dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement: All the data used in the manuscript will be made available on the data repository ORDAR (Research Data Repository of the 'Observatory Earth Environment of Lorraine'), including (i) discharge data, (ii) phage data (weekly and storm runoff events sampling), (iii) genome data (weekly and storm runoff events sampling), (iv) suspended solid concentration, turbidity, temperature data (weekly and storm runoff events sampling), (v) cross-section phage and suspended solid concentration data, (vi) in situ turbidity and temperature measurements, and (vii) core phage and genogroup data. Rainfall, air temperature and solar radiation data can be made available by Météo France upon request (https://publitheque. meteo. fr, accessed on 30 June 2021). Wastewater treatment plant data can be downloaded from the Water Information System Rhine-Meuse (https://rhin-meuse.
Author Contributions: Conceptualisation: N. M. -C. , L. O. and C. H. ; installation of field equipment: J. -F. I. ; sampling: N. M. -C. , L. O. , C. W. , C. M. , E. M. -P. and J. -F. I. ; FRNAPH protocols and FRNAPH data acquisition: L. O. , C. W. , C. M. and C. G. ; other laboratory work: N. M. -C. , E. M. -P. and C. H. ; data treatment: N. M. -C. and L. O. ; writing-original draft preparation: N. M. -C. , L. O. and C. H. ; writing-review and editing: N. M. -C. , L. O. , C. W. , C. M. , E. M. -P. , C. G. , J. -F. I. , H. -M. C. and C. H. ; funding acquisition and project administration: C. H. and E. M. -P. All authors have read and agreed to the published version of the manuscript. Funding: This research was mainly funded by the Luxembourg National Research Fund (FNR) and the French National Research Agency (ANR-14-CE01-0019) in the framework of the FNR INTER ANR research program (contract no.
Finally, we have assessed qualitatively and quantitatively this computational framework in view of clinical applications. In the context of computational models, a coupled UQ/SA analysis is crucial for the scientific research, especially in biology and medicine. The use of such mathematical framework may be employed in the process of product development or diseases understanding, decreasing the number of physical tests necessary and therefore the economic cost. The low fidelity model employed already provided useful information for analysis, however this study must be pursued with higher fidelity models such as System II and System III, see Figs. 1b and 1c respectively. The current methodology could thus be further improved, in particular by (i) using a multilevel multifidelity estimator, as for instance the one developed in Dakota toolkit, see  [32, 30] ; (ii) devising a reduced order modelling approach for the 3D elastic and poroelastic models, following the reduced basis framework  [31, 33] , developed in the open source software Feel++  [36, 37].
Identification of ACR-16-Like and ACR-19 Sequences from Trichuris muris Trichuris muris is, as with T. suis, used as a model for T. trichiura. Oxantel was previously shown to act on the nAChR made of ACR-16-like subunits from T. suis  [30]. To investigate the ACR-16-like nAChR from T. muris, we identified and cloned the full-length coding sequence of the T. muris acr-16-like subunit cDNA (Tmu-acr-16-like). The cloned T. muris acr-16-like sequence differed from the putative full-length Tmu-acr-16-like cDNA sequence available in WormBase (accession number WBGene00290200) by two synonymous nucleotide substitutions (CCG → CCA at codon 327 and AGT → AGC at codon 427), and was therefore deposited in GenBank under the accession number MZ322663. To explore all the members of the ACR-16 group of Trichuris spp, we performed searches in genomic databanks that led to the identification of the C.
elegans acr-19 gene orthologue in T. muris  [30]. Consequently, the full-length Tmu-acr-19 cDNA sequence was amplified by PCR and subsequently cloned and sequenced. Our clone was identical to the putative full-length Tmu-acr-19 cDNA sequence available in WormBase (accession number WBGene00291941). An amino-acid alignment of the Tmu-ACR-16-like and Tmu-ACR-19 sequences with their related counterparts from T. suis and T. trichiura is shown in Figure  1. The nematode ACR-16 nAChR subunit has been described as a homolog of the mammalian α7 nAChR subunit, for comparison, the amino-acid sequence of the human α7 subunit is therefore included in the alignment  [36] [37] [38]. The Tmu-ACR-16-like subunit is 464 amino acids long and shares 90 and 91% identities with the ACR-16-like subunits from T. suis and T.
