dataesr/scientific-paragraphs-categorization huggingface dataset:
For SO 2 , the fitting window 314-330 nm (traverses), 314-326 nm (stationary), was selected to minimise the dilution effect  (Kern et al. 2009; Mori et al. 2006 ) and obtain a near-random fit residual structure with minimal standard deviation. High-resolution laboratory SO 2 [at 273 K:  (Bogumil et al. 2003) ] and O 3 [at 246 K:  (Voigt et al. 2001) ] absorption cross sections were convolved with the instrument line shape (FWHM = 0. 45 nm) for use in the fitting procedure. A background spectrum (to account for the solar Fraunhofer lines), and a Ring spectrum (calculated from a solar spectrum  (Kurucz 1995)  using WinDOAS software, to account for the effect of rotational Raman scattering in the atmosphere), were also included in the fit, and a third order polynomial was used to remove broadband structures and the effects of Rayleigh and Mie scattering.
A GPS was used to obtain the path of the traverse, and this was then combined with the gas data using calcflux software to calculate the SO 2 flux. The plume speed required in the calculation of the fluxes was derived from wind speed observations at plume altitude obtained from the Icelandic Meteorological Office station closest to the site. To analyse BrO, spectra were co-added in groups of five (stationary) or ten (traverse at Eyjafjallajökull, on top of original co-adds) to improve the signal-to-noise ratio. Retrievals were then performed in the spectral range 332-357 nm, which includes five BrO absorption bands and has minimal interference from the SO 2 absorption feature discussed above. Absorption cross sections for BrO [at 273 K:  (Fleischmann et al. 2004 )], SO 2 [at 273 K:  (Bogumil et al.
OP-FTIR measurements FTIR measurements were made during the Holuhraun eruption as reported by  Pfeffer et al. (2018) , and analysed for SO 2 , HCl, HF, CO, CO 2 , H 2 O, and OCS. Here, we report additional measurements made over the lava field (excluding the main plume from the field of view), using the same methods as detailed in  Pfeffer et al. (2018). The spectrometer was pointed at an overflow on the side of the spatter cone, using the lava as a source of infrared. This allowed us to measure the gas content over the lava field. Measurements of the main plume used the lava fountains as the source  (Pfeffer et al. 2018 ). The data were analysed using the code of  Burton et al. (2007). Measurements of H 2 O and CO 2 were corrected to account for the atmospheric contents of these species.
Visual and audio observations (Fimmvörðuháls) A Sony High Definition video camera (frame rate 25 fps) was used to film several episodes of fire fountain activity in the evolving fissure system at Fimmvörðuháls. This video was analysed qualitatively using Eagle Eye software, and semi-quantitatively by spectral analysis of the audio track. The audio was contaminated with extraneous noise from tourist helicopters and vehicles, and signal processing to remove the noise met with limited success because (i) the high sampling frequency (44100 Hz) was important for maintaining the shape of the signal, so downsampling was not an option and datasets were therefore very large; (ii) both high-pass and low-pass filters ran into similar issues and also failed to eliminate the noise. However, the explosions related to bubble bursts had sufficiently distinctive signals to allow their precise identification.
Glass chemistry We analysed glasses from the lava and tephra from this eruption. Lava samples were almost completely crystalline, with the exception of a quenched sample, which contained some glassy pools that allowed analysis of the glass composition. Representative tephra and lava glass compositions are provided in Table  2. Only sulphur was lower in the lava than the tephra, suggesting that it continued to degas in the lava flows. Cl and F were enriched in lava-though quite variable, with no discernible spatial pattern. Cl/F was relatively constant in the lava, with lower values than in the tephra due to higher F. Major element data are also significantly different between the tephra and the lava, which is expected because Average error on BrO (molec/ cm 2 ): 5 × 10 13. Average error on SO 2 (molec/cm 2 ): 6 × 10.
BrO measurements BrO was detected in traverse data (TR2 to TR10) performed on 23 and 24 April 2010, and in stationary data (S1) from 24 April 2010 (Table  4 ). We note that BrO was not detected in TR1 on 23 April since the spectral fits were of lower quality: the amplitude of the fit residuals from TR1 was significantly larger (1 × 10 -2 molec/cm 2 ) than from other traverse data (typically 4 × 10 -3 molec/cm -2 ). Considering the maximum peak-to-peak amplitude of the BrO differential cross section, ~ 2 × 10 -17 cm 2 molecules -1 , we would need at least 5 × 10 14 molecules cm -2 of BrO in the measured column for it to be detected in spectra from TR1. The maximum column amount of BrO measured from other traverse and stationary datasets, however, was 4 × 10 14 molecules cm -2 , that could be detected given the lower amplitude of residual noise in the data (4 × 10 -3 ).
