dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS A. Weigelt ,  L. Mommer , and  J. van Ruijven  conceived original idea. A. Weigelt  and  L. Mommer  received funding for this project with input from  N. Buchmann ,  H. de Kroon ,  N. Eisenhauer ,  A. Hildebrandt ,  A. Milcu ,  C. Roscher ,  M. Scherer-Lorenzen , and  B. Schmid. K. E. Barry  collected data sets, analyzed data, wrote initial drafts, and incorporated feedback from all authors. J. van Ruijven,  Y. Bai ,  C. Beierkuhnlein ,  A. Ebeling ,  N. Eisenhauer ,  C. Guimaraes-Steinicke ,  F. Isbell ,  A. Milcu ,  C. Neßh€  over,  P. Reich ,  C. Roscher ,  B. Schmid ,  L. Sauheitl ,  D. Tilman ,  M. Scherer-Lorenzen  and  S. von Felten  provided data and provided detailed feedback on manuscript drafts. The authors would like to thank  Alyssa Hallett  for the illustrations contained in this manuscript.
The Jena Experiment is funded by the  German Research Foundation  (DFG, FOR 1451) and provided funding for KB. The study conducted in the Montpellier European Ecotron benefited from the  CNRS human  ( Damien Landais ,  Sebastien Devidal ,  Clement Piel , and  Olivier Ravel ) and technical resources allocated to the  ECOTRONS Research Infrastructure as well as from the state allocation 'Investissement d'Avenir '  ANR-11-INBS-0001. The BioCON experiment at Cedar Creek Ecosystem Science Reserve was supported by  U. S. National Science Foundation (NSF) Long-Term Ecological Research  grants  DEB-0620652  and  DEB-1234162 ,  Long-Term Research in Environmental Biology  grant  DEB-1242531 , and  Ecosystem Sciences  grant  DEB-1120064  and by  U. S. Department of Energy Programs for Ecosystem Research  grant  DE-FG02-96ER62291.
The  Big Biodiversity Experiment at Cedar Creek Ecosystem Science Reserve  was supported by  NSF   Long-Term Ecological Research grants   DEB-0620652  and  DEB-1234162. The  BIODEPTH  project was funded by the  European Commission  within the  Framework IV Environment and Climate programme  ( ENV-CT95-0008 ). B. Schmid was supported by the  University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB). N. Buchmann  was financially supported by the  Swiss National Science Foundation  ( 315230E-131194 , www. snf. ch). Further financial support came from the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by  DFG  ( FZT 118 ). LITERATURE CITED.
Acknowledgments The method to measure root exudate content was developed in the  Microbial Ecology laboratory  jointly with  Dr. L. Simon  and published by Guyonnet et al. (2018a) Ecology and Evolution DOI:  10. 1002/ece. 3. 4383. We thank the "Serre et chambres climatiques" platform ( Université Lyon1, FR BioEnviS ) for growing the plants, as well as the " Ecologie Isotopique" platform (Université Lyon 1, UMR 5023 ) for elemental and isotopic analysis. This work was supported by the  French National Research Agency  ( ANR-18-CE32-0005 , DIORE).
Structure of the plasma facing surfaces Surface structure and chemical composition of the plasma facing surfaces were assessed by the means of scanning electron microscope equipped with the EDX detector. Surface structure differs not only depending on tile position in the vacuum vessel but also across single tiles. Structure of the samples from central part of the tile from inner wall guarding limiter IWGL indicates direct interaction with plasma as there can be observed prolonged beryllium structures that are similar to the surface morphology of beryllium exposed to the light ion in plasma device Pisces-B  [22]. Whereas both side parts of the particular tile have scale like deposition layer that according to EDX data contains nickel, oxygen, carbon and traces of tungsten and iron. Similar surface structure was observed in both campaigns -ILW1 and ILW2 (Fig.
