dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, viral isolates identified as belonging to the same species, i. e NSPaV-P. cerasus, MaLV-P. incisa, MaLV-P. armeniaca, and ChLVA-Cigany, displayed more than 90% of aa identity in their various proteins with those of their respective reference isolates, thus confirming their taxonomic assignation (Supplementary Fig. S2 ). To determine whether recombination has played a role in the evolution of the newly identified luteoviruses, an RDP4 recombination analysis was performed on a full genome multiple alignment. No recombination signature with significant support involving Prunusinfecting luteoviruses was detected (data not shown). S1 ). Amplicons from positive samples were subjected to direct Sanger sequencing in order to confirm the specificity of the amplification and assess the genetic diversity of the various viruses (see below).
Discussion This study describes five novel luteoviruses identified from different Prunus species. Compared to the previously reported three Prunus-infecting luteoviruses (NSPaV, PaLV and ChALV), these results provide further evidence of the power of HTS-approaches for the discovery of unknown viruses, even in situations of latent or mixed infections. However, the in silico discovered PhaLV should be considered with caution since it has not been possible to experimentally validate its presence in this host. However, the fact that PhaLV could also be identified in RNASeq data independently generated (PRJNA683804) is in favor of the existence of PhaLV in P. humilis.
brigantina, the first report of a wild NSPaV host. GenBank data available to date indicate rather narrow natural host ranges for Prunus-infecting luteoviruses. On the other hand, experimental graft inoculations have demonstrated that NSPaV is able to infect P. tomentosa and Bing cherry (P. avium) indicators  (Villamor et al. 2016) , suggesting the possibility of a broader natural host range as reported here. Unlike most other luteoviruses, the genome organization of Prunus-infecting luteoviruses shows significant ORF presence/absence variability depending on virus or isolate (ORFs 3a, 4, 6 and 7, Fig. 1A  and Table  2 ). P3a and P4 (MP) have been shown to be involved in luteovirus movement  (Ali et al. 2014; Ju et al. 2017; Smirnova et al. 2015). However, these two proteins appear to be dispensable in at least some of the Prunus-infecting luteoviruses as already described for NSPaV and AlLV1  (Bag et al.
, with the proposed names of mume-associated luteovirus (MaLV), peach-associated luteovirus 2 (PaLV2), cherry luteovirus A (ChLVA), and Prunus mahaleb-associated luteovirus (PmaLV), respectively. The genomic sequences of the PaLV2, MaLV, and ChLVA isolates were completed by filling internal gaps by PCR if needed, and by determining 5' and 3' genome ends by RACE. The 5,822 nt contig for PmaLV, lacking only 10 nt and 40 nt at the 5' and 3' ends respectively, as judged from a comparison with the most closely related luteovirus, ChALV (NC_031800) was not completed. The corresponding genome sequences have been deposited in the GenBank database under the accession numbers ON408234 (PaLV2), ON408236 (MaLV), ON408238 (PmaLV) and ON146357 (ChLVA).
The HTS reads datasets were also analyzed by mapping trimmed reads on reference luteovirus genomes and the results were validated by RT-PCR using corresponding virus-specific detection primers. Altogether, the results showed that 96% of the 50 peach accessions are infected by NSPaV, compared to 38% for PaLV and 54% for PaLV2. Resampling of the luteovirus-infected trees was performed in 2021, two years after the original sampling, as well as observations for any leaf or wood symptoms. No clear symptoms of viral infection could be identified in the field-grown trees and, in particular no symptoms of stem pitting on their bark or woody cylinder. RT-PCR testing of leaf samples showed that viral infection was detected again in 71%, 77%, and 87% of the trees initially found infected in 2019 by PaLV, PaLV2, and NSPaV, respectively, indicating that infection by any of the three viruses could persist over a 2-year period but also that no further spread had apparently occurred.