Sequence Analysis and Alignment The Tsu-ACR-16-like (accession number MT386096) and the Tsu-ACR-19 (accession number KFD70086) were used as queries in database searches for amino-acid sequences of ACR-16-like and ACR-19 from T. muris using the protein-protein BLAST (BLASTp) service at the National Center for Biotechnology Information (NCBI) service  [59]. The Tsu-ACR-16-like and the Tsu-ACR-19 were used in an alignment with the identified sequences. The accession numbers of the identified sequences used for the alignment are: Trichuris muris: ACR-16-like MZ322663; ACR-19 WBGene00291941. Trichuris suis: ACR-16-like MT386096; ACR-19 KFD70086. Trichuris trichiura: ACR-16-like CDW52185; ACR-19 CDW53523. Homo sapiens: α7 subunit isoform 1 P36544. Signal peptide was predicted using the SignalP 4.
1 server  [60]  and the transmembrane regions were predicted using the TMHMM version 2 server  [61]. Deduced amino-acid sequences were aligned using the MUSCLE algorithm  [62]  and additionally processed in GeneDoc (IUBio). The accession numbers of the ACR-16like and ACR-16 sequences from Clade I, III and V nematodes used for the alignment to show the ligand binding loops in the ECDs are: Trichuris muris: MZ322663; Trichuris suis: MT386096; Trichuris trichiura: CDW52185; Ascaris suum: KP756901; Parascaris equorum: AZS27834. 1; Caenorhabditis elegans: CCD64102. 1; Haemonchus contortus: AZS27833. 1; Ancylostoma ceylanicum: MT163735; Ancylostoma caninum: QEM53385; Necator americanus: MT163736.
Statistical Analysis All acquired electrophysiological data were analyzed with Clampfit 10. 7 (Molecular Devices, Sunnyvale, CA, USA) and GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). All data analysis was performed on normalized current values. For the efficacy tests, the normalized drug-group means were statistically analyzed using a non-parametric Kruskal-Wallis Test with a Dunn's multiple comparison test, where p < 0. 05 was considered significant. The dose-response relationships were established by fitting the normalized currents as a function of drug concentration to a Hill equation using non-linear regression analysis with a variable slope model. The following equation was used: where I rel is the mean relative current, I max , is the relative current obtained at saturating agonist concentration, I min is the relative current obtained at agonist concentrations 0 µM, EC 50 is the concentration of agonist at which 50% of the maximal current response is obtained, [D] is the drug concentration and n H is the Hill coefficient.
Study population ELITA called for a COVID-19 study which was circulated on March 30, 2020 among 149 LT centres affiliated to ELTR) and located in 30 European countries. All centres that reported at least one case were provided with a database and instructions on how to record structured data. Data collection was managed by ELTR. One hundred and fourteen centres (76. 5%) responded, with 56 centres (38%) having observed COVID-19 cases in adult LT recipients between March 1st and May 19th, 2020. All patients with symptoms and having SARS-CoV-2 infection confirmed by a positive result on a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay of a specimen collected on a nasopharyngeal swab or on broncho alveolar lavage, were included in the study.
Data collection and definitions Demographic and clinical data, including clinical symptoms or signs at presentation, laboratory and radiologic results during COVID-19 management as well as administered antiviral therapies and anti-thrombotic prophylaxis were retrospectively collected. All laboratory tests and radiologic assessments were performed on the discretion of the treating physician discretion. Serum creatinine was converted to mg/dl for analysis. Information on baseline immunosuppression and on changes during Covid-19, namely reduction or discontinuation, were also obtained. Obesity was defined as a given BMI of >30 kg/m2. Liver injury during Covid-19 was defined as alanine amino-transferase (ALT) level > 30 IU/L for male and 19 IU/L for female in those patients with normal ALT levels at last outpatient visit.
