dataesr/scientific-paragraphs-categorization huggingface dataset:
Plasmid Preparation pMC MTS-EmGFP and pMC MTS-Dendra2 plasmid were produced from pMC plasmid containing an MTS by restriction and insertion of the DNA-sequence encoding EmGFP and Dendra2, respectively. The gene of Saccharomyces cerevisiae SUMO was composed of Escherichia coli class II codons  (Hénaut and Danchin, 1996)  with the use of the DNA Builder software (The University of Texas Southwestern Medical Center at Dallas, USA). The nucleotide sequence at the 5 -end was additionally optimized using the RNA WebServer (Institute for Theoretical Chemistry, University of Vienna, Austria) in order to reduce the probability of RNA hairpin formation. The designed gene was synthesized by PCR from overlapping oligonucleotides  (Stemmer et al. , 1995)  designed by means of DNA Works  (Hoover and Lubkowski, 2002).
All the experiments were conducted with 1024 × 1024 (141 × 141 µm) image size using 100× (NA = 1. 46, oil immersion) objective. The autofocus functionality of the LSM780 was utilized to the same z-plane using laser reflection on the sample dish glass surface before every image was taken. Afterward spectral unmixing was performed in the ZEN software (Zeiss, Germany) using saved spectra from the non-injected cells (MTOrange or MTS-Dendra2) and a droplet of a pure protein solution before the injection (MTS-EmGFP or MTS-SNAP-tag protein), respectively. dSTORM single molecule localization based super-resolution fluorescence microscopy was performed with the ELYRA. PS1module of the Zeiss LSM780 microscope in TIRF-mode with iXon 997 (Andor, UK) camera with 256 × 256 px resolution (2 × 2 binning), with 20 ms frame for 30 s and excitation by 6 mW 642 nm laser.
Data Processing The fluorescent images were manually segmented in Fiji  (Schindelin et al. , 2012)  by a polygon tool into the regions with single, separated cells. In individual cells, a mitochondrial mask was created from the image of the used mitochondrial marker (MTS-Dendra2 or MTOrange). Next, we determined the average fluorescence intensity in the mitochondria and in the whole cell. The two values allow quantification of relative amounts of FP in mitochondria and whole cell at any given time point, since they are independent of the quite significant movements of cells and mitochondria within cells during the experiment (up to 90 min). To normalize for photobleaching and to remove the influence of the out-of-plane fluorescence signals from the cellular environment of the mitochondria, we calculated the ratio of the signal inside mitochondria to the signal of the cell.
Methods Study design TITAN (NCT02321241) was an observational 12-month study to assess the effectiveness and safety of IVT-AFL in patients with nAMD refractory to ranibizumab. The study was conducted in 28 centres in France and enrolled patients both retrospectively and prospectively. Data were analysed from patients who received IVT-AFL treatment between January 1, 2014 and December 31, 2015. The date of the first IVT-AFL injection was considered the baseline visit, and data collection continued for a maximum of 12 months after the first injection. For retrospectively enrolled patients, the number of office visits, eye exams and treatments were collected from medical records; for prospectively enrolled patients, this information was recorded at routine follow-up visits. The study protocol was approved by a French data privacy committee (Comité Consultatif sur le Traitement de l'Information en Matière de Recherche dans le Domaine de la Santé and Commission Nationale de l'Informatique et des Libertés).
Outcomes The primary endpoint was treatment success rate at 12 months (defined as a gain of ≥1 letter in BCVA and/or any decrease in CRT [in μm] between initial visit [first injection of IVT-AFL] and 12-month follow-up visit). BCVA was measured using ETDRS letters (preferentially) or any other visual scale. For data analysis, we transformed any other visual acuity score to ETDRS letter score. Secondary outcomes included change in BCVA between baseline and final study visits (12 months after the first injection of IVT-AFL or study discontinuation), mean duration of ranibizumab treatment before initiation of IVT-AFL, and frequency and mean number of IVT-AFL injections over the study period. No BCVA data were collected at the end of ranibizumab treatment; however, switching to IVT-AFL occurred relatively soon after the last ranibizumab injection (median of 44.
