dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We considered the problem of the tomographic reconstruction of the index of refraction using data acquired with XPCI techniques. An efficient method for the integration of the refractive index gradient vector field is proposed. The gradient vector field approach and the suggested integration method were applied to a set of differential phase contrast projections of a whole human breast sample acquired with the analyzer-based imaging method and synchrotron X-rays. It is shown that obtained refractive index CT images can be used to distinguish between various anatomical features with high accuracy. The quality of the CT reconstructions was evaluated by a comparative study with two other methods: (1) the filtered backprojection algorithm for gradient projections and (2) the CT reconstruction from projections of the phase delays.
S1 ). Although apo GrxS14 appeared to be a monomer on the gel filtration column, analytical ultracentrifugation analysis showed two peaks (with molecular weight about 24 kDa and 12 kDa) for apo GrxS14 without GSH, while there was only one peak at ∼12 kDa for apo GrxS14 with GSH (Fig. S2A  and S2B ). All these suggest that apo GrxS14 should be in a monomer-to-dimer equilibrium, and the dimerization can be inhibited by GSH. Thus, there exists another type of GrxS14 dimer in addition to the holo GrxS14 dimer assembled with a [2Fe-2S] cluster. Based on the analytical ultracentrifugation data (Fig. S2A ), the dissociation constant of GrxS14 monomer-to-dimer equilibrium is estimated to be ∼0. 4 mmol/L. We determined the solution structure of reduced monomeric GrxS14 using NMR data collected on protein samples in the presence of 20 mmol/L GSH.
FOOTNOTES All NMR experiments were carried out at the Beijing NMR Center. This research was supported by grants 2011IM030300 and 2006DFA31210 from Ministry of Science and Technology of China, grant CERS-1-51 from Ministry of Education of China, and grant 30570353 from the National Science Foundation of China to BX; and the ANR grant 2010 BLAN 1616 to NR and JPJ. Lei Wang, Yifei Li, Jean-Pierre Jacquot, Nicolas Rouhier, and Bin Xia declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors.
Computational Methods In order to obtain reliable isomerization energies, calculations have been carried out using the high-level, ab initio W2-F12 procedure with the Molpro 2012. 1 program suite. 11 W2-F12 theory  9  (and its earlier versions W2 and W2. 2 theories  12, 13  ) represent layered extrapolations to the CCSD(T) infinite basis-set-limit energy. These composite theories include scalar-relativistic, first-order spin-orbit coupling, diagonal Born-Oppenheimer, zeropoint vibrational energy, and enthalpic corrections and can achieve "near-benchmark accuracy" for atomization reactions (e. g. , W2-F12 theory is associated with a mean absolute deviation of 1. 3 kJ mol -1 for a set of 140 very accurate atomization energies). 9, 10, 12, 13, 14, 15, 16 vertheless, it should be pointed out that for isomerization reactions these theories yield even better performance due to a larger degree of systematic error cancelation between reactants and products.
ACKNOWLEDGEMENTS We are indebted to  Alicia M. Muro-Pastor  for providing the Western blot used in Fig. 4. The work in France was financially supported by the  Centre National de la Recherche Scientifique  ( UMR8221 ) and the  Commisariat al'Energie Atomique (Bioenergies DSV, IBITEC-S). The work in Spain was supported by  Ministerio de Economı ´a y Competitividad  co-financed by  FEDER  (grant  BFU2010-15708 ) and by  Junta de Andalucı ´a  (grant  P07-CVI-02792  and group  BIO-284 ). The anonymous reviewers are acknowledged for their constructive comments.
Acknowledgments Concordiasi is an international project, supported by the following agencies:  Météo-France ,  CNES ,  CNRS/INSU ,  NSF ,  NCAR ,  University of Wyoming, Purdue University, University of Colorado , the  Alfred Wegener Institute , the  Met Office , and  ECMWF. The measurements by the  University of Wyoming  within Concordiasi were supported by the  NSF  under  OPP  award  0636946. Concordiasi also benefits from logistic or financial support of the operational polar agencies  Institut Polaire Français Paul Emile Victor (IPEV) ,  Programma Nazionale di Ricerche in Antartide (PNRA) ,  United States Antarctic Program (USAP) , and  British Antarctic Survey (BAS) , and from  Baseline Surface Radiation Network (BSRN)  measurements at  Concordia. Concordiasi  is part of the  Observing System Research and Predictability Experiment-International Polar Year (THORPEX-IPY ) cluster within the International Polar Year effort.
