dataesr/scientific-paragraphs-categorization huggingface dataset:
e. those sources with a low strength and a large influence on the model output. The NUSAP approach therefore can be used to evaluate uncertainties that cannot be quantified, but can also be useful in identifying the most important uncertainties for further quantitative evaluation and/or additional work to strengthen the evidence base of the assessment. Pedigree matrices have been developed to evaluate model parameters and input data as well as assumptions. The method is flexible, in that customised scales can be developed. The NUSAP method is typically applied in a workshop involving multiple experts with various backgrounds in the subject matter of the assessment. The workshop would build on previous efforts to identify and characterise uncertainties using an appropriate typology. An introductory session would include presentations on the NUSAP methodology, the risk assessment to be evaluated and an open discussion about the identified uncertainties, followed by an introduction to the evaluation methodology and a discussion about the scoring methods.
For each assumption, all experts would then be asked to write down their scores on a score-card and to also describe their rationale. Scores and rationales are then reported by all experts to the group and are the basis for a discussion. Experts are then given the opportunity to adjust their scores and invited to submit their results. Computer-assisted tools may help to show the key findings of the workshop directly after completing scoring of all uncertainties. The group discussions and iterative process are an important characteristic of the NUSAP process that helps to create a better and collective understanding of uncertainties. However, the method can also be applied by a small number of experts, see e. g. Bouwknegt et al. (2014)  in which only two experts provided scores. Data analysis after the workshop involves developing diagnostic diagrams and possibly other data analysis.
Based on the simulation results, they also suggested that 'the datasets contained little uncertainty, and additional measurements would not significantly improve the quality of the dose estimates'. MC simulations are used by FAO/WHO committees supporting the work of the Codex Alimentarius Commission (JECFA, JMPR, JEMRA), as well as by national risk assessment agencies (RIVM, BfR, ANSES, and others). They are commonly used for exposure assessment in chemical risk assessment (US FDA), but not yet common in toxicology. In the USA, an interagency guideline document (USDA/ FDIS and US EPA, 2012) for microbial risk assessment features MC simulations prominently for exposure assessment and risk characterisation. There are many guidelines and books that provide detailed instructions on how to set up MC simulations.
Applicability in areas relevant for EFSA In principle, the method is applicable to any area of EFSA's work. It is restricted to situations where the model or part of the model involves adding or multiplying, but not both adding and multiplying, independent uncertain components. In such situations, distributions representing individual uncertainties can be approximated using distributions from the relevant family. The latter are then combined to provide a distribution from the same family which approximately represents the combined uncertainty. Gaylor and Kodell (2000)  proposed using this approach to derive assessment factors to apply to animal data in the context of toxicity to humans of non-carcinogenic chemicals. For the same context, IPCS (2014) developed a more sophisticated multiplicative model for determining a chemical specific assessment factor subject to assumptions about suitability of underlying databases.
0 software (Applied Biosystems, Foster City, CA, USA) as being present (1) or absent (0) in order to create a binary matrix. For AFLPs, the estimation of error rates was performed as suggested in Herrmann et al. (2010). Three individual DNAs (6. 67% of total sample) were duplicated and the output from GeneMapper (fragment sizes and peak heights) was analyzed using the R script "scanAFLPv1-3. r" (Herrmann et al. 2010).
Data Analysis In MSAP analyses, the banding patterns of EcoRI/HpaII and EcoRI/MspI were classified as follows: (i) fragments present in both profiles (1/1): unmethylated state, (ii) fragments present only in the EcoRI/MspI profiles (0/1): hemi-or full-methylation of internal cytosine ( HMe CG or Me CG sites), (iii) fragments present only in the EcoRI/HpaII profiles (1/0): hemi-methylation of external cytosine ( HMe CCG sites), and (iv) fragments absent in both profiles (0/0): uninformative state. We used the "Mixed-Scoring 2" approach as implemented in R script "MSAP_calc. r" (Schulz et al. 2013)  to generate a matrix of binary data for each of the three subepiloci: u-subepilocus (coding the unmethylated sites), m-subepilocus (coding the methylated sites: Hme CG or Me CG sites), and h-subepilocus (coding the hemimethylated sites: HMe CCG sites).
