dataesr/scientific-paragraphs-categorization huggingface dataset:
Ostreid herpesvirus-1 (OsHV-1) RNAs are enzymatically modified by Ato-I conversions during the infection of Crassostrea gigas. The increase of ADAR1 expression and hyper-editing activity parallel to OsHV-1 RNAs suggests a functional connection between dsRNA editing and antiviral responses. We analyzed 87 RNA-seq data sets from immuno-primed, resistant, and susceptible oysters exposed to OsHV-1 to compare the ADAR hyper-editing levels on host and viral transcripts and trace hyper-editing on the oyster genes. Host RNAs were more hyper-edited than viral RNAs, despite the increased editing of viral RNAs in late infection phases. A set of genes, representing ;0. 5% of the oyster transcriptome and including several tripartite motif-containing sequences, were constantly hyper-edited. Conversely, we identified genes involved in antiviral response, miRNA maturation, and epigenetic regulation that were hyper-edited in specific conditions only.
Despite technical and biological bottlenecks that hamper the understanding of the bivalve "RNA editome," available tools and technologies can be adapted to bivalve mollusks. IMPORTANCE Ostreid herpesvirus-1 (OsHV-1) is a harmful pathogen of bivalve species, such as oysters. However, knowledge is lacking about host-virus interactions at the molecular level, hampering the possibility of a correct management of viral outbreaks and related massive mortalities. Notably, OsHV-1 transcripts are massively modified by host RNA editing enzyme during infection, resulting in multiple A-to-I variations along RNAs assuming double-strand conformations. The impact of these modifications on host transcripts is, however, not completely clear. Analyzing RNA-seq data of oysters infected with OsHV-1, we revealed that ;0.
Malacoherpesviruses represent an intriguing exception, since the abundant ADAR activity has probably contributed to viral genome evolution toward a reduction of editable targets (3). However, ADAR's functional roles in mollusk-malacoherpesvirus combinations remain unclear, and which of the two currently benefits from this editing activity has not been established. Following a dedicated analysis of RNA-seq data sets of immuno-primed, OsHV-1susceptible (S) and OsHV-1-resistant (R) oysters infected with OsHV-1, we quantified host and viral ADAR hyper-editing and evaluated the editing impact on the C. gigas transcriptome. Then, we discuss the current limitations of this analysis and we trace future research lines, aiming to unravel the "RNA editome" at the edge of host-virus interactions in the marine environment.
RESULTS We analyzed 87 C. gigas RNA-seq data sets referring to (i) a susceptible oyster family treated with the dsRNA analog poly(IÁC) and, therefore, immuno-primed (IP) or with filtered seawater (FSW), and infected with OsHV-1 10 days later (45 samples, EXP1  [7] ); (ii) the same susceptible (S) family and a resistant (R) oyster family maintained in cohabitation with infected oysters and monitored for 72 h (hpi; 42 samples, EXP2  [8] ) (Table  1 , Table  S1 ). C. gigas ADAR expression correlated with the amount of OsHV-1 RNAs. The percentage of reads mapping on the OsHV-1 genome (NCBI ID: KY242785. 1) revealed a successful oyster infection, herein defined as samples with at least 0. 1% of viral reads, in 6 EXP1 samples and 9 in EXP2 samples (Table  S1 ). In EXP1, the amount of OsHV-1 was 176 and 329 times higher in not immuno-primed (non-IP) oysters compared to the IP oysters, at 12 and 24 hpi, respectively (Figure  1a ), with the oysters sampled at 24 hpi showing 0.
S1 ). In EXP2, ADAR1 expression mirrored the bimodal distribution of OsHV-1 RNAs, with an expression maximum of 173 TPMs at 24 hpi in R oysters and 239 TPMs at 60 hpi in S oysters (Figure  1d  and Fig. S2 ). ADAR hyper-editing differentially impacted C. gigas depending on experimental conditions. We used the hyperediting tool (https://github. com/hagitpt/Hyper-editing) (14)  to identify C. gigas and OsHV-1 hyper-edited reads, namely, reads gathering multiple variations of the same type that, in the case of A-to-G or T-to-C variations, are likely produced by the enzymatic activity of ADAR1. The level of hyper-editing, measured as the percentage of hyper-edited reads over genomic-mapped reads, revealed differences between the two experiments. EXP1 samples displayed a higher hyper-editing level (0.