Study Population Patients included in this study participated in previously published phase II and III randomized controlled trials (RCTs) including adult patients with both recurrent and newly diagnosed glioma (Supplementary Table  1 ). Over 6000 patients are included in the CODAGLIO (ie, COmbining clinical trial DAtasets in GLIOma) project. 15 or the purpose of the current analysis, focusing on identifying symptoms clusters at the time of diagnosis, only RCTs involving newly diagnosed glioma patients and using the European Organisation for Research and.
Statistical Analysis Descriptive methods were used to summarize baseline sociodemographic, clinical, and HRQoL data, including the prevalence and severity of symptoms. For this study, only fully completed baseline HRQoL forms were considered. To evaluate differences between patients with and without a completed HRQoL baseline form (ie, possible selection bias), several clinical characteristics were compared using the chi-square test for categorical data and an independent Student's t-test for continuous variables. Mean scores on the functioning scales of the included patients were compared with a healthy normgroup to have an indication of the level of functioning of the included patients. 19 lustering of the symptoms was carried out in 3 steps, and we chose to define a symptom cluster as having a minimum of 2 symptoms.
and type of surgery), to determine the independent association between the symptom clusters and the functioning scales and the global health status/QoL scale. All variables were included simultaneously, allowing adjustment for confounders for the associations between the symptom clusters and functioning. In each multivariable regression model, a two-tailed P-value < 0. 05 was considered statistically significant. In terms of clinical relevance, beta coefficients ≥10 were considered clinically relevant and beta coefficients ≥20 were considered a large effect, corresponding with, respectively, a 10 and 20 point change in HRQoL scores. 25 Analyses were performed using IBM SPSS v23. 0  26  and R  27  with the qgraph package. 22.
This could potentially limit the generalizability of the results. Another limitation is that only baseline data were used in the analyses, and we do not know if the clusters identified at pretreatment are stable during follow-up. Moreover, only symptoms were included that were measured with the QLQ-C30 and the QLQ-BN20 questionnaires. Some relevant symptoms, such as mood disorders or cognitive complaints, were not covered, and therefore the use of instruments that specifically and extensively measure symptoms may be more useful. Furthermore, we included glioma patients with different tumor types in the analyses and, besides the subgroups glioblastoma/nonglioblastoma, could not look further into different molecular subtypes. One implication could be that the results of our study may not be generalizable to all glioma subtypes, as we saw for the GI/seizures/bladder control cluster, which was not found in non-glioblastoma patients.
For over two decades, the World Organisation for Animal Health (OIE) has engaged in combatting antimicrobial resistance (AMR) through a One Health approach. Monitoring of antimicrobial use (AMU) is an important source of information that together with surveillance of AMR can be used for the assessment and management of risks related to AMR. In the framework of the Global Action Plan on AMR, the OIE has built a global database on antimicrobial agents intended for use in animals, supported by the Tripartite (World Health Organization (WHO), Food and Agriculture Organization of the United Nations (FAO) and OIE) collaboration. The OIE launched its first annual data collection in 2015 and published the Report in 2016. The second Report, published in 2017, introduced a new methodology to report quantitative data in the context of relevant animal populations, and included for the first time an annual analysis of antimicrobial quantities adjusted for animal biomass on a global and regional level.
3. ] and on the "Harmonization of national antimicrobial resistance surveillance and monitoring programmes" [(2) Terrestrial Animal Health Code Chapter 6. 8. ], and "Development and harmonization of national antimicrobial resistance surveillance and monitoring programmes for aquatic animals" [(3) Aquatic Animal Health Code Chapter 6. 4] (3). In the framework of the Global Action Plan on AMR, the OIE has also built a global database on antimicrobial agents intended for use in animals, supported by the Tripartite collaboration (WHO, FAO, OIE). The OIE ad hoc Group on Antimicrobial Resistance developed a template for harmonized AMU data collection, as well as guidance for its completion that are available in the three official OIE languages (i. e. , English, French, and Spanish)  (4). The OIE launched its annual data collection on AMU in 2015, and published the first Report in 2016  (5).