Acknowledgments This work was supported by  Centre National de la Recherche Scientifique (CNRS, France) ,  Faculty of Science University of Zagreb, Faculty of Forestry University of Zagreb (Croatia) , the  Croatian Ministry of Science, Education and Sport  ( 119-1191196-1224 ; V. Z. ), the  European Structural and Investment Funds (ZCI)  grant (contract # KK. 01. 1. 1. 01. 0010 ), Faculty of Sciences University of Sarajevo , and  Faculty of Forestry University of Sarajevo  (Bosnia and Herzegovina).
The area source model that was developed in SHARE  [18]  is sampled with the Monte Carlo method to generate the earthquake catalog (OpenQuake engine, Stochastic Event Set Calculator tool  [39] ). The  Boore (2003)  Stochastic Method  [40, 41]  is then applied to generate synthetic time histories at the bedrock site corresponding to all earthquakes in the catalog. Some specific adjustments and corrections were required to obtain ground motions compatible with the ground-motion model (AA14) used in the classical PSHA calculation. This step, which is not trivial, is described in detail in the hard rock section. The hard rock, correctly scaled time histories are then propagated to the site surface using two different 1D non-linear site response codes. One set of time histories on soil is generated that is based on the SHAKE91 equivalent-linear (EL) code  [42] [43] [44] , while the second set was derived using the NOAH  [45]  fully non-linear code (NL).
Host-to-Target Adjustments As presented in more detail in  [21, 22] , the bedrock beneath the Euroseistest basin (~196 m depth) has a shear wave velocity of about 2600 m/s, which is much larger than the validity range of most GMPEs. It is therefore mandatory to perform what is known in literature as "host-to-target adjustment" corrections (HTT  [31, 32, 33, 34] ). As discussed in  [14, 15] , the high level of complexity and uncertainty associated to the current HTT adjustments procedures  [16, 37, 49, 50, 51] , have motivated us to use here another, simpler, straightforward way to account for rock to hard rock correction for probabilistic seismic hazard purposes. This simpler approach is based on the work by Laurendeau et al. , 2017 (LA17)  [37] , who proposed a new rock GMPE that is valid for surface velocities ranging from 500 m/s to 3000 m/s and derived on the basis of the Japanese KiK-net dataset  [52].
The LA17 GMPE is based on a combined dataset including surface recordings from stiff sites and hard rock motion estimated through deconvolution of the same recordings by the corresponding 1D linear site response. The site term in the proposed (simple) GMPE equation is given by a simple dependence on rock S-wave velocity value VS30 in the form ( ). ( /1000), where is tuned from actual recordings for each spectral oscillator period ( ). The HTT adjustment factor can thus be computed, as follows in Equation (1):.
Host-to-Target Adjustments As presented in more detail in  [21, 22] , the bedrock beneath the Euroseistest basin (~196 m depth) has a shear wave velocity of about 2600 m/s, which is much larger than the validity range of most GMPEs. It is therefore mandatory to perform what is known in literature as "host-to-target adjustment" corrections (HTT  [31] [32] [33] [34] ). As discussed in  [14, 15] , the high level of complexity and uncertainty associated to the current HTT adjustments procedures  [16, 37, [49] [50] [51] , have motivated us to use here another, simpler, straightforward way to account for rock to hard rock correction for probabilistic seismic hazard purposes. This simpler approach is based on the work by Laurendeau et al. , 2017 (LA17)  [37] , who proposed a new rock GMPE that is valid for surface velocities ranging from 500 m/s to 3000 m/s and derived on the basis of the Japanese KiK-net dataset  [52].
The LA17 GMPE is based on a combined dataset including surface recordings from stiff sites and hard rock motion estimated through deconvolution of the same recordings by the corresponding 1D linear site response. The site term in the proposed (simple) GMPE equation is given by a simple dependence on rock S-wave velocity value V S30 in the form S 1 (T). ln(V S30 /1000), where S 1 is tuned from actual recordings for each spectral oscillator period (T). The HTT adjustment factor can thus be computed, as follows in Equation (1):.
