dataesr/scientific-paragraphs-categorization huggingface dataset:
Seed characteristics The applicant tested the germination rate of seeds harvested from maize MON 87403 (selfed F 2 seeds), the conventional counterpart and four non-GM maize varieties. 35 Germination was tested in growth chambers under controlled conditions at seven different temperature regimes. The endpoints analysed were the numbers of normal germinated seeds, abnormal germinated seeds, hard seeds, dead seeds and firm swollen seeds. No statistically significant differences in the germination rate of seeds were observed between maize MON 87403 and its conventional counterpart. Although the applicant referred to seed dormancy when discussing the generated data on maize MON 87403 seed characteristics, no data on induced seed dormancy were supplied. Therefore, the GMO Panel considers that only the conclusions on seed germination of maize MON 87403 are substantiated by the provided data.
Pollen characteristics The applicant reported data on pollen characteristics of maize MON 87403. The endpoints analysed were pollen diameter and viability via the Alexander stain method. No significant difference between maize MON 87403 and its conventional counterpart was observed for pollen diameter and general pollen morphology. Although Alexander's stain is intended to provide an indication of pollen viability, it does not directly measure pollen viability (Dafni, 1992). Therefore, the data on pollen viability supplied by the applicant in support of the comparative assessment of maize MON 87403 are not considered suitable by the GMO Panel. Given that the genetic modification of maize MON 87403 is not designed to target specific pollen characteristics, that maize is not a persistent and invasive crop and that the scope of the application EFSA-GMO-BE-2015-125 excludes cultivation, the GMO Panel considers that data provided on pollen are not required for the risk assessment of maize MON 87403.
Physiological characteristics tested under field conditions Physiological characteristics of maize MON 87403 and the conventional counterpart were assessed and compared under field conditions. 36 Information on R1 and R6 ear biomass, R1 and R6 stover 31 Dossier: Part II -Section 1. 3. 5; Additional information: 7/3/2016, 6/7/2016, 27/3/2017 (including the partial dataset received on 18/1/2017) and 10/7/2017. 32 For the endpoint early stand count, data for only three replications at the PAGR site were initially reported in the application, but complemented with data collected from another row (see Additional information: 7/3/2016). 33 The endpoints were: early stand count, days to 50% pollen shed, days to 50% silking, dropped ear count, ear height, plant height, final stand count, root lodged plants, stalk lodged plants, stay green, grain moisture, test weight and yield.
34 The endpoints were: visually observable responses to naturally occurring diseases (disease incidence), arthropod damage and abiotic stress responses. 35 Test materials were collected from three sites of the 2012 field trials: Story, Iowa (IALL); Warren, Illinois (ILMN); and Lehigh, Pennsylvania (PAGR). 36 Additional information: 7/3/2016, 6/7/2016, 27/3/2017 (including the partial dataset received on 18/1/2017) and 10/7/2017. biomass, R1 and R6 total plant biomass, ear partitioning, ear biomass, total kernel weight, total kernel number, single kernel weight, harvest index and dry matter production was gathered from field trials conducted at 13 sites in 2012 (Table  2 ). The R1 and R6 ear biomass, ear partitioning, total kernel weight and total kernel number were statistically significantly higher in maize MON 87403 than in the conventional counterpart in the combined-site analysis.
Compositional analysis 38 Maize forage and grains harvested from the field trials in the USA in 2012 were analysed for 78 different constituents (nine in forage 39 and 69 in grains 40 ), including the key constituents  37  Additional information: 7/3/2016, 6/7/2016 and 27/3/2017 (including the partial dataset received on 18/1/2017). 38 Dossier: Part II -Sections 1. 3. 3 and 1. 3. 4. 39  The endpoints were: protein, fat, ash, moisture, carbohydrates by calculation, ADF, NDF, calcium and phosphorus. 40 The endpoints were: protein, fat, ash, moisture, carbohydrates by calculation, acid detergent fibre (ADF), neutral detergent fibre (NDF), total dietary fibre (TDF), alanine, arginine, aspartic acid, cysteine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, caprylic acid (C8:0), capric acid (C10:0), lauric acid (C12:0), myristic acid (C14:0), myristoleic acid (C14:1), pentadecanoic acid (C15:0), pentadecenoic acid (C15:1), palmitic acid (C16:0), palmitoleic acid (C16:1), heptadecanoic acid (C17:0), heptadecenoic acid (C17:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), c-linolenic acid (C18:3), nonadecanoic acid (C19:0), arachidic acid (C20:0), eicosenoic acid (C20:1), eicosadienoic acid (C20:2), eicosatrienoic acid (C20:3), arachidonic acid (C20:4), behenic acid (C22:0), b-carotene, thiamin, riboflavin, pyridoxine, a-tocopherol, niacin, folic acid, calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium, zinc, phytic acid, raffinose, furfural, ferulic acid, and pcoumaric acid.
