Sensibility analysis of VORIS lava-flow simulations: application to Nyamulagira volcano, Democratic Republic of Congo

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This discussion paper is/has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). Please refer to the corresponding final paper in NHESS if available. Discussion Paper Nat. Hazards Earth Syst. Sci. Discuss., 3, 1835–1860, 2015 doi:10.5194/nhessd-3-1835-2015 © Author(s) 2015. CC Attribution 3.0 License. | Correspondence to: A. M. Syavulisembo ( Published by Copernicus Publications on behalf of the European Geosciences Union. | 1835 A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close Full Screen / Esc Discussion Paper Received: 26 November 2014 – Accepted: 8 January 2015 – Published: 10 March 2015 Sensibility analysis of VORIS lava-flow simulations | Goma Volcanological Observatory, Goma, DR Congo University of Liège, Department Geology, Sart Tilman B52, 4000 Liège, Belgium 3 European Center for Geodynamics and Seismology, rue Josy Welter 19, 7256 Walferdange, Luxembourg 4 Vrije Universiteit Brussel, Pleinlaan 2, 1085 Brussels, Belgium 5 Royal Museum for Central Africa, Leuvensesteenweg 13, 3080 Tervuren, Belgium 6 National Museum of Natural History Geophysics/Astrophysics Department, rue Josy Welter 19, 7256 Walferdange, Luxembourg 7 Institute of Earth Sciences Jaume Almera, CSIC, Lluís Solé i Sabarís s/n, 08028 Barcelona, Spain 2 Discussion Paper 1 3, 1835–1860, 2015 | A. M. Syavulisembo1 , H.-B. Havenith2 , B. Smets3,4,5 , N. d’Oreye3,6 , and J. Marti7 Discussion Paper Sensibility analysis of VORIS lava-flow simulations: application to Nyamulagira volcano, Democratic Republic of Congo NHESSD Printer-friendly Version Interactive Discussion 5 NHESSD 3, 1835–1860, 2015 Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Discussion Paper 20 Discussion Paper 15 | 10 Assessment and management of volcanic risk are important scientific, economic, and political issues, especially in densely populated areas threatened by volcanoes. The Virunga area in the Democratic Republic of Congo, with over 1 million inhabitants, has to cope permanently with the threat posed by the active Nyamulagira and Nyiragongo volcanoes. During the past century, Nyamulagira erupted at intervals of 1–4 years – mostly in the form of lava flows – at least 30 times. Its summit and flank eruptions lasted for periods of a few days up to more than two years, and produced lava flows sometimes reaching distances of over 20 km from the volcano, thereby affecting very large areas and having a serious impact on the region of Virunga. In order to identify a useful tool for lava flow hazard assessment at the Goma Volcano Observatory (GVO), we tested VORIS 2.0.1 (Felpeto et al., 2007), a freely available software ( based on a probabilistic model that considers topography as the main parameter controlling lava flow propagation. We tested different Digital Elevation Models (DEM) – SRTM1, SRTM3, and ASTER GDEM – to analyze the sensibility of the input parameters of VORIS 2.0.1 in simulation of recent historical lava-flow for which the pre-eruption topography is known. The results obtained show that VORIS 2.0.1 is a quick, easy-touse tool for simulating lava-flow eruptions and replicates to a high degree of accuracy the eruptions tested. In practice, these results will be used by GVO to calibrate VORIS model for lava flow path forecasting during new eruptions, hence contributing to a better volcanic crisis management. Discussion Paper Abstract | Full Screen / Esc 25 Introduction During the past century Nyamulagira (or Nyamuragira), the westernmost volcano of the Virunga Volcnaic Province (VVP), erupted at least 30 times at intervals of 1– 4 years (Tedesco, 2002, 2003; Smets et al., 2010). It is considered one of the most active African volcanoes, with at least 39 documented eruptions since 1882 (Smets | 1836 Discussion Paper 1 Printer-friendly Version Interactive Discussion 1837 | Discussion Paper 3, 1835–1860, 2015 Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Full Screen / Esc Discussion Paper 25 NHESSD | 20 Discussion Paper 15 | 10 Discussion Paper 5 et al., 2013). Most of historical eruptions of Nyamulagira occurred along its flanks, sometimes