dataesr/scientific-paragraphs-categorization huggingface dataset:
A: E gene assay, evaluated with SARS-CoV genomic RNA. B: RdRp gene assay evaluated with SARS-CoV genomic RNA. C: E-gene assay, evaluated with 2019-nCoV-specific in vitro-transcribed RNA standard. D: RdRp gene assay evaluated with 2019-nCoV-specific in vitrotranscribed RNA standard. The x-axis shows input RNA copies per reaction. The y-axis shows positive results in all parallel reactions performed, squares are experimental data points resulting from replicate testing of given concentrations (x-axis) in parallels assays (eight replicate reactions per point). Technical limits of detection are given in the panels headings. The inner line is a probit curve (dose-response rule). The outer dotted lines are 95% CI. signals, all assays were tested 120 times in parallel with water and no other nucleic acid except the provided oligonucleotides.
In addition to information on reagents, oligonucleotides and positive controls, laboratories working under quality control programmes need to rely on documentation of technical qualification of the assay formulation as well as data from external clinical evaluation tests. The provision of control RNA templates has been effectively implemented by the EVAg project that provides virus-related reagents from academic research collections  [18]. SARS-CoV RNA was retrievable from EVAg before the present outbreak; specific products such as RNA transcripts for the here-described assays were first retrievable from the EVAg online catalogue on 14 January 2020 (https://www. european-virus-archive. com). Technical qualification data based on cell culture materials and synthetic constructs, as well as results from exclusivity testing on 75 clinical samples, were included in the first version of the diagnostic protocol provided to the WHO on 13 January 2020.
Acknowledgements This work was funded by  European Union DG Research through projects Prepare  ( GA602525 ), Compare ( GA643476 ), and  EVAg  ( GA653316 ); by  European Union DG SANCO through EVD-LabNet , as well as by the  German Ministry of Research through projects RAPID  ( 01KI1723A ) and  DZIF  ( 301-4-7-01. 703 ). We gratefully acknowledge the authors, the originating and submitting laboratories for their sequence and metadata shared through GISAID, on which this research is based. All authors of data may be contacted directly via www. gisaid. org: National Institute for Viral Disease Control and Prevention, China CDC  ( Wenjie Tan ,  Xiang Zhao ,  Wenling Wang ,  Xuejun Ma ,  Yongzhong Jiang ,  Roujian Lu ,  Ji Wang ,  Weimin Zhou ,  Peihua Niu ,  Peipei Liu , Faxian Zhan ,  Weifeng Shi ,  Baoying Huang ,  Jun Liu ,  Li Zhao ,  Yao Meng ,  Xiaozhou He ,  Fei Ye ,  Na Zhu ,  Yang Li ,  Jing Chen ,  Wenbo Xu ,  George F.
[10]by the MERS-CoV upE assay as published before[10]. Additional samples were selected from biobanks at the Rijksinstituut voor Volksgezondheid en Milieu (RIVM), Bilthoven, at Erasmus University Medical Center, Rotterdam, at Public Health England (PHE), London, and at the University of Hong Kong. Samples from all collections comprised sputum as well as nose and throat swabs with or without viral transport medium. Cell culture supernatants containing typed coronavi-ruses and other respiratory viruses were provided by Charité and University of Hong Kong research labo-ratories. Respiratory samples were obtained during 2019 from patients hospitalised at Charité medical centre and tested by the NxTAG respiratory pathogen panel (Luminex, S´Hertogenbosch, The Netherlands) or in cases of MERS-Faecal samples containing bat-derived SARS-related CoV samples (identified by GenBank accession numbers) were tested: KC633203, Betacoronavirus BtCoV/Rhi_eur/BB98-98/BGR/2008; KC633204, Betacoronavirus BtCoV/Rhi_eur/BB98-92/BGR/2008; KC633201, Betacoronavirus BtCoV/Rhi_bla/BB98-22/ BGR/2008; GU190221 Betacoronavirus Bat coronavi-rus BR98-19/BGR/2008; GU190222 Betacoronavirus Bat coronavirus BM98-01/BGR/2008; GU190223, Betacoronavirus Bat coronavirus BM98-13/BGR/2008.