Acknowledgements The authors wish to thank the TITAN study investigators:  Dr. Aouizerate ,  Dr. Averous ,  Professor Baillif ,  Dr. Benouaich ,  Dr. Benyelles ,  Professor Berrod ,  Dr. Cantaloube-Bessière ,  Dr. Chahed ,  Professor Chiambaretta ,  Dr. Cohen ,  Dr. Coscas ,  Dr. De Bats ,  Dr. Deudon-Combe ,  Dr. Dominguez ,  Dr. Donati ,  Professor Dot ,  Dr. Dumas ,  Dr. Giocanti-Auregan ,  Dr. Guigui ,  Professor Kodjikian ,  Dr. Oubraham ,  Dr. Rothschild ,  Dr. Rumen ,  Dr. Sampo ,  Dr. Scholtès ,  Dr. Sibille-Dabadie ,  Professor Souied ,  Dr. Stanescu ,  Dr. Tran ,  Dr. Uzzan ,  Dr. Wolff  and  Dr. Zerbib. Medical writing assistance was provided by  Apothecom, UK , and was funded by  Bayer Consumer Care AG, Basel, Switzerland.
Numerical model for 0D plasma chemistry In this work, the GlobalKin code is used to describe the RF quasi-homogenous discharge. It is a 0D global plasmachemical kinetics model which is described in detail in  [55]. Briefly, it consists of a reaction chemistry and transport module, a Boltzmann equation solver and an ordinary differential equation solver. In the ordinary differential equation solver, the mass continuity equations for each charged and neutral species are solved as a function of time, accounting for surface and gas phase production and consumption processes: dN i dt = S V   - D i N i γ i γ i Λ D + 4Di v th,i + ∑ j D j N j γ j f ij γ j Λ D + 4Dj v th,j   + S i. (3) Here, N represents the number density of a species i in the gas phase, S V is the surface area to volume ratio of the source, Λ D is the diffusion length, D the diffusion coefficient, γ the surface loss probability, f the return fraction of species from surfaces and S i represents gas phase production and consumption processes.
h Born-Bethe fit to data in the cited reference. i Obtained from reverse process by detailed balance. j In the reference reaction rates were calculated using Bolsig+ [104] and cross sections obtained from the Morgan database [105] for a He/H O plasma. k Cross section assumed to be the same as that for CO. [179, 180]  a In m 3 s -1 and m 6 s -1 for two-and three-body collisions, respectively. b Value is an upper limit in reference. c Estimated value in reference. d Estimated branching ratio. e Branching ratios taken from Sanders  [147]. f Third body is Ar instead of He in reference. The gas efficiency factor is assumed to be 1. g Third body is Ar instead of He in reference. The gas efficiency factor is assumed to be 0. 65. This factor is calculated by dividing reaction rate coefficients for He and Ar as background gases for the same reaction measured by Zellner et al  [175].
CML8 Gene Expression Increases in Roots Following Rs Inoculation As CML8 was shown to act as a positive regulator of plant defense responses against Pst [26], and considering that CML8 expression was restricted to LR initiations that are considered an entry point for many plant soil pathogens, we investigated the contribution of CML8 in response to the soilborne bacterium Rs  [33]. Firstly, we analyzed the expression levels of CML8 6 h after inoculation with Rs GMI1000 WT strain using the promoter CML8::GUS reporter lines. As shown in Figure  2A , Rs inoculation strongly induces the expression of CML8 mainly in the vascular tissues of roots and leaves that correspond to the multiplication areas of this vascular bacterium. We confirmed this data using RT-qPCR and showed that the expression of CML8 is on average respectively 1.
Moreover, once induced or repressed at the first kinetic time point, a progressive increase of the downor upregulation of these genes is observed, while being accompanied by the identification of new DEGs, leading to a greater number of DEGs at 24 hpi in KD (Figure  4B ) as well as in OE2 (Figure  4C ). To verify up-and downregulated DEGs, seven genes either induced or repressed at different kinetic time points were used for RT-qPCR experiments using specific primers (Supplementary Materials, Table  S5 ). These genes were chosen according to their expression profiles and correspond to candidate genes detailed hereinafter. The RT-qPCR data (Supplementary Materials, Figure  S3 ) were consistent with RNA-seq analysis. of Rs GRS100 strain. (A) Photographs of disease symptoms were taken at nine dai.
