints on regional emissions than the surface observations (Fig. 6 ). This is particularly obvious in the tropics and subtropics, including the Amazon, eastern Brazil, southern South America, northern Africa, tropical Africa, southern Africa, the Mideast, India, and Southeast Asia.
Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ministry of the Environment for the GOSAT data and their continuous support as part of the joint research agreement. We also thank François Marabelle and the LSCE IT team for computing support at LSCE. Financial support. This research is supported by NASA ROSES IDS (grant no. 80NM0018F0583), the NASA Carbon Monitoring System (grant no. 80NM0018F0583), and a grant from the Grantham Foundation. This work also benefited from HPC resources from GENCI-TGCC (grant no. 2018-A0050102201). Review statement. This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
Acknowledgements. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. We acknowledge the  University of Leicester  for the GOSAT X CH 4 retrievals, the  NCAR MOPITT group  for the CO retrievals, and the  Goddard Earth Sciences Data and Information Services Center  for the  SAO OMI HCHO retrievals. We thank the  WDCGG, NOAA, AGAGE , and TCCON archives for publishing the ground-based observations and we are very grateful to all the people involved in maintaining the networks and archiving the data. Specifically, we acknowledge the following networks for making the measurements available: NOAA,  CSIRO, ECCC, AGAGE, JMA, UBAG, NIWA, LSCE, MGO, DMC, Empa ,  FMI ,  KMA ,  RSE ,  SAWS ,  UMLT ,  UNI-URB , and VNMHA.
Patients A cohort of 138 NSCLC patients with stage II and III treated with curative (chemo) radiotherapy at the CHU (Centre Hospitalier Universitaire) Milétrie in Poitiers, France was exploited in the present work (Table  1 ). Out of 138 patients, 87 were retrospectively collected and were included in our previous study  [13]. Fifty-one additional patients were prospectively collected between 2016 and 2018. Inclusion criteria were: confirmed NSCLC, available FDG PET/CT imaging, stage II or III and (chemo) radiotherapy treatment, and at least one-year follow-up. All patients provided signed permission for the use of their clinical data for scientific purposes and informed consent for the anonymous publication of data. The "comité de protection des personnes (CPP Ouest III)" (local ethics committee) from the University Hospital of Poitiers approved this study.
PET/CT Imaging Procedure All patients had an 18 F-FDG PET/CT acquired as part of their diagnosis and staging procedure prior to treatment, which was exploited in the present work. A Biograph mCT 40 ToF with axial field of view of 21. 6 cm (Siemens, Erlangen, Germany) was used, relying on the routine clinical protocol. PET/CT acquisition began after 6 h of fasting and 60 ± 5 min after injection of 2. 5 MBq/kg of 18 F-FDG (421 ± 98 MBq, range 220-695 MBq). Non-contrast-enhanced, non-respiratory-gated (free-breathing) CT images were acquired (120 kVp, Care Dose ® current modulation system) with an in-plane resolution of 0. 853×0. 853 mm 2 and a 5 mm slice thickness. PET data were acquired using 3. 5 min per bed position and images were reconstructed using a CT-based attenuation correction and the OSEM-TrueX-TOF algorithm (with time-of-flight and spatial resolution modelling, three iterations and 21 subsets, 5 mm 3D Gaussian post-filtering, voxel size 4 × 4 × 4 mm 3 ).
Image Analysis The 87 retrospectively collected datasets were not re-processed and the previously considered volumes of interest  [13]  were used. A single expert processed the 51 new prospectively collected datasets following the exact same procedure (Figure  1 ), as follows: primary tumor volumes were characterized independently in FDG PET and in the associated low-dose CT images. Primary tumor volumes were first detected and isolated in a rough volume of interest (VOI) containing the tumor and excluding other surrounding physiological uptakes or anatomical structures as much as possible. Determination of the metabolic tumor volume in the PET image was then automatically performed using the Fuzzy Locally Adaptive Bayesian (FLAB, in-house software, LaTIM, Brest, France) algorithm  [14, 15]  applied to that VOI, whereas anatomical volumes were semi-automatically delineated in low-dose CT images using 3D Slicer TM  [16].