DISCUSSION By tracing ADAR hyper-editing along OsHV-1 infection in C. gigas, we demonstrated that in the early stage of infection host RNAs are more impacted than viral RNAs, with viral-directed editing increasing along the time course and suggesting that at later stages ADAR1 will mostly target viral dsRNAs. This increase is in agreement with data reported demonstrated for OsHV-1 infection in S. broughtonii  (9) , and likely mirrors the different abundances of viral and host dsRNAs during infection, with the latter increasing as a consequence of sense-antisense gene pair cotranscription  (9). Next, we showed that host hyper-editing levels varied along each experiment. Arguably, ADAR activity depends upon several factors: dsRNA availability, amount of functional protein, protein-protein interactions, and chemico-physical cell conditions.
In the context of a viral infection, the amount of dsRNA is viral-dependent rather than host-dependent and cellular chemical-physical conditions might change to promote inflammation and/ or apoptosis. The sustained expression of oyster ADAR1 up to 10 days after IP suggested a continuous protein production, as a possible counteraction of the instability of ADAR, as showed for human protein due to SUMOylation  (15). The absence of ADAR1 in proteomic data of OsHV-1 infected oysters (  16 ) possibly confirmed a rapid degradation. Although protein-protein interactions are mostly unknown in bivalves, it is possible that proteins other than ADAR1 can either interact with it or compete for the same dsRNA substrates, as shown for the enzymatic inactive human ADARB2  (13). Despite these variables, IP oysters maintained stable hyper-editing levels along OsHV-1 infection, whereas R oysters showed higher hyper-editing at the first infection peak compared to S oysters, and lower levels at the second peak.
Second, the different strategies adopted for the RNA library preparation likely biased the identification of ADAR edits, with the polyA depletion showing a lower efficiency than the ribosomal rRNA depletion in collecting key substrates such as ncRNAs  (20). Lastly, available data sets designed for purposes other than the "RNA editome" are suitable for a preliminary evaluation only, due to the lack of paired DNA-seq data, relatively limited number of samples, and a general heterogenicity. How future research can improve bivalve RNA editomics. Several relevant questions regarding computational and biological aspects remain to be answered and challenge future research toward a deeper understanding of the bivalve ADAR editome. How precise is the quantification of ADAR edits and what is the penetrance of these variations? Are other RNA editors active in oysters and can their activity explain the abundance of G-to-A variations (Fig.
S3 )? What is the fate of the edited transcripts and how do these regulate host/virus interactions? And how is ADAR1 interconnected within the oyster proteome? We highlight the necessity of dedicated projects, which could leverage on existing approaches and implement them to species having specific genome structure, plasticity, repeat contents, and distributions, as bivalves  (21). By producing individual paired DNA/RNA-seq data, useful to discriminate ADAR edits from genomic/transcriptomic variations, it will be possible to develop dedicated SNP databases, such as the human/mouse REDI portal  (22). The use of rRNA-depleted, stranded RNA libraries can allow the identification of strand-specific edits beyond coding genes and improve the identification of ADAR-editing versus other variation sources.
e. at Q = (-0. 387, 0. 512, 0) and (-0. 504, 0. 382, 0), respectively. Additional reciprocal lattice scans on the 1. 06 g sample were taken at intermediate temperatures to confirm the validity of the temperature dependent peak intensity measurement. Additional temperature dependent background data on the 55 mg sample were measured at Q = (-0. 504, 0. 347, 0) and (-0. 504, 0. 417, 0).
Acknowledgements We acknowledge enlightening discussions with  K. Lefmann  and his group, and  K. Beauvois  and  A. Piovano  for assistance at the  Institut Laue-Langevin, Grenoble. We thank the  Institut Laue-Langevin  for the allocated beamtime on IN22 ( CRG-2807 ) and  ThALES  ( 5-41-1154 ), and the  Paul Scherrer Institut  for the beamtime at  GPS. This work was additionally supported by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  through a  CRG-grant. The project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  884104  ( PSI-FELLOW-III-3i ). J. K. , Q. W. , and J. C. acknowledge support from the  Swiss National Science Foundation  ( 200021188564 ). J. K.
is further supported by the PhD fellowship from the  German Academic Scholarship Foundation. N. B. C. thanks the  Danish Agency for Science, Technology, and Innovation  for funding the instrument center  DanScatt  and acknowledges support from the  Q-MAT ESS Lighthouse initiative. M. M. is funded by the  Swedish Research Council (VR)  through a  neutron project grant  ( Dnr. 2021-06157 ), the  Swedish Foundation for Strategic Research (SSF)  within the  Swedish national graduate school in neutron scattering (SwedNess ), the  Carl Tryggers Foundation for Scientific Research  (CTS-18:272), as well as the  KTH Materials Platform.