Creation of a reference sectional view The reference sectional view has been digitized manually in the previously resampled point cloud. Based on cutting planes of 6 cm thickness, the selected slices of point clouds have been exported to AutoCAD software (Autodesk) for digitizing the contours, after 35 hours work (Figure  15 ). By curiosity, this sectional view has been compared on 90 points to the one drawn in 1968 (Figure  4 ) after horizontal flip. Incoherencies have been emphasized and deviations of 18 cm +/-23 cm have been observed, but this was expectable given the measurement techniques used in the past.
5 kW. This instrument worked with a good temporal resolution (3. 5 -6 minutes) and offered two modes: the low mode and the high mode. The low mode was based on a 150 m pulse length, and on 95 level gates spaced every 75 m starting at 100 m from the radar. Although the high mode offers measurements at higher levels, the low mode data were used in this study for the calculation of the vertical wavenumber (m) due to the higher radial resolution and because the GWs detected in this work propagated mainly below 2000 m AGL. Wind profiles from UHF measurements were compared to measurements from another UHF wind profiler located 5 km apart from the former. This comparison (not shown) served as an additional quality control data. Both UHFs showed similar values but with a coherent time lag among observations, which agreed with the storm direction and speed of propagation.
Data from several barometers located at different distances from site A have been analysed in order to track the GWs propagation. Figure  5   I  for locations). According to these data we can affirm that the wave propagated for at least 5 km (presumably more distance) and was firstly detected at site C (at the southernmost barometer in Lannemezan), which proves that the GWs propagation had a northward component of movement, in accordance with wavelet-derived parameters. Surface pressure from further locations (sites D and E) do not show apparent GWs oscillations. However, a decrease in surface pressure followed by an increase is clearly visible and could be an indicator of the passage of the weak low. This surface pressure drop was observed at site D approximately 25 minutes before than at site A, and at site E 10-15 minutes later than at site A.
Higher frequency data from instruments at site A were used to check whether the oscillations in these meteorological parameters were also encountered near surface. A combination of cooling produced by the evaporation of the rain water at the surface and radiative cooling during sunset most likely caused the formation of a surface-based temperature inversion from 2000 UTC onwards (Figure  9 ). Later, the arrival of the GWs to the site caused oscillations in wind speed, wind direction, temperature and vertical velocity measurements at different heights close to the surface. Figure  10    The observed oscillations in the wind speed projected in the same direction than the GWs have the same periodicity than the surface pressure records. This suggests that the effect of the GWs during narrowings (maxima in pressure) was to force the main flow to approach their own direction of propagation, as can be also seen in the changes in wind direction (closer to southwest during maxima).
ACKNOWLEDGMENTS This research has been funded by the  Spanish Government  (projects  CGL2009-12797- C03-03 ,  CGL2011-13477-E  and  CGL2012-37416-C04-02 ). BLLAST field experiment was made possible thanks to the contribution of several institutions and supports:  INSU-CNRS (Institut National des Sciences de l'Univers ,  Centre national de la Recherche Scientifique, LEFE-IDAO program ),  Météo-France ,  Observatoire Midi-Pyrénées  ( University of Toulouse ),  EUFAR (EUropean Facility for Airborne Research)  and  COST ES0802  (European Cooperation in the field of Scientific and Technical). The field experiment would not have occurred without the contribution of all participating European and American research groups, which all have contributed in a significant amount. BLLAST field experiment was hosted by the instrumented site of  Centre de Recherches Atmosphériques, Lannemezan, France (Observatoire Midi-Pyrénées, Laboratoire d'Aérologie).
BLLAST data are managed by SEDOO, from  Observatoire Midi-Pyrénées. The tower equipment and UHF radar have been supported by  CNRS ,  University of Toulouse and European POCTEFA FluxPyr program  and  FEDER program  (Contract  34172 -IRENEA -ESPOIR ). Corn and Moor stations were funded by the  CNRS INSU and Météo-France  and implemented by the CNRM-GAME team GMEI/ 4M. We thank  François Gheusi  and  Gilles Athier  for providing data from the Pic du Midi station --part of the P2OA research platform. These measurements have been supported by the  French national institute CNRS-INSU  and the  Observatoire Midi-Pyrénées  ( University of Toulouse ). The edge-site measurements were financed by the.
