dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent studies  [48, 49, 51, 52, 53]  have investigated models of epidemic spread on temporal networks and studied the role of the networks' temporal properties, sometimes with contrasting results, as discussed in Ref. [54]. The comparison is however difficult, because different datasets, different epidemic models, and different parameters values have been used  [54]. In particular, many studies have focused on processes that unfold rather fast with respect to the temporal scale of the network dynamics, so that the precise ordering of individual interactions is important, as well as the distribution of time intervals between contacts. In our case, conversely, the timescales of the spreading process and of the network dynamics are well separated, which explains why static representations yield results that are similar to the timeresolved representations like DYN.
Acknowledgments This work has been partially supported by the  Italian Ministry of Health  (Grant n. 1M22 ) to CR, AET and CC, by the  French ANR  (contract no. ANR-12-MONU-0018 HARMSFLU ) to AB, and by the  EU FET  project  Multiplex 317532  to AB and CC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CC acknowledges a stimulating discussion with  John Edmunds  and inspiring discussions at the Data Science and Epidemiology workshop organized by  Marcel Salathé.
Precession microscopy: pattern acquisition and identification For this rather new technique, sample preparation is the same as in standard TEM observations, although when seeking the best possible spatial resolution for orientation maps obtained on thin foils, a compromise between expected grain size and sample thickness along beam direction is advisable; the PACOM technique will not easily discriminate superimposed spot diffraction patterns originating from piled-up crystals along beam direction. During data acquisition, electron diffraction spot patterns are collected sequentially with a dedicated external CCD camera (attached to the TEM viewing screen) while the sample area (typically from a fraction to tens of square micrometres) is scanned by the incident quasiparallel nanobeam (<2 mrad).
Workshop Methodology Forty-three experts (pediatric and adult endocrinologists, clinical and basic geneticists, epidemiologists, a nutrition specialist, an orthopedic surgeon, a psychiatrist, health technology assessment specialists, a bioethicist, a health economist, and a patient advocate; see author list in Acknowledgments) participated by invitation from the scientific committee (see author list). Clinical representatives from 5 manufacturers of rhGH also submitted their PWS-specific safety data. Prior to the workshop, an extensive literature review based on a multicriteria methodology  (30, 31)  was performed to identify relevant available data concerning rhGH treatment for patients with PWS. For clinical evidence in the pediatric population, randomized controlled trials (RCTs)  (20 -26, 33-41) , comparative observational studies  (42) (43) (44) (45) (46) (47) (48) , and long-term studies (Ͼ3.
5 y)  (5, 49 -58)  were included. Adult studies included RCTs of rhGH treatment for Ն 6 months  (7, 29, 59, 60)  and uncontrolled trials  (61) (62) (63) (64) , because data were more limited. Safety data from pharmaceutical registries (phase 4 trials)  1  and sponsored clinical trials (phase 3) were reviewed. Data on disease, therapeutic context, and economic, ethical, and societal aspects were also included to reflect a broad international context. Details on approach, evidence tables, and data summaries are available in Supplemental Table  1 , sections A and B (published on The Endocrine Society's Journals Online web site at http://jcem. endojournals. org) and on the workshop web site (http://bit. ly/PWGHIN; Ref. 65). The level of evidence was evaluated using the scoring procedure based on the Oxford Centre for Evidence-Based Medicine (CEBM) Level of Evidence scale  (66).
Acknowledgments The workshop participants thank the  Foundation for Prader-Willi Research and the EVIDEM Collaboration , particularly  Dr Mireille Goetghebeur  (president of the nonprofit EVIDEM Collaboration) for her help in preparing the methodological approach to this clinical practice guideline and, along with  Patricia Campbell , for the design and support of the  PWS GH Evidence Registry  and Workshop web site. We also thank  Dominika Kozubska ,  Centre Hospitalier Universitaire Sainte-Justine Research Center , for her expert administrative assistance to the workshop. We gratefully acknowledge the provision of rhGH safety data from the following companies:  Genetech-Hoffman LaRoche, Lilly ,  Novo Nordisk ,  Pfizer , and  Serono. Address  all correspondence and requests for reprints to:  Cheri Deal, PhD, MD ,  FRCPC ,  Endocrine Service ,  Centre Hospitalier Universitaire Sainte-Justine/Université de Montréal, 3175 Côte Ste-Catherine, Montréal, Québec ,  Canada   H3T 1C5.