Modeling The two endpoints investigated were overall survival (OS) below the median or below 6 months, as binary classification problems. The first endpoint corresponds to a balanced classification (i. e. , no need to correct for imbalance in the training data), whereas the second endpoint leads to unbalanced datasets but is also more clinically relevant as it corresponds to the identification of a small subset of patients with very poor prognosis that could be offered alternative treatment strategies, such as targeted therapies. In that case, Synthetic Minority Over-sampling Technique (SMOTE)  [18]  was applied to the training dataset, in order to facilitate learning of the models by generating additional synthetic data points for the minority class, so negative and positive classes are balanced.
Higher accuracy (69% (95% CI 61-77)) could be obtained in the testing set, at the cost of including a much larger number of features, which would likely reduce the performance of the model in external datasets. LR relying on 37 features reached lower accuracy 72% (95% CI 65-79) in the training set compared to RF and SVM, and performed with slightly reduced accuracy in the testing, with 63% (95% CI 55-71) accuracy. The fusion of the three ML algorithms with majority voting led to a perfect accuracy (100% (95% CI 100-100)) in the training set and slightly increased performance of 71% (95% CI 63-79) (sensitivity 75% (95% CI 68-82), specificity 67% (95% CI 59-75)) in the testing set. Using the average of the probabilities led to even better results of 78% (95% CI 71-85) accuracy (sensitivity 75% (95% CI 68-82), specificity 80% (95% CI 73-87)).
58% for median OS and 89% vs 53% for OS < 6 months. The performance was consistently improved for both endpoints when exploiting the consensus of the three ML algorithms, instead of each of them being used separately. Comparing the two strategies, majority voting vs. averaging output probabilities, the latter achieved a higher improvement in both scenarios. Several other recent studies have investigated the prognostic value of PET/CT radiomic features in NSCLC. Wang, et al. 2017 compared different machine learning and deep learning methods to classify mediastinal lymph node metastasis of NSCLC using 18 F-FDG PET/CT images  [19]. However, they did not consider the fusion of the machine learning techniques before moving to the use of deep learning methods. Mi, et al. 2015  proposed a novel wrapper feature selection algorithm to overcome the small sample size problem of datasets  [20].
Our approach yielded good initial results with about 200 responses per week. The survey has been promoted via social networks, direct contacts, and, more recently, with paid advertising. To ensure total anonymity, the surveys are hosted on a private instance of LimeSurvey  (15). Data is aggregated daily, and in this process the responses are shuffled so no single entry can be backtraced to its user. All the data is published in a public Github repository. The study design was reviewed and approved by the ethics committee of the IMDEA Networks Institute. The survey includes an informed consent. Once the data is collected, we remove outlier responses. A response is considered an outlier if (1) r i is outside 1. 5 times the interquartile range above the upper quartile (which for the data in this paper means r i > 175) or if (2) c i /r i is > 1/3 (to exclude participants with an exceptionally high contact with cases).
This results in n = 999 responses (59 ± 35 per region) across Spanish regions, with a cumulative reach of i r i = 67, 199 (0. 1827 ± 0. 0701% of the regional population, and 0. 1434% of the national population). On average, participants provide information for r i = 74. 6219 ± 38. 0291 members in their social circle, which is coherent with Dunbar's acquaintance group and related studies that take social networks into consideration  (17). Within this dataset, our outlier removal methods excluded 19. 8883 ± 9. 2692% of responses, including spurious contributions as the original average reach per participant before filtering was > 5 • 10 15. The Bland-Altman plot in Figure  1B  shows a high correlation between the CoronaSurveys estimates and the gold standard. A direct comparison of crude percentages, depicted in Figure  1B , also yields excellent results (R 2 = 0.
Results (n = 4 replicates) are represented as the mean percentage (±SD) of necrotic cells. e, f Cytotoxicity assays and representation are similar to (c,d), but cells were infected with the ExoU+ PP34 strain (n = 3 and 4 for (e) and (f), respectively). g T3SS-dependent injection of ExoU in A549 epithelial cells. A549 or DNAJC5 -/-cells were infected with PP34ΔexoU expressing ExoU S142A fused to the β-lactamase (Bla) or infected with uncomplemented PP34ΔexoU. Cells were loaded with CCF2, a fluorescent substrate for Bla, which shifts from green to blue fluorescence upon processing by the enzyme. Fluorescence was measured at 4 hpi on both channels and results are expressed as a blue/green ratio (n = 3; bar: median). Statistical differences between data in A549 and DNAJC5 -/-cells infected with the exoU-bla strain were calculated with a two-sided Student's test, and were not significant (n.