The demographic model The demographic model is constructed to represent the transmission of the relevant part of the estate from its owner to one of his/her heirs. Various details of its construction are mentioned in Appendix 1. We use directly the most recent data available from Insee, the French National Institute of Statistics and Economic Studies, corresponding to the year 2019  [23]. Note in this regard that the data for the years 2020 and 2021 may have been affected by the COVID-19 pandemic. These data represent average values for the French population, and it is clear that for a particular investor, his/her own values concerning the age of children or life expectancy may differ significantly. Our results will therefore be average values, and may therefore differ from the result of a valuation that would be made for a particular case.
The High-ResMIP protocol, requiring all simulations to be performed with a low and high resolution model configuration, allows us to investigate the spatial resolution dependence of the ocean-atmosphere interaction. The impact of increased resolution in HighResMIP simulations on climate and variability has been documented in several studies, including  Moreno-Chamarro et al. (2021)  who discuss the impact on biases. We have focused here on the VMM and PAM mechanisms and compared the results with available observations. In the following Sect. 2 we describe the models' configurations, the observation/reanalysis datasets and the techniques used for this analysis. In Sect. 3 we present our results, followed by a discussion in Sect 4. Finally, a summary of the conclusions is given in Sect. 5.
Optimum interpolation (OI) is used to produce a spatially complete map. Wind stress divergence and curl are obtained from Advanced SCATterometer (ASCAT) on MetOp-A  (Figa-Saldaña et al. 2002). It is a product of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) provided through the Royal Netherlands Meteorological Institute (KNMI). We use daily values from the L3 REP product, sampled on a 0. 25 • grid. SLP is derived from the European Centre for Medium Range Weather Forecasts Reanalysis 5 atmospheric ReAnalysis (ERA5)  (Hersbach et al. 2020). Data processing for ERA5 is carried out by using ECMWF's Earth System.
Coupling coefficient Since the Laplacian operator acts as a high-pass filter, it is sensitive to the grid on which it is computed. We argue that the linear relationship between the Laplacians is scaledependent, and for this reason a fair comparison between the different resolutions is not possible. In order to demonstrate this we compute the -∇ 2 SST and the ∇ 2 SLP both from ERA5, with the same numerical scheme, first using every grid point (effective resolution of 0. 25 degrees, "HR") and subsequently using every fourth grid point (effective resolution of 1 degree, "LR"). The coupling coefficient for the HR is 0. 26 while for the LR is 0. 41 (Supplemental Fig. S11 ). This increase of the coupling coefficient, by computing the Laplacians over larger distances using the exact same dataset, indicates that comparing the coupling coefficients between the Laplacians across different model resolutions, is not simply a comparison between the realism of the models.
Availability of data and material (data transparency) All data used in this study are publicly available on line. The model output is available on the Earth System Grid Federation (ESGF) under the references: HadGEM3-GC3. 1  (Roberts 2017b (Roberts  , c, a, 2018)), ECMWF-IFS (Roberts et al. 2017a(Roberts et al. , 2018a (Roberts et al. , 2017b)) , EC-Earth3P (EC-Earth 2019, 2018), CNRM-CM6  (Voldoire 2019b, a) , MPI-ESM1-2 (von Storch et al. 2017a, b), CMCC-CM2 (Scoccimarro et al. 2017b, a). The observational/reanalysis data are available at: the Copernicus Climate Change Service Climate Data Store (CDS) (DOI:https://doi. org/10. 24381/ cds. 68d2bb30) for ERA5; the Copernicus Marine Environment Monitoring Service (https:// resou rces. marine. coper nicus. eu/? option= com_ csw& view= detai ls& produ ct_ id= WIND_ GLO_ WIND_ L3_ REP_ OBSER VATIO NS_ 012_ 005) for ASCAT satellite data; http:// clime xp.