OIE Calculation of the Animal Biomass To compare quantitative data reported on antimicrobial agents intended for use in animals between regions and over time, a scale is necessary to evaluate these data in the context of the relevant animal populations, which vary in size, and composition. Therefore, the quantitative data reported on antimicrobial agents intended for use in animals needs to be adjusted for the relevant animal biomass according to the following calculation: antimicrobial agents reported (mg) animal biomass (kg) Animal biomass is calculated as the total weight of the live domestic animals in a given population present during a year in a specific area, used as a proxy to represent those likely exposed to the quantities of antimicrobial agents reported. Animal biomass is currently employed as a denominator in analysis of quantitative antimicrobial use data by other national and regional antimicrobial use surveillance groups, such as the European Surveillance of Veterinary Antimicrobial Consumption (ESVAC)-(  9 ), the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS)-  (10) , and the Japanese Veterinary Antimicrobial Resistance Monitoring System (JVARM,  (11) ).
In 2017 the US FDA proposed a method for estimating a Biomass denominator  (12). While several methodologies have been developed for the calculation of animal biomass by other surveillance groups, none could be directly used for the OIE global database on antimicrobial agents intended for use in animals. Particularly, these methodologies utilize available data on animal populations detailed by production class, estimates of live animal weights, import/export data, and total annual populations of production groups living <1 year (i. e. , poultry, veal calves, fattening pigs, lambs, and kids). On a global level, such detailed data are not yet available for many countries, and therefore a new methodology was developed by the OIE mainly using globally available datasets-the OIE World Animal Health Information System (WAHIS) and the United Nations Food and Agriculture Organization Statistics (FAOSTAT).
The formulas for calculating biomass by species were developed using the two globally available datasets, WAHIS and FAOSTAT, and were compared to references from countries where more detailed animal population data by production class were available. These references include animal biomass figures either directly supplied from Member Countries, or calculated from animal population data in Eurostat, the statistical office of the European Union. The formulas chosen for calculation of the OIE denominator reflect the best fit estimations using the more general global animal population data (WAHIS, FAOSTAT) when compared to these available reference figures. The derived formulas were then applied to all countries providing quantitative data for the target year. All weights and biomass figures are measured in kilograms (kg).
Data collected by these global animal surveillance databases, WAHIS and FAOSTAT, are point in time species-level census data  1  with little to no detail on the production class. Such data are difficult to interpret given that production classes within a species can have very different average weights, such as beef cattle and veal calves. Additionally, given that census data are collected at one point in time of the year, the total annual population is not known for production groups which are slaughtered and repopulated a certain number of times within 1 year (this multiplication factor is hereafter referred to as "cycle factor"). In development of the methodology for calculation of an annual animal biomass, the underlying effort was to best use globally available census data from the OIE WAHIS interface.
WAHIS data are reported by National Veterinary Services through OIE Focal Points for Animal Disease Notification, and the figures are subsequently validated by OIE staff. FAOSTAT animal population data are used as a complementary dataset. FAOSTAT data are similarly primarily obtained from national governments, but sources expand beyond National Veterinary Services to National Statistics Offices and other relevant agencies. When a national government does not report a figure to FAOSTAT, FAO uses local expert resources to estimate a figure, or their statistical team to imputate a data point. The two datasets are therefore similar but can display significant variation. Where census data were used, the WAHIS and FAOSTAT figures are first cross-referenced with each other, and then with national reports or literature as needed.
FAOSTAT data are used when a WAHIS data point is not available or is outside of expected variation without explanation. In addition to census data, FAOSTAT also reports numbers and tons of production animal species slaughtered by country each year, similarly undifferentiated by production class. As WAHIS does not collect this information, FAOSTAT slaughter data is used when these data were needed. For species living <1 year, it was necessary to use data on number of animals slaughtered to represent an annual population, as this information cannot be extrapolated from point in time census data without a cycle factor specific to each country's production model.
