dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank Shane Bussmann for leading a proposal to obtain some of the data presented in this paper. T. K. D. L. acknowledges support from the NSF through award SOSPA4-009 from the NRAO and support from the Simons Foundation. D. R. acknowledges support from the NSF under grant number AST-1614213 to Cornell University. A. J. B acknowledges support from NSF grant AST-0955810, to Rutgers, The State University of New Jersey. I. P. -F. acknowledges support from the Spanish research grants ESP2015-65597-C4-4-R and ESP2017-86852-C4-2-R J. L. W acknowledges support from an STFC Ernest Rutherford Fellowship (ST/P004784/1 and ST/P004784/2), and additional support from STFC (ST/P000541/1). The Flatiron Institute is supported by the Simons Foundation. This work is based on observations carried out with the IRAM PdBI Interferometer.
IRAM is supported by INSU/CNRS (France), MPG (Germany), and IGN (Spain). Support for CARMA construction was derived from the Gordon and Betty Moore Foundation, the Kenneth T. and Eileen L. Norris Foundation, the James S. McDonnell Foundation, the Associates of the California Institute of Technology, the University of Chicago, the states of Illinois, California, and Maryland, and the National Science Foundation. CARMA development and operations were supported by the National Science Foundation under a cooperative agreement and by the CARMA consortium universities. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration.
This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2016. 2. 00105. S; ADS/JAO. ALMA# 2013. 1. 00749. S; and ADS/JAO. ALMA# 2011. 0. 00539. S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. This research has made use of NASA's Astrophysics Data System Bibliographic Services. The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. This work is based in part on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA), and the Canadian Astronomy Data Centre (CADC/NRC/CSA).
This research made use of ASTROPY, a community-developed core Python package for Astronomy  (Astropy Collaboration et al. 2013). This research made use of APLPY, an open-source plotting package for Python hosted at http://aplpy. github. com. [-3, 3, 4, 5, 6, 9 ]× σ ch , where σ ch = 0. 031 mJy beam -1. The star symbols indicate the positions of the two compact dust components detected at 635 µm (rest-frame 158 µm; XD1 and XD2; see last panel of Figure  16 ). The synthesized beam is shown in the lower right corner of the last panel (0. 94 ×0. 71 at PA = 31 • ).
TIV in different settings and populations. Despite significant advances in our understanding of influenza B  [14] , important knowledge gaps persist, in particular concerning the pattern of circulation and geographical distribution of B/Victoria and B/Yamagata viruses, and the age distribution of patients who are most often infected with either lineage. These aspects are of great importance, however, for instance in order to evaluate the cost-effectiveness of alternative influenza prevention strategies and thus enable more appropriate public health choices (e. g. around the choice of tri-and quadrivalent vaccines)  [15]. In fact, while some reports have addressed this topic in single countries  [16] [17] [18] [19] [20] [21] [22] , a global overview is still lacking. Here, we conducted a global analysis of the epidemiology of influenza B virus and its lineages using the database of the Global Influenza B Study (GIBS).
The Global Influenza B Study The rationale and methodology of the Global Influenza B Study has been described in detail elsewhere  [14, [23] [24]. The GIBS database encompasses epidemiological and virological influenza surveillance data from thirty-one countries around the world (Fig  1 ). Participating countries were classified as located in the Northern hemisphere (n = 11), inter-tropical belt (n = 15), or Southern hemisphere (n = 5) based on the latitude of the country's population centre  [25]. Participating countries were required to make available influenza surveillance data for as many consecutive years as possible (from 2000 onwards). Data include the weekly number of laboratory-confirmed influenza cases broken down by virus type (A, B), type A subtype (H1N1, 2009 pandemic H1N1, H3N2, A not subtyped), and type B lineage (Victoria, Yamagata, B not characterized) and weekly influenza-like illness/acute respiratory infection rates (per 100,000 population or 100 consultations, depending on country).