Results from Holuhraun At Holuhraun, the primary techniques used were stationary and mobile DOAS and OP-FTIR. Glass and melt inclusion chemistry were also measured for selected samples. The mobile DOAS and main vent FTIR data are published elsewhere  (Pfeffer et al. 2018 ), but will be discussed in the context of this paper here, particularly focussing on the outgassing of lava flows and the halogen content. The stationary DOAS involved measurement of BrO/SO 2 ratios. Video of fire fountain activity was also taken and analysed, but it was not possible to extract records of bubble bursts from the video. This was due to a distinct difference between the type of activity here and that at Fimmvörðuháls. At Holuhraun, the activity was characterised by churning of a lava lake and was considerably more chaotic than the gas pistoning activity that was observed at Fimmvörðuháls.
2021 ). The basalts that were feeding the eruptions at Eyjafjallajökull and Holuhraun exhibit key differences that are well documented both in composition and in eruptive volume  (Bali et al. 2018; Hartley et al. 2018; Moune et al. 2012; Sigmarsson et al. 2011). Our multidisciplinary datasets reinforce previous arguments that the magma feeding the Bárðarbunga eruption at Holuhraun was from a deeper source and richer in sulphur relative to halogens compared to that feeding Eyjafjallajökull and erupted at Fimmvörðuháls. There are substantial differences between the two fissure eruptions in terms of degassing, too. The most obvious is magnitude: the Holuhraun eruption was substantially more voluminous, and produced much more sulphur dioxide  (Bali et al. 2018; Halldórsson et al. 2018; Pfeffer et al.
Atmospheric processes: bromine chemistry There is in all our datasets a distinct peak in the BrO/SO 2 ratio in the early plume. This then dissipates slowly at a rate that depends largely on plume size at both the summit eruption of Eyjafjallajokull and at Holuhraun (the plume at Fimmvörðuháls was only measured very close to the vent and shows only the bromine explosion close to the vent). The BrO/ SO 2 ratios evolve in the plume in a consistent way for all the Eyjafjallajökull traverses, reflecting a consistent plume chemical evolution. Simulations using the PlumeChem model Fig. 11  BrO/SO 2 vs. distance from summit for the summit eruption of Eyjafjallajökull. Only points where the column amount of SO 2 exceeds 5 × 10 17 molecules cm -2 have been included so as to avoid large fluctuations in ratios caused by low column amounts of gas (and plume edge effects on the chemistry).
Study sample and data collection The NOWAC study has been described in detail previously  [20]  and has been used to investigate a wide range of lifestyle factors and health outcomes. In brief, the NOWAC study is a nationwide, prospective cohort consisting of approximately 172 000 adult female participants. Women invited to participate in the NOWAC study were randomly sampled from the Norwegian Central Person Register between 1991 and 2007 in multiple sub-cohorts. Consenting participants completed a self-administered questionnaire at enrolment and were invited to complete a maximum of three follow-up selfadministered questionnaires, where each questionnaire was distributed between 2 and 11 years apart. All questionnaires, including follow-up questionnaires, collected information on socio-demographic characteristics, reproductive and hormonal factors, self-reported health, physical activity level, height, weight, smoking habits, dietary habits, and family history of breast cancer.
Multiple imputation Missing data among variables constituting the HLI score at Q1 and/or Q2, and covariates for 21 830 participants were handled by multiple imputation chained equations (MICE) under the assumption that data were missing at random  [24]. All covariates included in the cancer models and the Nelson Aalen cumulative hazard estimator were included in the MICE model. MICE analysis employed fully conditional specification, whereby each incomplete variable was modelled iteratively by a series of multivariable regression models  [25]. A total of 100 datasets were generated with 10 iterations each. Parameter estimates in the Cox models from each imputed dataset were averaged through the Rubin's rule  [26]  to account for uncertainty in the MICE models to impute missing values. Model parameters were also estimated in complete-case analyses.
Descriptive statistics for each imputed variable were compared between observed and imputed values. Convergence of MICE models were assessed by visual inspection of plots of the mean HLI score change against iteration number for each MI dataset (not shown). All data treatment and statistical analysis were conducted in RStudio Version 1. 2. 959 with R Version 4. 0. 3 [27]. All statistical hypotheses were tested two-sided, allowing a Type I error rate of 5%.
