dataesr/scientific-paragraphs-categorization huggingface dataset:
A reed instrument model with N acoustical modes can be described as a 2N dimensional autonomous nonlinear dynamical system. Here, a simplified model of a reed-like instrument having two quasi-harmonic resonances, represented by a four dimensional dynamical system, is studied using the continuation and bifurcation software AUTO. Bifurcation diagrams of equilibria and periodic solutions are explored with respect to the blowing mouth pressure, with focus on amplitude and frequency evolutions along the different solution branches. Equilibria and periodic regimes are connected through Hopf bifurcations, which are found to be direct or inverse depending on the physical parameters values. Emerging periodic regimes mainly supported by either the first acoustic resonance (first register) or the second acoustic resonance (second register) are successfully identified by the model.
Typical bifurcation diagram obtained by continuation method To overcome the difficulties of the analytical analysis of small amplitude oscillations near thresholds, and to get results for any inharmonicity value arbitrarily far from the oscillation threshold, simulation techniques in time domain are often used. An alternative method is possible. A nice way to have an overview of the dynamics over small and large amplitudes is to use the bifurcation diagram representation. Very few of them can be obtained analytically (see the previous subsection). It is possible to obtain bifurcation diagrams numerically for a large range of situations by using continuation methods, such as in the AUTO software  [31]  or MANLAB software  [32]  for example. In order to use AUTO technique in the following section, the elementary model has to be mathematically reformulated in a set of first-order ODE equations.
The principle of continuation is to seek solution branches of a nonlinear algebraic system rather than solution points. A solution branch is an 1D-curve in a space whose axes are an unknown to the problem and a parameter of interest called a bifurcation parameter. In the following the dimensionless mouth pressure c is chosen as the bifurcation parameter. It provides more information than a set of solution points obtained for successive values of the bifurcation parameter. Branches of static and periodic solutions are computed numerically hereafter using the software AUTO, freely available online  [33]. The model analysed in this paper is a nonlinear dynamical system. In order to obtain a nonlinear algebraic system in which numerical continuation can be applied, some additional work may be required.
The values of the modal parameters of the two resonance's air column given in Table  1  are inspired from  [9]  and  [19]. The main plot displays the continuation results obtained with AUTO, whereas the six smaller plots above correspond to time-domain simulations of the same system between t = 0 and t = 0. 5 s, for different values of c pointed by numbers. Time integration is performed with an ordinary differential equation solver, namely ode15s from the Matlab ODE Suite. d dt X ð1Þ X ð2Þ X ð3Þ X ð4Þ 0 B B B @ 1 C C C A ¼ d dt p 1 p 2 dp 1 dt dp 2 dt 0 B B B B @ 1 C C C C A ¼ X ð3Þ X ð4Þ À x1 Q 1 X ð3Þ À x 2 1 X ð1Þ þ Z 1 x1 Q 1 du dt À x1 Q 2 X ð4Þ À x 2 2 X ð2Þ þ Z 2 x2 Q 2 du dt 0 B B B B @ 1 C C C C A : ð11Þ The main plot displays max|p|, the maximum of the absolute value of pressure in the mouthpiece over one period with respect to the blowing pressure c.
Conclusion Bifurcation diagrams of a basic reed instrument modeled by two quasi-harmonic resonances have been computed by using a continuation method (AUTO software), where the mouth pressure is the control parameter. Some of the mouth pressure thresholds results are interpreted in terms of the ease of playing of the reed instrument. When there is an inverse Hopf bifurcation (perfect harmonicity) or of a double fold after a direct Hopf bifurcation (moderate inharmonicity), there may be a minimum value c = c subthr lower than c thr1 for which periodic stable oscillations can be observed. This value c subthr may be considered as a quantitative characterisation of the ease of playing. It has been shown that the lowest value of c subthr is obtained when the two resonances are harmonic, harmonicity equal to 2.