Information on age (exact age or age groups) was also available. In most participating countries, the influenza surveillance system covers the whole national territory, and a sample of influenza virus positive specimens or virus isolates is routinely sent to a WHO collaborating centre for further investigations. However, countries differ in other important aspects of their surveillance system, e. g. the clinical case definition in use, the distribution of outpatients and inpatients being sampled, and the availability of population denominators  [14, 24]. The GIBS database was initially assembled in 2013-14 by merging influenza surveillance data up to as late as December 2013 from thirty countries  [14] , but was updated for the purposes of this paper with data until 2015 to 2018 (depending on the country) for twenty-five countries.
Statistical methods Similar to previous GIBS publications  [14, [23] [24] , the unit of analysis was the "season": this corresponded to the calendar year in tropical countries and countries located in the Southern hemisphere, and was defined as the period between the 27 th week of a year and the 26 th week of the following year for countries located in the Northern hemisphere. As previously explained  [23] , the purpose of this methodological approach is to give each "season" an equal weighting in the analysis, thus limiting the impact on results arising from any differences in reporting between countries (e. g. high-vs. low-resources countries) and over time within the same country (e. g. before vs. after the 2009 pandemic). For each country, we included in the analyses the seasons with at least 20 weeks of reported data and at least 100 laboratory-confirmed influenza cases.
The recent update of the GIBS database and the resulting increase in the number of cases with characterized influenza B viruses made it possible to compare the age distribution of influenza B/Victoria and B/Yamagata cases in a similar way to what had been done previously for influenza B vs. A (and its subtypes)  [14, 24]. This analysis was limited to countries for which information on the exact age was available for at least 50 B/Victoria and at least 50 B/ Yamagata influenza cases over all seasons available. The virus-lineage-specific age distribution of influenza B cases was visualized, separately in each country, using histograms with 5-ageyear-wide bars. Since the distributions were skewed to the right, we used the non-parametric Wilcoxon rank sum test to compare the median age of influenza B cases by virus lineage.
Results The database of the Global Influenza B Study encompassed a total of 1. 820. 301 influenza cases between 2000 and 2018, of which 419,167 (23. 0%) had type B influenza. Cases were unevenly distributed between countries, with USA and Australia contributing 54. 1% and 25. 3% of all influenza cases to the database, respectively. Information on age was not available for the USA; for the remaining countries, information on age was available for most (95. 0%) influenza cases. After applying our exclusion criteria, 299 seasons with 100 or more reported influenza cases overall were included in the analysis, of which 110 were from Northern hemisphere countries, 131 from countries located in the inter-tropical belt, and 58 from Southern hemisphere countries. The median number of seasons per country was nine, ranging from three (for Turkey) to eighteen (for New Zealand).
Frequency of B/Victoria and B/Yamagata lineages viruses There were 84 seasons where the virus lineage was determined for 50 or more influenza B cases (out of a total 299 seasons, 28. 1%) and this data covered 18 countries (out of 31). In these seasons, the B/Victoria and B/Yamagata lineage viruses caused a similar range of proportions of influenza B cases. More precisely, the median proportion of influenza B cases reported in a season that were caused by B/Victoria lineage viruses was 46. 0% (IQR 5. 3-89. 6%). The range of the proportion of B/Victoria over all influenza B cases was 0%-100%, meaning that there were seasons in which all influenza B cases were caused by either virus lineage. Co-circulation of the two lineages was frequent: in 27 (32. 1%) of 84 seasons, both lineages accounted for at least 20% of influenza B cases, while the B/Victoria and B/Yamagata lineages caused over 80% of all influenza B cases in 27 (32.