Therefore, all presented estimates were obtained from MICE  data models, except for those in figures displaying HLI score change modelled with restricted cubic splines, where estimates from complete-case data models were described. After adjusting for covariates, for every 1 SD increase in HLI score change, the HR was 0. 93 (95% CI: 0. 90-0. 96) for lifestyle-related cancers, 0. 96 (95% CI: 0. 91-0. 99) for alcohol-related cancers, 0. 92 (95% CI: 0. 88-0. 96) for tobacco-related cancers, 0. 94 (95% CI: 0. 91-0. 98) for obesity-related cancers, 0. 90 (95% CI: 0. 84-0. 98) for reproductive-related cancers, 0. 96 (95% CI: 0. 91-1. 01) for breast cancer, and 0. 98 (95% CI: 0. 90-1. 07) for colorectal cancer (Table  2 ). When the HLI score change was modelled using restricted cubic splines, there were no indications of nonlinearity (all p-values > 0.
We note that another non-ideal MHD effect, Ohmic dissipation, produces a thermally supported midplane where fragmentation should occur in the massive protostellar context  (Oliva & Kuiper 2023b). Moreover, when including all three non-ideal MHD effects (ambipolar diffusion, Ohmic dissipation, Hall effect) in low-mass pre-stellar core collapse calculations,  Wurster & Bate (2019)  found less disk fragmentation in their magnetized models than in their purely hydrodynamical models. For these reasons, we include MHD with ambipolar diffusion in our magnetized models, as those are already implemented in the RAMSES code  (Teyssier 2002; Masson et al. 2012). Finally, radiative transfer and its coupling to hydrodynamics are taken into account in order to accurately compute the gas temperature, essential for accurate Jeans instability modeling.
Radiation-magnetohydrodynamical model Simulations were performed with the RAMSES code  (Teyssier 2002; Fromang et al. 2006). RAMSES is an adaptive mesh refinement code (AMR) integrating the equations of radiation-MHD (RMHD). Non-ideal MHD is taken into account in the form of ambipolar diffusion (ion-neutral drift;  Masson et al. 2012). For the radiative transfer part, we used a hybrid A134, page 2 of 17 radiative transfer method  (Mignon-Risse et al. 2020). In this method, the M1 method  (Levermore 1984; Rosdahl et al. 2013; Rosdahl & Teyssier 2015 ) is employed to model the propagation and absorption of protostellar radiation, while the flux-limited diffusion (FLD;  Levermore & Pomraning 1981; Commerçon et al. 2011b Commerçon et al. , 2014 ) is used to model dust-gas emitted radiation. The RMHD model consists of this set of equations ∂ρ ∂t + ∇ • [ρu] = 0, ∂ρu ∂t + ∇ • [ρu ⊗ u + PI] = -λ∇E fld + κ P, ρ c F M1 + F L -ρ∇φ, ∂E T ∂t + ∇ • u E T + P + B 2 /2 -(u • B)B -E AD × B = -P fld ∇ : u + κ P, ρcE M1 -λu∇E fld + ∇ • cλ ρκ R,fld ∇E fld -ρu • ∇φ, ∂E M1 ∂t + ∇ • F M1 = -κ P, ρcE M1 + Ė M1 , ∂F M1 ∂t + c 2 ∇ • P M1 = -κ P, ρcF M1 , ∂E fld ∂t + ∇ • [uE fld ] = -P fld ∇ : u + ∇ • cλ ρκ R,fld ∇E fld + κ P,fld ρc a R T 4 -E fld , ∂B ∂t -∇ × [u × B + E AD ] = 0, ∇ • B = 0, ∆φ = 4πGρ, (1) where, ρ is the gas density, u is the velocity vector, P is the gas thermal pressure, λ is the flux-limiter for the FLD module, E fld is the FLD radiative energy, κ P, is the Planck mean opacity computed at the effective temperature of the primary star, c is the speed of light, F M1 is the M1 radiative flux, F L = (∇ × B) × B is the Lorentz force, φ is the gravitational potential, E T is the total energy (which is defined as E T = ρ + 1/2ρu 2 + 1/2B 2 + E fld , where is the specific internal energy), E M1 is the M1 radiative energy, B is the magnetic field, E AD is the ambipolar electromotive force, P fld is the FLD radiative pressure (which is not evolved as such but related to the other FLD moments; this is the FLD approximation), κ P,fld is the Planck mean opacity in the FLD module (computed at the local gas temperature), κ R,fld is the Rosseland mean opacity, a R is the radiation constant, P M1 is the M1 radiative pressure, and Ė M1 is the injection term of the primary stellar radiation into the M1 module.
