dataesr/scientific-paragraphs-categorization huggingface dataset:
The statistics of these changes made for the S&MPO database are summarized in Figure  12 , as given in  [21]. Corrected (ν corr ) and uncorrected (ν uncorr ) line positions correspond to the SMPO2020 and SMPO2017 line lists, respectively. A particular case of a very highly excited state assigned as (1. 6. 3) was discussed in  [33] ; the largest value of the corresponding RMS deviation ~0. 075 cm -1 is due to the issues of the applicability of the conventional EH model in this energy range  [32]. One of the ways to obtain more accurate values of line positions in databases is to replace the calculated transition frequencies with observed values (or with the difference in empirical energy levels obtained from the observed line positions) when the differences will be greater than some chosen value (for example 0.
001 cm -1 ). In a recent paper  [21] , this was done for the S&MPO database  [64]  and will be integrated in the two other databanks  [24, 223]. Note that these changes correspond to long and tedious work, since the same change must be applied to all transitions of the same upper energy level. This corresponds to three transitions in the A-type band and six in the B-type band. In addition, this change should also be made for hot bands, in particular, having the (010), (001), (100), and (020) states as lower ones. The statistics of these changes made for the S&MPO database are summarized in Figure  12 , as given in  [21]. Corrected (ν corr ) and uncorrected (ν un- corr ) line positions correspond to the SMPO2020 and SMPO2017 line lists, respectively.
1-0. 3%. The comparisons show excellent agreement between ab initio calculations [17,133] and new empirical line lists. Average deviations for strong and medium lines are within 0. 5% or 1%, corresponding to uncertainty estimates for both theoretical and experimental results. For 5 microns, a comparison was made between the ab initio predictions [17] and the experimental results of the LERMA  [20]  and GSMA  [21]  laboratories. The modeling of GSMA experiments with ab initio intensity corrections led to the final line list published in the S&MPO database  [18]  and partly included in the last release of the HITRAN database  [24]. The comparisons confirming the sub percent level of accuracy of the obtained intensities are given in [17,  18, 24]  and were presented at the AGU meeting in Vienne  [63].
Discussions In the past, there have been several reviews devoted to the analysis of high-resolution infrared spectra of ozone: Bacis and Flaud  [85] , Rinsland et al. [224] , Campargue et al. [140] , and Barbe et al. [93]. Each of them summarized the latest publications to date. Many corresponding results from various laboratories were incorporated in spectroscopic databanks, including dedicated line lists such as the atlas of ozone spectral transitions below 3000 cm -1  [87]  and the "Spectroscopy and Molecular Properties of Ozone" (S&MPO) information system  [18, 21, 64, 239]  as well as generic compilations GEISA  [224]  and HITRAN  [24]  for atmospheric applications. Some recent data are available via the VAMDC European web portal  [240]. This review also updates the results in this domain and provides a lot of additional information, including a brief history of the discovery of ozone; issues of the formation and destruction of atmospheric ozone; a decrease of its concentration in the stratosphere and an increase in the troposphere; a possible solution of these problems and their potential connection with the supersonic air fleet; a brief overview of absorption in the UV, visible, and IR ranges; and the history of IR spectral recordings in various wavelength ranges and some other issues.
In addition, they make it possible to achieve higher intensity accuracy than direct observation of weak bands, for which very large values of (pressure × path length) are used and which are difficult to measure with sufficient precision. Finally, these calculations are able to predict all the hot bands  [133] , which are not directly observable, since most of them are hidden by stronger cold bands. Recent validations of the ozone line lists carried out in the Jet Propulsion Laboratory, NASA by  Toon [158]  using atmospheric balloon, ground-based laboratory measurements simultaneously in 37 spectral infrared windows have indicated that band intensities corrected by ab initio calculation  [18]  and included in the last release of the S&MPO database were much more consistent from window to window than all other available lists in terms of variance-to-mean ratio scaling factors (VSF).