Acknowledgments The authors would like to thank the  Platform GetPlaGe (GenoToul, INRAE, Toulouse, France)  for the Illumina sequencing,  P. Briard  and  T. Mauduit  ( UMR BFP, INRAE, Bordeaux ) for plant indexing,  D. Racofier  and  M. L. Greil  ( UE Arboricole, INRAE, Toulenne ) for arranging access to Prunus BRC,  Dr. F. Paprštein  ( Research And Breeding Fruit Institute, Holovousy, Czech Republic ) for arranging access to Prunus samples, as well as  L. Svanella-Dumas  ( UMR BFP, INRAE, Bordeaux ) for help in collecting samples. The authors declare that they have no conflict of interest.
This study was funded by the  European Union  through the  Horizon 2020 Marie Skłodowska-Curie Actions Innovative Training Network  ( H2020   MSCA-60 ITN)  project " INEXTVIR " (Grant agreement number  813542 ). The ChLVA research part was financed by  Academy of Sciences of the Czech Republic support  ( RVO60077344 ). The plant indexing biological tests conducted by CTIFL were funded by  INTERFEL  (Fresh fruit and vegetable interprofessional association). D. Safarova  and  M. Navratil  have received support from the  Ministry of Agriculture of the Czech Republic, National Agency for Agricultural Research  (Project no. QK1920124 ). M. Glasa  and  D. Mihálik  have received support from the  Slovak Research & Development Agency  (project  APVV-18-0005 ).
Luteovirus species from publicly available Prunus RNASeq data Maryam Khalili (Edgar et al. 2022)luteoviruses infecting Prunus, the Serratus tool(Edgar et al. 2022)was used with RdRp sequences of PaLV2 and MaLV, two of the four newly identified viruses in this study, as queries. At the species level, only one RNAseq SRA (P. humilis from China, SRR12442710) was identified, with a contig showing 83% aa identity in the highly conserved RdRp motif with both queries, indicating that this sequence likely represents a new species in the genus Luteovirus of this tentative agent. The SRA dataset was downloaded and, following de novo assembly using CLC Genomics Workbench, a large contig of 5,202 nt (nearly full-length, in comparison to other Prunus-infecting luteoviral genomes) was identified. This contig shows only 48-73% nt identity with any known luteovirus species, suggesting this isolate belongs to a novel species in the genus Luteovirus.
Electroweak symmetry breaking explains the origin of the masses of elementary particles via their interactions with the Higgs field. Besides the measurements of the Higgs boson properties, the study of the scattering of massive vector bosons (with spin one) at the Large Hadron Collider allows to probe the nature of electroweak symmetry breaking with an unprecedented sensitivity. Among all processes related to vector-boson scattering, the electroweak production of two jets and a Z-boson pair is a rare and important one. This article reports on the first observation of this process using proton-proton collision data corresponding to an integrated luminosity of 139 f b -1 recorded at a centre-of-mass energy of 13 TeV by the ATLAS detector. Two different final states originating from the decays of the Z-boson pair, one containing four charged leptons and the other containing two charged leptons and two neutrinos, are considered.
Data and simulation The data for this analysis were recorded using single-lepton and multi-lepton triggers, corresponding to an integrated luminosity of 139 fb -1. The overall trigger efficiency for the inclusive Z Z j j events selected for this analysis ranges from 95% to 99%. The EW Z Z j j production was modelled using the M G 5_aMC@NLO 2. 6. 1 event generator  [19]  with matrix elements (ME) calculated at leading order (LO) in perturbative QCD (pQCD) and with the NNPDF2. 3LO [20]  parton distribution functions (PDF). The QCD Z Z j j production was modelled using S 2. 2. 2  [21]  with the NNPDF3. 0NNLO [22]  PDF, where events with up to one (three) outgoing partons were generated at NLO (LO) in pQCD. The production of Z Z j j from the gluon-gluon initial state with a four-fermion loop or with the exchange of a Higgs boson was generated separately.
[47]. The MD distribution in the SR for the non-resonant-process is estimated from simulation. The Z + jets background is largely suppressed, and the yield is evaluated by extrapolating the low E miss T -significance region distribution in data to the high E miss T -significance region using an exponential function, while the MD distribution in the SR is modelled by simulation. Small background contributions from triboson and ttV production are modelled with simulation. All of those backgrounds collectively yield an estimated contribution of about 1% to the selected data sample in the νν j j channel.