more than 10 km far from the central edifice. Nyamulagira eruptions commonly last few days to few weeks, but some voluminous and less frequent historical events lasted more than 2 years (Pouclet, 1976; Smets et al., 2010). Nyamulagira’s activity is characterized essentially by lava fountaning activity along an eruptive fissure, which produces lava flows and progressively build, together with ejected tephra, a spatter-and-scoria cone along the fissure (Pouclet, 1976; Smets et al., 2013) On 2 January 2010, an eruption of Nyamulagira started in the central caldera and 6 3 along the SSE flank, emitting ∼ 45.5 × 10 m of lava. These lava flows caused an enormous loss of vegetation in the Virunga National Park and the dense plume of gases that scaped from the eruptive vents affected the surrounding population. The estimated surface area covered by the 2010 lava flows is 15.17 ± 2.53 × 106 m2 (Smets et al., 2013). Due to the presence of densely populated zones and a National park in the vicinity of Nyamulagira, hazards associated with its frequent fissural eruptions must be assessed. In addition to detailed field surveys and literature reviews performed to better characterise Nyamulagira’s eruptive activity and, hence, better assess eruption hazards, simulations are practical tools to create hazard maps and detect the most probably affected areas during an eruption. For lava flows a wide diversity of both probabilistic and deterministic simulation models exist in the volcanological literature (e.g. Crisci et al., 1986, 1997; Ishihara et al., 1989; Wadge et al., 1994; Kuauhicaua et al., 1995; Felpeto et al., 2001; Favalli et al., 2005; Damiani et al., 2006) and offer different degrees of accuracy depending on the mathematical methods used to make calculations and the input parameters considered. Unfortunately, most of these models only exist in the scientific literature and their source codes are not freely available. Furthermore, some require complex calculations and significant CPU usage, which is not always available. Felpeto et al. (2007) have built a volcanic hazard assessment software package © (VORIS 2.0.1) within ArcGIS 9.1 ( ESRI), which includes a probabilistic lava-flow model based on a previously developed model (Felpeto et al., 2001). Unlike existing Printer-friendly Version Interactive Discussion | Discussion Paper 10 Discussion Paper 5 lava-flow models, VORIS is free (downloadable from, easy-to-use and does not entail high computing requirements. However, VORIS may be less accurate than other more sophisticated deterministic lava-flow simulation models, which thus creates a dilemma as to whether a high precision or a quicker, easier-to-use – but less precise – tool is preferable. The Goma Volcano Observatory (GVO) is in charge of conducting volcano monitoring and assessing volcanic hazards in the Democratic Republic of Congo (DRC). Thus, as part of its work, it needs to adopt adequate methodologies and tools – tried and tested in other areas – that can be used by its scientists and technicians to comply with the most essential of its tasks. To check whether VORIS could be of use to the GVO for lava flow hazard assessment in the DRC volcanoes, and for quickly estimating the potential impact of new lava flows in the event of a new eruption, we tested this package’s lava-flow simulation model by replicating the Nyamulagira 2010 eruption, most of whose parameters and pre-eruption topography are known. NHESSD 3, 1835–1860, 2015 Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Abstract 20 Geological setting | 1838 Full Screen / Esc Discussion Paper The VVP is part of the western branch of the East African Rift and is situated between Lake Edward in the north and Lake Kivu in the south (Fig. 1) (Verhoogen, 1938; Smets, 2007). It is composed by eight large volcanic edifices: Muhabura (4127 m), Gahunga (2474 m), Sabinyo (3647 m), Visoke (3711 m), Karisimbi (4507 m), Mikeno (4437 m), Nyamulagira (3058 m), and Nyiragongo (3470 m) (Komorowski et al., 2003). Although volcanic activity in the VVP dates back to the Upper Miocene (Pouclet, 1977), Nyiragongo and Nyamulagira are currently the main active volcanoes of this volcanic province (Tedesco et al., 2007; d’Oreye et al., 