s. ). h Cytotoxicity assay. Increasing concentrations of quercetin were added to A549 cells in the presence of PA14 (ExoU+) at an MOI of 10. Necrosis was monitored by PI incorporation and recorded by time-lapse microscopy. Results (n = 8 replicates) are represented as the mean percentage (±SD) of necrotic cells. Source data are provided as a Source Data file. We next examined whether DNAJC5 also contributes to the activity of other toxins delivered by P. aeruginosa. To do so, we first performed a cytotoxicity assay using bacteria CHAΔexoT secreting ExoS (and not ExoU or ExoT) through the T3SS. The toxic activity of ExoS results in dismantling of the actin cytoskeleton, hence provoking cell rounding 2. The similar kinetic profiles recorded for the two cell lines (Supplementary Fig. 3a, b ), indicate that the action of ExoS in host cells does not require DNAJC5.
One z-section is shown. For each section, a region was enlarged and an arrow was drawn (represented on the right) to establish the intensity profiles for both green and red fluorescences. d, e. Effect of decreased Hsc70 and Hsp70 expression on ExoU toxicity. A549 cells were transfected with siRNAs for Hsp70 (si_Hsp70) or Hsc70 (si_Hsc70) to knock down their expression. Knockdown was monitored by Western blot (d). KD cells were subjected to an infection assay (e)(n = 5), in the same conditions as in (b). Source data are provided as a Source Data file. For exoU-bla fusion, the DNA fragment encompassing the exoU promotor and coding sequence was amplified by primers ExoU-BamHI and ExoU-XbaI (Supplementary Table  2 ) using pIAexoU S142A spcU as template  39. The PCR fragment was then transferred into BamHI-and XbaI-digested pIAexoS-bla plasmids, where exoS was excised.
Then, each sequencing library was quantified on both NanoDrop and Qubit and a quality control of DNA was performed on Agilent Bioanalyzer system. Finally, a high throughput sequencing was performed on the pool of amplicons using a NextSeq 500 device (Illumina) at the CNRS platform of Orsay (Institut de Biologie Intégrative de la Cellule) and raw data were processed and analyzed using a web-based analysis platform named CRISPR-AnalyzeR. The adjusted p-values were analyzed according to MAGeCK method to identify overrepresented genes targeted by gRNAs in output compared to input. Cytotoxicity assay. For infection assays, 1. 5 × 10 4 A549 cells or derivatives were seeded per well in a 96-well plate 48 h before infection in DMEM supplemented with 10% FBS and 200 µg mL -1 neomycin for transfectants.
Thirty minutes before infection, Syto24 was added to the medium at 0. 5 µM to label cell nuclei. Then, medium was removed and replaced by DMEM supplemented with PI at 1 µM. Cells were subsequently infected at MOI 20 with bacteria (OD1) unless indicated. The kinetics of PI incorporation was followed using an IncuCyte live-Cell microscope (Sartorius). Acquisitions were done every 15 min for 5 h using a ×10 objective. Images from bright field (phase), green channel (acquisition time 200 ms) and red channel (acquisition time 400 ms) were collected. The percentage of necrotic cells was calculated by dividing the number of PI-positive cells by the number of Syto24-positive cells. For the cell retraction assay, cells were labeled with the CellTracker Red CMTPX (1 µM). Images were treated with ImageJ software.
Statistics. Statistics on genomic screening data are described above. GraphPad 7. 04 software was used for all other statistical analyses. For cytotoxicity studies or cell retraction assay, no statistical test was used. For Bla activity assay, a Student's t test was used between conditions using ExoU-Bla. For PLA2 activity test, a one-way ANOVA was employed, followed by Tukey's posthoc test for data comparison. For the two latter assays, data distribution was normal according to Shapiros-Wilk's test and the tests were two-sided. For fly infection, a Log-Rank test was used. Data were considered significantly different when p < 0. 01. For CFU comparison in PP34-vs PP34ΔexoU-infected flies, a Mann-Whitney's test was used. The number of experimental repetitions for each figure is shown in Supplementary Table  7.