dataesr/scientific-paragraphs-categorization huggingface dataset:
g. basal area increment) and carbon fixation from the tree ring data. Tree-ring data from tropical forests are scarce at the ITRDB (partially due to the inherent difficulty of ring formation and cross-dating in these tropical sites), but ITRDB data have been successfully used in global analyses (e. g. Anderegg et al. , 2015). Palaeoecological data at the European level are harmonised in the Neotoma Paleoecology Database (Neotoma) and the European Pollen Database (also accessible via Neotoma, see Appendix B). The main datalimitations relate to the spatial coverage (uneven distribution of sites across Europe), multisource and sampling effects (i. e. time interval can differ between sampling sites). Neotoma and the EPD are open-access standardized databases of published palaecological records to foster broad-scale (global or continental-scale) vegetation and land-use history studies  (Williams et al.
There is an increasing amount of openaccess large-scale airborne LiDAR data across Europe (generally at regional scale) and the recently launched GEDI Mission will provide global coverage of spaceborne LiDAR (though over a relative short duration, Appendix B). TLS has the potential to move forward forest inventory datasets by providing new structural measurements at fine spatial scales  (Liang et al. , 2016 , White et al. , 2016)  as well as new means to determine uncertainty of forest properties quantified by spaceborne and airborne methods.
Environmental data Climate databases at European or global levels differ in spatiotemporal resolution and extent. Mean climatic conditions for the 20 th century are often directly available at high spatial resolution and at global or European scales from databases such as Worldclim, E-OBS, Chelsa, Climatic Research Unit (CRU, see Table  3  and Appendix B) either for a certain period (e. g. WordClim data provide mean values for  or even monthly values for each year (e. g. E-OBS, CRU-TS, CRU-CL or CRU-SR, Appendix B). Temporal data on past temperature and precipitation (i. e. daily, monthly or yearly records) are available at the global and European level (e. g. CRU and E-OBS, respectively). There are new databases that combine the spatial resolution of WorldClim (1 km 2 ) with the temporal resolution of CRU (1901 -2014)  (Fréjaville & Benito Garzón, 2018) , and European climate data has been downscaled at 1 km 2 for large temporal frameworks (i.
e. 1951-2012, see  Moreno & Hasenauer, 2016). There is also an R packags available to interpolate and downscale coarse climate data and obtain daily weather variables at landscape level  (meteoland, De Caceres et al. , 2018). Past climatic data can be used to calculate changes in climate (i. e. climatic anomalies based in annual data, e. g. Ruiz-Benito et al. , 2014). Drought effects are derived from climatic databases that are available at detailed spatial and/or temporal resolution (e. g. precipitation and drought indices; see Appendix B). Climatic data for future scenarios are available globally and bias-adjusted from the Intersectoral Impact Model Comparison Project  (ISIMIP, Frieler et al. , 2017)  and for Europe at different spatial resolutions from the EURO-CORDEX (https://www. hzg.
de/ms/ euro-cordex/) to CRU database or Wordclim (see Appendix B). Other environmental drivers include topographic information (e. g. elevation, slope and aspect), soil classification and properties, disturbance and management information, atmospheric nitrogen or sulphur deposition and CO 2 concentrations, etc. Topographic information can be easily obtained from digital elevation models at different resolutions (e. g. from 2 m 2 to 1 km 2 , Table  3 ). The Soil Grid dataset (https://soilgrids. org/) provides global information about site characteristics, physical and chemical properties (Appendix B). European Soils Data Centre (ESDC) and ISRIC World Soil Information provide a wealth of soil science information, and the FAO a global soil organic carbon map, which is mostly open-access and directly downloadable at 1 km 2 (Appendix B).