Human dietary exposure 65 Only acute dietary exposure estimates to the AtHB17Δ113 protein were provided by the applicant. Dietary exposure was estimated across different European countries on different population groups: young population (toddlers, other children) and adult population (adolescents, adults, elderly and very elderly). For the purpose of estimating dietary exposure, the limit of detection (LOD) (0. 00025 lg/g fresh weight) was used as the assumed maximum amount of the AtHB17Δ113 protein present in maize MON 87403 grains (including sweet corn); this assumption most probably overestimates the real concentration of the AtHB17Δ113 protein in maize grains. Consumption figures were retrieved by the applicant from the available summary statistics present in the EFSA Comprehensive European Food Consumption Database.
66 The EFSA consumption database contains information on food consumption data at individual level from the most recent national dietary surveys in different EU Member States (EFSA, 2011; Huybrechts et al. , 2011; Merten et al. , 2011). A conservative scenario with 100% replacement of conventional maize by the GM maize was considered. Food commodities derived from maize for which no proteins are expected to occur (e. g. maize oil, maize syrup, bourbon whiskey among others) were excluded. Acute dietary exposure was estimated using for each population group the food commodity with the highest acute consumption among consumers only (95th or 97. 5th percentile depending on the number of consumers),  67  and multiplying these values by the reported LOD for the AtHB17Δ113 protein. Processing factors or recipes that may decrease the amount of the AtHB17Δ113 protein found in the consumed commodities as compared to that in maize grains were not considered.
Among the young population, the highest acute exposure would be achieved in toddlers following the consumption of sweet corn (0. 002 lg/kg bw per day), while for adults the consumption of popcorn would lead to the highest acute exposure estimate (0. 001 lg/kg bw per day). The use of the highest acute consumption for only one food commodity could slightly underestimate the dietary exposure to the AtHB17Δ113 protein in certain population groups. The GMO Panel estimated chronic dietary exposure to the AtHB17Δ113 protein, which was not provided by the applicant. Individual consumption data on food commodities from dietary surveys with at least two days consumption and covering a total of 19 European countries 68 were retrieved from the EFSA Comprehensive European Food Consumption Database. As for the acute exposure estimations provided by the applicant, the LOD of the AtHB17Δ113 protein was used as the maximum assumed protein concentration in maize grains.
65 Dossier: Part II -Section 2. 4. 66 https://www. efsa. europa. eu/en/applications/gmo/tools 67 EFSA (European Food Safety Authority), 2011. Evaluation of the FoodEx, the food classification system applied to the development of the EFSA Comprehensive European Food Consumption Database. EFSA Journal 2011;9(3):1970, 27 pp. https://doi. org/10. 2903/j. efsa. 2011. 1970 68 Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Germany, Denmark, Spain, Finland, France, the United Kingdom, Greece, Hungary, Ireland, Italy, Latvia, the Netherlands, Romania and Sweden. 69 Example: 100 g of corn bread contains 26. 4 g of corn flour that are derived from 31. 7 g of maize grains (processing factor of 1. 22); this would result in 0. 00008 µg of AtHB17Δ113/g of corn bread.
2-mL microfuge tube. Each pair of glands was used as one biological replicate (10-12 replicates were carried out for each treatment). These samples were immediately frozen in liquid nitrogen and stored at À80°C for RNA isolation. RNA was isolated using the RNeasy kit from Qiagen according to the manufacturer's instructions. 2 lg of total RNA was reverse-transcribed into cDNA using Bio-Rad's iSCRIPT cDNA synthesis kit in a 40 lL reaction volume also according to the manufacturer's instructions. Primers (Table  S1 ) were designed based on sequence information of Pieris brassicae. iQ SYBRGreen Supermix (Bio-Rad) was used to perform the real time qPCR reactions in duplicate. The following PCR program was used for all PCR reactions (annealing temperature in Table  S1 ): 95°C, 3 min followed by 40 cycles of 95°C for 10 s, annealing temperatures for 10 s and 72°C for 30 s, with data collection at 72°C.