Consistently, many induced genes are involved in defense response to pathogens, and in particular to Rs, such as PAD4, EDS1, and EDS5, as well as several genes coding for characterized NLRs, such as SNC1 and RPP1. In addition, several genes also code for factors known or predicted to be involved in Ca 2+ signaling, such as CML41, CDPK22, and CRK17. To assist in the selection of the most relevant candidate DEGs, we used the GeneMANIA Cytoskape app to construct the gene-gene functional interaction network from our list and identify the most-related genes (Supplementary Materials, Figure  S4 ). Based on the relevance of the functional category, the predicted function, and coexpression network, seven promising candidate genes were selected for further analyses. They corresponded to cell wall-related genes strongly induced in OE2 line and previously demonstrated to participate in host defense against pathogens: the plant natriuretic peptide A (PNP-A), also named EGC2 (At2g18660), upregulated at 12 and 24 hpi, and the lipid transfer protein LTP4.
A significant overlap was obtained between our DEGs lists and studies focused on Rs infections. Strikingly, while comparing with transcriptome data of leaves of four-week-old Arabidopsis root, inoculated at 6, 12, and 24 hpi and 5 and 8 dai  [55] , genes involved in basal resistance were found enriched at five dai but not at 12 and 24 hpi. This result could be explained by the fact that Hu et al. [55]  used leaves to perform their analysis, while we used roots. We also found significant overlap with studies carried out on Arabidopsis secondary cell wall mutants that displayed enhanced resistance to Rs: the wat1 mutant, in which the resistance is regulated by SA and indole metabolism and efficient against multiple pathogens including Xcc  [56]  and irx5-5 and irx1-6 secondary cell wall mutants  [57].
Plant Inoculations and in Planta Quantifications Rs inoculations were performed on four-week-old plants using the method described in Aoun et al. [86]. To promote bacterial entry and obtain a homogeneous infection, and to better assess the magnitude of the effect of CML8 on plant immunity, roots were cut, and plants were soaked for 15 min in 2 L per tray of a bacterial suspension at 1 × 10 7 cfu mL -1 for symptom notations and IGC and transferred in growth chambers with controlled 27 • C light/26 • C dark conditions (75% relative humidity, 12 h light, 100 µmol m -2 s -1 ). The wilting symptoms were scored on an established zero, one, two, three, and four disease index scale corresponding to healthy, 25%, 50%, and 75% wilted and dead plants, respectively. Data in Figure  3  are representative of three independent experiments consisting of 69 plants per genotype.
Data in Supplementary Materials, Figure  S2  are representative of three independent experiments consisting of 75 and 73 plants for GMI1000 and GRS100 inoculations, respectively. Quantification of bacteria in planta was performed as described in Deslandes et al. [94]. For Xcc, piercing inoculations were carried out on four-week-old plants as described by Meyer et al. [95] , and disease index scoring varies from zero (no symptom) to four (leaf death). In planta bacteria quantifications were performed after seven days as described by Xu et al. [96]. For Pc, inoculations and symptoms quantifications were performed as described by Larroque et al. [93]. Symptoms were quantified from photos with ImageJ software (ImageJ. Available online: https://imagej. nih. gov/ij/, accessed time: 25 September 2021); the leaf surface of green area was used as proxy for disease development.
The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina, San Diego, USA). After cluster generation, the libraries were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. Oriented paired-end RNA sequencing was carried out by Novogene Bioinformatics Technology Co. , Ltd. (Tianjing, China). Four GB raw data were obtained for each sample library. Clean reads were obtained by removing adapter and reads containing poly-N, and low-quality reads were eliminated. Raw sequence data were submitted to the Sequence Read Archive (SRA) database (Accession SRP280329).
Statistical Analysis of RNA-seq Data Pair-end reads from the 63 RNA-seq runs were aligned on the Arabidopsis TAIR10 genome with tophat-2. 1. 1. Read countings were performed with htseq-0. 9. 1. An average of 36 million reads with quality scores over 90% per sample were obtained. To identify DEGs, htseq-counts files were analyzed with the R software, also using EdgeR package version 3. 24. 3 [98]. A genotype comparison between treated and nontreated plants at a given time point was performed. Genes that did not have at least one read after a count per million normalization in at least one half of the samples were discarded. Raw counts were normalized using TMM method and count distribution was modeled with a negative binomial generalized linear model where the genotype, the treatment, the time, and all double interactions between factors were taken into consideration, and dispersion was estimated by the edgeR method.