2012). Nyamulagira (1.41 S, 29.20 E) is located in the Virunga National Park, which was declared as a World Heritage Site in 1979 and has an endangered one since 1994 ( This composite volcano produces alkali basalts, hawaiites, basanites, and tephrites, with a SiO2 content ranging from 43 to 56 wt % | 25 2 Discussion Paper 15 Printer-friendly Version Interactive Discussion Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close Full Screen / Esc Discussion Paper | 1839 3, 1835–1860, 2015 | 25 Discussion Paper 20 We used VORIS 2.0.1 (Felpeto et al., 2007; to simulate the lava flows emitted by Nyamulagira during the 2010 eruption. VORIS (Volcanic Risk © Information Systems) was developed in an ArcGis 9.1 ( ESRI) Geographical Information System (GIS) framework and allows eruptive scenarios and probabilistic hazards maps to be created rapidly for the commonest volcanic hazards (lava flows, fallout and pyroclastic density currents). The lava flow model included in VORIS is a probabilistic (Monte Carlo algorithm) model based on the assumption that both topography and flow thickness play major roles in determining the path followed by a lava flow (Felpeto et al., 2007 and references therein). The model computes several possible paths for the flow on the basis of two simple rules: (i) the flow will only propagate from one cell to one of its eight neighbors if the difference in corrected topographic height between them is positive, and (ii) the probability that the flow will move from one cell to one of its neighbors is proportional to the difference in height. The calculation of the probability of a point being invaded by lava is performed by computing several random paths using a Monte Carlo algorithm (see Felpeto, 2009 for more details). The input data for this simulation includes a Digital Elevation Model (DEM), from which we can chose either a single vent or a vent area including several vents, the maximum flow length (i.e. the total length of the path followed by the lava flow, not just the maximum longitudinal distance), and a height correction (i.e. the average thickness of the flow). All these input parameters remain essentially constant during the NHESSD | 15 Methodology Discussion Paper 10 | 3 Discussion Paper 5 (Aoki et al., 1985). In recent decades, the eruptive activity took the form of periodic flank fissure-fed effusive eruptions, with intervals of summit lava lake activity (e.g. Hamaguchi and Zana, 1983; Wadge and Burt, 2011; Smets et al., 2014), but since April 2014, it is restricted to episodic fountaining and lava lake activity in a pit crater located in the NE sector of the central caldera (Smets et al., 2014). Printer-friendly Version Interactive Discussion 1840 | Discussion Paper 3, 1835–1860, 2015 Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | Full Screen / Esc Discussion Paper 25 NHESSD | 20 Discussion Paper 15 | 10 Discussion Paper 5 simulation and characterize the resulting model. The maximum flow length and the height correction or average thickness are integers defined in meters. In addition, the “iterations number” must be considered. This number is proportional to the number of calculations made and will determine the CPU time required and the accuracy of the results obtained. However, the greater the number of iterations, the more accurate the results, but also the greater the risk that the microprocessor will be overloaded. This is why in our study we paid special attention to this input parameter in order to determine the most appropriate number of iterations for guaranteeing obtaining reliable results without using too much computing time. The DEM is a fundamental boundary condition needed for modelling volcanic processes and associated risks including pyroclastic, lava, and mud flows (Sheridan et al., 2004). In this case study, the DEM plays a major role in determining the pathways of lava flows (Felpeto et al., 2007) as it is the numerical base used to compute the path the lava will follow based on the principle used by VORIS. To test the model, we used three different DEMs corresponding to the 2010 pre-eruption topography: SRTM1, SRTM3, and ASTER GDEM. They differ either by their resolutions or by the way they were produced. SRTM (Shuttle Radar