Primers (Table  S2 ) were designed based on sequence information of the used plants or were designed earlier by  Poelman et al. (2011). iQ SYBRGreen Supermix (Bio-Rad) was used to perform the real time qPCR reactions in duplicate. The following PCR program was used for all PCR reactions (annealing temperatures in Table  S2 ): 95°C, 3 min followed by 40 cycles of 95°C for 10 s, annealing temperatures for 10 s and 72°C for 30 s, with data collection at 72°C. The PCR reactions were followed by a melt curve analysis to check for primer-dimer formation or unspecific PCR products. Delta-delta Cq values are calculated using the Cq values of the untreated plants and normalising using the Cq values of the reference genes BTUB and ACT2 which were selected (using geNorm,  Vandesompele et al. 2002)  out of a total of six known reference genes.
ACKNOWLEDGEMENTS The authors thank three anonymous reviewers whose constructive comments contributed to improving the quality of the manuscript. We thank  L eon Westerd ,  Andr e Gidding  and  Frans van Aggelen  for culturing insects and the experimental farm of  Wageningen University (Unifarm ) for rearing plants. Fruitful discussions with  Gary Felton  ( Penn State University ), which helped to improve this manuscript, have been greatly appreciated. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement nr:  655178  to AC and the  European Research Council (ERC)  grant agreement nr:  677139  to EHP.
Data were collected on a FACSCanto (BD Biosciences) and were analysed with FlowJo software (TreeStar). Antibodies. The following anti-mouse antibodies were used for FACS (Supplementary Table  1 ): B220-FITC (RA3-6B2), B220-PE (RA3-6B2), B220-PE-Cy7 (RA3-6B2), CD3-PerCP-Cy5. 5 (145-2C11), CD86-APC (GL-1), CD4-APC-Cy7 (GK1. 5), CD23-FITC (B3B4), IFN-γ-FITC (XMG1. 2), Bcl-6-PE (7D1), IL-21R-APC (4A9), Streptavidin-PerCP-Cy5. 5, NK1. 1-PE-Cy7 (PK136), TCR-β-chain-APC (H57-597), ICOS-PE-Cy7 (C398. 4A), and IFN-γRα-Biotin (2E2) were from Biolegend; B220-APC (RA3-6B2), CD11b-FITC (M1/70), CD21-PE (eBio8D9), PD-1-PE (J43), CD4-PE (GK1. 5), CXCR4-PE (2B11), CD40-PE (1C10), CD69-PerCP-Cy5. 5 (H1. 2F3), Foxp3-PerCP-Cy5. 5 (FJK-16s), Foxp3-APC (FJK-16s), IFN-γ-APC (XMG1. 2), Ki-67-eFlur660 (SolA15), GL7-eFluor660 (GL7), CD11c PE-Cy7 (N418), MHC Class II I-Ab PerCP-eFluor710 (AF6-120.
DNA microarrays. For gene-expression analysis, purified B cells from 8-week-old WT and Gal-3 KO mice were lysed with TRIzol reagent. Total RNA was extracted with the RNAeasy Mini Kit (Qiagen). RNA quality was verified in an Agilent Bioanalyser and measured with a Nanodrop 1000 (Thermo Scientific). Microarray experiments were performed on GeneChip Mouse Gene 2. 1 ST arrays (Affymetrix). The working lists were created by filtering probes with detection P values <0. 05 for all chips and discarding overlapping probes. Each dataset was derived from two biologically independent replicate samples. Independent samples were compared by computing fold ratios and filtered at a 1. 2-fold threshold for Venn diagrams and pathway analysis. For pathway analysis, GenBank accession numbers were mapped to the Ingenuity database (IPA, http://www.
ingenuity. com) to retrieve relevant biological processes. Heat maps were generated using the web interface programme Matrix2png. Statistical analysis. All data are expressed as mean ± s. e. m. Replicates were biological replicates and n represents the number of individual mice analysed per experiment. Mice were assigned to groups at random for all mouse studies and, where possible, mixed among cages. Careful consideration has been given to calculate the number of mice needed to give statistically significant results, while using the minimum number of mice possible. Numbers of mice for each experiment were decided after consultation with a statistician at UNC. Experiments were performed independently in triplicate to control for experimental variation. No exclusion was performed. Statistical comparisons were determined using unpaired or paired twotailed Student's t-test or one-way analysis of variance (ANOVA) followed by Tukey-Kramer multiple comparison test when appropriate.