In addition to soil property and quality datasets, the ESDC hosts information on different soil functions and threats to soil functioning. Soil water content, temperature and snowpack has been estimated from 1979 to 2010 in the ERA-INTERIM/Land at a resolution of 0. 125°  (Balsamo et al. , 2015)  and soil organic carbon is mapped at 1 km 2 resolution in the Global Soil Organic Carbon Map (Appendix B). However, potential drivers of forest responses to climate change as soil fertility or water retention  (Wardle et al. , 2008)  is not easily accessible at detailed resolution for the European extent. Disturbances such as fires, pests or pathogens are major drivers of forest vulnerability that can strongly interact with climate change (e. g. Pausas & Keeley, 2009). Palaeoecological records often include charcoal data to reconstruct changes in fire activity through long timescales, which can be freely accessed and downloaded from the Global Charcoal Database (GCD;  Power et al.
, 2010)  and Neotoma  (Williams et al. , 2018). The Database of Forest Disturbances in Europe (DFDE; Appendix B) provides historical data on abiotic (i. e. wind and snow damage) and biotic (pathogens and insects) disturbance agents. DFDE has been used at the country-scale to empirically parameterise landscape models to predict future disturbance levels under different climate change scenarios  (Seidl et al. , 2014). European initiatives to record and disseminate forest disturbance information include the EFI database, European Forest Fire Information System (EU-EFFIS) and the European Storms Catalogue (Appendix B). However, there is a considerable lack of geo-referenced data on pest and alien species in European forests and they are poorly linked to other databases on forest health such as ICP forests.
Some initiatives involving citizen science are providing georeferenced data of forest pests at regional levels (e. g. http://www. alertaforestal. com/es/). The European Network of Alien Species (EASIN) provides access to records of alien species in Europe, via a mapping tool and a geo-referenced database of published scientific reports (EASIN-lit; Appendix B), although there are few records regarding forest ecosystems. Data availability on forest management practices across Europe is limited because it is difficult to assign a management system to a forest stand based on signs of its recent management; long-term historical records are essential, but they are largely missing across most of Europe. NFIs are a valuable source of information on recent forest management but harmonising the descriptions across countries will remain challenging until a common classification system is used.
The scarce information about management in vegetation inventories has generally led to harmonisation as a binary indicator field (managed or unmanaged), which provides only minimal information to aid in the understanding of forest responses to management (see e. g. Vayreda et al. , 2012). The Natura 2000 and Nationally Designated Areas (CDDA; see Appendix B) initiatives provide spatial information on the protected sites at the European level. These datasets cannot be used to infer the development of a particular management activity, but they could be used as an indication of different forest policy and management objectives. Given the limited availability of management information, historical reconstruction maps (e. g. McGrath et al. , 2015) , forest management simulators  (Härkönen et al.
Considerations for harmonised data use in modelling forest responses to climate change Harmonised and quality-controlled data at the European scale are needed for robust assessments of forest responses to climate change  (Serra-Diaz et al. , 2018; Reyer et al. , 2019). We have demonstrated that data availability at the European extent has increased in the last few decades for a multitude of forest properties ranging from genetics to demography, forest structure and occurrence/abundance (Table  2 ) as well as for the potential interacting drivers of climate change (Table  3 ). We have also identified many open and semi-restricted databases across Europe, which will facilitate future integrative research on forest responses to climate change using multiple data sources. We found several limitations that should be considered when developing models and frameworks based on the databases presented here, relating to spatial and temporal coverage and the effects of using multisource data and data with different sampling methodologies.
Firstly, for specific forest properties data are not publicly available at high resolution or for many European species, particularly for intraspecific trait variability, adaptation and phenotypic variation, and physiological and dispersal responses. Secondly, the temporal coverage of key responses to climate change such as defoliation, mortality and recruitment is short (e. g. the main sources are vegetation inventories, which are only available since the 1980s). In addition, there are sampling issues such as the under-representation of big trees, no individual or harmonised data of tree recruitment and extreme responses might be under-represented when permanent plots of forest inventories are used. Thirdly, long-term data are available for forest cover and tree growth, but researchers should be aware of data limitations regarding spatial coverage (i.
e. generally localised data) and sampling effects (e. g. selection of sensitive species/sites for study). The main limitations regarding underlying drivers of forest responses to climate change that we identified are the availability of meaningful and detailed soil information, long-term data about disturbances and forest management and legacy effects on forest functioning. Finally, most of the databases cannot deliver cause-effect mechanisms except emerging ecosystem experiments (see e. g. meta-phenomics database, Appendix B) and plant responses can differ in field-conditions  (Poorter et al. , 2016). The lack of data on key mechanisms of forest responses to climate change either at high spatial resolution or long temporal span at the European scale can strongly hamper modelling of forest tree responses to climate change (Table  4 ).