Acknowledgments: Thanks are due to  Professors Bernard Dreyfus  and  Jacques Friedel. The idea of this work started 50 years ago with criticisms of Bernard Dreyfus who considered that the nucleation equation would have to depend on the Fermi energy of conduction electrons. My first model developed in 2006, introduces an energy saving in the classical Gibbs free energy change for a nucleus formation that I had attributed to a transfer of conduction electrons from nucleus to liquid. Jacques Friedel  noted that this transfer does not exist. I have proposed in 2011 that a Laplace pressure change acting on growth nuclei induces the enthalpy saving and the equalization of  Fermi  energies without electron transfer. A virtual transfer is nethertheless considered to quantify the energy saving in agreement with undercooling rates.
The map comes thus with an associated error map, ranging from 20 m (on measured points) up to 1000 m (in unobserved areas), and it is worth noting that this error map is obtained thanks to the kriging method, with no ice flow consideration. Data assimilation (DA) covers the methods used to combine model and data in order to estimate initial states or parameters. There are two main classes of DA algorithms: variational, based on optimal control theory (the prototype being 4D-Var), and sequential, based on optimal statistical estimation (the prototype being the Kalman filter). DA is widely known in weather and oceanography forecasting, but its introduction in glaciology is fairly recent, in particular for the initial state estimation problem for sea-level rise. MacAyeal (1992)  and  MacAyeal (1993)  introduced control methods to infer basal drag in ice-stream models, using in particular the self-adjoint property of such models, leading to many application papers  (Rommelaere and MacAyeal, 1997; Vieli and Payne, 2003) , and later for full Stokes models  (Morlighem et al.
, 2010; Jay-Allemand et al. , 2011). Later on, many DA and inverse methods were introduced in glaciology. The Best Linear Unbiased Estimation (BLUE) and Optimal Interpolation (OI) methods were introduced by  Arthern (2003)  and  Berliner et al. (2008). The Robin inverse method due to  Chaabane and Jaoua (1999)  has been introduced by  Arthern and Gudmundsson (2010)  for ice sheet models, and finally,  Heimbach and Bugnion (2009)  presented the first adjoint ice sheet model derived automatically. As the ice surface elevation is pretty well (in terms of accuracy and data density) observed by satellite sensing, initial state estimation focuses on bedrock topography and basal sliding law estimation. The joint identification of bedrock topography and basal friction law is a difficult problem. Indeed, different configurations (e.
Acknowledgements. The authors would like to thank the editor  Olivier Talagrand , reviewer  Istvan Szunyogh  and another anonymous reviewer for their comments that helped to improve the quality of the manuscript. B. Bonan  would also like to thank  N. K. Nichols ,  M. J. Baines  and the  University of Reading  for their support. This work has been partially supported by the  French National Research Agency (ANR)  through the  SYSCOMM programme  (project  ADAGe  number  ANR-09-SYSC-001 ). Edited by: O. Talagrand Reviewed by:  I. Szunyogh  and one anonymous referee.
0‰ and 263 +5. 3‰ and thus confirm previous observations from small rivers  (Kisakurek et al. , 2005; 264  Pogge von Strandmann et al. , 2010). Considering both SPM and RBS, we found similar 265 δ 7 Li and Li concentration ranges within a single depth-profile and across the overall 266 dataset. Each river sediment depth profile defines a hyperbolic relation between the δ 7 Li 267 and the Al/Si ratio (Fig. 3 ). The Al/Si ratio is a grain size proxy  (Bouchez et al. , 2011a; 268 Lupker et al. , 2011) , and the trend suggests that any river sediment sample taken on a 269 river depth-profile can be described as a mixture between fine surface particle and coarse 270 bottom sediment components. 271 Using the depth-integrated values for the Amazon, Mackenzie, Ganga, Brahmaputra 272 rivers and surface sediments of the Orinoco  (Huh et al.