Acknowledgements We thank  N. G. Núñez ,  M. P. Abadie ,  M. P. Crespo ,  V. Blanco ,  F. Navarro ,  D. Lutti ,  C. Mas ,  M. C. Sampedro  and  L. Almada  for their excellent technical assistance. This work was supported by grants from the  Agencia Nacional de Promoción Científica y Técnica  ( PICT 2013-2007 ),  CONICET  and the  Secretaría de Ciencia y Técnica-Universidad Nacional de Córdoba. E. V. A. R. , C. L. M. , J. M. , O. C. and A. G. are researchers from  CONICET. C. G. B. , F. F. V. , M. C. R. , J. T. B. , M. G. S. thank  CONICET  for the fellowship awarded.
Network activities The network is organized via closed biannual network meetings and monthly executive committee meetings and task force meetings. Task forces are formulated to organize and stimulate six key functions of the ERN: (1) Coordination and management, (2) Organization of care, (3) Guidelines, outcomes and quality, (4) Education and training, (5) Research, data registration and grants, and (6) Patient empowerment, communication and dissemination. Organization of care The ERN GENTURIS is an expert reference network for patients with or suspected of having a genetic tumour predisposition syndrome. Only a physician can consult the network and/or refer a patient with any question related to the health care pathway of these syndromes. This includes topics as identification, diagnostics, treatment and risk management.
Each guideline will relate to one of six modules of the clinical pathways: (1) General description of the syndrome, (2) Identification of patients, (3) Surveillance and follow-up, (4) Treatment of symptoms, (5) Prophylactic treatment, and (6) Psychosocial counselling. Registry The ERN GENTURIS registry covers a standardised minimal dataset on genotype and phenotype which will be captured following international classifications and ontology's. Patients who were referred to the ERN GEN-TURIS and who gave permission on their ERN informed consent form for data registration will be included in the registry. Rules of governance are being established that will include procedures for reviewing data requests, data access and data sharing of pseudonymised data.
Observed changes in permafrost and in rock-glacier flow velocities and ice volume Permafrost borehole temperatures are monitored in many European mountain ranges (documented and available in the Global Terrestrial Network for Permafrost (GTN-P) database;  Biskaborn et al. , 2015) , several of the sites being accompanied by meteorological stations and ground surface temperature measurements  (Gisnås et al. , 2014; Staub et al. , 2016). However, as mountain permafrost is usually invisible from the surface, various indirect methods need to be employed to detect, characterize, and monitor permafrost occurrences. These methods include surface-based geophysical measurements to determine the physical properties of the subsurface, including water and ice content distributions  (Kneisel et al. , 2008; Hauck, 2013) , and geodetic and kinematic measurements to detect subsidence, creep, and slope instabilities  (Kääb, 2008; Lugon and Stoffel, 2010; Kaufmann, 2012; Kenner et al.
, 2012). Considering the multiple factors that affect rock slope stability, however, it is generally difficult to attribute individual events to a single one  (Huggel et al. , 2013). Improved integrative assessments are therefore necessary. Further evidence of climatic impacts on high mountain rock slope stability comes from the analysis of historical events. For the Alps, inventories documenting such events exist since 1990  (Ravanel and Deline, 2011; Huggel et al. , 2012)  and indicate a sharp increase in the number of events since 1990. This makes the temporal distribution of rock slope failures resembles the evolution of mean annual temperatures. Given the fact that monitoring and documentation efforts have been intensified during the past decades, it remains unclear to which degree this correlation is affected by varying temporal completeness of the underlying datasets.
, 2014). For the Scandinavian mountains, the observed increase of runoff in winter and spring and the earlier snowmelt are projected for the future (RCP8. 5), with relatively small changes of total annual runoff (NCCS 2017). This holds for all scenarios, with the changes being less in the moderate scenarios RCP4. 5 and RCP2. 6, respectively. More than by changes in the cryosphere, hydrological extremes in the alpine region are affected by the changing climate itself. Periods of persistent and exceptional dry conditions have been identified based on a simple drought index around the mid-1940s and for the late 1850s to the 1870s (van der  Schrier et al. , 2006). For the 21st century, Heinrich et al. (  2014 ) found increasing temperatures and negative trends of precipitation in the ENSEMBLES multimodel dataset (reference period , the latter mainly in the southern and eastern parts of the Alps  (Brunetti et al.