The only exception (i. e. the only country in which B/Victoria cases were older than B/Yamagata cases, albeit not significantly) was Australia. The reason for this divergent pattern is not clear; however, since the majority of B/Yamagata cases in the database from Australia occurred in 2008 (see S1 Table ), this finding might be driven by a single season with an unusual age distribution of B/Yamagata patients. Importantly, our findings support earlier work showing that each influenza A subtype (H3N2, pre-2009-pandemic H1N1, and H1N1pdm09) tends to affect a different age group  [24]. A recent study has shown that the B/Victoria viruses antigenic drift parallels that of A(H3N2), with limited cross-reactivity between phylogenetic clusters compared to B/Yamagata (which, in contrast, resembled A(H1N1) under this regard, with multiple variants circulating and greater levels of antigenic cross-reactivity)  [40].
B/Victoria viruses  [41]. Finally, antigenic original sin (i. e. preferential response to influenza B viruses encountered in the past  [42] ) may also play a role. If this were true, the mean age of patients infected with either B lineage would be expected to drift over time: this has occurred in Australia between 2008-2011  [40] , but we were unable to replicate these results in the GIBS database (results not shown). These findings have important implications for assessing the impact of influenza B viruses in a population each season. Knowing early in the season (through surveillance activities) which influenza B lineage virus is circulating will allow one to predict which age groups will be more affected, with B/Yamagata seasons being generally characterized by more adults and elderly people being infected and, therefore, by a greater burden of disease compared to B/Victoria seasons.
This appears to have been the case during the 2017/18 season in Europe, when B/Yamagata was dominant (and there was a lineage-level vaccine mismatch) and a high burden of influenza B was observed in many European countries  [43]. We analyzed influenza surveillance data from around the world to determine the epidemiology of influenza B virus and its lineages in the 21 st century. We have enriched the analysis of an earlier GIBS paper  [14]  by including more countries  (31 vs. 26)  and additional years (data to up to 2018): the overall number of reports analysed is now twice as large (>1. 8 million vs. 935,000 cases). This allowed us to conduct a comprehensive comparative analysis of the epidemiological characteristics of B/Victoria and B/Yamagata virus lineages, thus achieving a better understanding of the epidemiology of influenza B globally.
Throughout this paper, we have used statistical methods that were intended to minimize the impact of this variability on the results. These include the use of the 'season' as a unit of analysis for the comparison of the frequency of B influenza across countries, the standardization offered by the EPIPOI software in the analysis of temporal patterns of influenza epidemics, and the use of medians to compare the age distribution of influenza cases between countries. However, we acknowledge that these sources of variability may have affected our findings to an extent that is difficult to quantify precisely, and a further improvement in the harmonization of influenza surveillance data remains desirable as it would allow researchers to conduct more detailed and in-depth analyses and obtain more reliable results.
Acknowledgments The " Global Influenza B Study team " (group authorship) includes the following scientists:  Juan Manuel Rudi  (jmrudi@anlis. gov. ar), National Institute of Respiratory Diseases "Emilio Coni" ,  Santa Fe , Argentina;  Dorji Wangchuk  (dorjiwangchuk@health. gov. bt) and  Sangay Zangmo  (szangmo@health. gov. bt), Royal Centre  for Disease Control,  Department of Public Health, Ministry of Health, Thimphu, Bhutan ;  Daiana Araujo da Silva  (daiana. silva@saude. gov. br), Ministry of Health, Department of Surveillance of Transmissible Diseases, Brası ´lia/ DF, Brazil ;  Winston Andrade  (wandrade@ispch. cl), Sub-Department of Viral Diseases, Instituto de Salud Publica de Chile, Santiago, Chile ;  Jiandong Zheng  (zhengjd@chinacdc. cn) and  Ying Qin  (qinying@chinacdc. cn), Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing , P.
however, to be approved by the study group at  NIVEL  and by contributors from each participating The study is supported by a research grant from  Sanofi Pasteur : the ' Global Epidemiology of Influenza B ' research project. The funder provided support in the form of salaries for two authors (CEGS and JP) but did not have any additional role in the data collection, analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the "author contribution section". None of the surveillance schemes in the participating countries was supported by this grant. Clotilde El-Guerche Seblain is an employee of Sanofi Pasteur. She  was the coordinator of the research project at  Sanofi Pasteur , she helped define the study objectives, and critically revised the manuscript.