The architecture then would only be provided with the list of the candidate registry list before the voting. When the list of candidates is ready, the poll creator must send the list of candidates to the Poll Creation (see Fig. 4 ) module of the architecture. This module is used for modeling the list of candidates into a machine-readable format. The data model used for modeling a candidate is described in Def 1. Definition 1: Candidate: A Candidate, c, is a choice within a poll that a voter can cast a vote to. It is modeled as a 3-tuple c =< id, desc, score >, where: • id: is a unique number for identifying c; • desc: is a user-friendly short textual description for describing c; • score: is a number representing number of votes that c got from voters. After all the candidates are modeled, the Poll Creation would automatically pass the list of modeled candidates to the BlockVOTE Contract Handler module.
return T rue Successfully cast a vote 11: end if fulfill the voter validation/auditing. Each of the voters would have to be verified by the voting authorities before they can cast their votes. The degree of anonymity is not lost after the voting authorities have verified the voters as the voters would cast their votes to the blockchain without the requirement of the voting authority to oversee their votes. Based on that, the architecture adopts the identity verification process outside the voting contract, in which would fit the process better than adopting the identity verification process inside the voting contract. After the poll creator uses Poll Creation module and BlockVOTE Contract Handler module to create a new BlockVOTE contract, voters can invoke the vote functionality of the contract to cast a vote.
2 BlockVOTE: HyperLedger-based Implementation HyperLedger is an opensource blockchain platform hosted by Linux foundation. Unlike Ethereum, the designed of HyperLedger is not designated for creating a public cryptocurrency-based ecosystem. Instead, it is designed for developing a private blockchain ecosystem where a private organization can create its own blockchain network internally, and deploy their business logic onto their network using a smart contract-based mechanism. The architecture overview of the HyperLedger-based BlockVOTE implementation that we develop in this study is given in Figure  6. The architecture of BlockVOTE in Ethereum and Hyperledger implementations are mostly similar. The difference between both implementations are the languages for modeling a contract and a set of tools required.
In HyperLedger, data models and built-in functions are needed to be programmed separately. Data models are written using a language specific to HyperLedger named CTO language, while built-in functions are written using Javascript. The template https://web3js. readthedocs. io/en/1. 0/ After the contract is generated, it is compiled and deployed using HyperLedger Composer toolsets. The compiled BlockVOTE contract is deployed in a network of a pre-deployed HyperLedger Fabric 7 nodes. It is to be noted that this step is obligatory when using HyperLedger because HyperLedger is a private blockchain-based platform. Hence, a set of nodes is required for creating the infrastructure. This step is not necessary for the Ethereum-based prototype because Ethereum already has an extensive network of Ethereum node clusters available.
Hence, the challenges in securing both infrastructure and voting data can be overcome. The proposal system was implemented using Ethereum and HyperLedger in this study. Both of the Ethereum and HyperLedger-based prototypes can be used for conducting a poll and tallying for the result in our validation experiment. One major issue to be considered in both of the prototypes are the fact that the Ethereum-based prototype requires voters to own an Ethereum coin wallet before they can cast a vote, while there is no such a requirement in our HyperLedger-based prototype. This comes from the fact that deploying BlockVOTE on Ethereum means that all the voting data has to be stored in the public Ethereum network. Hence, all voters need to follow the Proof-of-Stake consensus protocol of Ethereum by paying some amount of Ether before a vote can be cast.
Fraud that is committed is also difficult to detect. From a security standpoint, online systems have high risk. The proposed blockchain decentralized system (i. e. , our BlockVOTE) would be considered the most accountable system compared to the previous approaches. The system itself has mechanisms for fraud detection built within the blockchain. By doing so, voting data is now stored among the decentralized blockchain nodes. Tampering with voting results in one of the nodes is considered extremely difficult because the tampered result will not be accepted by other nodes through the consensus handling mechanism of blockchain. It is possible to have a selfauditing system that could not be easily modified due to the nature of the distributed blockchain.
Study design This cross-sectional study was nested within the BIRDY program. 8 Briefly, it consists of a cohort of pregnant women and their newborns in Madagascar, Senegal, and Cambodia with an urban and rural site in each country (Figure  S1 ). Pregnant women enrolled between January 2015 and December 2016 were invited to participate in the present Klebsiella carriage study. A maternal stool sample was taken at delivery or, in case of home delivery, shortly after delivery. Data on maternal socio-demographic characteristics, medical history, and exposures, possibly related to Kp and its resistant or virulent subpopulations carriage, including dietary habits, antibiotic consumption, and contacts with animals, were collected.
Genomic sequencing and bioinformatics analyses NextSeq-500 sequencing of Nextera XT 2×150 nt libraries was performed. Genomic assemblies (Table  S1 ) were obtained using SPAdes v3. 9. 12  Multilocus sequence typing (MLST) was performed using the BIGSdb-Kp database (https://bigsdb. pasteur. fr). 11, 13 This resource was also used for detecting quinolone target genes mutations, and variants of the wzi and intrinsic class A beta-lactamases. 13, 14 Novel variants were submitted to BIGSdb (for blaSHV, blaOKP, and blaLEN variants) and to NCBI beta-lactamases database (blaSHV-206 to blaSHV-228). Other beta-lactamases and resistance gene families were detected using Kleborate (https://github. com/katholt/Kleborate). Isolates that carried only blaSHV (or blaOKP or blaLEN), fosA and oqxAB genes were considered as having an ancestral resistome.