57 The quality of datasets also needs to be thoroughly validated to avoid possible biases before the models developed from them can be integrated in clinical decision-making. 58 o address this issue, an increasing number of institutions are creating initiatives for data-sharing platforms, aiming at reusing existing   datasets and verifying published research works. 59 Governments, regulatory agencies, and philanthropic funders are promoting the open science culture, enforcing publishing patient-level data by means of compliance to product launching, funding application, and journal publishing. 60 nother approach to improve the generality of data is the generation of synthetic cases of a representative wider population. The core idea is to expand the average mechanistic model to obtain populations of models, all of them parameterized within the range of physiological variability obtained by experimental protocols.
These solutions have thus met regulatory approval, where they are referred to as 'software as a medical device', and where guidelines from the International Medical Device Regulators Forum are accepted by the EU and the USA. A digital twin will follow the life journey of each person and harness both data collected by wearable sensors and lifestyle information that patients may register, shifting the clinical approach towards preventive healthcare. A notable challenge is the integration of these data with healthcare organizations, where security and confidentiality of the sensitive information remain paramount. The currently still fragmented and incipient concept of the digital twin will be gradually crystallized and adopted during the next 5-10 years. The holistic integration of a Digital Twin is the aspiration that will be reached through two complementary and synergetic pathways: the first is the refinement of key decision points in the management of cardiac disease, driven by personalized mechanistic models that are informed by key pieces of patient's data; and the second is the disease-centred optimization of the patient's lifetime journey through the healthcare system, driven by statistical models being informed by the electronic health record of a large population.
Organizational and societal challenges ahead Access to data is the main challenge in both the development and the clinical translation of the digital twin, caused by infrastructural, regulatory, and societal reasons. Information systems and electronic health records are fragmented, highly heterogeneous and difficult to interoperate. Information is often contained in unstructured format, and its extraction requires either manual work or further research efforts of automation through natural language processing technologies. 79 imulations may also require specialized skills and supercomputers. Consent and confidentiality are key ingredients to address the societal concerns when handling the personal data needed to develop and validate digital twin technologies. The EU General Data Protection Regulation (GDPR) has imposed new legal requirements, such as the right to withdraw consent and the right to be forgotten, causing controversy about the cost and feasibility of its enforcement.
Funding This work was supported by the  EU's Horizon 2020 Marie Sklodowska-Curie ITN  Projects (g. a. 764738 and  766082 ), the  EU 's  Horizon 2020 research and innovation programme  (g. a. 675451 and  823712 ), the  Wellcome/EPSRC Centre for Medical Engineering  ( WT 203148/Z/16/Z ), the  National Research Agency (ANR)  (g. a. ANR-10-IAHU-04 ), the  NC3RS  ( NC/P001076/1 ) and the  British Heart Foundation  ( RE/13/2/ 30182 ,  RE/13/1/30181 ,  TG/17/3/33406 ,  PG/16/75/32383 ,  FS/17/22/ 32644 ,  CH/16/3/21406 ,  RG/16/14/32397 ). E. Pueyo holds an  ERC Starting Grant  (g. a. 638284). B. Rodriguez  and  P. Lamata  hold  Wellcome Trust Senior Research Fellowships  ( 214290/Z/18/Z ,  209450/Z/17/Z ).
With this dataset we can study the isotopic fractionation of Si around a Sunlike protostar, and better constrain the origin of the SiO high velocity component. Furthermore, we complement the SOLIS data with previously obtained single-dish observations of SiO emission towards L1157-B1 from J=2-1 to 12-11. The paper is structured as follows: the observations carried out with NOEMA, the IRAM 30m telescope and Herschel-HIFI are presented in Section 2, the results of the single-dish and interferometric observations are presented in Section 3, with a focus on the SiO isotopic fractionation in L1157-B1 and the kinematics. The non-LTE analysis of the IRAM 30m spectra are summarised in Section 4. In Section 5 we discuss the isotopic fractionation of SiO in the framework of the Galactic chemical evolution, as well as the signature of the jet coming from the protostar L1157-mm.
Single-dish spectra We detected 6 lines of 28 SiO (up to J = 12-11), 5 lines of 29 SiO (up to J = 7-6), and 4 lines of 30 SiO (up to J = 6-5), thus collecting a large SiO dataset (spanning from 86 GHz to 521 GHz). The spectra are shown in Figure  2  and the line properties are summarised in Table  1. The SiO lines peak at blue-shifted velocity, ∼ -4 km s -1 with respect to systemic velocity (v sys = +2. 6 km s -1 ). Moreover, the SiO lines show a high-velocity wing extending up to -20 km s -1 likely originating in the strong shock occurring where the jet impacts the cavity walls (see Section 3. 4). The singledish spectra are used to characterise the physical properties of the emitting gas via the LVG analysis described in Section 4, and therefore the integrated intensities reported in Table  1  are divided into three velocity intervals: low velocity (LV) from -3 to +5 km s -1 , high velocity (HV) from -13 to -3 km s -1 , and extreme high velocity (EHV) from -23 to -13 km s -1.