This is probably due to the very simple functional form that is used in LA17, and to the fact that it was elaborated only from Japanese data. Its main interest however is to provide a rock adjustment factor calibrated on actual data from a large number of rock and hard rock sites, without any assumptions regarding non-measured parameters, such as κ0. Once the approach that is proposed here and initially based in LA17 is tested on other data sets, the corresponding results can be used to provide alternative scaling factors between hard rock and standard rock. Note, however, that this modulation approach through another kind of GMPE is exactly similar to what has been proposed in  [16] , where stochastic simulations are used for the VS-κ corrections without being used as GMPEs.
Synthetic Earthquake Catalog Once defined the target hazard on hard rock, we proceed to generate a long enough seismic catalog to build synthetic hazard curves both on rock and soil at the selected Euroseistest site. The One might wonder why scaling AA14 curve instead of using directly LA17 in the calculations. The main reason was that AA14 (V S30 = 800 m/s) provides a good approximation to the median hazard estimate at the Euroseistest on standard rock from seven explored GMPES  [15] , while the estimates with LA17 (V S30 = 800 m/s) is located outside the uncertainty range of the same selected representative GMPEs. This is probably due to the very simple functional form that is used in LA17, and to the fact that it was elaborated only from Japanese data. Its main interest however is to provide a rock adjustment factor calibrated on actual data from a large number of rock and hard rock sites, without any assumptions regarding non-measured parameters, such as κ 0.
We use the well-known stochastic method that was proposed by D. Boore  [40]  for the generation of ground-motion time histories, and its corresponding Fortran code SMSIM  [41] , which is freely provided by the author on his website. Any other suitable method to generate synthetic time histories could be used. This step is one of the most time consuming of the entire process, since the length of the earthquake catalog implies a very large number (~21,800) of events and thus the generation of numerous time histories at the site. The main input parameters are  [40] : the source characteristics (seismic moment M0 and stress drop Δσ or corner frequency fc), the path terms with geometrical spreading G(R), anelastic attenuation Q(f), and duration terms Tgm(f) related to hypocentral distance Dhyp, the frequency , and the site terms including the crustal amplification factor CAF(f) derived from the site velocity profile VS(z), together with the site specific shallow attenuation factor characterized by the κ0 value.
We use the well-known stochastic method that was proposed by D. Boore  [40]  for the generation of ground-motion time histories, and its corresponding Fortran code SMSIM  [41] , which is freely provided by the author on his website. Any other suitable method to generate synthetic time histories could be used. This step is one of the most time consuming of the entire process, since the length of the earthquake catalog implies a very large number (~21,800) of events and thus the generation of numerous time histories at the site. The main input parameters are  [40] : the source characteristics (seismic moment M 0 and stress drop ∆σ or corner frequency f c ), the path terms with geometrical spreading G(R), anelastic attenuation Q(f), and duration terms Tgm(f) related to hypocentral distance D hyp , the frequency f , and the site terms including the crustal amplification factor CAF(f) derived from the site velocity profile V S (z), together with the site specific shallow attenuation factor characterized by the κ 0 value.
Hence, we opt for a much faster, easier, and entirely pragmatic scaling technique, as described below. Figure  7. Hazard curves at the Euroseistest site for three different spectral periods (PGA, 0. 2 s, 1. 0 s), applying AA14 GMPE (blue for standard rock, green for hard rock-with HTT adjustments) or built from stochastic time histories on hard rock generated using SMSIM code (Vs = 2600 m/s, κ0 = 0. 024 s, Δσ = 30 ± 0. 68 bars, CAF(f) from  [26] ). First we calculated the median accelerations predicted by AA14 (VS30 = 800 m/s), for the magnitude set Mw = [4. 7, 5. 3, 5. 9, 6. 5, 7. 1, 7. 3] and distances Dhyp =  [1, 5, 10, 20, 50, 75, 100, 150]  km at a series of spectral periods T = [0. 01, 0. 05, 0. 2, 0. 2, 0. 5, 1. 0, 2. 0, 3. 0, 4. 0] s. These AA14 predictions were corrected while using the site term of the LA17 GMPE to derive the expected motion for a hard rock with Vs = 2600 m/s (see HTT, Section 4.