Perspectives In complex tracking situations (e. g. Z info missing, 3 bees overlapping), our GNN method reaches its limit. We are currently working to use a combined MHT-IMM tracking method to improve tracking results. This work is a preliminary for a behavioral analysis. Based on 3D trajectories collected, we will be able to build a database of bees' behaviors of front at the beehive entrance. To ensure the feasibility of behavior identification of bees outside the beehive, we will implement a behavior recognition system based on HMM as the image of  [5]. Then as  [8]  or  [6] , we plan to take advantage of the behavior knowledge to improve the tracking step. The first idea is to drive the choice of the motion model in IMM. To go further, we also plan to use data coming from the platform such as weather, historical behaviors and even crossed data between beehives.
Using slightly modified diffusivities for some sites, and neglecting samples for which the firn fractionation signals are strongest, a combined multisite inversion can be performed, which returns an isotope reconstruction that is consistent with firn data. However, the isotope trends are lower than what has been concluded from Southern Hemisphere (SH) archived air samples and highaccumulation ice core data. We conclude that with the current datasets and understanding of firn air transport, a high precision reconstruction of δ 13 C of CH 4 from firn air samples is not possible, because reconstructed atmospheric trends over the last 50 yr of 0. 3-1. 5 ‰ are of the same magnitude as inherent uncertainties in the method, which are the firn fractionation correction (up to ∼ 2 ‰ at individual sites), the Kr isobaric interference (up to ∼ 0.
Data The δ 13 C(CH 4 ) firn air results are separately presented for the Northern Hemisphere (NH) sites and the Southern Hemisphere (SH) sites in Fig. 1. At each location, δ 13 C(CH 4 ) shows a stable or slowly decreasing trend with depth in the upper firn and stronger isotope depletions in the bubble close-off zone (deeper firn). The firn structure and thickness depend on the snow accumulation rate and on temperature at each site (Table  1 ) hence these parameters cause differences in the depth profiles observed among the sites (Fig. 1 ). For Table  1. Information on the eleven firn air pumping sites used in this paper. The NH sites are Devon Island (DI), North GRIP (NGR), NEEM-2009 (NM-09), NEEM-EU hole (NM-EU-08). The SH sites are DE08-2 (DE08), Berkner Island (BI), Vostok (VOS), Dronning Maud Land (DML) and Dome Concordia (DC)  (Bräunlich et al.
, 2001 ), South Pole 1995 (SPO-95)  and South Pole 2001 (SPO-01)  (Sowers et al. , 2005). (1) Institute where the measurements used in this study were performed. (  2   (Sowers et al. , 2005). The numbers associated with the names on the legend are the dates (year) of the firn sampling for each site. the NH sites, the differences are small between the Greenland sites (NEEM and NGR), because the surface conditions (temperature, snow accumulation, etc. ) are rather similar at these sites. The Devon Island (DI) dataset shows a different pattern, as surface conditions differ significantly from the Greenland sites. Numerous melt layers have been detected in the DI firn column (e. g. Clark et al. , 2007; Martinerie et al. , 2009) , which strongly reduce molecular diffusivity with depth. The SH data show larger differences between the sites, because the meteorological/glaciological variables (temperature, snow accumulation, snow morphology, etc.
Single-site δ 13 C scenarios In order to investigate the consistency among the different sites, the inverse model is first applied to the isotope data from each site individually and single site δ 13 C(CH 4 ) atmospheric trends are shown in Fig. 2a  and d. A regularization term is used to control the smoothness of the scenario and find a unique solution (see  Wang et al. , 2012 and Supplement). Figure  2b  and e  show the comparison of the measurements with modeled δ 13 C(CH 4 ) firn profiles using the scenarios shown in panels a and d. The differences between the modeled and measured firn air datasets (panels c and f) are in the majority of the cases similar to or smaller than the assigned measurement uncertainties. This shows that mathematically the inversion set-up performs well, i. e. the reconstructed atmospheric δ 13 C(CH 4 ) scenarios produce firn air isotope profiles, which are in agreement with the measurements.