Integrated intensity maps Figure  1  shows the image of the emission of the J=2-1 transitions of 28 SiO, 29 SiO and 30 SiO obtained with ∼7 km s -1 spectral resolution. The intensity has been integrated between -22. 95 and 6. 25 km s -1 , i. e. in 4 channels centred at 2. 6, -4. 7, -12, and -19. 3 km s -1 respectively, where emission from the rarer SiO isotopologues is detected (see Section 3. 3). The channel maps for all three isotopologues are presented in the Appendix A ). The map of http://www. iram. fr/IRAMFR/GILDAS/ 28 SiO (2-1) had already been reported in  Gueth et al. (1998)  using IRAM-PdBI with 4 antennas. The present NOEMA dataset has an improvement in the noise level of a factor of 20 (rms = 18. 3 mJy/beam vs. 0. 9 mJy/beam) and allows us to image higher (blue) velocities, up to -20 km s -1 , as described in more detail in Section 3.
3 km s -1 is 1. 14, while in the channels at -12. 0 km s -1 and -4. 7 km s -1 ranges from 1. 1 to 1. 5. Considering an uncertainty of ∼20% on our maps and of ∼5% on the Solar isotopic ratio  (Anders & Grevesse 1989) , these values are consistent with the Solar value of 1. 5, and do not show any variation across the source. Studying the isotopic fractionation in galactic sources (as L1157-B1), and comparing it with the Solar value (defined 4. 6 Gyr ago), gives information on the environment where the Sun and Solar system were formed, as well as the processes that formed them. Our results confirm what was reported in  Podio et al. (2017)  based on single-dish observations: there has not been a significant change in the Si isotopic ratios in the past 4. 6 billion years. Furthermore, with the present dataset, we can now exclude local changes in the isotopic ratios within the shocked region in L1157-B1.
70 km s -1 ) is associated with the B1a clump  (Benedettini et al. 2007 ). This position is indeed considered the youngest impact between the jet (never directly imaged Article number, page 5 of 16page. 16 outside the protostar;  Podio et al. 2016 ) and the B1 cavity (see also  Busquet et al. 2014 and Benedettini et al. 2012  on H 2 O, OI and high-J CO PACS Herschel data). Furthermore, the present dataset allows us to image, for the first time, very high velocity SiO emission outside B1, namely towards the northern B0 clump (see the channel map centred at -19. 72 km s -1 in Figure  5 ). The comparison between the spectra extracted in the three different intensity peaks clearly shows how the low velocity emission looses its importance in the B1a and B0 peaks. The green spectrum in Figure  5  shows a peak close to the systemic velocity of the source (+2.
(2003). We used the collisional coefficients of SiO with H 2 computed between 10 and 300 K for the first 20 levels by  Dayou & Balança (2006)  and provided by the BASECOL database  (Dubernet et al. 2013). We run a large grid of models (≥7000) covering a SiO column density N SiO from 1. 5×10 13 to 8×10 15 cm -2 , a H 2 density n H2 from 1×10 3 to 2×10 7 cm -3 , and a temperature T from 30 to 300 K. We then find the solution by simultaneously fitting the 28 SiO, 29 SiO and 30 SiO lines, leaving as free parameters N SiO , n H2 and T , while the emitting sizes and line widths are those measured. Note that we assumed the 28 Si/ 29 Si=19. 7 and 28 Si/ 30 Si=29. 8, as obtained by  Anders & Grevesse (1989)  and confirmed by our SOLIS observations for L1157-B1 (see Section 3. 3), and we added the 20% of calibration uncertainty in the observed intensity error bars.
Conclusions We present here the first isotopic ratio maps of SiO towards a shocked region. From these interferometric maps we can infer that the (2-1) transition of the main isotopologue is optically thick within the whole region imaged, also at high velocities (up to -20 km s -1 ). The isotopic ratio map of the two rarer isotopologues, 29 SiO and 30 SiO, shows a uniform value consistent with the Solar value of [ 29 Si/ 30 Si]= 1. 5. Coupling the high spatial resolution of the new SOLIS data with the large single-dish dataset, we were able to put stringent constraints on the physical properties of the gas in L1157-B1. Our observations can be reproduced by a more compact, denser and warmer gas component (n H2 = 6×10 5 cm -3 , T = 180 K and X Si = 5×10 -7 ), and a more extended colder and less dense gas component (n H2 = 2×10 5 cm -3 , T = 40 K and X Si = 0.