There are several examples of high-quality data at national, European or global extent that could serve as models for future data infrastructures. At the national and continental level forest inventories and the ICP databases are examples of systematically collected data that are widely used to asses forest vulnerability to climate (e. g. ICP database, UNECE & ICP Forests Programme Co-ordinating Centre, 2016). At global scales GFBI, ITRBD, FLUXNET data  (Aubinet et al. , 2012)  and the TRY database  (Kattge et al. , 2011)  combine high-quality data with established quality and assessment controls. The increasing availability of data will further allow us to investigate complex mechanisms relevant for the assessment of forest impacts to climate change and to integrate them in a wide variety of forest models.
The main data priorities to improve our understanding and model forest impacts to climate change are: (i) to maintain monitoring in existing data networks and start targeted new monitoring that addresses the identified gaps such as measuring climatic extremes and responses and to obtain long-term high-quality data on critical biological mechanisms driving forest responses to climate change, such as adaptation capacity, physiological responses, dispersal and regeneration, and mortality; (ii) to promote the availability and provision of harmonised freely-available databases and further develop the standardisation methods and quality assessment approaches; (iii) to increase discussion and networking between those scientists primarily involved in data collection and those in modelling and data integration; (iv) to encourage data integration methods from different sources, because they have the potential to use the existing information in the data more effectively and provide detailed information at large spatial and long temporal scales that can be used in different modelling frameworks.
Funding This work was supported by the  European Cooperation in Science and Technology (COST)  www. cost. eu[grant number  FP1304  PROFOUND]; the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) [grant agreement No. PCOFUND-GA-2010-267243  (Plant Fellows)];  Ministerio de Ciencia, Innovación y Universidades, Spain  [grant number,  CGL2015-69186-C2-2-R FUNDIVER  and  RTI2018-096884-B-C32 DARE]  and  Comunidad de Madrid  [grant number  S2013/MAE-2719 Remedinal-3  and  2016-T2/AMB-1665 ]. CPOR acknowledges funding from the  German Ministry of Science and Education [BMBF , grant numbers  01LS1711A  and  01LS1201A2 ]. MBG was funded by the  'Investments for the Future' program IdEx Bordeaux  [grant number  ANR-10-IDEX-03-02 ]. FH acknowledges funding from the  Bavarian Climate Research Network  via the research network BLIZ.
ago- centennial timescale relevant for forest ecosystems; uneven present spatial occurrence, sometimes quite localised; no large-scale (Multi-decadal to spatial coverage at high resolution, relatively low time millennial) resolution) Ground RS Structure Local Restricted access, highly localised, no large-scale databases (Terrestrial laser scanning, leaf (height, dbh, biomass, fine-scale crown metrics (cm -ha) available spestoscopy) and canopy gaps) Since 00 s (Easy sampling of fine spatial explicit measurements, require (NA to decadal) fieldwork and data processing) Airborne RS Structure Local-Regional- From open-to restricted access, highly localised (Photogaphy, optical, LiDAR (canopy and sub-canopy including height, National (cm) (Detailed structural data, require data processing) SAR) biomass, crown metrics) Since 00 s (NA to decadal) Space-borne RS* Demography, structure and composition Global-continental Open-access (Computational challenges in interpreting the (Landsat, AVHR; MODIS, SPOT, (forest cover/area, biomass, LAI, spectral (30 -10 m) data and integrating them with existing ground data at RADARSAT, diversity or phenology (NDVI, EVI), Since 80-90s different scales) ALOS PALSAR, SENTINEL) productivity) (day-month).