Note that for the readability of the figure, the age spread is not represented in Fig. 6 , but that no discrete age exists for a given firn air sample. The lower panel shows that the data from DE08 and SPO-01 are near the lower envelope of the multi-site reconstruction, whereas the data from VOS are for a large part of the record near the higher end. This reflects the results from the single site reconstructions, where the VOS data required only a weak δ 13 C(CH 4 ) atmospheric trend, whereas DE08 and SPO-01 required a strong δ 13 C(CH 4 ) atmospheric trend. The multi-site reconstructions are constrained by δ 13 C(CH 4 ) data from 10 sites presented in Table  1  (i. e. excluding DI). For the NH sites, the multi-site scenario is constrained by only a few data points before 1990. Furthermore, the firn profiles at the three sites that enter the multi-site inversion (NGR, NM-EU-08 and NM-09) are alike, as shown by the similar vertical profiles in Fig.
7a  a comparison with five data points from EUROCORE  (Sapart et al. , 2012)  covering the period 1800-1950. Figure  7  shows that the direct measurements over the last decade (light grey lines) fall within the uncertainty envelopes of the δ 13 C(CH 4 ) reconstruction from the firn air samples. The Cape Grim archive data from the SH, which are not affected by firn air fractionation (dashed grey line in Fig. 7b ), show a steeper temporal trend than our reconstruction. Interlaboratory calibration offsets can considerably hamper direct 37 Figure  6 : Firn air δ 13 C(CH4) datasets corrected for isotopic fractionation in firn (diffusion and gravitational settling) as a function of age. A: NH sites, NM--EU--08 (purple), NM--09 (red), NGR (green). b: SH sites, DE08 (orange), BI (purple), SPO--95 (dark blue), SPO--01 (light blue), DML (black), DC (green), VOS (brown).
The dashed grey line in b) represents air--archive data from Cape Grim  (Francey et al. , 1998)  and the solid grey lines represent continuous atmospheric monitoring data from the NOAA--ESRL network. Note that the age spread of each data point is not presented here in order to keep the figure readable. comparison of the datasets and an absolute offset could potentially be attributed to uncertainties in the calibration scales. However, the Cape Grim data also show a somewhat stronger δ 13 C(CH 4 ) trend than the multi-site firn air scenario. Experimental uncertainties are further discussed in the next section. One other possible parameter that can influence the model results is the uncertainty in the best-guess CH 4 input scenario. A sensitivity test (Supplement, Fig. S2 ) shows that using atmospheric CH 4 values at the upper end of their uncertainty envelope may lead to a slope nearly consistent with the one from the Cape Grim air archive.
The red continuous lines are the "best--estimate" scenarios for each hemisphere with uncertainties (short dashed lines). The red long--dashed--line is the SH scenario shifted by 0. 5‰ a: NH raw δ 13 C(CH4) data (light blue dots) and δ 13 C(CH4) data corrected for firn fractionation from the EUROCORE ice core  (Sapart et al. 2012 ). b: SH raw δ 13 C(CH4) firn and ice data at DEO8 (yellow dots) and the same data corrected for firn fractionation with the CSIRO model (orange dots)  (Francey et al. , 1999 , Ferretti et al. , 2005) , with the LGGE--GIPSA model using regular diffusivity (black stars) and with the LGGE--GIPSA model but with "slightly modified diffusivity" as in Fig. 4  (red stars). The grey lines represent continuous atmospheric monitoring data from the NOAA--ESRL network (NH and SH).
4  (red stars). The grey lines represent continuous atmospheric monitoring data from the NOAA-ESRL network (NH and SH). were also calculated with the LGGE-GIPSA model using regular diffusivity (Fig. 7 : black stars) and "slightly modified diffusivity" as explained in Sect. 5. 3. (Fig. 7 : red stars). The corrected firn air data with the CSIRO model (orange dots) and with the LGGE-GIPSA model with modified diffusivity (red stars) show similar results which suggests that the difference in firn air models is not responsible for the differences between the Law Dome records and our SH firn reconstruction. The differences between the raw and firn fractionationcorrected δ 13 C(CH 4 ) shown in Fig. 7  demonstrate that the DE08 young ice core data are also significantly affected by isotopic fractionation in the firn.