Other antimicrobial resistance genes were considered acquired. To evaluate the correlation between the antimicrobial resistance phenotype and genotype, and to depict co-resistance networks, we constructed a correlation matrix for binary variables (in the case of resistance genes: 1, presence; 0, absence; in the case of antimicrobial drugs: 1, resistant/intermediate; 0, susceptible) using the 'corr. test' function (Pearson method, which for a pair of binary variables equates to the Phi coefficient) from the 'corrplot' R package. Significant correlations were visualized utilizing the 'corrplot' function from the same package. Plasmids were detected using PlasmidFinder (https://cge. cbs. dtu. dk/services/PlasmidFinder/). 15 Kleborate (https://github. com/katholt/Kleborate) was used to search for yersiniabactin, colibactin, aerobactin, and salmochelin clusters, and to predict the capsular types.
Phylogenetic and statistical analyses Genome phylogeny was derived from MASH distances using JolyTree (https://gitlab. pasteur. fr/GIPhy/JolyTree) and visualized using iTOL (https://itol. embl. de/). 19 ue strains were defined with a ≤ 10 allelic mismatch cutoff within allelic variation recorded using BIGSdb genome comparator function based on the 5,073 protein-coding genes of strain Kp616 (GCA_003076555. 1; 90% identity and 90% length coverage). Statistical analyses were performed using Stata version 15 (Stata Corp. , College Station, TX). We defined multidrug resistant (MDR) strains as being resistant to at least one antimicrobial agent in at least 3 antibacterial categories. Chi square or Fisher exact test was used to compare the prevalence of Kp subsets (all, MDR, ESBL) and antibiotic resistance patterns among countries.
Kp carriage A total of 911 pregnant women from Madagascar (n=423), Cambodia (n=152), and Senegal (n=336) were enrolled. For 874 (95. 9%) of these, both epidemiological data and a fecal sample were obtained (Figure  S2 ). Women enrolled in the urban sites represented 32. 6%, 55. 5%, and 35. 2% of the total in Madagascar, Cambodia, and Senegal, respectively. The study population (Table  1 ) was young (mean 27 years; SD 6. 4) and underprivileged, as a large majority (79. 3%-88. 9%) did not complete secondary school and an important fraction did not have toilets within the housing (Madagascar: 93. 1%; Cambodia: 49. 3%; and Senegal: 29. 9%). Kp colonization was detected in 489 of 874 (55. 9%) women. Carriage rate per country (Figure  1A ) was significantly lower in Senegal (40. 2%, 95% CI [36. 3-46.
Acknowledgements We thank all collaborators of the  BIRDY program :  Andrianonimiadana Lova Maminirina  ,  Andriambololona Mino ,  Herisoa Feno ,  Rabearitiana Lydos ,  Raheliarivao Bodonirina Tanjona ,  Rakotobe Miora ,  Rakotoarimanana Feno Manitra Jacob ,  Randriarilala Heritiana ,  Randrianirina Frédérique ,  Volahasina Antsa Tanjona ,  Andrianirina Zafitsara Zo ,  Vincent Richard ,  Awa Ndir ,  Amy Gassama Sow ,  Balla Sy ,  Marguerite Diatta ,  Pape Samba Dieye ,  Jean Baptiste Diouf ,  Joseph Faye ,  Abibatou Ndiaye ,  Bouya Ndao ,  Thida Chon ,  Veronique Ngo ,  Arnaud Tarantola ,  Sok Touch ,  Patrice Piola ,  Sophie Goyet ,  Siyin Lach ,  Elsa Kermorvant-Duchemin ,  Michael Padget ,  Laurence Watier , and  Abdou Armya Youssouf. We thank all physicians, laboratory staff, field interviewers, and community workers involved in the project.