5% Triton X-100, and intracellular bacteria were counted after transfer to BHI agar plates. Percentage invasion was determined as the ratio of intracellular bacteria to the number of bacteria in the initial inoculum. Experiments were performed in triplicate wells for at least four independent assays. Results were analysed by a nonparametric one-way analysis of variance (ANOVA). Further statistical analysis or post-hoc comparisons were done using the Tukey test and the GraphPad Prism software.
Acknowledgements We thank  A. Boukadiri ,  C. Cherbuy ,  C. Longin ,  M. A. Nahori  and  J. Rivière  for technical advice or assistance. We thank  I. Podglajen  ( AP-HP, Hôpital Européen Georges Pompidou, France ) for providing the strains  VE14818 ,  V14819  and  VE14821  ( Centre cardiologique du Nord ), and  P. Y. Allouch  ( Service Hygiène hospitalière, CH de Versailles ) for the gift of strain  VE14518. We are grateful to  D. Griffin  ( Chiron Biopharmaceuticals ) for the gift of daptomycin. We also thank members of staff members from the conventional animal facilities (IERP, INRA, 78352 Jouy-en-Josas, France) and the germfree animal facilities (ANAXEM, INRA, 78352 Jouy-en-Josas, France). We thank the histology facility of UMR 1313 GABI, 78350, Jouy-en-Josas, France. This work has also benefited from the facilities and expertise of MIMA2  MET -GABI (INRA, AgroParisTech , 78352 Jouy-en-Josas, France; www6.
The thermal and magnetic signals during martensitic transformation were recorded by using differential scanning calorimetry (DSC, TA-Q100) and superconducting quantum interference device (SQUID, MPMS), respectively. The scanning rate for DSC measurement was 10 K/min. The mechanical properties were measured by a universal mechanical testing machine (AG-XPlus 100 kN) at room temperature. The temperature change of elastocaloric effect was determined by using a thermocouple welded to the lateral surface of the specimen. The spin-polarized total energy and electronic structure were calculated based on the density function theory (DFT) using the Vienna Ab initio Simulation Package (VASP 5. 4). The projector augmented wave (PAW) pseudopotential method and Perdew-Burke-Enzerh (PBE) exchangecorrelation functional were used.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  (Grant Nos. 51431005 ,  51801020 ,  51922026 ,  51771044 ), the  Fundamental Research Funds for the Central Universities  (Grant No. N170203009 ), the  Liao Ning Revitalization Talents Program  (Grant No. XLYC1802023 ), the  Doctoral Starting up Foundation of Liaoning Province  (Grant No. 20180540115 ) and  Postdoctoral Science Foundation of China  (Grant No. 2018M641700 ).
The wider and wider use of very high-resolution (VHR) remotely sensed images has stimulated the development of object-based methods and data mining techniques, which were much more effective for change detection  [35]. Bauman et al. [24]  have developed a methodology for detecting and analysing windthrow for various meteorological phenomena (from simple storms to meteorological extreme events). Moreover, they have managed to separate the disturbances due to the storms as compared to those caused by massive forest cuttings in the forest areas, as well as to the other land cover classes. The methodology applied in this study was based on the windthrow detection using the Disturbance Index that was designed considering the Tasselled Cap Transformation and the "dark object" approach. The purpose of our application is to detect the forest areas in the Vosges Mountains affected by the Storm Xynthia in February 2010, as it is well known that there are scattered windthrows, without taking into account more sophisticated and more expensive imaging resources, such as those mentioned above, but, on the contrary, using Landsat's least expensive satellite imagery.