Acknowledgements. We thank the anonymous referee for valuable comments and suggestions. This work is based on observations carried out under project number  L15AA  with the IRAM NOEMA Interferometer [30m telescope]. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). We are very grateful to all the IRAM staff, whose dedication allowed us to carry out the  SOLIS  project. This work was supported by (i) the  PRIN-INAF 2016  " The Cradle of Life -GENESIS-SKA (General Conditions in Early Planetary Systems for the rise of life with SKA )", (ii) the program PRIN-MIUR 2015 STARS in the  CAOS -Simulation Tools for Astrochemical Reactivity and Spectroscopy in the Cyberinfrastructure for Astrochemical Organic Species  ( 2015F59J3R ,  MIUR Ministero dell'Istruzione, dell'Universitá della Ricerca e della Scuola Normale Superiore) , (iii) the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , for the Project "The  Dawn of Organic Chemistry" (DOC) , grant agreement No  741002 , and (iv) the European  Marie Skłodowska-Curie  actions under the European Union's  Horizon 2020 research and innovation programme , for the Project " Astro-Chemistry Origins" (ACO ), Grant No  811312.
Mutation of a miR-124-3p binding site within the SINV E1 coding region prevents miRNA-mediated regulation As miRNA-mediated viral regulation can happen by direct binding of the miRNA to the viral RNA  (34) , we bioinformatically searched for predicted miR-124-3p binding sites on SINV genome. Using the prediction algorithm ViTa  (35)  we found three putative binding sites at positions 2454-2475, 3828-3853 and 10904-10926 in the viral genome. We further studied the latter, localized within the virus ORF2 and more specifically in the glycoprotein E1 coding region (Figure  4A ), which possesses a canonical 6-nt seed-match (nucleotides 2 to 7) for miR-124-3p. Using available SINV sequences in the NCBI Virus  (36)  database we sought to determine the conservation of this region among SINV strains.
Small RNA library preparation, sequencing and analysis RNA was extracted from Huh7. 5. 1 cells and a small RNA library was prepared from 25 µg of total RNA as previously described in  (62, 65, 66). Single-end sequencing was performed at the IGBMC Microarray and Sequencing platform, Illkirch, France on an Illumina Genome Analyzer IIx machine with a read length of 36 bp. Deep sequencing data analysis was performed as in  (62)  with slight modifications. Briefly, FASTX-Toolkit (http://hannonlab. cshl. eu/fastx_toolkit) was first applied to remove instances of the 3' adaptor. Remaining reads between 18 and 32 nt in length were then mapped to the human genome (assembly version hg19 -UCSC repository), using Bowtie 1. 0. 0  (67). Up to 2 mismatches in total with no more than 1 mismatch in the first 15 nucleotides of each read were permitted.
ACKNOWLEDGMENTS We thank our collaborators  Carla Saleh  who kindly provided us with SINV-GFP and CHIKV-GFP vectors,  Diane Griffin  for the SINV antibodies,  Olivier Petitjean  for producing the hsapri-miR-124-3p pDONR221 vector and  Sophie Reibel-Foisset  ( Chronobiothron, UMS 3415 ) for the access to the BSL-3 facilities. The pLenti6. 2-3xFLAG-V5-ccdB was a kind gift from  Susan Lindquist  (Addgene, # 87072 ) (72). Sequencing was performed by the GenomEast platform, a member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ).
FUNDING This work was funded by the  European Research Council  ( ERC-CoG-647455 RegulRNA ) and was performed under the framework of the  LABEX :  ANR-10-LABX-0036_NETRNA , which benefits from a funding from the state managed by the  French National Research Agency  as part of the  Investments for the future program. This work has also received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Program  ( FP7/2007-2013)  under  REA  grant agreement n°  PCOFUND-GA-2013-609102 , through the  PRESTIGE program  coordinated by  Campus France  (to EG).
This concept is discussed in  Mothersill et al. (2017)  and has been applied to datasets from  CEZ and Fukushima (Omar-Nazir et al. 2018; Geras'kin et al. 2019; Hancock et al. 2019b Hancock et al. , 2020)). Therefore, a clear understanding of exposure history is of fundamental importance for interpreting field effects studies and subsequently for predicting how populations and ecosystems recover from radiation exposure.