V. ARCHITECTURES BASED ON FLUXONIUM AND TRANSMON In this section we provide some architectural considerations for multiqubit transmon-fluxonium chips based on either the CR gate or the CPHASE gate analyzed in the previous sections. For concreteness, we focus on a surface code architecture, where each qubit is directly coupled to at most four neighbors  [9, 10, 36]. In this case, either the transmons or the fluxonia play the role of the data or ancilla qubit in the surface code. For both the CR and CPHASE gate, we drive the fluxonium qubit and since up to four transmons couple to the same fluxonium qubit we need to examine how to avoid operation crosstalk. We then explore the expected yield for the architecture based on the CR gate, for which we expect frequency collisions to impact the fidelity of operations.
ACKNOWLEDGMENTS A. C. acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy -Cluster of Excellence Matter and Light for Quantum Computing  ( ML4Q )  EXC 2004/1 -390534769  and from the  German Federal Ministry of Education and Research  in the funding program "quantum technologies -from basic research to market" (Contract No. 13N15585 ). B. M. V. and B. M. T. are supported by  QuTech NWO  funding  2020-2024 -Part 1  " Fundamental Research " with Project No. 601. QT. 001-1. C. K. A. is supported by  QuTech NWO  funding  2020-2024 -Part 1-3. The numerical analysis was carried out using an in-house Python package for superconducting qubits available on github [79], that relies on the QuTiP package for the dynamical simulations [80].
SF-FLS and inflammation During RA, SF-FLS actively participate in synovial inflammation. It has been shown that RA SF-FLS release multiple inflammatory cytokines dominated by CCL2 and IL6 without exogenous stimulation  (19). This corroborates the findings of previous data showing that RA SF-FLS secrete IL6, IL8, IL1b and CCL2  (17, 34). In addition, RA FLS from the intimal lining have been considered primary sources of IL6, as shown by in situ hybridization and immunohistochemistry studies  (6). However, recent data showed that SF-FLS express higher levels of IL6 than tissue FLS and fibrocytes from patients with RA  (34). The release of these cytokines contributes to joint inflammation and cell recruitment into the inflamed synovium  (1, 6). The inflammatory profile of RA SF-FLS reflects the activation of numerous signaling systems, including the Wnt pathway  (62).
Explainability, causability, and the need for robustness The concepts of explainability and causability have been developed by Visvikis et al. , and it is clear that they are the foundations for trusting any AI application in clinical practice  [1]. However, we are also convinced that more is needed. Current AI models are mostly static, in that they have been trained at a certain point in time using samples corresponding to a particular population and in a setup that was appropriately validated when the model was built. This validation should have involved the use of multiple datasets originating from centres external to the one in which the model was developed, in order to test its robustness on datasets that are as heterogeneous as possible (for example in terms of acquisition and reconstruction protocols).
Instead, alternative approaches such as software challenges need to be implemented. In these challenges, a given dataset is made available for developers to evaluate the performance of their algorithms within a controlled environment. Numerous software challenges have been organised throughout the years, mainly within the field of image segmentation but also in other fields of interest and increasingly targeting clinical endpoints (https:// grandchall enge. org/ chall enges/). Software challenges should be based on a predetermined categorisation of potential applications, using different factors such as impact and potential utility and uptake in clinical practice. Challenges should address one or more of the following points: 1. Specify the need for data and associated requirements (volume, annotation, QC), defining training and validation datasets.
2. Clarify the dependency of data volumes necessary for a given task, which should also include standardisation and/or harmonisation for the exploitation of multicentre datasets. 3. Define algorithm-related aspects such as the number of parameters, their optimisation, robustness, levels of uncertainty, and transferability to datasets from different instruments or body locations. 4. Deal with model interpretability (white/grey box concept as opposed to black box), and thus with acceptability to professionals (medical physicists, physicians) and the public (patients and families). In order to accelerate clinical adoption, validation of the outcomes/results of individual trials/studies is required, and this is where multicentre and multigroup cooperation is of the utmost importance. Such cooperations within the field of NM can and should be triggered by challenges that link methodological and clinical objectives.
