dataesr/scientific-paragraphs-categorization huggingface dataset:
g. for January, P was the mean of the values presented in the map of Figure  5a1 ). The resulting Honduras index P was highly correlated with the AaS estimated in a similar manner (Figure  5 a2 ). At the city level, we estimated index P in time using daily temperature and humidity data during the period 2005-2013 (from a weather station at the centre of Tegucigalpa, see Data accessibil-ity3). Similarly to what was done for the Brazilian cities (Figure  4 ), we averaged P over each month across the years. The AaS estimation was performed as in Thézé et al. , based on a geopixel located within the geographical boundaries of Tegucigalpa  (Thézé et al. , 2018). The resulting Tegucigalpa index P was highly correlated with the AaS estimates (Figure  5b ). We next compared the correlations of both the index P and AaS with incidence patterns for ZIKV, DENV and CHIKV at the city level (Figures  5c1-c3 ).
| CON CLUS IONS In this manuscript, we have introduced in detail a mosquito-borne viral suitability measure which we named index P. We apply and exemplify the capacities of the index P to estimate transmission potential and characterize it in space and time using real world examples. We also introduce and provide MVSE, an R-package that implements various functionalities allowing to estimate and analyse the index P in regions for which humidity and temperature time series are available. We believe that both MVSE and the index P are unique in the current literature. Other suitability approaches exist and have been widely used, but are generally based on complex computations, dependent on many variables from various data sources (not necessarily available), with little direct biological interpretation on the measured scale, and critically, for which no freely available (estimation) software tools exist.
In contrast, index P is simply based on the R0 expression of a mosquito-borne dynamic model and two climate variables, has a direct biological interpretation, and is easily calculated and explored through MVSE, a complete and freely available software tool. With its aforementioned mathematical simplicity, dependency on only two climatic variables at any possible time-scale (hours, days, weeks, months) and freely available R-package MVSE, we foresee the potential of the index P not only for direct research purposes, but also for education (e. g. epidemiological courses on arboviruses) and field ento-epidemiology (e. g. for entomologists and epidemiologists collecting real-time data). New ideas and methodological updates will be implemented in future versions of MVSE as the user base will keep growing and our own research projects on mosquitoborne viruses' progress.
was supported by an EMBO postdoctoral fellowship. PNPG received travel and accommodation expenses from the Department of Global Health and Oriel College, University of Oxford. N. R. F. is supported by a Royal Society and Wellcome Trust Sir Henry Dale Fellowship (204311/Z/16/Z), internal HEFCE GCRF grant 005073 and John Fell Research Fund Grant 005166. JL received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013, ERC grant number 268904 -DIVERSITY) and was supported by a Lectureship from the Department of Zoology, University of Oxford. C. J. V. A. was supported by an European Research Council Starting Grant (ERC-2017-STG, project ID: 757688). J. T. was funded by the European Research Council European Unionâ€™s Seventh Framework Programme (FP7/2007-2013, ERC grant number 614725 -PATHPHYLODYN).
Funding information Higher Education Funding Council for England , Grant/Award Number:  005073 ;  John Fell Fund, University of Oxford , Grant/Award Number:  005166 ;  European Molecular Biology Organization ;  Wellcome Trust and Royal Society , Grant/Award Number:  204311/Z/16/Z ;  H2020   European Research Council , Grant/Award Number:  ERC-2017-STG  and  757688 ;  FP7 Ideas: European Research Council , Grant/Award Number: 268904 -DIVERSITY and 614725 -PATHPHYLODYN.
22 Supercomputing Frontiers and Innovations tions for parameters that are already known, and to use the already computed quantities at the micro-scale, stored in a database, to build a surrogate model. The database and surrogate model, in conjunction with an HMC manager, restores previously computed data, interpolates them where necessary using the surrogate model, and provides input to the macro-scale model(s). Depending on the state of the surrogate model and the history of the data required, this mechanism can reduce the number of micro-scale simulations significantly, potentially by orders of magnitude. Generally, this approach is practical and has already been demonstrated in multiple fields  [22, 31]. The exchange of data should not be a bottleneck in this approach, because the exchange between scales usually involves a few floating-point numbers that represent specific properties.