Acknowledgments: The GSMA laboratory of Reims University acknowledges support from the French-Russian program of collaboration SAMIA. The authors thank Alain Campargue, Samir Kassi, Didier Mondelain, and Semen Vasilchenko for the CRDS spectra recording. Analyses of these spectra have permitted an extension of information on band origins and rotational constants to the high-energy range as reported in various tables for comparisons with theoretical predictions. The authors also thank Sergei Tashkun, Roman Kochanov, and Viatcheslav Kokoouline for collaboration in computational methods; Yurii Babikov for the S&MPO software development; Bruno Grouiez for the help in drawing Figures  16  and 17 ; and Jean-Marie Flaud and Christof Janssen for valuable discussions. We thank Juan Cuesta (PI of the IASI + GOME2 multispectral satellite retrieval) from the LISA laboratory (UMR CNRS 7583/UPEC/UP) for providing us global maps of the ozone distribution at the troposphere and stratosphere, as well as the support from AERIS data centre, the CNES, and the project SURVEYPOLLUTION from TOSCA/CNES.
Statistical analysis All statistical analysis were performed with RSTUDIO (v. 1. 4; R Core Team 2017). We compared the survival rates of females after the LD 50 and during the reproductive period by using two global mixed effects Cox proportional hazard regression models, built with coxme package  (Therneau et al. 2003). We entered the females' treatment (SAP, LBP, NP, control) as fixed effect, and Hazard Ratios (HR) were estimated thanks to the instantaneous risks of death between NP or control and other females' treatments (SAP, LBP, and NP). Body weight differences of females before reproduction were tested with a linear mixed effects model built with lme4 and car package  (Fox and Weisberg 2011; Bates et al. 2014) , including the treatment as fixed effect. Concerning the first reproductive event, we tested the probability of producing the first clutch with one generalised linear mixed effects model with binary logistic regression (i.
For all models (i. e. , survival, weight, probability of producing the first and second clutches, time to produce these clutches, the number of offspring in each clutch, haemocyte parameters and β-galactosidase activity), we entered the experimental replicates as random factor. This factor allows to correct the non-independence of samples within the same replicate of treatment  (Harrison et al. 2018). Whether the treatment influenced the considered variable, we compared the pairs of means between each treatment by using Tukey adjustment (lsmeans packages; Lenth 2016). When interactions with treatment and weight were significant in our models, we performed the Pearson's correlation test for each treatment, to obtain the effect of weight in each treatment. The R script and the datasets used to perform our analysis are available on the open access repository Mendeley Data https://data.
Acknowledgements We would like to thank the reviewer and editor for their very constructive comments. We would also like to thank  Richard Cordaux  ( Team Manager ) for supporting our publication proposal,  Benjamin Macchi  (Teaching assistant) for his technical support and  Sebastien Thobie  for reviewing the English language. We sincerely thank  Julie Tee  for her careful proofreading of the manuscript and her corrections to the English language. This work was supported by the  Vivipary and Immune Priming grant  ( PEPS EXO-MOD CNRS ), State-Region Planning Contracts, the  European Regional Development Fund , the  French National Centre for Scientific Research , the  French Ministry of Higher Education and Research , and the  University of Poitiers.
I. INTRODUCTION The quantum computing field has been evolving at an increasing rate in the past few years and is currently gaining more traction. Several quantum chips, the underlying hardware that enable researchers and companies to run quantum algorithms, have been announced by different research teams. The error rates and number of qubits provided by these chips greatly improved, with quantum hardware that have up to 65 qubits in early 2021. Software has also seen a tremendous rise with the emergence of several quantum computing frameworks and languages such as Qiskit  [2] , Q#  [29] , PyQuil  [7] , Cirq  [6]  or myQLM  [28]  to name a few. These frameworks help in speeding-up the process of implementing a quantum algorithm by providing their own "standard library". Most of them also include specialised libraries whose purpose is to facilitate the development and testing of new quantum algorithms.
Currently, the only other way one has to optimise a given quantum implementation is "trial and error": try to locate a "hot spot" in the implementation, either by a tedious theoretical analysis or a manual counting of the routine calls, optimise the potential hot spot found and finally check if the optimisation performed improved the performances. This process has a severe drawback that makes it impractical on real-world implementations: the first step that consists in finding the hot spots is either imprecise or potentially very long, tedious and errorprone on large implementations. qprof aims at replacing this manual, tedious and errorprone step of analysis by automatically generating a report with all the useful information needed to find the hot spots of the given quantum program implementation.