Funding This work was supported by the  Institut Pasteur programme Action Concertee Inter-Pasteurienne  (Grant  A-14-2014 ) and by the  French government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (grant  ANR-10-LABX-62-IBEID ). Support to the  BIRDY  project was provided by the  Total Foundation ,  MSDAVENIR  and the  Monaco Department of International Cooperation. CR was supported financially by the  MedVetKlebs  project, a component of European Joint  Programme One Health EJP , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  773830. The funders had no role in study design, data collection and analysis, interpretation, or writing of the manuscript.
Contigs 47 48 48 40 64 157 64 47 54 38 76 55 51 46 113 75 5346312 5174574 5321869 5365452 5330747 5449031 5230669 5264571 5175960 5444783 5262563 5462273 5203937 5161973 5357522 5613895 Assembly size (nt) 5062967 5226977 5138345 5092929 5323070 5460594 5549052 5228028 5259561 5234949 5291083 5501182 5095900 5164377 5704285 5375242 N50 ERS3557105 ERS3557126 ERS3556782 ERS3557100 ERS3556951 ERS3556971 ERS3556989 ERS3556888 ERS3557025 ERS3556899 ERS3556820 ERS3556789 ERS3557082 ERS3557014 ERS3556975 ERS3557055 Accession Number ERS3557106 ERS3557127 ERS3556901 ERS3557101 ERS3556953 ERS3556972 ERS3556990 ERS3557009 ERS3557027 ERS3557039 ERS3557056 ERS3556912 ERS3556790 ERS3557034 ERS3556978 ERS3556821 SB5624 SB5658 SB5690 SB5720 SB5648 SB5380 SB5411 SB5441 SB5472 SB5507 SB5534 SB5569 SB5595 SB5622 SB5542 SB5430 SB5586 Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae similipneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae Klebsiella pneumoniae quasipneumoniae Klebsiella variicola subsp.
Nevertheless, the secretion index obtained with perfused cryopreserved adult dog islets was six, that is, twofold higher than that of our ACT-164 cells. Working with dog tissue has some strong limitations. Most of the tools developed for islet studies were produced for rodent and human islets. Fortunately, antibodies developed for humans are appropriate for dogs, including those to insulin, glucagon, and Ki67. It would have been of interest to qualitatively evaluate the expression of Pdx1 and neurogenin 3, but neither of these dog compounds was recognized by human antibodies. We were able to quantitatively evaluate insulin, glucagon, and Pdx1 mRNA but not the other markers of interest. The dog neurogenin 3 sequence is incomplete in publicly available databases, and consequently, specific probes to detect dog neurogenin 3 could not be designed.
Acknowledgments We thank  Alice Pailleret  and  Noe ¨mie Guedj  for their excellent technical assistance in developing the cell culture model. This research work benefited from equipment and services from the  Celis  and iVector platforms at the  Paris Brain Institute  supported by  Programme Investissements d'Avenir  [ ANR-10-IAIHU-06 ]. We are especially indebted to  Océane RIBAULT  and  Laetitia STREHL  for the ACT-164 culture and transduction experiments.
Validity of the IRES The hypothesis was assumed that the confirmatory factor analysis model with twocorrelated factors would show adequate fit. The values of the scale parameters were estimated and verified by the Maximum Likelihood (MLR) method by means of confirmatory factor analysis performed using the SPSS Amos package. The properties of the confirmatory factor analysis model are presented in Table  4 , and the size of the loadings of individual statements is shown in Fig. 1. Absolute indices of goodness of fit were used to evaluate the model. Criteria indicated in Table  4  were assumed. Examining all the indices of goodness of fit considered in this study the values of the adapted model fit indicators for the tested model have proved satisfactory including RMSEA =. 06 (Marsh and Hocevar 1985).
We describe both the NIKA2 observations and raw data analysis in Sect. 2. We characterize the radio source contamination of the SZ signal in the NIKA2 data in Sect. 3. In Sect. 4, we give the details of the Chandra observations and the X-ray data reduction. We study the cluster morphology based on our multi-wavelength dataset in Sect. 5. We combine the X-ray and SZ data to estimate the radial profiles of the ICM thermodynamic properties in Sect. 6. In Sect. 7, maps of the ICM temperature and entropy are obtained from the combination of the Chandra and NIKA2 data at the pixel level. We present our perspectives given the results of this study in Sect. 8 and give a summary of our work in Sect. 9. In this paper, we assume a ΛCDM cosmology based on the latest results from the Planck collaboration (Planck Collaboration et al.