This is expected since these samples contain air from a period when CH 4 mixing ratios in the atmosphere strongly increased. The same is true for the only available ice core data from the NH site EUROCORE (Fig. 7a ). However, here the δ 13 C(CH 4 ) data corrected for isotopic fractionation are rather at the high end of the firn air reconstruction. The firn/ice data comparison for the Northern Hemisphere is limited by the absence of overlapping period but the large difference between the EUROCORE and Law Dome data at 1950 additionally implies a possible unidentified interlaboratory offset between these datasets.
Additional model and measurement uncertainties As discussed above, the discrepancies between the different trend reconstructions may be due to calibration issues, uncertainties of the CH 4 scenario and/or insufficiently constrained diffusivity, but also to other features. A major drawback while working with multiple datasets measured at different times and in different laboratories is the uncertainty associated with intercalibration differences between measurement systems, but also reference gas drifts over time which may affect datasets measured in the same laboratory. Sapart et al. (2011)  suggested that the intercalibration should be carried out using at least 3 reference datapoints with different δ 13 C(CH 4 ) values to identify and quantify possible system offsets and scale contractions.
This exercise could not be carried out here, because most of the datasets presented were measured long ago and some systems have been dismounted. The available information about intercalibration is presented in Table  S1. It has been discovered recently that Krypton (Kr) produces a strong interference for CH 4 isotope measurements in many of the currently used analytical systems  (Schmitt et al. , 2013). When the Kr/CH 4 ratio of the sample differs from the Kr/CH 4 of the reference air, which is generally the case for firn air over the last century and for ice air samples, this introduces a strong bias in the δ 13 C(CH 4 ) measurements. This system specific interference introduces a strong bias dependent on both the concentration of the air sample and the analytical set-up. Considering the CH 4 concentration trend (about 800 ppb) between the lock-in zone and the firn surface, the bias induced by the Kr interference could reach -0.
8 ± 0. 2 ‰  (Schmitt et al. , 2013). For the IMAU system, it was possible to correct for the Krinterference  (Schmitt et al. , 2013). Using this correction, we carried out a δ 13 C(CH 4 ) reconstruction for the NM-EU-08, NM-09 and NGR datasets with Kr correction (Fig. 8 ). The temporal trend of the Kr-corrected scenario is about 0. 5 ‰ larger than the non-Kr corrected scenario showing the significance of the Kr-correction on δ 13 C(CH 4 ) trend reconstruction. Unfortunately, the Kr-correction is not possible for the SH datasets, which limits the interpretation of our SH reconstruction in term of atmospheric trends. Buizert et al. (2012)  showed that different firn air models produce isotopic fractionation effects of different magnitude. For δ 13 C(CO 2 ), the LGGE-GIPSA model produces the largest diffusive fractionation below 65 m depth at NM-EU-08, because a dispersive mixing term is not considered in the lock-in zone.
Conclusion and perspectives We reconstructed δ 13 C(CH 4 ) atmospheric scenarios over the last 50 yr for both northern and southern high latitudes using a firn transport model and taking as input δ 13 C(CH 4 ) firn air measurements from 10 firn sampling campaigns. Our calculations reveal discrepancies between sites and show that the modeled isotopic fractionation between 12 CH 4 and 13 CH 4 along the firn column is highly sensitive to slight variations in the diffusivity profiles at each site. After plausible adjustments of diffusivity profiles at individual sites, and neglecting the deepest firn air samples (where the isotopic fractionation is strongest), it is possible to reconstruct an estimate of δ 13 C(CH 4 ) for each hemisphere from a multi-site inversion, which fits the selected measurements within reasonable uncertainties; however, discrepancies between the different datasets and with previously published results remain.
At present, it is difficult to discriminate between different sources of uncertainty such as scale differences between laboratories, the recently discovered Krinterference affecting the δ 13 C(CH 4 ) trends, the representation of fractionation effects in the lock-in zone and the uncertainty of the 12 CH 4 scenario used. After the recent discovery of a Kr interference, analytical systems will need to be revised. This could eliminate an important source of discrepancies between datasets in the future. Our analysis showed that results of firn air models for δ 13 C(CH 4 ) strongly depend on accurate determination of the diffusivity profiles. Sampling at high-accumulation rate sites, such as DEO8 is an advantage, because firn fractionation is less strong at such sites, hence the δ 13 C(CH 4 ) reconstruction will be more robust.