These values are displayed in Figure  8b  as a function of the spectral period, showing that the largest correction corresponds to rather long periods (around 2. 0 s), suggesting that part of this discrepancy could be due to the fact that only point sources were considered. Indeed, the correction factors in Figure  8a  increase with magnitude, which suggests that the use of a code considering extended source should be preferred. Figure  8b. Hazard curves at the Euroseistest site for three different spectral periods (PGA, 0. 2 s, 1. 0 s), applying AA14 GMPE (blue for standard rock, green for hard rock-with HTT adjustments) or built from stochastic time histories on hard rock generated using SMSIM code (Vs = 2600 m/s, κ 0 = 0. 024 s, ∆σ = 30 ± 0. 68 bars, CAF(f) from  [26] ). First we calculated the median accelerations predicted by AA14 (V S30 = 800 m/s), for the magnitude set M w = [4.
001:0. 001:0. 01; 0. 01:0. 01:0. 1; 0. 1:0. 1:2]. Posteriorly, we calculated efficiency curves showing the percentage of random samples where the number of accelerograms, n, predicted a median ln AF( f ) within a 10% error margin (with respect to median ln AF( f ), estimated from the full set of stochastic time series; Figure  17  and Table  4 ). One can observe that the number of accelerograms that is required is larger using SHAKE91 than using NOAH. This is due to the larger variability of SHAKE91 data with respect to NOAH data. Hence, more accelerograms are needed for a robust estimation. From this particular example, we determine that an amount of 60 accelerograms per acceleration bin S r a ( f ) are required to robustly predict the median of the amplification function ln(AF). The different centers of the S r a ( f ) bins that were used are displayed in Figures  18  and 19.
To test this assertion, we randomly selected from 2 to 200 accelerograms (1000 times each) in our set of stochastic time histories and for different ( ) bins, ( )bins = [0. 001:0. 001:0. 01; 0. 01:0. 01:0. 1; 0. 1:0. 1:2]. Posteriorly, we calculated efficiency curves showing the percentage of random samples where the number of accelerograms, , predicted a median ( ) within a 10% error margin (with respect to median ( ), estimated from the full set of stochastic time series; Figure  17  and Table  4 ). One can observe that the number of accelerograms that is required is larger using SHAKE91 than using NOAH. This is due to the larger variability of SHAKE91 data with respect to NOAH data. Hence, more accelerograms are needed for a robust estimation. From this particular example, we determine that an amount of 60 accelerograms per acceleration bin ( ) are required to robustly predict the median of the amplification function ( ).
The median standard deviation as a function of the input hazard on hard rock, S r a ( f ), does not vary significantly when increasing the number of accelerograms, for both SHAKE91 and NOAH results. The apparent larger variation at high input acceleration levels is due to the lack of data on such acceleration levels. With respect to the robustness of the standard deviation of AF( , ), nothing was said by the authors. We decided to explore the behavior of the standard deviation while considering 30, 50, and then whole set of accelerograms. Figures  18  and 19  display the amplification factor, AF( , ), (top) and its standard deviation (bottom), versus the input hazard on hard rock, ( ), using SHAKE91 and NOAH results, respectively. The median standard deviation as a function of the input hazard on hard rock, ( ), does not vary significantly when increasing the number of accelerograms, for both SHAKE91 and NOAH results.
The apparent larger variation at high input acceleration levels is due to the lack of data on such acceleration levels. With respect to the robustness of the standard deviation of AF( , ), nothing was said by the authors. We decided to explore the behavior of the standard deviation while considering 30, 50, and then whole set of accelerograms. Figures  18  and 19  display the amplification factor, AF( , ), (top) and its standard deviation (bottom), versus the input hazard on hard rock, ( ), using SHAKE91 and NOAH results, respectively. The median standard deviation as a function of the input hazard on hard rock, ( ), does not vary significantly when increasing the number of accelerograms, for both SHAKE91 and NOAH results. The apparent larger variation at high input acceleration levels is due to the lack of data on such acceleration levels.
This can be partially explained by the larger variability of amplification AF( f ) using SHAKE than NOAH (Figure  15 , Table  3 , σ(AF)). Additionally, our numerical results are in qualitative agreement with observations from real recordings on soil and rock  [57, 58] , where the standard deviation on soil, σS s a ( f ), is smaller than the standard deviation on rock, σS r a ( f ), for all ground motion levels (see Figure  13 ). Currently, this phenomenon is presumed to be due to nonlinearity of the soil response during severe shaking  [11] , and also the effect of a lower damping at low strain levels on rock that makes the ground motion more sensitive to the input motion. Furthermore, we compared the predictions with current available data at the site. Figure  21  plots the spectral acceleration on soil, as a function of the corresponding input spectral acceleration on rock, for both nonlinear codes tested, together with recordings from the Euroseistest database  [17].