Biological Pathway Enrichment, Gene Network Analysis, Comparisons with Available Transcriptome Datasets Biological pathways significantly overrepresented were identified with the Classification SuperViewer tool using MapMan classification categories (The Bio-Analytic Resource for Plant Biology. Available online: http://bar. utoronto. ca/ntools/cgi-bin/ntools_ classification_superviewer. cgi, accessed time: 25 September 2021)  [99]. We used Gene-MANIA to predict the contribution of DEGs in co-expression or interaction networks ( https://genemania. org/, accessed time: 25 September 2021)  [100]. To infer the putative function of DEGs, hierarchical clustering analyses were performed using the Genevestigator toolbox (Genevestigator. Available online: https://www. genevestigator. com/gv/, accessed time: 25 September 2021) and microarrays and RNA-seq data coming from stress, biotic, elicitor, and development data, with distance measured as Euclidian distance.
This model was implemented with the function lm() in R software environment (R version 3. 6. 1 (2019-07-05) R Core Team 2019). The dynamics of mutant responses to Rs and Xcc were presented using the ggplot2 package  [106]  showing the least-square means (LSmeans) ± the standard error of the LSmeans. For phenotyping experiments for Pc inoculations, data were analyzed using variance analyses performed using the version 9. 4 of SAS software. To analyze bacteria quantifications in planta, each transgenic plant was analyzed with the following mixed model to test whether the transgenic differed from the WT background: number of CFU/FW (g) ij = µ + block i + genotype j + block i × genotype j + ε ij  (2)  where µ is the overall mean of the phenotypic data, "block" accounts for differences in microenvironmental conditions between the experimental blocks, "genotype" corresponds to the genetic differences between the transgenics and the wild-type background, "block x genotype" accounts for variation in between genotype differences among blocks, and "ε" is the residual term.
All factors were considered as fixed. Model (2) was applied separately to each pair of transgenic plant and its corresponding WT. This model was implemented with the function lm() in R software environment. The log of cfu per gram of fresh weight averaged over the two plates for Rs inoculations and the log of cfu per cm 2 for Xcc inoculations were represented as a boxplot per genotype using the ggplot2 package  [106]. Error bars represent standard error means (SEM) (R version 3. 6. 1 (2019-07-05) R Core Team 2019).
M. is fellow from the French Ministry of National Education and Research. This work was supported by the Université Paul Sabatier (Toulouse, France), the CNRS (France) and by the Agence Nationale de la Recherche (ANR) (ANR-17-CE20-0017-01) thanks to the CaPPTure project, the International Science and Technology Cooperation Major Project Cultivation Special Fund of SCAU (2019SCAUGH05), and Pearl River Talent Program for Young Talent (grant no. 2017GC010321). This study is set within the framework of the "Laboratoires d'Excellences (LABEX)" TULIP (ANR-10-LABX-41) and of the "École Universitaire de Recherche (EUR)" TULIP-GS (ANR-18-EURE-0019). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Acknowledgments: We would like to thank  C. Mazars ,  D. Aldon , and  M. Mbengue  for their comments on this work,  E. Belmas  ( De Sangosse ) for the inoculations with Pc,  A. Leru  ( TRI GénoToul ) for her help with image analyses, and  L. Casagrande  for her help in gene expression analyses.
They were the seeds around which many modelling developments have coalesced, leading to more well-motivated variants and tunes. The maturation of the collaborative GENIE development paradigm, along with the substantial effort invested in curating extensive data archives of neutrino, electron and hadron scattering data, developing advanced frameworks for data/MC comparisons, tuning, and continuous integration underpinned a marked improvement both in the volume of deployed simulations and in release frequency. Steady progress continued following the release of GENIE v3. 0. 0, with a large number of new modelling elements, comprehensive model configurations and tunes planned for release in v3. 2. 0. A comprehensive description of GENIE v3 is much beyond the scope of this article, which will only highlight some of the most recent technical and physics developments, with particular emphasis on new developments that followed the release of v3.