14;  Hammer et al. , 2001). To better constrain the relationship between species variability and environmental conditions, principal component analysis (PCA) and canonical correspondence analysis Fig. 2. Environmental parameters: bottom water oxygenation, organic carbon (OC; 0-1 cm), chlorophyll a (Chl a, 0-1 cm), oxygen and nitrate penetration depths. In red: the results from this study; in blue: the results published in  Koho et al. (2013) ; in black: the results published in  Kraal et al. (2013). The dashed line at 22 µM corresponds to the definition of the OMZ according to  Levin et al. (2003). (CCA) were conducted using Statistica 8 software. The PCA was run on species that contributed > 2 % to the total abundance of the entire database. Two PCA analyses were carried out; the first one investigating the entire core (down to 10 cm, > 125 µm fraction), and the second one investigate the top 1 cm of the sediment (0-1 cm, > 63 µm fraction).
Dissolved oxygen and nitrate In short, dissolved O 2 concentrations in the water column and on the seafloor measured with an oxygen sensor (Sea-Bird SBE43, accuracy 2 % (Sea-Bird Electronics Inc. , 2011)) that was built into the framework of a conductivity, temperature and depth (CTD) profiler (all data published in  Koho et al. (2013) ; see Fig. 2  for details). The pore water oxygen concentration was determined immediately following core recovery in a temperaturecontrolled laboratory setup at in situ temperature (some data published in  Kraal et al. , 2013 ; see Fig. 2  for details). All measurements were done with Unisense oxygen microelectrodes (OXO100 or OXO50). The microelectrodes were twopoint calibrated at 100 % O 2 saturated seawater (bubbled with oxygen) and in a solution containing sodium ascorbate (0.
1 M) to obtain the 0 % reading. The details of pore water extraction for nitrate analyses are outlined in  Kraal et al. (2013). In short, sediment slicing was carried out in a N 2 -purged glove box in a temperaturecontrolled laboratory set at in situ temperature (some data are published in  Kraal et al. , 2013 ; see Fig. 2  for details). Sediment samples were transferred to 50 ml plastic centrifuge tubes under N 2 atmosphere and centrifuged for 20 min at 4500 rpm outside the glove box. The centrifuge tubes were then transferred back into the glove box where the supernatant pore water was filtered over 0. 45 µm Teflon filters and frozen (-20 • C) until the analyses. Pore water nitrate concentrations were measured by an autoanalyser at the Royal Netherlands Institute for Sea Research (NIOZ) in Den Burg, the Netherlands.
Organic carbon and phytopigment analyses The details of organic carbon analyses are outlined in  Koho et al. (2013)  (all data published in  Koho et al. , 2013 ; see Fig. 2  for details). In short, the sediment from which the pore waters were extracted was further used for solid phase analyses and frozen at -20 • C until analyses. In the laboratory, sediment was weighed, freeze-dried and decalcified by reacting twice with 1 mol L -1 HCl (4 and 12 h). After two subsequent rinses with ultrapure water, the decalcified samples were freeze-dried and organic C and N were measured with a CNS analyser (Fisons Instruments NA 1500). The phytopigments were extracted from a different core than the one used for the organic carbon analyses. However, the core came from the same multicore cast. The details of phytopigment analyses are outlined in  Koho et al.
As in the PCA presented above, three very similar groups were obtained (see Appendix C in Supplement), with only few changes in species composition (e. g. Nuttallides pusillus, Bolivina spp. ) compared to the first PCA. Due to the similarity of the two PCA analyses, only the PCA based on the > 125 µm size will be discussed further. We assume this one is more robust as it is based on the complete cores with foraminifera analysed down to 10 cm depth in sediment. Moreover, most paleoceanographic studies are based on the > 125 µm fraction. We consider that species groups 1, 2 and 3 each contain a number of taxa with rather similar ecological requirements, which therefore can be lumped together. Subsequently, CCA was performed on the geochemical data (depth, BWO, OC, oxygen and nitrate penetration depth) and on the cumulative percentages of these three species groups (Fig.
; 0-200 ppm range) and H 2 S (EZ3H electrochemical sensor by City Technology Ltd. ; 0-200 ppm range). All the sensors, powered by an internal battery, were housed in a water-proof box, through which the plume gas was pumped at 1. 2 l/min rate. The sensor readouts were sampled at 0. 5 Hz by an onboard data-logger, and saved into an internal memory board. The acquired data were postprocessed using the methodology described below (cf. Section 4). Our measurements at the BL were taken on February 26, 2012, from a fixed position (Lat, N15. 31867; Long, E61. 2947; elevation 792m) on the north-northwestern outer rim of the depression hosting the lake (see Figures  1a  and 3d ). Weather conditions, which had remained unstable over the morning of February 26, significantly improved between mid-day and 3pm, when our observations were taken in relatively dry, stable (no precipitation, relative humidity of ~70%, T ambient of ~24°C) conditions.