2 s and 1. 0 s). Furthermore, we compared the predictions with current available data at the site. Figure  21  plots the spectral acceleration on soil, as a function of the corresponding input spectral acceleration on rock, for both nonlinear codes tested, together with recordings from the Euroseistest database  [17]. The same amount of observed earthquakes recordings (28 records) are used at the three different spectral periods, but accelerations on rock below 0. 001g are not displayed. From visual inspection, the few observations that are available at low/intermediate ground motion levels are roughly within the range predicted by the two models. We can thus expect the actual hazard curve of the site to be comprised between the two proposed hazard models. SHAKE91 might better predict site surface motion for low input ground motion levels [S r a ( f ) < 0.
Acknowledgments: Special thanks are due to  Benjamin Edwards ,  Adrián Rodriguez-Marek ,  Valerio Poggi  and an anonymous reviewer for fruitful discussions. Also, special thanks to  Fabian Bonilla  for facilitating the nonlinear code NOAH and additional technical assistance on its usage, and to  Fabrice Hollender  for providing valuable geotechnical and site-specific data to build the nonlinear models. All ground-motion records used in this study were generated using Boore 2003  Stochastic Method  and his open source code SMSIM (http: //www. daveboore. com/). Hazard calculations on rock have been performed using the Openquake engine (http://www. globalquakemodel. org/), as well as the SHARE area source model (http://www. share-eu. org/). The post-processing toolkits developed by the  GEM Foundation have been used.
, 2005; Danek et al. , 2016). This hypothesis is supported by the finding that the Flot1 diffusion coefficient is decreased in plants treated with methyl-β-cyclodextrin, a sterol-depleting agent  (Li et al. , 2011 (Li et al. , , 2012;; Hao et al. , 2014). Moreover, it was also observed that the knocking-down of Flot1 affected the internalization of sterol into membranes  (Li et al. , 2012). Since proteins involved in vesicular trafficking and endocytosis (e. g. , ESCRT proteins, exocyst and SNARE subunits or Rab-GTPase) were proposed to be Flot2 and Flot3 interactors by Associomics, a split-ubiquitin yeast system-based database of direct protein-protein interactions 1   (Jones et al. , 2014) , this suggests that plant flotillins could play a similar role in membrane transport to mammalian ones.
The flow rate during the gradient separation was set to 0. 3 µLmin -1. Peptides were eluted directly to the ESI source-captive spray (Bruker Daltonics). Measurements were performed in DDA mode with precursorion selection in the range of 400-1400 Da; up to 10 precursor ions were selected for fragmentation from each MS spectrum. Peak lists were extracted from the raw data with the software Data Analysis 4. 1 (Bruker Daltonics). Proteins were identified in the software Proteinscape 3. 1 (Bruker Daltonics) using in-house Mascot server 2. 4. 1 (Matrix Science) with the A. thaliana protein database downloaded from 2  (October 2016). The parameters for the database search were set as follows: carbamidomethyl (C) as fixed modification, oxidation (M) and CAMthiopropanoyl (K, N-terminus) as variable modifications, tolerance 10 ppm in MS mode and 0.
Determination of Flot2 Interactors We used IP-MS to suggest potential interacting partners of Flot2. IP/AP-MS has become widely used nowadays, mainly thanks to the development of MS instrumentation that enables more efficient data acquisition. On the other hand, unfiltered IP/AP-MS data sets could give a large number of false positive interactions. To deal with this, a high number of repetitions and high number of controls should be analyzed  (Pardo and Choudhary, 2012) , different tags  (Ho et al. , 2002)  as well as tag combinations  (Van Leene et al. , 2015)  can be used, or some computational or informatics strategies can be applied for the evaluation of specific protein interactors  (Collins and Choudhary, 2008; Choi H. et al. , 2011; Nesvizhskii, 2012). To identify specific interactors from the obtained IP-MS data set, some independent techniques such as Förster resonance energy transfer (FRET) or yeast two-hybrid assay can be used.