Conclusion The rare electroweak production of Z Z j j events is observed using 139 fb -1 of √ s = 13 TeV proton-proton collision data collected with the ATLAS detector. The events are identified within a large hadron-collision background, owing to an optimised analysis strategy as well as the excellent performance of the LHC and the ATLAS detector. The measurement of the cross-section for electroweak boson scattering producing a pair of Z bosons is consistent with Standard Model predictions. This result is a milestone in studies of electroweak VV j j production at the LHC, and an important contribution to understanding the nature of electroweak symmetry breaking in the Standard Model. [13] CMS Collaboration, Measurement of vector boson scattering and constraints on anomalous quartic couplings from events with four leptons and two jets in proton-proton collisions at √ s =.
In addition, individual groups and members have received support from  BCKDF ,  CANARIE ,  Compute Canada  and  CRC ,  Canada ;  ERC ,  ERDF ,  Horizon 2020 ,  Marie Skłodowska-Curie Actions  and  COST ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel; CERCA  Programme Generalitat de Catalunya and PROMETEO Programme Generalitat Valenciana , Spain; Göran Gustafssons Stiftelse, Sweden; The  Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA) , the Tier-2 facilities worldwide and large non-WLCG resource providers.
Trial registration number NCT02402855 ⇒ This is the first study to assess the perception of conditional cash transfer (CCT) among women (of different backgrounds and pregnancy follow-up) and among healthcare professionals (HPs) who participated in a prenatal CCT trial implemented in France. ⇒ This study shows for the first time that the use of CCT is not perceived, by those for whom they are intended, as a stigmatising or infantilising approach. ⇒ Many women either did not respond to our request or refused to answer our questions, which may limit the representativeness of our data. ⇒ Women who did not adhere to prenatal follow-up are under-represented in our study and our findings may not adequately describe their perspective. ⇒ It was even more difficult to interview HPs, notably those who refused to evaluate the CCT programme, and although their opposition to the principle of CCT was expressed, the strength of this opposition may have been underestimated.
32 In our case, the ethics committee expressed concern that CCT would stigmatise women and disrupt the relationship of trust between pregnant women and their HP by putting economic interests before health interests. To address these legitimate ethical concerns, we conducted a qualitative survey during the first year of recruitment in the NAITRE Study. 32 Results were to be presented to the independent Data and Safety Monitoring Committee, so that the study could be stopped if the qualitative assessment showed that women felt degraded or stigmatised. Considering that the care relationship is a two-way street, the negative perception of a CCT programme by HPs could also have a paradoxical negative effect on the quality of antenatal care. We thus also included HPs in our qualitative survey. Results of the interviews with women and HPs are presented here.
We did not survey woman whose babies died or had been diagnosed with a severe medical condition at birth because we thought these women might not be prepared to discuss or to accurately recall their experiences during pregnancy. In the first wave (figure  1 ), we recruited from three including centres to broaden the representation of underserved women included in the NAITRE Trial (rural/urban, former industrial area, or area with high proportion of immigrants). In the second wave of recruitment (figure  2 ) we then asked our data manager to select women from any of the including centres who met specific criteria (primiparous, non-adherent) to balance our sample. We sought the perspectives of women with diverse backgrounds that might influence their perception of medical follow-up. We contacted women by phone to inform them about the qualitative study and to invite them to participate.
Data collection Two hospital-based health sociology researchers (AG-M, a female PhD and NM-B, a male PhD) collected the data and facilitated the interviews conducted between May 2017 and July 2019. Both sociologists are clinical researchers trained to conduct qualitative interviews. They used either the face-to-face guide for interviewing participating women or for interviewing HPS. We took a grounded theory approach in writing our interview guides. They were designed to help interviewers lead subjects through a series of topics designed to elicit data we would analyse to meet our study objectives. The researchers selected the topics and the clinicians validated them. We tested the guides and tuned them in preliminary interviews to ensure that interviewees understood the open-ended questions and that they elicited useful information.