The qprof tool has been strongly inspired by classical profilers such as gprof  [11, 17]  which try to solve the exact same issue but in classical (non-quantum) programming. The rest of the paper is organised as follows. In Section II we explain the internals of qprof and explain in details its architecture, the design choices made, and their impact on the tool efficiency, extensibility and usability. We then include code snippets and simple practical examples in Section III to illustrate the tool usage. The overall workflow of qprof is schematically explained in Figure  1. In this workflow, qprof can be seen as a black-box that takes a "quantum circuit" as input and returns a "profiler report". This black-box view should be enough for users that only want to use the qprof tool, but experienced users or plugins developers might need more details on the internals of qprof in order to understand how it works.
C. Framework support The goal of the qprof tool is to provide a unique profiling interface for quantum programs. As such, it should be able to support as much quantum computing programming frameworks as possible. Taking into account that several of the most successful quantum computing frameworks such as Qiskit, Cirq, PyQuil or myQLM are Python libraries, and in order to ease its integration with these already existing frameworks, qprof has naturally been designed as a Python library too. But being written in the same language as a given quantum computing framework does not grant a direct compatibility. In fact, all the previously cited frameworks use a different representation for quantum programs. Qiskit implements a QuantumCircuit class, Cirq comes with a Circuit class, PyQuil uses a Program structure and finally myQLM uses either QRoutine or AbstractGate instances.
Grover's algorithm oracle diffusion ccx X C n X H × 1 × 1 × n × 2 × ( n + 1 ) × 2 n ×1 × 2 n FIG. 5. Call-graph representation of one possible implementation of Grover's algorithm. Each node represents one routine rather than one call to the routine. Edges have been regrouped and labelled with the number of calls for readability purposes. In the internal representation used by qprof, edges are not regrouped and are ordered to account for the original quantum program subroutine call order. terface, Figure  3  shows a representation of one possible Grover's algorithm implementation. Even though this representation is valid according to the general definition of a call-graph, it contains a lot of redundant information that scrambles the useful data in visual noise. Because of this, most of the call graphs representations avoid the duplication of nodes, i.
E. Exporters Along with framework support, the plugin architecture of qprof is also used to implement exporters that will transform the abstract quantum program representation described in Section II D 2 to a more usable format. Just as the framework plugins, exporter plugins should implement a specific interface schematised in Figure  6. qprof natively implements two textual exporters: one that outputs a gprof-compatible format and another that returns a JSON-formatted string that directly represents a flat call-tree structure used internally by the qprof-compatible exporter.
III. CODE EXAMPLES AND PRACTICAL APPLICATIONS This section includes several examples of qprof usage on various quantum circuits ranging from a simple Toffoli gate decomposition in Section III A to more complex algorithm implementations such as Grover's algorithm in Section III B. All these benchmarks are performed on circuits generated using the qiskit framework. An example of benchmarking a quantum implementation of a 1dimensional wave equation solver written using the myqlm framework is finally provided in Section III C.
A. Benchmarking a simple program One of the most simple quantum program that can be benchmarked is the implementation of a Toffoli gate. Such a benchmark has the benefit of being simple enough to be studied by hand which means that we will be able to verify qprof results by re-computing them. The decomposition of a Toffoli gate as implemented in the qiskit framework is depicted in Figure  7. A com-plete example using qprof to profile the default Toffoli gate decomposition in qiskit is shown in Listing 1. from qiskit import QuantumCircuit from qprof import profile # Circuit construction circuit = QuantumCircuit(3, name="one_ccx_circuit") circuit. ccx(0, 1, 2) # Profiling gate_times = { "u1": 0, "u2": 10, "u3": 30, "u": 30, "cx": 300 } gprof_output = profile(circuit, gate_times, "gprof", include_native_gates=True) with open("out.
qprof", "w") as f: f. write(gprof_output) Listing 1: Python code needed to use qprof on the Toffoli gate implementation and save the profiler report in a gprof-compatible format in a file named out. qprof. The output of qprof, which is here in a gprof format with some slight adaptations as discussed in Section II E, can then be analysed. For the sake of readability and brevity, the full gprof-compatible profiler report will not be included verbatim in this paper and will rather be visualised using the gprof2dot tool that allows representing gprof reports as call-graphs. The call-graph obtained from the report generated in Listing 1 is depicted in Figure  8. From the call-graph depicted in Figure  8 , it is clear that the cost of a Toffoli gate comes from its 6 controlled-X gates, that account for more than 98% of the total execution time.