In order to obtain consistent data to compare with our BL dataset, we also explored the fumarolic field of the Valley of Desolation (VoD). In this case, the technique was employed in a walking traverse mode [see  Aiuppa et al. 2005] , i. e. , while traversing the fumarolic field with the MultiGAS transported in a backpack (Figure  3f  ). At each degassing vent, the traverse was stopped, and the instrument was left to acquire for a few minutes with its inlet being positioned at ~0. 5m from the vent. Acquiring gas concentration data at 0. 5 Hz, and with simultaneous GPS positioning (Figure  3a ), the Multi-GAS had the required temporal/spatial resolution to map the chemical heterogeneity of a fumarolic field [e. g. ,  Aiuppa et al. 2005 ]. Unfortunately in this specific VoD survey, however, the plume was condensing (e.
The peak concentrations (3680, 101 and 25 ppm for respectively H 2 O, CO 2 and H 2 S), evident in the central part of our record (at measurement number 600), were obtained in a (few-minutes long) temporary phase of wind speed decrease, during which the gas was forced to accumulate at our observation site. SO 2 , instead, was virtually absent in the plume, confirming that H 2 S is the only S species emitted, and that no rapid oxidation process is occurring in the plume. From the concentration data of Figure  4 , the scatter plots of Figure  5  were obtained. These were then used to derive -by calculating the slopes of the best fit regression lines -the characteristic CO 2 /H 2 S (5. 2±0. 4) and H 2 O/CO 2 (31±6) volatile ratios in the BL atmospheric plume (all ratios quoted here are molar).
Discussion The BL gas plume, for which we provide here the first compositional report (at least to our knowledge), is manifestly H 2 O-rich (H 2 O/CO 2 of 31±6). With the objective of facilitating comparison between our observations and other volcanic gas datasets (Figure  6 ), we recalculated -from the plume characteristic ratios described above -an air-free composition for the BL gas. This operation, while mathematically correct, leads to a "real" volcanic gas composition only at condition that other volcanic gas species (not detected by the Multi-GAS: e. g. , HCl, CO, H 2 , N 2 , etc. ) are unimportant in the BL gas. This assumption, while not strictly true, is a good approximation, however, considering that H 2 O, CO 2 and H 2 S generally make up ~100% of the total gas phase in low-temperature hydrothermal gas manifestations  [Chiodini and Marini 1998 ], including in Dominica  [Joseph et al.
Models of gas-lake interaction and scrubbing Modelling is initialised and performed following the steps detailed below. Step 1, Fixing feeding gas composition -The composition of the gas supplying the lake from below is unknown, but is required to initialise a simulation of gas scrubbing. Given the proximity between BL and VoD, we can make the guess that the composition of the feeding gas is approximated by a VoD-type gas. This latter is evaluated at ~94. 4% H 2 O, 4. 7% CO 2 , 0. 8% H 2 S, and 0. 1 HCl%, using our H 2 O/CO 2 (20) and CO 2 /H 2 S (5. 7) ratios (cf. Section 4), and assuming an average H 2 S/HCl ratio of 13 (the mean ratio calculated from the dataset of  Joseph et al. [2011] ). The temperature of the feeding gas, prior to its entrance in the lake, is taken at 200 °C (the equilibrium temperature estimated from application of the Na/K aqueous geothermometer to BL lake-water;  Joseph et al.
The BL gas in a regional context If, as proposed above, the BL gas is only marginally affected by scrubbing in the lake, comparison is then possible with available volcanic gas datasets in the region. Figure  6  puts our Dominica measurements in the context of published gas analyses from active volcanoes in the Lesser Antilles. Research on volcanic gas compositional signature in the Lesser Antilles arc has received significant impetus after the 1995-present eruption of Soufrière Hills (Montserrat); still, the available information remains fragmentary and incomplete -at least relative to other better characterised arc regions  [Hilton et al. 2002 , Mather et al. 2006 , Fischer 2008 ]. The high-temperature (>700°C) gas emissions issuing from the Soufrière Hills dome should in principle represent a source of invaluable information to constrain the truly magmatic gas composition in the region.