Permafrost modeling Challenges that need to be addressed in permafrost modeling include the development of (1) static large-scale permafrost distribution models, (2) high-resolution and site-specific permafrost evolution models, and (3) transient hemispheric permafrost models or land-surface schemes of RCMs and global climate models (GCMs). Current state-of-the-art permafrost distribution models (e. g. ,  Gisnås et al. , 2017)  are forced not only by statistical and topo-climatic variables such as mean annual air temperature and potential incoming radiation but also by operationally gridded datasets of daily air temperature and snow cover. Statistical distributions of snow and other surface characteristics (soil type, roughness) allow for the representation of sub-grid variability of ground temperature  (Gubler et al.
New high-resolution satellite images make the required input data available, thus opening the way toward improved future global estimates of glacier thicknesses. Another challenge in glacier modeling is the use of approaches that explicitly consider ice dynamics. Jouvet et al. (2009)  showed that full 3-D Stokes models representing ice flow without approximation can be applied if the required input data are available (e. g. , glacier thickness, surface velocities). At the regional scale, however, simplified approaches are still required  (Clarke et al. , 2015). For estimating future glacier evolution, ice dynamics models need to be coupled to adequate representations of glacier surface mass balance. The Glacier Model Intercomparison Project (Glacier-MIP, www. climate-cryosphere. org/ activities/targeted/glaciermip) assesses the performance of regional-to global-scale glacier models to foster the improvement of the individual approaches and to reduce uncertainties in future projections.
Modeling uncertainty Predicting the future evolution of cryospheric components is challenging in many respects. On the one hand, these challenges stem from the incomplete understanding of the physical processes leading to given changes and from the lack of more comprehensive and consistent datasets (see Fig. 7 ). On the other hand, predictions are intrinsically affected by a range of uncertainties. Adequately evaluating and communicating such uncertainties is anything but a trivial task, both because the interplay between individual systems can be complex and because end users of projections are typically adverse to uncertainty. Outside the scientific community, "uncertainty" and "error" are two concepts often not sufficiently distinguished. This can lead to important misunderstanding and misinterpretations.
Logistical challenges and related costs are introducing a bias in the availability of cryosphere-related observations. Even in European countries with reasonable financial and infrastructural resources, such observations have a bias towards low and mid-elevations and easily accessible regions. This also applies to related environmental variables, in particular meteorological variables  (Orlowsky and Seneviratne, 2014). Substantial efforts and new technical solutions are required to improve the spatial coverage and representativeness of the variables of interest. The high logistical and monetary costs of acquiring data at high elevations also explains why many cryosphere-related data across Europe still have restricted ownership. The consequences include lack of data, limitations to accessing existing data, delays in obtaining them, and duplication of datacollection efforts.
The current push for open-data policies of major funding agencies (e. g. , EC, ERC, NERC, ANR, DFG, SNF) is therefore to be welcomed. Some examples for successful data portals related to cryospheric data include the Global Earth Observation System of Systems (GEOSS)a data catalogue of a partnership of more than 100 states and the European Commission -the National Snow and Ice Data Center (NSIDC), the World Glacier Monitoring Service (WGMS), the International Network for Alpine Research Catchment Hydrology (GEWEX-INARCH), and the Swiss Open Support Platform for Environmental Research (OSPER). The recent establishment of a Global Cryospheric Watch program at the WMO facilitates the exchange of cryospheric data. To be successful, however, the definition of common standards for different data types is required, as well as adequate mechanisms for rewarding and acknowledging groups and agencies investing in field-data collection.
An effort towards rigorous open-data policies for the mountain cryosphere seems particularly important, since mountain topography often introduces sharp administrative boundaries such as country borders. The latter often impose artificial limitations in spatial coverage. Large-scale studies on snow cover changes based on in situ measurements, for instance, barely exist because such measurements are typically acquired from individual institutions or authorities. These cause the data to be subject to different jurisdictions, competences, practices, or priorities, all of which hamper free data exchange. Rather than adhering to administrative borders, environmental data should sample regions defined on the base of geomorphological, topographical, and climatologic considerations. We call upon the European community working in the domain of the mountain cryosphere to be an example when it comes to open-data policies.