B. Grover's algorithm The Toffoli gate is a good example to start and understand the meaning of qprof's output but the end goal of qprof is to be able to profile large and complex quantum circuits. A good first candidate to show how qprof performs on a more complex circuit is Grover's algorithm. In this example we Grover's algorithm on four qubits to find the three quantum states that verify the following formula: (q 0 ∨ ¬q 1 ) ∧ (¬q 2 ∧ q 3 ). (1) The only three 4-qubit quantum states verifying Equation (1) are |0001 , |1001 and |1101 , q 0 being the leftmost qubit in the bra-ket notation. The code needed to generate the gprof-compatible output for Grover's algorithm with the oracle presented in Equation (  1 ) is given in Listing 2. The resulting call-graph, included in Figure  9 , clearly shows that the 100.
61% 5 = 3. 72% improvement to the overall execution time, which might not be worth the effort. On the other hand, optimising the c3z subroutine to reduce its execution time by 20% improves the overall running time by 9. 22%, which is nearly 10% and might be an interesting optimisation target. Finally, the call-graph visualisation conveys clearly the information that the cx gate is the most costly subroutine of the Grover's circuit, meaning that even a slight optimisation of the cx execution time will have a high impact on the overall implementation run-time. "u1": 0, "u2": 10, "u3": 30, "u": 30, "cx": 300 } gprof_output = profile(circuit, gate_times, "gprof", include_native_gates=True) with open("out. qprof", "w") as f: f. write(gprof_output) Listing 2: Python code needed to use qprof on the Grover implementation and save the profiler report in a gprofcompatible format in a file named out.
C. Quantum wave equation solver Finally, we include in this paper a more complex example that has been implemented in a previous work with myQLM, a quantum computing framework maintained by Atos. The code used to generate the benchmarked quantum program is available at https://gitlab. com/ cerfacs/qaths/ and is explained in  [27]. This example demonstrates that, as can be seen in Figure  10 , qprof interface stays nearly the same even though the framework used is now completely different. The only exceptions are some additional parameters (such as linking set in Figure  10 ) that are directly forwarded to the framework plugin used and additional gate definitions in the gate times data structure because of the way gate decomposition is handled in myQLM. The call graph obtained by running Figure  10  is reproduced in Figure  11.
IV. CONCLUSION In this paper we introduced an open-source and, to the best of our knowledge, novel library that is able to generate profiling reports in well-known formats from a quantum circuit implementation. Our library is able to natively read quantum circuits from multiple frameworks -currently Qiskit and myQLM -and can be easily extended to support more quantum computing libraries. It generates consistent reports independently of the underlying framework used. qprof opens new optimisation opportunities for quantum scientists and programmers by allowing them to view their quantum circuit implementation in a well-known, familiar, synthetic and visual representation. In this paper, we first presented the main concepts used in the internals of qprof and required to make it as extendable as possible: the plugin interface used to wrap different quantum computing frameworks, the call-graph data structure and finally the different native exporters.
The fifth Betacoronavirus subgenus, Embecovirus, is primarily associated with rodents and bovids, though a few bat hosts have been documented  (17, 18). Since the emergence of SARS-CoV in 2002, there has been increasing interest in surveying potential hosts of coronaviruses and contributing new virus sequences to public databases, with most effort focused on sampling bats from Asia  (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) , the continent of origin for both the SARS-CoV epidemic and the SARS-CoV-2 pandemic. Recently, more concerted efforts have arisen to survey the landscape of bat-borne coronaviruses in other regions of the world, including Africa and Europe  (11, 12, (29) (30) (31) (32) (33). The family Coronaviridae is considered one of the most likely viral taxa to switch host species  (34, 35)  in part because many CoVs utilize well-conserved cell surface receptors presented on a wide variety of mammalian host cells.