All characteristic analyzed in univariate model were included in a stepwise selection process that identified the best multivariate model. The same process was repeated after excluding age from potential predictors. Given the exploratory nature of the study and the limited sample size, a 0. 1 significance level was established to retain predictors in the final multivariate models possibly favoring the tracing of borderline significant associations that could be the basis for further studies on wider samples. All statistical analyses were conducted using SAS version 9. 4 (The SAS institute, Cary, NC) and R version 4. 0. 0 (R Core Team, Vienna, Austria). The map was drawn using QGIS software version 3. 10 (QGIS Development Team).
Another potential driver of the TAC protective effect could be related to the immunosuppressive property of this CNI  (17). By inhibiting calcineurin and suppressing the early phase of T-cell activation, TAC reduces the production of many cytokines, notably pro inflammatory cytokines, as TNFα and IFNγ, and possibly mitigate the cytokine storm which characterizes Stage III COVID 19. Interestingly, this background recently prompted a group of Spanish investigators to test the effect of TAC in combination with steroids in the management of COVID 19 occurring in immunocompetent subjects (clinicaltrials. gov/ct2/show/NCT04341038). While waiting for studies on larger cohorts of transplants that would allow a more precise estimate of the protective effect of TAC, reducing or withdrawing the doses of TAC during Covid-19 should be discouraged, if not indicated for other clinical reasons.
qPCR was realized on CFX96 Touch Real-Time PCR Detection System (Biorad). Analysis of input RNA was performed in the same way on 100 ng of total RNA. In order to determine the amount of neosynthesized miRNAs relative to the input, we first calculated the enrichment of miRNA levels in the pull-down relative to the input after normalizing the data to Let-7. This ratio was then compared between the specific treatment (LNA @K1*) and the control treatment (LNA Ctrl), which was arbitrarily set to 1. Primary transcript pri-miR-K10/12 was analyzed after prior treatment with DNase I (Invitrogen) or TURBO™ DNase (Invitrogen). 5 l of purified or 1 g of input RNA was treated and subsequently reverse-transcribed (using 1 4 of reaction volume for non-reverse-transcribed control) with Superscript IV (ThermoFisher Scientific) according to the manufacturer protocol.
cDNA was diluted twice before using 1 l for quantitative PCR Maxima SYBR Green qPCR Master Mix (Thermo Scientific). The same approach than for mature miRNAs was used to determine the amount of neosynthesized pri-miRNAs except that the data were normalized to the CYC1 mRNA instead of Let-7. RT-qPCR analysis of the primary transcript in HEK293Grip cells transfected with LNA was performed according to the same procedure, however by diluting the cDNA 10 times. miRNA expression in HEK293FT-rKSHV cells transfected with LNAs without metabolic labeling was measured similarly to the 4sU-samples, except for the reverse transcription step, which was performed individually for each RT stem-loop primer (0. 5 l) and without diluting the resulting cDNA. 100 ng of total RNA was used for each RT reaction. Primer sequences are indicated in the Supplementary Table  S1.
ACKNOWLEDGEMENTS The authors would like to thank members of the  Pfeffer laboratory for discussion. They also would like to thank  Prof Narry Kim  for the kind gift of plasmids expressing flagtagged  Drosha  and  DGCR8. Author contributions: A. F. , S. P. , M. C. and M. V. conceived the project. M. C. , M. V. , S. P. and A. F. designed the work. M. C. , M. V. , R. R. and A. F. performed the experiments and analyzed the results. P. D. performed the kinetic analysis. E. E. and P. M. O. generated the HEK293FT-rKSHV cells. A. F. and S. P. coordinated the work and S. P. assured funding. M. V. , P. D. , S. P. and A. F. wrote the manuscript with input from the other authors. All authors reviewed and approved the final manuscript.
FUNDING This work was funded by the  European Research Council [ERC-CoG -647455 RegulRNA] and was performed in the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm ;  IdEx Unistra  [ ANR-10-IDEX-0002 ];  SFRI-STRAT ' US  project  [ANR 20-SFRI-0012 ];  EUR   IM-CBio [IMCBio   ANR-17-EURE-0023 ] under the framework of the  French Investments for the Future Program as well as from the previous Labex NetRNA  [ ANR-10-LABX-0036 ]. It also received funding from the  French Minister for Higher Education, Research and Innovation  (PhD contract to M. V. ). Funding for open access charge:  European Research Council [ERC-CoG -647455 RegulRNA].