Background Surgery for obesity and metabolic diseases has been evolved in the light of new scientific evidence, long-term outcomes and accumulated experience. EAES has sponsored an update of previous guidelines on bariatric surgery. Methods A multidisciplinary group of bariatric surgeons, obesity physicians, nutritional experts, psychologists, anesthetists and a patient representative comprised the guideline development panel. Development and reporting conformed to GRADE guidelines and AGREE II standards. Results Systematic review of databases, record selection, data extraction and synthesis, evidence appraisal and evidence-todecision frameworks were developed for 42 key questions in the domains Indication; Preoperative work-up; Perioperative management; Non-bypass, bypass and one-anastomosis procedures; Revisional surgery; Postoperative care; and Investigational procedures.
BPD/DS versus RYGB A matched cohort study on 73,702 patients from the Bariatric Outcomes Longitudinal Database reported BPD/DS to be associated with the greatest adjusted change in BMI compared to RYGB and sleeve gastrectomy. The study also suggested that BPD/DS was superior in terms of diabetes remission  [111]. Due to conflicting evidence and generally low certainty across outcomes, no recommendation was provided by the panel.
Acknowledgements We would like to thank  Prof. Lilian Kow , President of IFSO and Director of the  Adelaide Bariatric Centre ; and  Prof. Scott Shikora , Director of the  Center for Metabolic and Bariatric Surgery at Brigham and Women's Hospital and Professor of Surgery at Harvard Medical School, President Elect of IFSO  for their external review of these guidelines. The input of  Mr. Carlos Oliveira  as patient representative is greatly appreciated. We would like to extend our special thanks to  Zbigniew Leś  for kindly providing a trial version of GRADEpro for enterprises, including PanelVoice 2. 0.
First, the sample size is small, and more data are required to confirm our results. Second, no routine postoperative screening by RT-PCR or chest CT was performed. However, no patient reported symptoms or adverse outcomes during the median follow-up of 15 days (range, 9e22 days), which is longer than the expected incubation period of SARS-CoV-2. Implementing routine postoperative RT-PCR and chest CT may additionally help to formally exclude postoperative infection with SARS-CoV-2. In conclusion, our preliminary data show feasibility and efficacy of systematic preoperative screening for SARS-CoV-2 by chest CT and RT-PCR on nasopharyngeal swabs. This screening strategy allowed the safe performance of the majority of scheduled oncologic interventions.
Spectral analysis revealed intra-species reproducibility and inter-species specificity that were consistent with the morphological classification. A blind test of 248 specimens was performed against the in-lab database upgraded with new spectra and validated using molecular tools. With identification percentages ranging from 76% to 100% alongside high identification scores (mean = 2. 115), this study proposes MALDI-TOF MS as an effective tool for discriminating lice species.
Alternative methods such as molecular approaches have been developed to identify arthropods including lice  [19, 27]. These are based on comparative analyses of gene sequences such as the 18S rRNA or the cytochrome c oxidase subunit I (COI) genes widely used for the identification of lice  [19, 27]. However, the NCBI GenBank database is still far from comprehensive regarding animal lice gene sequences  [24]. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is an ionization technique that generates specific spectra from protein extracts from organisms  [48]. The acquisition of the spectra allows the creation of a database based on reference spectra of the formally identified organism  [25]. In recent years, this proteomic approach has revolutionized clinical microbiology for the identification of bacteria and fungi  [36, 39].
The validity of the spectra obtained with protocol 2 was confirmed by testing 24 fresh lice P. humanus corporis from laboratory rearing by MALDI-TOF MS. Reproducibility and spectra quality was confirmed using FlexAnalysis v. 3. 3 software and the gel view tool of ClinProTools 2. 2 software (Bruker Daltonics, Leipzig, Germany) (Fig. 3 ). Non-engorged fresh P. humanus corporis lice were later used as controls for each MALDI-TOF MS assay.
MALDI-TOF MS parameters Protein mass profiles were obtained using a Microflex LT MALDI-TOF Mass Spectrometer (Bruker Daltonics), using Flex Control software (Bruker Daltonics), with the parameters described previously  [49]. The profiles of the spectra obtained were viewed using FlexAnalysis v. 3. 3 software and exported to ClinProTools v. 2. 2 and MALDI-Biotyper v. 3. 0 software  (Bruker Daltonics) for data processing (smoothing, basic subtraction and peak selection) and cluster analysis.