Acknowledgements This work received support from the grants from the Q-life  ANR-17-CONV-0005 , Cell(n)Scale  ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 , the  French National Research Agency   ANR-18-0013-02. We thank the  Cell and Tissue Imaging core facility (PICT IBiSA) ,  Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10 -.
Supervised machine learning typically requires clean targets from which to learn. However, such targets do not exist in this case. Therefore, a training dataset is generated by substituting clean targets with naturally smooth climate model data and generative machine learning networks are employed to replicate geodetic noise, providing noisy input and clean target pairs. Prior knowledge of the geodetic noise is exploited to develop a more realistic training dataset. A convolutional denoising autoencoder (CDAE) is then trained on these pairs. The trained CDAE model is then applied to unseen real geodetic ocean currents. It is demonstrated that our method outperforms conventional isotropic filtering in a case study of four key regions: the Gulf Stream, the Kuroshio Current, the Agulhas Current and the Brazil-Malvinas Confluence Zone.
Generating Training Pairs In this paper we implement a blind image denoising method using deep learning, for which we require training pairs. In the case of image denoising, training pairs refers to the pairings of noisy and corresponding clean example images, from which the CDAE can learn a denoising transformation. In our case, the true noise-free field (or ground-truth as it is referred to in the machine learning literature) does not exist. A commonly used strategy in computer vision to overcome a lack of training pairs, is to apply synthesised noise to a dataset of analogous, but naturally smooth targets  (Chen et al. , 2018). By learning the mapping between synthesised noisy inputs and noise-free targets, the model is able to learn a denoising transformation by which similar types of noise can be removed from new images.
Clean Target Data In order to generate training pairs for a case where a noise-free field does not exist, we first require a dataset of analogous, naturally noise-free images possessing characteristics similar to those we wish to denoise to which we can add synthetic noise. For this purpose, we compute surface geostrophic current velocity maps from a set of ocean model MDTs, as in Figure  1 , to be used as noise-free training targets. Ocean model data is a suitable choice as it contains the types of oceanographic features we wish to preserve during noise-removal while being free from the noise that contaminates geodetic MDTs. The ocean components of several global climate models from the Coupled Model Intercomparison Project (CMIP)  1  provide the target data for this study. Utilising the CMIP data provides a large dataset of naturally smooth MDTs, containing the same types of oceanographic features we wish to retrieve from the geodetic data through denoising.
Thus, there is a clear motivation to develop a realistic noise model which achieves a good approximation of real geodetic noise. Since noise can be considered to be an image, we employ a deep generative convolutional network to create a realistic noise model which creates images emulating the type of noise present in the geodetic current maps, i. e. the type of noise we wish to remove. (To the best knowledge of the authors, this is the first time deep learning has been used to replicate the geodetic noise present in geodetically-determined geostrophic surface currents. ) Generative networks aim to learn the 'true' underlying statistical distribution of a given dataset in order to generate new synthetic data that could plausibly have been drawn from said dataset. Such networks use unsupervised learning, and thus do not require a dataset of labeled targets.
GANs tend to learn a variety of non-linear patterns well and thus have been shown to reproduce highly realistic images. However, the method of training, i. e. reaching an equilibrium rather than an optimum, can be unstable  (El-Kaddoury et al. , 2019). VAEs extend the autoencoder  (Vincent et al. , 2010)  to enable generation of new data by introducing regularity to the distribution over the latent space from which new data samples can be drawn  (Cai L et al. , 2019). This regularisation can prevent the generation of samples that clearly lie outside the target distribution. However, the smooth assumption of the latent space may also restrict the VAE network from sufficiently learning the distribution of some target datasets. The WAE, a modified VAE whose encoded distribution is forced to form a continuous mixture matching the prior distribution, rather than matching a single sample, is included in this investigation as it has been shown to produce better quality images compared to the standard VAE in some cases  (Tolstikhin et al.