3D fluorescence maps 3D fluorescence maps of raw and hot-water pretreated CWR were acquired using a JASCO FP8300 spectrofluorometer (Japan). The 3D maps were acquired with excitation wavelengths scanning from 300 to 550 nm, with a wavelength increment of 2 nm, emission wavelengths scanning from 350 to 600 nm, with wavelength increment of 0. 2 nm. The sensitivity was set to 500 V. The 3D maps were analysed with Jasco SpectraManager software.
03 ppm. Chemical shifts, half width, and the peak area of the samples were determined using a least-squares fitting method with Peakfit ® software (Systat Software Inc. , USA). According to the method of Larsson et al. [116] , cellulose crystallinity was calculated from deconvolution of cellulose C 4 peaks in the region of 80-91 ppm according to the following equation: This method uses three Lorentzian peaks corresponding to cellulose Cr(Iα) (88. 1 ppm), cellulose Cr(Iα + β) (86. 8 ppm) and cellulose Cr(Iβ) (86. 2 ppm). An additional Gaussian peak associated with the para-crystalline (PCr) contribution (87. 9 ppm) was also used. Three peaks were used in the amorphous C4 region, the two Gaussian peaks corresponding to the accessible surface cellulose C4 (82. 9 and 84. 1 ppm) and the other to the inaccessible surface C4 (83.
Acknowledgements The authors would like to thank some members of the  FARE  staff:  David Crônier  for his help in performing Klason lignin and thioacidolysis analyses,  François Gaudard  and  Anouck Habrant  for their help in performing in HPAEC-PAD analysis; and some members of the  BIA  staff:  Audrey Geairon ,  Sylviane Daniel  and  Luc Saulnier  for their help in performing hydroxycinnamic acids analysis.
sigappfr. org/). OpenCEMS, which is currently under development, offers several solutions to manage the energy data in connected environments (e. g. , smart buildings, cities, and factories). It can connect (1) objects (with variant/invariant location) providing static or dynamic resources, and (2) Web applications, which are established to be always available in OpenCEMS, and thus, published as static resources. The resources, described in Hydra and registered in a triplestore, are mainly used for: collecting heterogeneous on-site data, preprocessing collected data, and analyzing data. In our scenario, a building manager desires to predict his office temperature, to check if the installed temperature-controlled system works properly. His demand, which is expressed using the ATP (Air Temperature Prediction) function, can be sent via 2 requests types: (1) r nca , which refers to a non-context aware request, where the necessary data for his demand is collected using objects (devices) that are in his office, without considering his position, and (2) r ca , a context-aware request, in which the required data for his demand is provided by devices dependant from where he is standing in his office (e.
g. , from the 3 nearest devices located in his office with a 2m range). To satisfy the building manager's request, it is important to identify the required resources and select the appropriate ones to answer his demand. Nevertheless, as illustrated in Figure  1 , the following challenges emerge: 1. Discover the suitable resources that collect the required data. To have accurate data for the building manager demand, identifying the resources that are (i) located in his office, or (ii) the nearest to his standing point in his office, is important. For instance, object o m5 provides a resource offering the ATC (Air Temperature Collection) function, which is necessary to collect the necessary air temperature data, but it is not located in the office of the building manager. Therefore, the provided resource would be unuseful for the request r nca at instants t and t + n.
K-Compositions Approaches Authors in  [20]  present an automatic service composition approach to retrieve the top-k compositions. For this matter, the work applies the MapReduce concept, by mapping, into several tasks, the top-k compositions which can be run in parallel. In the solution, only one QoS metric, i. e. , Response time, is used, and similarities between I/O services are computed for services filtering. The work in  [21]  proposes an approach that allows one to compose the top-k DaaS (Data as a Service) services. The formed top-k service compositions are produced using a fuzzy score that is associated with every service and service composition, along with the given fuzzy user preferences, which are defined by fuzzy terms (e. g. , "cheap" for services price). Authors in  [22]  resolve the QoS-aware service composition problem through a relational database.