Observations and data reduction The X-ray observations of MOO J1142+1527 were obtained in the VFAINT data mode for a total exposure of 46. 96 ks using the Advanced CCD Imaging Spectrometer (ACIS) I-chips on board the Chandra X-ray Observatory. We follow the data reduction procedure described in  McDonald et al. (2017)  and references therein. We have reduced the data using the Chandra Interactive Analysis of Observations (CIAO) software v4. 10 based on the calibration database (CALDB) v4. 8. 0 provided by the Chandra X-ray Center (CXC). We have used the chandra repro script to reprocess the level 1 event files using the latest charge transfer inefficiency corrections and time-dependent gain adjustments. The ACIS particle background for very faint mode observations is also cleaned based on outer pixel pulse heights during this analysis step.
As the raw data analysis method detailed in Sect. 2. 2 has allowed us to measure an almost flat noise power spectrum in the NIKA2 map at 150 GHz, the pixel-to-pixel correlation induced by residual correlated noise in the NIKA2 map is negligible compared to the RMS noise within each pixel at this frequency. We therefore assume that the NIKA2 χ 2 is given by: χ 2 NIKA2 = N NIKA2 pixels i=1 [(M NIKA2 -M )/M RMS ] 2 i (12) where M RMS is the map of the RMS noise in each pixel of the NIKA2 map at 150 GHz. It is computed from simulated noise maps including the instrumental, atmospheric and astrophysical contaminants (see Sect. 2. 3). The fact that we do not need to use a noise covariance matrix at 150 GHz to account for the residual noise properties at this frequency allows us to decrease the computation time of the likelihood value at each step of the MCMC from 0.
The temperature profiles (left panel) estimated with these two deprojection centers are significantly different at radii r ∼ < 100 kpc. We measure a monotonically decreasing temperature from the X-ray centroid up to 0. 7R 500. The central temperature around the SZ peak reaches high values around 14 keV within the enclosed region defined in Fig. 5  (orange line). However, the profile estimated by considering the X-ray peak has the typical shape expected for a cool-core cluster. The temperature increases from a low central value of ∼4 keV up to a maximum of 9 keV at 170 kpc from the X-ray peak. It then decreases at larger radii. These profiles estimated from the combination of X-ray and SZ results are also compat-ible with the mean ICM temperature estimated from the Chandra spectroscopic data (blue point).
We emphasize the large leap forward that such a joint analysis of SZ and X-ray data allows us to make on the characterization of the ICM temperature profile at z > 1. Indeed, having a precise measurement of the central temperature, the inner slope of the profile, and the location of the maximum temperature value with X-ray spectroscopy would require an increase of the Chandra exposure of at least an order of magnitude. The entropy profiles estimated from the combination of the NIKA2 and Chandra results are shown in the right panel of Fig. 7. The outer slopes of both profiles are compatible with the self-similar expectation obtained from non-radiative simulations and shown with a pink dashed line  (Voit et al. 2005). However, the radial distribution of the gas entropy estimated by using the X-ray peak is significantly different from the one obtained with the large-scale centroid at r ∼ < 100 kpc.
HSE Mass [M ] X-ray peak X-ray centroid Fig. 8. -Left: Profiles of the ICM cooling time (magenta) and of the ratio of the cooling to free-fall times (grey) estimated from the density, temperature, and hydrostatic mass profiles issued from the joint analysis of the NIKA2 and Chandra data using the X-ray peak as deprojection center. Right: Hydrostatic mass profiles estimated from Eq. 15 using the density and pressure profiles deprojected with respect to the X-ray peak (purple) and the X-ray centroid (green). In both panels, the black line gives the best-fit estimate and the dark and light regions show the 1σ and 2σ confidence regions. The unconstrained regions are shown as in Fig. 6.
ACKNOWLEDGEMENTS We would like to thank the  IRAM  staff for their support during the campaigns. The NIKA dilution cryostat has been designed and built at the  Institut Néel. In particular, we acknowledge the crucial contribution of the Cryogenics Group, and in particular  Gregory Garde ,  Henri Rodenas ,  Jean Paul Leggeri ,  Philippe Camus. This work has been partially funded by the  Foundation Nanoscience Grenoble  and the  LabEx FO-CUS   ANR-11-LABX-0013. This work is supported by the  French National Research Agency  under the contracts "MKIDS", " NIKA " and  ANR-15-CE31-0017  and in the framework of the  "Investissements davenir" program  ( ANR-15-IDEX-02 ). This work has benefited from the support of the  European Research Council Advanced Grant ORISTARS  under the European Union's  Seventh Framework Programme  (Grant Agreement no.