However, the number of micro-scale models should be mapped efficiently to available hardware resources to avoid potential load-balancing required at runtime issues  [5]. The role of the HMC manager is to build the surrogate model and evaluate it when needed. In this process, at every time-step the macro-scale model sends a request to the HMC manager for required quantities. The manager then consults the surrogate model for the required information. For this purpose, a user-defined script is implemented to decide whether the cached or interpolated data is sufficient. If not, the manager will start new micro-scale models to obtain more accurate results. To prevent the manager itself from becoming the bottleneck, so as we will assume asynchronous I/O and separate computing resources for the HMC manager.
In the stateless mode, the number of processors per micro-scale model P µi is totally dynamic. In this case, the pattern software decides how to utilise P µ for each macro-scale iteration. The main advantage of this method is that when the computation of the micro-scale models is complete, their nodes/cores can be used for pre-calculating some of the database properties of the surrogate model, or simply release them to the system. The distribution of the number of nodes/cores per micro-scale model depends on the number of micro-scale models requested and the number of available processors for these micro-scale models. In the following subsections, we will discuss a mathematical model for the dynamic num-Towards Heterogeneous Multi-scale Computing on Large Scale Parallel Supercomputers.
Case (a): Surrogate model from scratch If the user starts building the surrogate from scratch, i. e. for g -→ 0, then it is meaningless to run DoF micro-scale models to fill the database at the beginning. This will be inefficient, since the degree of freedom can easily reach 10 6 or more. What can be done is cluster input parameters into an input subsample. In this case, the initial batch of micro-scale models (η init ), from which a surrogate model is built, is first executed, then run the next batch (either look it up in the database or run the micro-scale model), and so on. Figure  5  shows the performance of a surrogate model (top graphs), expressed by g(t), and the corresponding number of micro-scale jobs (lower graphs) for two different performance levels of the surrogate model. Both examples had a (DoF = 48818).
After reservation, these resources were then managed using a python script, the pilot job manager. In this script the user can launch, request and kill jobs dynamically. In our benchmarks, the pilot job reserves a number of processors first. Then, in the pilot job manager, the macro-scale model and the HMC manager are submitted. The macro-scale, after an iteration requests a number of parameters from the surrogate model. The surrogate model looks in the database, interpolates the missing quantity and requests to run a number of microscale models for the missing quantities. In the benchmarks, we mimic this operation by using the performance of the surrogate model. The number of micro-scale models and the available resources are then sent to the HMC manager to suggest the right distribution of the resources to the pilot job manager.
When a previously developed surrogate model is used, the resulting utilisation of the system is as shown in Fig. 13  (top panel), and the corresponding number of micro-scale jobs and the average number of processors per micro-scale jobs are shown in Fig. 13  (middle and lower panels, respectively). This case shows the situation where the surrogate model must initially run a large number of micro-scale models at the outset to fill the database rapidly and then becomes sufficiently effective to replace the need for micro-scale models. The utilisation of the performance of the surrogate model presented in Fig. 6a  (model = developed, good performance) Figure  13  (top panels) appears similar to the previous case, but we notice that because the difference in the number of micro-scale jobs requested for each macro-scale step is high, there are more idle cores in the first phase than for the previous case.
Acknowledgments We acknowledge partial funding from the  European Union   Horizon 2020 research and innovation programme  under grant agreement No  671564  for the ComPat project (http://www. compatproject. eu/). SA acknowledges funding from  King Abdulaziz City for Science and Technology (KACST) ,  Saudi Arabia. P. V. C. thanks the  MRC Medical Bioinformatics  project ( MR/L016311/1 ), the  EU H2020  CompBioMed grant (http://www. compbiomed. eu,Grant.
431195(9) Å at room temperature). Analysis of all diffraction data was carried out by the Rietveld method 21 implemented in the Topas Academic software. 22, 23 hemical Analysis. The elemental composition of SSGO was measured by inductively coupled plasma mass spectrometry (ICP-MS) analysis performed on ∼50 mg of powdered sample dissolved in nitric acid using a Thermo Scientific Neptune Plus Higher Resolution Multicollector. Thermogravimetric Analysis. Any mass losses that occurred in SSGO on heating were recorded in air on a PerkinElmer TGA 8000 instrument. Two different samples were heated from 30 to 600 °C at 10 °C min -1. Variable-Temperature X-ray Diffraction. PXRD patterns were recorded in air over a temperature range of 25-1000 °C on a Bruker D8 Advance (Lynx-eye detector, Cu Kα radiation) with an Anton Paar HTK1200 furnace attachment.