Acknowledgements. R. M. R. thanks  A. Oliva  and  R. Kuiper  for useful discussion. This work was supported by the  CNRS "Programme National de Physique Stellaire" (PNPS). This work has received funding from the  French Agence Nationale de la Recherche (ANR)  through the project  COSMHIC  ( ANR-20-CE31-0009 ). The numerical simulations we have presented in this paper were produced on the CEA machine Alfvén and using HPC resources from  GENCI-CINES  (Grant  A0080407247 ). The visualisation of RAMSES data has been done with the OSYRIS (https://github. com/nvaytet/osyris)python package, for which R. M. R. thanks  Neil Vaytet.
Changes in the abundance and diversity of neural cell types, and their connectivity, shape brain composition and provide the substrate for behavioral evolution. Although investment in sensory brain regions is understood to be largely driven by the relative ecological importance of particular sensory modalities, how selective pressures impact the elaboration of integrative brain centers has been more difficult to pinpoint. Here, we provide evidence of extensive, mosaic expansion of an integration brain center among closely related species, which is not explained by changes in sites of primary sensory input. By building new datasets of neural traits among a tribe of diverse Neotropical butterflies, the Heliconiini, we detected several major evolutionary expansions of the mushroom bodies, central brain structures pivotal for insect learning and memory.
Extensive variability in Heliconiini mushroom body size We generated an extensive dataset of brain composition for 318 wildcaught individuals from 41 Heliconiini species (average 8 individuals/ species), including 30 species and sub-species of Heliconius and representatives from each Heliconiini genus (Fig. 1 ). 3D volumetric reconstructions of whole brains stained with a synaptic marker revealed a high degree of variation in mushroom body size across the tribe. In raw volume, mushroom bodies vary by 26. 5-fold, from ~3 × 10 6 μm 3 to ~70 × 10 6 μm 3 per hemisphere (Supplementary Data 1 and 2). This level of variation is unmatched by major visual or olfactory brain structures (medulla: 7. 9-fold variation; antennal lobe: 6. 2-fold variation), or the remaining volume of the central brain (7.
5 (Fig. 2A ), representing increases in relative mushroom body size at the internal branches leading to Heliconius+Eueides (post. prob. = 0. 64) and Heliconius (post. prob. = 0. 63), as well an independent expansion in Dryadula (post. prob. = 0. 98), and a reduction at the base of Eueides (post. prob. 0. 71). These shifts result in phylogenetic groupings of species having approximately common allometric scaling of the mushroom body, with convergent allometries between Eueides and Dryadula, which are intermediate between Heliconius and all other outgroup genera (Fig. 2B ). These results are supported by pairwise comparisons among all genera (Fig. S1  and Tables  S1  and 2 ), and by ancestral state reconstructions which again imply that serial shifts in mushroom body size occurred independently of central brain size, and culminated in Heliconius, where the estimated ancestral state is within the range of extant Heliconius species (Fig.
Sensory brain regions, conversely, do not show similar non-allometric shifts or co-incident periods of accelerated rates of evolution (Fig. 2E-H ). This again demonstrates that increases in mushroom body size are not primarily caused by changes in the sensory periphery. However, whether mushroom body expansion is associated with further circuit changes in downstream neuropils, such as the central complex, which has important roles in spatial navigation in other insects  16  , remains to be determined. Finally, to contextualise this variation within a broader sample of butterflies, we used the same approach to reanalyse a phylogenetically broad dataset of 41 species of North American butterflies which includes Heliconius charithonia and the non-pollen feeding Heliconiini Agraulis vanillae  46.
The scaling relationship between calyx volume and total Kenyon cell number is significantly different across species (χ 2 = 90. 749, d. f. = 5, p < 0. 0001, Tables  S3  and S4 ), and results in elevation shifts (W = 90. 06, d. f. = 5, p < 0. 0001). Post-hoc pairwise analysis indicates that this is primarily due to a shift between Heliconius+Eueides and other genera, which results in a smaller volume of calyx per Kenyon cell in these two genera (Fig. 3F  and Table  S5 ). We also quantified the density of synapses within the calyx to indirectly test for evidence of altered connectivity (Supplementary Data 4). Although we do find an effect of species on synapse density (χ 2 = 17. 846, d. f. = 5, p = 0. 003), there is no evidence that Heliconius consistently differ from other genera (χ 2 = 0. 077, d.