0. 0 1. This article is organised as follows: technical developments, with particular emphasis on the core generator framework improvements, a new event library interface that allows experiments to re-use the mature GENIE experimental interfaces with thirdparty neutrino generators, and the GENIE global analysis of neutrino scattering data are discussed in Sec. 2. New developments in the description of neutrino, electron and hadron-nucleus scattering, which are discussed in Sec. 3, are a focal point of this article. Special emphasis is given in the expanded modelling of the nuclear ground state, the careful validation and improvement of electron-nucleus scattering simulations, new models of zero-pion production (i. e. quasielastic (QE) and multi-nucleon mechanisms such as meson exchange current (MEC) or two-particle-two-hole (2p2h) excitations in general), single-pion production (including both resonant (RES) and non-resonant (NONRES) contributions to the amplitude), new models for coherent (COH) single-photon production and coherent elastic scattering, and new advanced models of final state interactions (FSI) delivered through interfaces to the INCL 2  [3]  and Geant4  [4]  codes, a complete new set of high-energy simulation modules including a next-to-leading order (NLO) deep-inelastic scattering (DIS) simulation, and a high-level description of all recent comprehensive configurations and tunes.
They include the: a) Comparisons, containing curated archives of neutrino, charged-lepton and hadron scattering data, as well as highly-developed software to produce a comprehensive set of data/MC comparisons, b) Prof-GENIE, implementing the GENIE interface to the Professor tool  [5] , and c) Tuning, containing the procedures implementing the GENIE global analysis of neutrino scattering data. The latter group of products plays a central role in the development and characterization of GENIE comprehensive models and tunes and in the GENIE global analysis. While this analysis is in active development, these products do not have open source releases and, therefore, a detailed description of the numerous developments there-in is not in the scope of this brief article. All the repositories corresponding to the suite of GENIE products changed from svn [6] to git  [7] , and they are hosted in the GENIE organization on GitHub, https://github.
The new GENIE tuning process Tuning is a necessary step for all MC generators. In the specific case of neutrino generators, it is required to merge together different models in order to avoid double counting, since there is no single model able to cover all possible interactions across the whole energy range. Indeed, development of a global analysis of scattering data for the tuning and uncertainty characterization of comprehensive neutrino interaction models has been a central activity of the core GENIE team over the past few years. The GENIE Generator is the main outlet for the GENIE global analysis results, and our goal is that, for each supported comprehensive model, several selected tuned versions shall be made available. The GENIE global analysis was made possible through the continued development of curated data archives, and their successful interface to the Professor tool  [5].
Noticeably better agreement is achieved in the backwards-angle bins with the Valencia procedure. Interestingly, further overall improvement is seen if binding energy corrections are neglected entirely (dashed blue). The importance of binding energy corrections for low-energy lepton-nucleus scattering is further highlighted in the right-hand plot of Fig. 3 , which shows a comparison of GENIE predictions to data obtained for 560 MeV electrons on 12 C at a scattering angle of θ = 60 •. Here the default GENIE approach to binding energy (solid green) achieves much better agreement with the measured QE peak location than the prediction where binding energy is neglected entirely (dashed blue). The Q 2 values represented by the data points in the right-hand plot (ranging between 0. 17-0. 31 GeV 2 ) are roughly comparable to the MiniBooNE flux-averaged mean Q 2 ≈ 0.
The MK model is the first resonance model implementation in GENIE that fully incorporates the interference effects and predictions for pion angular distributions. Figure  5  shows the total CC1π production cross sections for ν µ and ν µ induced reactions with the bare nucleons as predicted using the G18 10b 02 11b tune and MK model  [51] [52] [53] [54] , in comparison with the experimental data using bubble-chambers filled with the hydrogen and deuterium. Data from CERN BEBC  [62] [63] [64] [65]  are used without modification. The data of ANL 1982  [58]  and BNL 1986  [59]  are reanalysed in Ref. [66]. The data of CERN BEBC 1990  [65]  with the cut W < 2 GeV are revised in Ref. [67]. The data of ANL 1973  [56]  and FNAL 1978  [60, 61]  with the cut W < 2 GeV are obtained as the cross sections of ∆ production.
This document was prepared by the GENIE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. The authors acknowledge support from  US Department of Energy  under contract No. DE-SC0007914. A. P. acknowledges support from the  Visiting Scholars Award Program  of the  Universities Research Association. A. G. acknowledges support from the  European Union 's  H2020-MSCA Grant  Agreement No. 101025085. LAR has been supported by the  Spanish Ministerio de Ciencia e Innovación  and the  European Regional Development Fund (ERDF)  under contract  FIS2017-84038-C2-1-P , the  EU   STRONG-2020  project under the program  H2020-INFRAIA-2018-1 , grant agreement no.