Data were collected every 20 °C on heating and cooling, and patterns were analyzed by Rietveld fitting in Topas Academic. 22, 23 Furnace temperatures were calibrated based on an external Al 2 O 3 standard. 24 eutron Diffraction and Total Scattering. A ∼6 g powder sample of SSGO was used to collect neutron total scattering data on the POLARIS instrument at the ISIS facility at Rutherford Appleton Laboratory. The sample was loaded and packed into an 8 mm diameter V can. High-quality data sets for pair distribution function (PDF) analysis were acquired at room temperature (6 sets of 175 μAh collections, 6 h) and 1000 °C (4 sets of 175 μAh collections, 4 h). The scattering due to the empty furnace and the empty can were measured at room temperature for 1 h each for background subtraction. In addition, 4 min data sets were collected at 45 temperatures on heating.
Neutron total scattering data were normalized onto an absolute scale using GudrunN software,  25  and Bragg data were extracted using the established analysis routines in Mantid. 26 Rietveld and large-box local structure analyses were performed using Topas Academic. 22, 23 RMCProfile 27 was used to confirm the local structure models and gave good fits to F(Q) in addition to PDF and Bragg data. The three highest resolution data banks (banks 3, 4, and 5) of POLARIS were used for variable-temperature neutron refinements and covered a d-spacing range of 0. 15-5. 75 Å. Bank 3 was used for joint PDF/Bragg analysis as it offers a good compromise between resolution and d-spacing range. For large-box fitting, PDF patterns were calculated using a 20 736-atom approximately isotropic box, and the Bragg scattering was calculated by folding the large-box coordinates back into a single crystallographic unit cell.
In initial refinement cycles (both data sets fitted simultaneously), coordinate changes were constrained to prevent large atomic shifts; these constraints were relaxed during fitting. An equal weighting was applied to each point in the PDF such that the PDF and Bragg data gave a similar contribution to the overall χ 2. Large-box refinements converged within a couple of hours on a standard desktop PC. To avoid impurity peaks influencing the average structure of the large-box models, the peaks due to small amounts of Sc 2 O 3 and Sr 10 Sc 4 Ga 6 O 25 impurities were subtracted from the Bragg data before fitting. Inelastic and Quasielastic Neutron Scattering. Inelastic neutron scattering data were collected on the time-of-flight spectrometer IN6 and on the backscattering spectrometer IN16B at the Institut Laue Langevin.
The IN6 experiment was performed with an incident neutron wavelength of 5. 12 Å and an elastic energy resolution of 70 μeV. A 10 g sample of SSGO was placed in a cylindrical, 8 mm diameter Nb sample holder, and data were collected at 77, 200, 400, 600, 800, and 1000 °C, with a collection time of 6 h at each temperature. Background scattering from the empty sample holder was measured for 6 h at room temperature. Vanadium was measured at 20 °C for 6 h for normalization and to describe the instrument resolution function. The IN16B experiment was carried out with an incident neutron wavelength of 6. 271 Å and an energy resolution of 0. 75 μeV, to give access to nanosecond dynamics. A 10 g powdered sample was placed in a cylindrical 8 mm diameter Nb sample holder. Initially the measurements of elastic intensity (E = 0 μeV) and inelastic intensity at 2 μeV, were performed on heating from 20 to 900 °C using a heating rate of 0.
04 °C s -1 and on cooling to 290 °C. Data points were collected in pairs with elastic intensity (EFWSelastic fixed window scan) measured for 30 s, immediately followed by an inelastic intensity (IFWSinelastic fixed window scan) measurement lasting 2 min. 28 Quasielastic neutron scattering (QENS) measurements were performed at room temperature and 1000 °C for 6 h at each temperature (as six 1-h repetitions) within an energy transfer window of ±20 μeV. The background scattering from the empty can was measured for 5 h, and a resolution function from a standard vanadium sample was also acquired at room temperature. The data from both experiments were analyzed using the LAMP software. 29 mpedance Spectroscopy. Samples of SSGO and Sr 2 Sc 0. 95 Zn 0. 05 GaO 4. 975 (SSGO-5%Zn) were uniaxially pressed into 10 mm pellets and sintered for 24 h at 1200 °C.