However, the shift in Heliconius is, again, more extreme than this pattern would predict (Fig. 4J ). This demonstrates that a major change in the degree of visual processing performed by the mushroom bodies coincided with the origin of pollen feeding. Notably, our data suggest this marked expansion of visual processing is not repeated in other periods of mushroom body expansion, such as in Dryadula or the stem Heliconius+Euiedes branch.
First, Heliconiini share larval host plants from the Passifloraceae, and levels of host plant generalism could explain some variation in mushroom body size and plasticity  51, 52. However, using a dataset of host plant records  53  we found no association between the number of host plants used and mushroom body size (pMCMC=0. 492). Second, some Heliconiini form aggregated roosts at night, and it has been argued that this could facilitate social transfer of information on resource location  54  and exert distinct selection pressures on the processing of conspecific cues in social species. However, we again found that the degree of social roosting had no power to explain variation in mushroom body size (pMCMC = 0. 857). One additional explanation for the independent increases in mushroom bodies in non-trap lining species, such as Eueides and Dryadula, is that they possess an ability to form visual memories as part of their wider behavioural repertoire.
Animals To build an extensive dataset of neuroanatomical traits across the Heliconiini tribe, we caught 318 wild butterflies from 41 species from sites in Costa Rica, Panama, French Guiana, Ecuador and Peru (Supplementary Method 1). For measurements of the cellular composition and sensory innervation of the calyx, we obtained pupae from 8 species representing key lineages, from commercial suppliers. These butterflies were reared in controlled temperature room at 28 °C with 80% humidity level and a 12-h light cycle.
The tissues were immuno-labelled with a combination of anti-peroxidase antibody (HRP: Rabbit anti-horseradish peroxidase) and Cy3conjugated secondary antibody (Cy3 goat anti-rabbit IgG), while cell nuclei were stained with DAPI. Data on synapse number was estimated from the same individuals, with the addition of 7 individuals of H. charithonia and D. iulia stained with anti-SYNORF1 (as above) and Alexa 488 phalloidin (A12379, Thermo Fisher Scientific) to reveal both post and presynaptic (microglomeruli) structures (Supplementary Method 5 and 7).
Statistical analysis All statistical analyses were performed in R v 4. 1. 2. Ecological data were collated from the literature (Supplementary method 11), and phylogenetic comparative analyses were conducted using a new phylogenetic tree of the Heliconiini generated from newly assembled genomes  70  (Supplementary method 12). To assess how the volumes of neuropils vary across the Heliconiini, we ran a series of phylogenetic generalised linear models (GLMMs) with gaussian distributions using the R package MCMCglmm v 2. 32  71  , using the rest of the central brain (rCBR) as an allometric control. Ecological explanations for mushroom body expansion were tested by taking the best fitting of these models and then including ecological factorsthe degree of social roosting, host plant number and pollen feeding.
The R package bayou v 2. 0 72 was used to identify regions of the Heliconiini tree showing evidence of a shift in the scaling relationships between specific neuropils and rCBR. rCBR is used as an allometric control as it excludes all neuropils of interest (MB, AL, MED, vLOB) and therefore allows comparison of these results while excluding their influence on one another. Nonallometric "grade-shifts" are widely viewed as signatures of adaptive change in brain composition, reflecting a response to selection pressures acting on specific brain components  43. The bayou analyses were supplemented by an analysis of allometric shifts between mushroom body and rCBR volumes using the R package smatr v 3. 4-8  73. We subsequently used two methods to test for shifts in the evolutionary rate of change in mushroom body size within the Heliconiini.
First, we used BayesTraits v3 to compare two independent contrasts MCMC model of evolution, one allowing for a rate scaling parameter to vary across branches, and one with a fixed average rate  74. Second, we used a recently-published method that uses Brownian motion to model variations in evolutionary rates included in the R package phytools v 0. 7-90  45. These analyses allow us to confirm that the grade-shifts identified by bayou are the result of variation in the rate of MB evolution, rather than rCBR evolution, supporting the inference of adaptive MB evolution. Ancestral states for mushroom body size and rCBR at key internal nodes in the Heliconiini tree were also estimated using two methods. First, using BayesTraits we estimated ancestral values using a nondirectional model and the scaled trees generated from the previous varied rates analyses.