Acknowledgements. We thank the teams involved in the firn air sampling at NEEM site during the 2008 and 2009 field seasons. NEEM is directed and organized by the  Centre of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs ). Aside from NEEM, firn air sampling programs were supported by: at  DI, IPEV (France)  and the  Canadian Continental Polar Shelf  Project; at  BI , IPEV and the  British Antarctic Survey ; at VOS, IPEV; at SP:  US-NSF ; at DC,  IPEV ,  ENEA (Italy)  and the  ESF/EC program  EPICA; at DML:  British Antarctic Survey ; at DE08: the  Australian Antarctic Division , the LGGE and CNRS; at NGRIP:  Funding Agencies in Denmark (SNF) ,  Belgium (FNRSCFB) ,  France  ( IPEV  and  INSU/CNRS) ,  Germany (AWI) ,  Iceland (RannIs), Japan (MEXT), Sweden (SPRS) ,  Switzerland (SNF)  and the  United States of America (NSF).
The isolates were representative of the S. agalactiae population and belong to the main clonal lineages defined by MLST  [13]. MLST was performed previously and data were not available for the 24 isolates from patients with infection of the respiratory tract  [14]. Briefly, PCR was used to amplify fragments of about 500 base pairs from seven housekeeping genes (adhP, pheS, atr, glnA, sdhA, glcK and tkt) as described by Jones et al. [13]. The seven PCR products were purified and sequenced, and an allele number was assigned to each fragment on the basis of its sequence. A sequence type (ST), based on the allelic profile of the seven amplicons, was assigned to each isolate. This previous work made use of the Streptococcus agalactiae MLST website (http://pubmlst. org/ sagalactiae/)  [24]. Based on allelic profile data, a dendrogram was drawn using BioNumerics 6.
The arrays were processed in the PCS 4000 ProteinChip Reader (Bio-Rad) which was programmed in a positive ion mode and at ion acceleration potential of 20 kV. Spectra processing and statistics using ProteinChip Data Manager 3. 0. 7 software (Bio-Rad) After calibration and normalization of all spectra using the total ion current method, clusters of peaks with the same mass were defined at the following settings: S/N (first pass) $5, minimum peak threshold: 20%, mass error: 0. 3%, S/N (second pass) $2. Three types of computer-generated statistics were used for data analysis: the non-parametric Mann-Whitney U test, the Kruskal-Wallis H test, and the method of heat maps/hierarchical clustering.
Database search and processing of results SEQUEST was used through a Bioworks 3. 1. 1 interface (ThermoFinnigan, San Jose, CA) to search a subset of the NCBI non-redundant database restricted to Streptococcus agalactiae entries. Peak lists were created using extract-msn (BioWorks 3. 3. 1 Thermo Scientific) with the default settings. Data files in the DTA specific format (DTA stands for the extension ''. dat'') were generated from the MS/MS spectra that attained a minimal intensity (n$100) and a sufficient number of ions (n$5). The DTA file generation authorized the averaging of several MS/MS spectra corresponding to the same precursor ion with a tolerance of 50 ppm. Spectra from peptides with molecular masses between 600 Da and 4500 Da were retained. The search parameters were as follows: mass accuracy of the monoisotopic peptide precursor set to 10 ppm and that for the peptide fragments was set to 0.
EDM and candidate biomarker selection The protein patterns of 170 isolates of GBS and of three reference strains of the same taxon were obtained on two types of ProteinChip arrays, CM10 and Q10. The spectral data obtained were subjected to statistical analysis: univariate p-value tests (Mann-Whitney and Kruskal-Wallis) and multivariate heat maps/ hierarchical clustering. The initial multivariate analysis (Figure  1 ) provides a global view of 164 protein biomarkers found in the five GBS groups of isolates (bovine mastitis, respiratory infections, meningitis, endocarditis, vaginal carriage) that are clustered by mass and intensity. Some combinations of biomarkers (arbitrarily grouped into the yellowand white-bordered rectangles on Figure  1 ) allow one or two GBS groups to be discriminated from the others, but none of these combinations was completely homogenous in terms of overexpression or underexpression (i.