RNA Extraction RNA was extracted from a randomly selected subset of fecal, throat, and urine swab samples in the Virology Unit at the Institut Pasteur de Madagascar, with each sample corresponding to a unique individual from the field dataset. Samples undergoing mNGS corresponded to individuals captured in Feb-Apr, Jul-Sep and December 2018 or in January 2019. Water controls were extracted in conjunction with samples on each unique extraction day. Extractions were conducted using the Zymo Quick DNA/RNA Microprep Plus kit (Zymo Research, Irvine, CA, USA), according to the manufacturer's instructions and including the step for DNAse digestion. Postextraction, RNA quality was checked on a nanodrop to ensure that all samples demonstrated 260/280 ratios exceeding 2 and revealed quantifiable concentrations.
An initial equivolume library pool was generated, and the quality and quantity of that pool was assessed via electrophoresis (High-Sensitivity DNA Kit and Agilent Bioanalyzer; Agilent Technologies, Santa Clara, CA, USA), real-time quantitative polymerase chain reaction (qPCR) (KAPA Library Quantification Kit; Kapa Biosystems, Wilmington, MA, USA), and small-scale sequencing (2 x146 bp) on an iSeq platform (Illumina, San Diego, CA, US). Subsequent equimolar pooling of individual libraries from each plate was performed prior to performing large-scale paired-end sequencing (2 × 146 bp) run on the Illumina NovaSeq sequencing system (Illumina, San Diego, CA, USA). The pipeline used to separate the sequencing output of the individual libraries into FASTQ files of 146bp paired-end reads is available on GitHub at https:// github.
CZID Raw reads from Illumina sequencing were host-filtered, qualityfiltered, and assembled on the CZID (v3. 10, NR/NT 2019-12-01) platform, a cloud-based, open-source bioinformatics platform designed for microbe detection from metagenomic data (78), using a host background model of "bat" compiled from all publicly available full-length bat genomes in GenBank at the time of sequencing. Samples were deemed "positive" for coronavirus infection if CZID successfully assembled at least two contigs with an average read depth >2 reads/nucleotide that showed significant nucleotide or protein BLAST alignment(s) (alignment length >100 nt/aa and E-value < 0. 00001 for nucleotide BLAST/ bit score >100 for protein BLAST) to any CoV reference present in NCBI NR/NT database (version 12-01-2019). To verify that no positives were missed from CZID, all non-host contigs assembled in CZID underwent directed, offline BLASTn and BLASTx (79) against a reference database constructed from all available fulllength nucleotide and protein reference sequences for Alpha-and Betacoronavirus available in NCBI Virus (last access: August 15, 2021).
Genome Annotation and BLAST Three full genome-length Nobecovirus contigs returned from CZID (two from R. madagascariensis and one from P. rufus) were aligned with Nobecovirus homologs from NCBI (see 'Phylogenetic Analysis') and annotated in the program Geneious Prime (2020. 0. 5). We then used NCBI BLAST and BLASTx to query identity of our full length recovered genomes and their respective translated proteins to publicly available sequences in NCBI  (79). We queried identity to reference sequences for four previously described Nobecovirus strains (accession numbers: MG762674 (Rousettus bat coronavirus HKU9), NC_030886 (Rousettus bat coronavirus RoBat-CoV GCCDC1), MK211379 (Rhinolophus affinis coronavirus BtRt-BetaCoV/GX2018), and NC_048212 (Eidolon helvum bat coronavirus), as well as to the top BLAST hit overall.
Phylogenetic Analysis Contigs returned from CZID were combined with publicly available coronavirus sequences in NCBI to perform phylogenetic analysis. We carried out four major phylogenetic analyses, building (a) a full-genome Betacoronavirus maximum likelihood (ML) phylogeny, (b) a Betacoronavirus ML phylogeny corresponding to a conserved 259 bp fragment of the RNAdependent RNA polymerase (RdRp) gene encapsulated in the CoV Orf1b, (c) four amino acid ML phylogenies derived from translated nucleotides corresponding to the spike (S), envelope (E), matrix (M), and nucleocapsid (N) proteins from a subset of full length genomes, and (d) a time-resolved Bayesian phylogeny corresponding to all available full genome Nobecovirus sequences in NCBI virus, which we computed in BEAST2  (80). Detailed methods for the construction of each phylogeny are available at https://github.