Topography Mission). DEMs were produced by radar interferometry, using radar images acquired during a space mision, with NSA, space shuttle. SRTM1 in the studied area has a spatial resolution of 31 m, while that of SRTM3 has 93 m-wide pixels. In recent years, SRTM DEMs were the main source of height data for the VVP. The ASTER GDEM (ASTER Global Digital Elevation Model) is not obtained by stereophotogrametry, using nadir and backward images acquired by ASTER (Advanced Spaceborne Thermal Emission and Felection Radiometer) optical sensor, on board the Terra satellite (NASA/MATI/J-Spacesystems). ASTER GDEM data are published with a spatial resolution of 30 m (Hormann, 2012). In order to carry out a sensibility analysis of the input parameters we conducted several simulations using different DEMs, different values for the flow lengths and average lava-flow thicknesses, and different numbers of iteration. The comparison of the results obtained with the three dierent DEMs is important for testing the suitability of each Printer-friendly Version Interactive Discussion 4.1 Total length 10 Discussion Paper 1. “True lava flow pixels”: number of pixels of the DEM actually covered by the lava flow we are trying to simulate, NHESSD 3, 1835–1860, 2015 Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close | 15 The sensibility of the length parameter was evaluated using SRTM1 and 12 different simulations. A vent location was fixed at point A (UTM 35S 746161E/9840963N) with an average lava flow thickness of 3 m; 5000 iterations were applied with a different length value for each: L1 = 1 km, L2 = 5 km, L3 = 10 km, L4 = 15 km, L5 = 20 km, L6 = 25 km, L7 = 30 km, L8 = 35 km, L9 = 40 km, L10 = 45 km, L11 = 50 km, and L12 = 55 km. The results obtained are given in Figs. 2 and 3 and in Table 1 with the values for the following parameters: Discussion Paper Simulations and sensibility analysis of input parameters | 4 Discussion Paper 5 model in this type of lava-flow simulations and for testing their aptness for using with VORIS. In equatorial zones, such as in the VVP, the quality of the ASTER GDEM is considerably affected by cloud cover. As a aconsequence, artifacts apperar in the lava fields (Arefi and Leinartz, 2011). Compared to different GIS ground control points acquired in the southern Nyragongo lava field (Albino et al., 2014), the SRTM DEM has a better vertical accuracy than the ASTER GDEM. Hense, it is initially expected to have more reliable results using the SRTM DEMs. | 2. “Simulated pixels”: number of pixels of the DEM corresponding to the surface covered by the simulated lava flow, 3. “Well-estimated pixels”: number of pixels of the DEM corresponding to probable pixels that coincide with true pixels, | 4. “Under-estimated pixels”: number of pixels of the DEM corresponding to true pixels that were not covered by probable pixels, 1841 Full Screen / Esc Discussion Paper 20 Printer-friendly Version Interactive Discussion 6. “Length parameter”: value of the input parameter “length” introduced into the model for the simulation, 5 7. “Modeled likely length”: maximum longitudinal length of the simulated lava flow, Sensibility analysis of VORIS lava-flow simulations A. M. Syavulisembo et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close Full Screen / Esc Discussion Paper 25 3, 1835–1860, 2015 | 20 | We arbitrarily selected 10 simulations with different numbers of iterations (I1 = 10, I2 = 50, I3 = 100, I4 = 500, I5 = 1000, I6 = 5000, I7 = 10 000, I8 = 50 000, I9 = 100 000, and I10 = 500 000). Simulations were carried out on SRTM1, with the same point A (UTM35S, 746161E/9840963N), which corresponds to the flank vent location of the 2010 eruption, and fixed average thickness and total length of 3 m and 33 km, respectively. The results obtained are shown in Figs. 4 and 5 and Table 2, and indicate that the number of simulated pixels, well-estimated pixels, and pixels outside the 2010 lava flow increase from simulations I1 to I6, but remain more or less constant from I6 to I10. Simulation I6 has the largest number of well-estimated pixels, the lowest number of 1842 Discussion Paper 4.2 equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. 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