None contacted the psychologist after participating in the study. In order to guarantee confidentiality, data was collected anonymously and files were stored under lock outside the institution. All the participants gave their informed consent to be enrolled in the study. The study was conducted in accordance with the terms of the institution's ethics and adhered to the tenets of the Declaration of Helsinki. 3. 1. Childhood Trauma Questionnaire (CTQ, Bernstein et al. , 2003 , French version : Paquette, Laporte, Bigras, & Zoccolillo, 2004)  The CTQ is a 28-item self-report inventory that provides information on the level of abuse experienced during childhood and adolescence assessed according to five dimensions: emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse along a 5-point scale from 1 (never true) to 5 (very often true).
6. The same situation is observed when introducing, along with w and γ, further extensions to ΛCDM like massive neutrinos, although the latter allows to reduce the tension to 2σ, even when combining with BAO datasets. We also explore other common extensions by comparing the case when allowing a dynamical w following a CPL parametrisation in addition to a constant growth index, against that when the latter is expanded through a second parameter γ 1 along with a constant w. In the former, we reach the same conclusions as with the case of a constant w and γ, where the discrepancy was alleviated only if we do not constrain w by BAO, while in the latter case, we observe that introducing γ 1 drives (1b) towards lower values that would rather increase the discrepancy on σ 8. We conclude that none of these common extensions to ΛCDM is able to fix the discrepancy and a misdetermination of the calibration factor is the most preferred explanation.
2014; Giusarma et al. 2014; Dvorkin et al. 2014; Di Valentino et al. 2016 ). In the companion papers  Sakr et al. (2018, SK18 hereafter)  and  Ilić et al. (2019, IL19 hereafter) , we also examined, with Monte-Carlo Markov chains (MCMC) techniques, the role of neutrinos in fixing this discrepancy using an X-ray cluster sample or SZ detected cluster sample, in combination with CMB datasets and additional secondary probes like BAO and Ly-α and found that massive neutrinos are not able of alleviating the tension due to the fact that the introduction of massive neutrinos modifies the confidence contours on σ 8 , from cluster counts and those from the CMB data, in the same direction of the σ 8 -Ω m degeneracy thus not reducing the gap on the value of σ 8 obtained from the two probes. We also explored in addition another possible origin for this discrepancy through a modification of general relativity.
Here we also investigate whether the variation of dark energy parameter, alone or combined with neutrinos, could significantly reduce the cluster counts discrepancy and alleviate the tension. Furthermore, we test how the σ 8 -Ω m degeneracy behaves when we allow the growth index to vary along with the Dark energy equation of state parameter. We perform this study, using a SZ cluster sample in combination with CMB Planck C as our main datasets in a Markov chain Monte Carlo (MCMC) parameter inference analysis. And, in line with our previous investigations, we leave the cosmological parameters of the standard model, the nuisance parameters, and the calibration parameter free to vary, in a model independent approach, letting the data fix the last parameter. To alleviate possible degeneracies, we also consider an additional constraining probe, the BAO feature angular distance, more sensitive to certain expansion parameters, like the Hubble constant, or to certain redshift ranges than the others.
The study is divided in the following way. In Sect. 2 we review briefly the theoretical determination of the cluster counts observable within the extensions we consider, i. e. , the dark energy density equation of state parameter, the growth index and the neutrino mass. In Sect. 3 we describe the datasets we used and how we implemented them in the MCMC analyze. We then present the results obtained in Sect. 4 and conclude in Sect.
Datasets and methods As one of our primary probes, we use the CMB C of the temperature, polarisation and their cross correlations, from the publicly available datasets of the Planck mission Aghanim et al. (2020) and their publicly provided likelihood. We combine them with the SZ detected clusters sample PSZ2 containing a total of 439 (Planck Collaboration et al. 2016a) spanning the redshift range from z∼0. 0 to z∼1. 3. We also further constrain dark energy EoS using the scale of the baryonic acoustic oscillation (BAO) of the galaxy distribution measurements  (Beutler et al. 2011; Ross et al. 2015; Alam et al. 2017). The inference of the constraints on the parameters is done using standard MCMC techniques where observations are confronted with theoretical angular power spectra calculated with the Boltzmann solver CLASS  Lesgourgues (2011).