Study plan To answer these objectives, the study was organized as follows. First, we selected an adequate study site consisting of a tunnel of which we precisely know the trajectory in order to be able to assess the precision of its detection with 2D-ERT. We also identified a second tunnel in the same area that we were susceptible to detect but of which we didn't know the trajectory (Section 2). Then, we realized two 2D-ERT measurements along one profile perpendicular to the main tunnel axis under different weather conditions. The measured apparent resistivity was inverted with the RES2DINV software (Section 3). After that, we realized forward models with the RES2DMOD software with resistivity values based on small scale in situ measurements for the purpose of giving explanation to one unexpected result obtained after inversion of the measured apparent resistivity (Section 4).
The way the resistivity of the model is iteratively modified to fit the measured apparent resistivity depends on the chosen norm of inversion. There are 2 common norms  (Loke, 2016) : the smoothness constrained least-squares method (l2-norm) and the robust method (l1-norm,  Claerbout & Muir, 1973) , also based on the least-squares method. The robust inversion is supposed to be used where there is great resistivity contrasts over short distances. Such contrast was supposed to occur between the masonry (≈500 Ω. m) and the tunnel atmosphere (∞), so the robust method proposed by the RES2DINV software was selected. Such as it is commonly recommended  (Loke, 2016)  the logarithm of the resistivity values was used instead of the resistivity itself for the results to be in a linear scale (instead of its natural logarithmic scale) which is more easily represented on the profiles.
Inversion of the filtered apparent resistivity The measured apparent resistivity was not initially filtered because no aberrant data were visible and the data point's coefficients of variation were small. However, the data show large differences between two successive points. We supposed that this scattering, which was not considered as an artifact a priori, was at the origin of the non-detection of the tunnel with the inverse models. Therefore, we applied to the measured apparent resistivity the automatic average median filter followed by the automatic sliding mean filter proposed by the PROSYSII software (Iris Instrument, France). These filters consist in smoothing the measured apparent resistivity horizontally, i. e. along the lines corresponding to every depth levels successively, by changing the resistivity of every data point to be equal to the median and the arithmetic mean of the neighbor data points respectively.
The neighbor data points used for the smoothing were selected within a distance of 1 x AM around the considered data point for the sliding median and 0. 5 x AM around the considered data point for the sliding average (PROSYSII software recommendation), where AM is the distance between the closest injection (A) and potential electrodes (M). The resulting apparent resistivity is vertically similar but horizontally smoother than the unfiltered one (Fig. 10b ). It keeps the overall same trends except that the pics of resistivity observed at positions of 13 m and 37 m were significantly smoothed (Fig. 10b ). On the image obtained after inversion (Fig. 11 ), it is surprising to see that the main tunnel now clearly appears as one anomaly of low resistivity (≈70 Ω. m) in comparison with the surrounding material (300 to 400 Ω.
This layer can be composed of water, moisture and clay particles deposited by seepage water. The effect of such a layer was demonstrated by the authors according to synthetic forward resistivity models considering circular cavities without masonry. They showed that the ratio of the thin conductive layer resistivity to the surrounding soil resistivity has to be of at least 0. 1 for the cavity to be properly detected. In order to verify the validity of Putiska's results in the case of the old masonry tunnels we were studying, forward resistivity models were conducted with the RES2DMOD software  (Loke, 2014). Forward resistivity modelling aims to evaluate the apparent resistivity that would be measured and the inverse resistivity model that would be obtained with it considering a given "true" ground resistivity distribution (resistivity model) and a given electrode array.