Besides availability and resolution, the cost of data represents also an important factor to be considered. We also have to admit that sometimes it is not pos-sible to capture real-time images or create time series of images. Maybe this would be possible, but the cost of raw data purchases is too high. Therefore, we would like to assess if less expensive and easier-to-acquire images, such as Landsat, despite their lower resolution, would still be sufficient and capable of detecting scattered windthrows. Starting with 2014 Sentinel Radar images are also free, but obviously these cannot be considered because the event we studied occurred in 2010. If the purpose of a research is the restoration of the old windthrown trees, obviously, only the sources produced by the older satellites or having an archive (Landsat from 1980) can be accessed  [40] [41] [42] [43] [44] [45] [46] [47].
The reason in choosing Landsat sensor for this study was the wide swath and the low cost. The medium resolution Landsat data is not as sharp as high-resolution images, but the amount of information it contains along with the large area coverage make this data suitable for large-or small-scale studies. We studied the scattered windthrows in the Vosges Mountains and the exact location of these disturbances was unknown. Each type of satellite images has advantages and disadvantages in detecting windthrows. Malthus et al. [48]  have developed a comparative study to emphasize the advantages, limitations and, where possible, even prices of current remote sensing image sources used in commercial forestry. Similar comparisons have been developed by Suárez et al. [49] , which compared optical techniques with RADAR and LIDAR data.
Workflow In this study we aimed to highlight the applicability and efficiency of Landsat data in detecting scattered windthrows caused by the storm Xynthia that crossed the Vosges Moun-    1 ). We selected no clouds or very few clouds images that were recorded during the growing season and for the same vegetation season (June), to reduce the impact of vegetation in different seasons. The study was conducted using standard ArcGIS and ENVI software's. The main steps followed are illustrated in Figure  3. The first step consists in pre-processing the satellite imagery, namely converting them from digital numbers (DNs) into reflectance. This operation was done using the formulas given by Chander & Markham  [55, 56]. First, the DNs were converted into spectral radiance using the equation: Lλ = (gain * DN) + biasλ  (1)  where: L λ -the spectral radiance at the sensor level and referring to the wavelength λ for the specific band; DN -Digital Number; gain and bias -are specific calibration parameters determined before launch For the relatively clear Landsat images, a reduction in variability of images has been made by converting spectral radiance into surface reflectance, using the following equation: Rλ = (π * Lλ * d2)/(ESUNλ * sin θS) (2) where: R_λ -Spectral radiance at the sensor's aperture; Lλ -Spectral radiance at the sensor's aperture calculated through equation 3; d -Earth-Sun distance [astronomical units]; ESUNλ -Mean exoatmospheric solar irradiance  [56] ; SinθS -solar zenith angle in degrees.
As Darwish et al. [66]  stated, the segmentation techniques on the Landsat images emphasized the fact that the results provided a low accuracy. Therefore, this method is not recommended for medium resolution images. Instead, in this study we used the Disturbance Index to detect the affected areas. The last step in the "7-points method" is to integrate the results obtained by segmentation with the soil results. Thus, by classifying the disturbances produced, a map of damage was obtained. Due to lack of ground data, we have chosen to do a validation based on the error matrix. The Landsat images have been used successfully in other studies to detect forest area affected by wind storms using vegetation indices, image differencing, principal component analysis and others approaches. Landsat data and Disturbance Index was used by Healey  [23]  in assessing forest disturbances in multi-temporal data.
84). The results of this research highlight the usefulness of the Landsat satellite imagery, more easily to access than other types of images for the analysis and detection of windthrows. It is worth mentioning that when detecting and assessing windthrows, the choice of the appropriate sensor will depend on the availability of data, cost, scale of the fragmented landscape and area of interest, because the high resolution imagery is not available globally, like Landsat data. If the exact small scale (< 0. 5 ha) delineation of the windthrow is needed, then the high-resolution imagery is suitable. But if the goal is to detect windthrows quickly, then the medium resolution data is more useful.