, 2019). Said geoids are derived from a combination of satellite gravity field missions: GOCE  (Drinkwater et al. , 2003) , GRACE  (Tapley et al. , 2004) , surface data and altimetry data. Each geoid is expanded up to a common d/o 𝐿=280 before MDT calculation, to ensure noise patterns (which change with varying 𝐿) remain consistent across the training dataset. We utilise a recent global high resolution MSS product published by the Danish National Space Institute (DTU), termed the DTU18 MSS  (Andersen et al. , 2018) , for each MDT calculation. The DTU18 MSS is calculated from 20 years of altimetry data, over the period 1993-2012, with a spatial resolution of 1 arc minute. The spectrally truncated MSS, which is transformed from the gridded product to a set of spherical harmonic coefficients, is expanded up to the same degree/order 𝐿 as the geoid.
The data column summarises the datasets used in the development of each model, where A is for altimetry, S is for satellite and is G for ground data (e. g. terrestrial, shipborne and airborne measurements). The model in bold text is used to generate the DTU18_EIGEN-6C4 MDT which is filtered in final analysis of Section 4. 2. significant portion of the geodetic noise patterns, but small enough to prevent capturing significant long-range oceanographic features. This ensures that, after randomising the order of the tiles, the longrange oceanographic features are obfuscated, making the generative network more likely to learn the consistent distribution of the geodetic noise. Through visual investigation it was found that a tile size of 32 × 32 pixels (8 • × 8 • ) was the largest suitable size for training across all generative networks, and hence is used for all following experiments in this section; at larger sizes, higher frequency noise patterns were not reproduced and generative networks learned to focus on the more basic filamentary structures of the currents, which is undesirable.
, 2012). Following the best training configuration from the original WAE paper  (Tolstikhin et al. , 2017) , the MMD-based penalty is computed with the inverse multi-quadratics kernel (IMQ). DCGAN -The generator of the DCGAN consists of two 3x3 convolutional blocks, with 128 and 64 output channels, each followed by a ReLU activation function. The hyperbolic tangent function (tanh) is used as the activation function for the final output layer of the generator. The discriminator consists of four 3x3 convolutional blocks, with 16, 32, 64 and 128 output channels, followed by a fully connected layer which uses the Sigmoid activation function to generate a binary prediction. Binary Cross Entropy (BCE) is used for the adversarial loss function. Training Process -All generative networks are trained over 300 epochs (an epoch involves training the network over the whole training dataset exactly once) using the Adam optimiser (Kingma and Ba , 2014) on a dataset of 11,058 tiles from a set of six geodetic geostrophic current maps, with each global   3.
This is likely due to the inherent nature of VAEs, which tend to prioritise general shape rather than finer structural features  (Zhao et al. , 2017) , and the lack of constraint on the learned latent space representation. We compute the Fast Fourier Transform (FFT) from a batch of 50 randomly chosen tiles from the training dataset of real geodetic data and from each synthesised noise model. Figure  5  shows the shifted log magnitude of the FFT spectrum for each batch, in which lower frequencies are represented at the centre, and Figure  6  shows the corresponding difference in magnitude spectra between the training data and each synthesised noise model. The residual difference of the VAE differs by the greatest margin, managing to reproduce a similar distribution at low frequencies, but failing quite severely at the mid range.
To mitigate against these problems, we use an image quilting  (Efros and Freeman , 2001)  technique to stitch a random sampling of tiles together. For each stitching, a random subset of tiles is considered. The tile with the best neighbouring agreement (Figure  7b ) along the boundaries is selected for stitching. Finally, the minimal cost path is computed using the Dijkstra graph algorithm  (Dijkstra , 1959)  to find the optimal  seam boundary between the two tiles (Figure  7c ). We are, thus, able to generate a large dataset of 'noise quilts' of any size built from smaller synthesised noise tiles which can then be superposed onto naturally smooth numerical model target data (discussed in the following section). We generate noise quilts to match the size of training samples: 128 × 128 pixels (32 • × 32 • ), of which approximately 100,000 are generated.