Forward modelling considering a uniform surrounding We constructed a resistivity model with tunnel geometry as close as possible from the true tunnel geometry. However, we considered an intentionally simplistic geometry of the surrounding materials (Fig. 12 ). The objective was first to evaluate the detectability of the tunnel as a low resistivity anomaly where it is surrounded by a homogenous and uniform material. To do so, the resistivity of the backfill, the rock and the masonry was fixed according to Table  1  and the resistivity of the atmosphere was fixed at 100 000 Ω. m (infinite relatively to the other materials). The resistivity of the thin conductive layer around the masonry was logarithmically varied from 0. 01 to 10 Ω. m. The voltage that would be measured with a WS array at the surface and a unit electrode spacing of 1 m was calculated, as well as the apparent resistivity, with the RES2DMOD software.
Then the measured apparent resistivity was inverted with the RES2DINV software considering the settings described in section 3. 2. 1. The obtained inverse resistivity model was compared to that obtained considering the field measurement (section 3. 2. 3) along two profiles: a vertical line crossing the tunnel at mid-span and a horizontal line crossing the tunnel at mid-height. The results are synthetized in Fig. 13. With no conductive layer, the tunnel appears as a highly resistive anomaly. With a thin conductive layer of 10 Ω. m resistivity, the tunnel is detected as an anomaly of slightly higher resistivity than the surroundings (red curves) in both the horizontal (Fig. 13a ) and vertical directions (Fig. 13b ). With a thin layer resistivity lower than 1 Ω. m, the tunnel is detected as an anomaly of lower resistivity than the surroundings.
Acknowledgements We would like to thank  BpiFrance , the  European Union  ( FEDER-FSE program ) and la region Occitanie for the financial support of the research project  FUI 22 EREDOS n°LR0012285. We are also grateful to  Dr. David Salze  for the project coordination, to  Mr. Thibault Jeannin  for material assistance and to  Mr. Henri Chalvidan  and the municipality of  Robiac-Rochessadoule  for their collaboration.
A way to proceed is to include in the analysis a large number of isolates sampled in the same period and geographical area of the epidemic isolates and, when the information is available, belonging to the same or a close genetic group. In fact, at present, the construction of this control group is largely empirical and built on common sense principles. Here, we propose a non-parametric statistical approach to distinguish between outbreak and non-outbreak cases as an alternative to methods based on pairwise differences thresholds, bootstrap estimations or visual inspections of phylogenetic trees  (Lee et al. , 2015a,b). We extracted six genomic features at the coregenome, accessory genome or pangenome scales from genomic data of four historical Salmonella enterica outbreaks, and we evaluated the ability of three non-parametric tests-Wilcoxon rank-sum (WS), Kolmogorov-Smirnov (KS), and Kruskal-Wallis (KW)-to discriminate between outbreak and non-outbreak cases.
The clinical strains were obtained from the National Reference Center (NRC) for Salmonella at the "Institut Pasteur Paris. " Food, animal and environmental strains were obtained from the ANSES Salmonella Network at the French Food Safety Laboratory in Maisons-Alfort. The antigenic formulae were determined by glass slide agglutination according to the White-Kauffmann-Le Minor scheme  (Grimont and Weill, 2007) , and PCRs were performed following EFSA recommendations to confirm that all S. 1,4,[5],12:i:-isolates were monophasic variants of serovar Typhimurium  (EFSA, 2010; Tennant et al. , 2010). The sequence types (ST) were predicted using the version 2. 16. 1 of the program mlst developed by Seemann T 1. based on components of the PubMLST website 2  , integrating BIGSdb developed by  Jolley and Maiden (Jolley and Maiden, 2010).
Variant Calling (SNPs and InDels) The coregenome SNPs and small InDels were detected based on the variant caller HaplotypeCaller that was implemented in 3 www. icm-institute. org the iVARCall2 workflow  (Felten et al. , 2017) , used Salmonella Typhimurium LT2 (NCBI NC_003197. 1) as a reference genome and followed the best practices proposed by the Genome Analysis ToolKit (GATK)  (McKenna et al. , 2010). More precisely, secondary alignments around small InDels were performed and duplications were excluded before variant calling analysis via local de novo assembly of haplotypes in active regions. The matrices of pairwise SNP differences and pseudogenomes were computed using in-house Python scripts called 'VCFtoMATRIX' and 'VCFtoPseudoGenome, ' respectively. The pseudogenomes correspond to the reference genome where the genotypes of detected variants were replaced in each genome  (Felten et al.