The proposed solution generates all possible service combinations and stores them in a relational database. Based on a given user request, SQL queries are composed to search in the database and return the top-K Web service compositions having the best QoS. Discussion: All of the aforementioned works generate multiple service compositions and take into consideration QoS aspects. Nevertheless, they do not deal with service dynamicity, and they do not allow the generation of various composition services types to satisfy different user demands. Based on user request and user request type, the two processes allow forming icompositions satisfying user needs. The user request type can hold 3 main different composition types to be formed:.
The set of the required functions used for collecting data along with their corresponding discovered resources are saved in the dataCollectionRes array. The funResMap(dataCollectionRes) function is then used to map every function to its relevant resource, and stores the outputs in the discovered array (line 10). For each resource in dataCollectionRes, DP gets its relevant resource description (which is expressed in Hydra vocabulary in this work) by calling the getResDesc(id) function (line 13), to retrieve their linked resources ids that are to be traversed next. These resources ids, that are stored in the resToExploreNext array (line 15), are used later by the discover(algoType, resToExploreNext) function, which is presented in Appendix A, to discover the resources providing the required other functions.
If no resource is identified providing a required data collection function in the necessary location, DP will return a message (lines  [16] [17]. When the first identified resources are stored into resToExploreNext array, they are used by the discover(algoType, resToExploreNext) function as the resources from which discover(algoType, resToExploreNext) will start the traversal of the resource graph, to discover the rest of the necessary resources to satisfy f (line 18). As stated in line 19, the identified resources that provide all of the required functions are stored in the output discovered array (line 19). If there are no overlapped resources for any required function in WM, one resource composition is returned. If not, the discovered resources will take part in the Selection Process (SP).
Evaluation and Discussion In this section, we first compare our QoR model to existing works that considered services attributes in their service selection solution, and then, we evaluate the performance of the resource discovery and selection in a simulated Web environment provided by OpenCEMS. OpenCEMS offers 2 types of operation: real and simulated. As the real environment is currently being developed with a limited number of resources, in this paper, we evaluated our work in the simulated functioning of OpenCEMS. The evaluation in the real environment will be presented in a dedicated work. However, to prove the feasibility of our approach in the real world, we integrated Hydra-based descriptions for several resources implemented in OpenCEMS used for data collection, data preprocessing, and data energy prediction.
Hydra-based descriptions of the implemented resources in the real OpenCEMS environment are available online: https://tinyurl. com/yarlsslp. During the integration, and for each provided function in a resource description, we added an image link to allow composition visualization in OpenCEMS. Based on the user requested function (as EDP which stands for Energy Demand Prediction) and given QoR constraints, OpenCEMS can provide the user with the required visual composition answering his request, as shown in Figure  7. Details about the basic requests sent to the OpenCEMS server with their relative responses are available in Figure  8. For the resource discovery evaluation, we tested the approach according to the "Location" dimension  [27]  evaluates the discovery based on Function and Resource dimensions, while varying (1) the number of the needed data collection resources in the required location, (2) the number of locations relative to r, i.
e. , a non-context aware request (r nca ), and (3) the number of required data collection functions provided by resources in the necessary location. As for the selection, the tests were conducted on different DRAG graphs, while varying (1) the number of candidate resources per function, and (2) the number of required functions. The experiments have been conducted by using a Linux Debian (64 bits) virtual machine, having 1 dedicated Intel® Core™ i7-46000 CPU @ 2. 10GHz 2. 70GHz processor, and 1 GB of RAM. In the results, we present, for each test, the response time (expressed in ms), computed according to 5 sequential executions on average.
In DOMAJEUR2, which is based on the finite element analysis code CAST3M  [16] , the quasi-static thermomechanical evolution of the fuel pin claddings, the spacer wires, and the hexcan is modeled by a finite element representation. The effects of void swelling, and irradiation and thermal creep under the action of the internal fission gas pressure are considered by means of experimentally determined laws. The thermal expansion of the materials and the contacts between fuel pins and between the pins and the hexcan are also considered in the analysis. To explore different modeling strategies, models with different geometrical representations (described in Section 3. 3) are available in DOMAJEUR2, ranging from a fully 3D model that employs volumetric finite elements to a beam-based model.