Creation of a reference spectra database In order to obtain reference spectra and upgrade our arthropod database, a subgroup of lice specimens identified both morphologically and using molecular tools were subjected to MALDI-TOF MS (Tables  2  and 3 ). Lice species from the same genus were run on the same MALDI-TOF MS target plate to rule out any plate bias. Intra-species reproducibility and interspecies specificity of MALDI-TOF MS spectra were visually evaluated using the gel view, dendrogram and principal component analysis tools of ClinProTools 2. 2 and MALDI-Biotyper v3. 0. (Bruker Daltonics). Dendrograms are based on the results of Composite Correlation Index (CCI) matrices. CCIs are calculated by dividing spectra into intervals and comparing these intervals across a dataset. The composition of correlations of all intervals provides the CCI which is used as a parameter that defines the distance between spectra.
A CCI match value of 1 represents complete correlation, whereas a CCI match value of 0 represents an absence of correlation  [25]. Spectral dendrograms were created to assess the profile diversity within each species and high-quality spectra from separate clusters were selected using FlexAnalysis software v. 3. 3. (Bruker Daltonics) to update the reference spectra database. Reference spectra were selected based on intensity, overall spectrum quality and intra-species reproducibility. For each reference sample, a main spectrum profile (MSP) was created using the automated function of MALDI-Biotyper software v. 3. 3. (Bruker Daltonics). Spectra from a spot of lower quality were sometimes removed to obtain a high-quality MSP. MSPs were created on the basis of an algorithm using peak position, intensity and frequency data.
Blind tests and cluster analysis New specimens of lice collected at different study sites were tested. Each spectrum obtained by MALDI-TOF MS analysis as described above was subjected to a blind test analysis against the upgraded database. The significance of the identification was determined using the log score values (LSV) given by MALDI-Biotyper software v. 3. 3. corresponding to a signal intensity level of the mass spectra of the query and reference spectra. The LSV range was from 0 to 3. LSVs allow for good evaluation of reproducibility between a queried spectrum and a reference spectrum, as they result from a thorough comparison of the position of peaks and the intensity between those two spectra (MALDI BioTyper Help, Bruker). In order to visualize MALDI-TOF MS profile similarities and distances, hierarchical clustering of the mass spectra of all tested species was performed using the dendrogram function of MALDI-Biotyper software v.
Molecular identification of lice Of the 4112 lice morphologically identified, 159 lice specimens preserved at À20 °C and belonging to 13 species were randomly selected to be included in the study. Randomly selected specimens of each species were subjected to molecular identification targeting the 18S rRNA gene. A GenBank request revealed that 18S rRNA gene reference sequences were available for 8 of the 13 lice species. Amongst these eight available sequences on GenBank, four had an average quality and the remaining four were of very poor quality. No sequences were available for five species including Li. caponis, C. meleagridis, G. gallinae, Go. gigas, and Me. stramineus (Table  3 ). For 13 species of lice, the BLAST analysis of 18S rRNA reference sequences of the lice specimens of the same species demonstrated high identity ranging from 99% to 100%, supporting correct morphological identification (Table  3 ).
MALDI-TOF MS analyses A total of 427 lice preserved at À20 °C were tested by MALDI-TOF MS using two protocols. An analysis of the spectral profiles using FlexAnalysis software showed that the spectra obtained using the second protocol provided MALDI-TOF MS profiles of higher intensity and superior quality to those obtained with the first protocol (Fig. 4 ). Based on spectra quality MALDI-TOF MS, the second protocol provided good intra-species reproducibility and interspecies specificity between specimens of the same species and variability between different species. This protocol was therefore selected for further MALDI-TOF MS analyses (Fig. 5 ) to create a reference spectra database. Therefore, protocol 2 was used for 408 specimens concerning the following species: B. ovis, B. bovis, B. caprae, L.