Principles of Animal Biomass Calculation Methodology The overall objective of the methodology is to obtain the biomass of animals present during the year of analysis in a specific country using internationally available data. For a given species, animal weight varies by age and production class, and therefore the structure of the population of a given species must be taken into account. The first approach is to distinguish animals from production classes with a lifespan >1 year, and those with a lifespan of <1 year. For animals living for more than 1 year, it was considered that census data (number of animals present at one time) can be a good basis to evaluate the number of animals present in the country during the year. In this case, the biomass can be obtained by multiplying the number of animals present at one point in time (census data) by a calculated weight (if available or possible to calculate) or standard weight.
Generally, census data available represent the number of animals present at one point in time and include all animals within the species regardless of their age and production class. Thus, it is necessary to estimate the number of adults vs. young animals to ensure appropriate average weights were applied. Different methods for this estimation were used depending on the species and available data. For example, to differentiate breeding swine (sows) with a production lifespan >1 year, from the fattening pigs, an estimation of the percentage of sows in the total pig population was calculated based on Eurostat data, where production-class detailed information is available. To calculate the number of sows, the percentage obtained was applied to the census data as a constant for all the countries.
For cattle, the approach is different due to the large variation of body weight between production classes (calves, young cattle, and adults), and a broader diversity of production models (veal meat, beef meat, milking cows, etc. ). For this species a model was built to estimate the structure of an average bovine population, based on Eurostat production-class information, and was applied to all the countries. The mean weight of adult animals was generally based on existing standard weights, adapted regionally by livestock unit classification  (13). For animals living <1 year, production data (number and weight of animals slaughtered annually) were considered as a good basis to estimate the average weight of animals present in the country during the year. The application of these principles for calculating mean weights depends on the species; in some species, like poultry, mainly young animals are slaughtered (with a production life less than a year).
The live weight of the animal before slaughter was most easily accessible, and was considered to be a best representation of average weights for a global calculation of animal biomass. The live weights of animals before slaughter were calculated using FAOSTAT annual slaughter data, for all species and regions where these data were available, using the following two formulas: carcass weight (kg) = total weight of species slaughtered (kg) number of species slaughtered (heads) Carcass weights were converted to live weights at time of slaughter using conversion coefficients (k) as defined by Eurostat, also known as dressing percentages  (14). Conversion coefficients represent the difference between a processed carcass weight and the expected live weight of that animal species before slaughter, expressed as a fraction.
These population ratios were calculated in the reference Eurostat database and consider an anticipated renewal rate of 30%. Bovine biomass was calculated by multiplying the representative weight determined for each sub-region by the census population of bovines for each country within the sub-region, according to the following formula: census population × [ sub regional mean live weight ×LSU calves × P. pop calves + sub regional mean live weight ×LSU young 1-2yrs × P. pop young 1-2yrs + sub regional mean live weight × LSU adults × P. pop adults ] Whereby, P. pop calves, P. pop young 1-2years, P. pop adults represents, respectively, the proportion (P. pop) of calves, young (between 1 and 2 years of age) and adults in the total living cattle population, as calculated from Eurostat animal population data.
Determination of the Correction Coefficient Using reference datasets (Eurostat and several national detailed reports), where slaughter data are detailed by production category [adults, young (between 1 and 2 years of age), calves (<1 year of age)], it was estimated that, on average, the mean weight of live adult cattle was 15% higher than the Generic mean live weight at slaughter. mean live weight of adults at time of slaughter Generic mean live weight at slaughter = 1. 15 Therefore, applying an add-on factor of 15% (×1. 15) to the Generic mean live weight at slaughter is the best fit model to obtain the mean live weight of adult cattle when compared to the reference datasets (Eurostat and country specific data).