Provocative Statement 3 "Mechanistic studies need to employ systems biology and be field-based to be useful (field v laboratory studies reveal discrepancies)" Summary Answer: Both field and laboratory data have a role, and laboratory studies can be used to substantiate field observations under controlled conditions. But laboratory data should be validated in the field, whenever possible, before being used to develop ecosystem models. The corollary of this is that suspected field effects can and should be confirmed in the laboratory The aim of this statement was to explore the suggestions that field-derived radiation sensitivities have been reported to be up to 10-fold greater than laboratory-based values, based on a comparison of between field and laboratory data (Garnier-Laplace et al. 2013).
There are multiple reasons for the difference including stress, other pollutants, predation and disease, which could compromise survival of organisms from contaminated environments. However, the reason for the question was to discuss whether ONLY field data should be considered when developing an ecosystem approach or whether laboratory data has merit. A secondary issue is whether or not the difficulties in obtaining good field data, present a major block to developing an ecosystem approach? The discussion centred mainly on the types of mechanistic analysis that might help in probing laboratory-field discrepancies, including the influence of neurological, immune and humoral responses and DNA damage. Although there is increasing evidence to suggest that ionising radiation, like chemicals, can induce a variety of biological responses e.
Novel concepts like multiscale modelling  (Powathil et al. 2015) , and bio-energetic model-based network analysis  (Yodzis and Innes 1992 ) can be very beneficial for exploring the population dynamics and other ecological interactions under multi-stress scenarios, including radiation stress. Beyond these modelling approaches, current developments in Artificial Intelligence (AI) and Machine Learning (ML) techniques offer novel opportunities to gain insights from large data sets (see  Jarry et al. (2003)  or  Feng et al. (2018)  for an example in radiation oncology) and the availability of complex and large datasets in ecological studies permits the application of a large range of data-driven modelling approaches. Machine learning in particular provides several main benefits: classifiers may be derived for data using supervised learning and requiring large training datasets, unsupervised learning can be used to find patterns in complex data and relationships between entities or measurements that had not been suspected.
Convolutional artificial neural networks can find feature representations for datasets to facilitate, for example, the application of similarity metrics or both supervised and unsupervised learning for classification. These approaches are in many ways related to established techniques of data-driven model building as they provide functions for describing datasets. Both can use data collected in the field as training material or to derive functions in modelling, such as estimation of the rate of change of occupation of state spaces ( position of a population in a multidimensional space such as that defined by population size, nutritional status and age distribution), but can also use synthetic data, discussed below. A recent review  (Christin et al. 2019)  succinctly captures the range of recent data types and applications of machine learning in ecological studies which range from identifying individual bird calls from environmental recordings  (Potamitis 2016)  to wild animal counts  (Norouzzadeh et al.
The LTQ-Orbitrap was operated in data-dependent acquisition mode with Xcalibur software. Survey scan MS spectra were acquired in the Orbitrap on the 300-2000 m/z range with the resolution set to a value of 60,000. The 20 most intense ion survey scans were selected for CID (collision-induced dissociation) fragmentation and the resulting fragments were analyzed in the linear trap (LTQ). Dynamic exclusion was used within 60 s to prevent repetitive selection of the same peptide.
Protein Identification and Quantification: Database Search and Data Analysis Acquired MS and MS/MS data as Raw MS files were converted to the mzdb format and were processed with the mzdbaccess library 1  to generate peaklists. Data were searched with Mascot (version 2. 6. 1 2  ) against a custom-made database containing Anas platyrhynchos & Cairina moschata entries from the UniProtKB database (Swiss-Prot/TrEmbl release 20190524, 35,568 entries). The search included methionine oxidation as a variable modification and carbamidomethylation of cysteine as a fixed modification. Specificity of digestion was set for cleavages after lysine or arginine for trypsin-digested samples and only one missed cleavage was allowed. The mass tolerance was set to 6 ppm for the precursor ion. It was set to 0. 8 Da for fragment ions in CID mode (detection in the ions trap).
Validation of identifications was performed through a false-discovery rate set to 1% at protein and peptide-sequence match level determined by target-decoy search using the in-house-developed software Prolinesoftware version 1. 6 3   (Bouyssié et al. , 2020). Raw MS signal extraction of identified peptides was performed with Proline across different samples. The mass spectrometry proteomics data has been submitted to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD019866.