This weight was adapted by region using livestock unit ratios (Americas = 240 kg, Asia and the Pacific = 240 kg, Africa = 192 kg); • 0. 09 is the expected percentage of sows in a given swine population, as calculated from Eurostat animal population data. Poultry biomass was calculated according to the following formula: The live weight of horses, donkeys, and mules was calculated for regions where equine slaughter is common and data were available. For regions where equine slaughter is not practiced and/or where data were unavailable, live weights were adapted using livestock unit ratios. Sheep and goat biomass were calculated according to the following formula: × 75 kg represents the expected biomass of animals retained for breeding purposes, calculated with the following considerations: • 1.
5 is the average number of breeding cycles per year; • The standard weight of a breeding small ruminant in Europe is 75 kg  (9). This weight was used globally based on livestock unit ratios. Rabbit biomass was calculated according to the following formula: Farmed fish biomass was included in the total biomass only for countries that included aquaculture in their reported data on antimicrobials intended for use in animals. Aquaculture data are collected in WAHIS and FAOSTAT as tons produced annually, which were converted to kilograms for the animal biomass calculation. Data on farmed crustaceans, molluscs and amphibians were excluded given the relatively small size of these populations, and inconsistency in their reporting.
Limitations The OIE data collection on antimicrobials intended for use in animals is at an early stage of development and caution should therefore be taken when interpreting the data of the first years of data collection. Multiple data sources are used by the different countries including imports, wholesalers data, marketing authorization holder declarations, and veterinary prescription. The level of accuracy of the data may be different according to the data source, for example import data may be relatively imprecise compared to marketing holder declarations or veterinary prescriptions. In some cases, reporting of many different data sources can also result in over-estimated, duplicated or overlapping data. The OIE works with its Member Countries to correct thee issues wherever possible. The OIE will continue to support its Member Countries through its Regional Trainings for National Focal Points for Veterinary Products, where the guidelines are reviewed and Member Countries can ask the OIE questions and share experiences with their peers.
In the absence of global animal population data detailed by age and production, extrapolations were also calculated from European references. The extent to which these age class distributions of species apply to other countries is still undergoing a validation process. The methodology for calculating biomass in several species is based on the mean standard weight of animals for breeding. An effort was undertaken to adapt these mean standard weights between regions using livestock units  (13). A review of how well these standard weights depict the variability at a regional and national level has been initiated. For cervids, camelids, and equines in some regions, data on breeding cycles were not collected at the time of reporting, nor was slaughter data. Therefore, this information was taken from literature where necessary, or extrapolated from regions where data was available (such as in the case of live weights of equines).
Data on populations of cats and dogs are available in WAHIS, however, many countries do not report these figures, or report them inconsistently. Another consideration is the need to better understand whether reported cat and dog populations represent owned or stray animals, as this would affect the likelihood of their treatment with antimicrobials. For the countries where cat and dog populations were available, it was seen that their contribution to overall biomass was minor (<1%). However, as some countries do include antimicrobials used in companion animals in their reported quantitative data, there is expected to be a small effect on results by excluding these species. As excluding them decreases this denominator, this effect, if any, would be a minor increase in antimicrobial quantities adjusted for animal biomass.
Methods The panel included 22 members with recognized clinical and research expertise in ITP representing North America (United States, 4; Canada, 1), Europe (13), Australia (1), China (2), and Japan (1). There were 3 pediatric hematologists and 18 adult hematologists (2 with expertise in obstetric hematology). Two members were experts in clinical trials methodology. There was 1 patient representative. The methodology of the original consensus report was closely followed. A literature search of the electronic database PubMed was performed in July 2018. The following search terms were used: "immune thrombocytopenic purpura," "idiopathic thrombocytopenic purpura," "autoimmune thrombocytopenic purpura," "autoimmune thrombocytopenia," "idiopathic thrombocytopenia," "immune thrombocytopenia," and "ITP.