dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Most of recorded species (14 species of 17 in total) are represented in the ecological zone 4. Some among them are unique to this area (Adiantum incisum, Doryopteris kirkii, Haplopteris guineensis var. guineensis, Pteris atrovirens, Pteris similis and Pteris tripartita). On the other side, Actiniopteris radiata and Acrostichum aureum are respectively exclusive to the ecological zones 2 and 5. The entire chain of the mountains of Togo is rich: 16 species out of 17. This distribution shows the role played by biodiversity refuges such as mountains  (Moran 1995) , which are then comparable to islands. These results also confirm works of  Aldasoro et al. (2004)  on the biogeography of ferns in sub-Saharan Africa which show that ferns prefer mountainous and altitudinal areas. Three families in the RIHA database of the herbarium of Lomé correspond to the current delimitation of the Pteridaceae  (Christenhusz et al.
2011). These are the former families of "Adiantaceae", "Vittariaceae" and "Pteridaceae". They are now treated within a single taxon: the Pteridaceae family. About 84% (16 names on 19) of the species names in the RIHA database match the current delimitation of the Pteridaceae. However, two other species of the genus Nephrolepis are currently integrated within the Nephrolepidaceae  (Hennequin et al. 2010). The name Pellaea doniniana Link. does not correspond to any described taxon (The Plant List 2015). Among the 16 names corresponding currently to the Pteridaceae, 6 are synonyms (Table  2 ). Moreover, Vittaria guineensis Desv. belongs henceforth to the genus Haplopteris but remains a Pteridaceae genus  (Crane 1997). Finally, Pteris similis Kuhn, harvested in the ecological zone 4 is not present in the collections of Lomé and Paris, referents herbaria of this study, nor in GBIF and RIHA databases.
Naturally existing electromagnetic (EM) fields recorded at the surface of the Earth can be used to infer the electrical conductivity distribution of the subsurface. In the magnetovariational sounding (MVS) technique, the transient variations of orthogonal components of the Earth's magnetic field are measured. In the frequency domain, the magnetic transfer function relates the vertical component to the horizontal components of the magnetic field. This paper describes the thin-sheet modeling of MVS data on a regional scale. The integrated conductivity variations in the thin-sheet model were estimated by applying the Markov chain Monte Carlo (MCMC) inversion algorithm. The application of this method to MVS data from the Finland part of the Fennoscandian Shield has illustrated the utility of both the thin-sheet approximation and MCMC inversion modeling.
Background Transient variations of the Earth's main magnetic field due to solar wind activity serve as the primary field in electromagnetic (EM) induction methods, which allow for the estimation of the subsurface electrical conductivity distribution. The magnetovariational sounding (MVS) technique is a specific EM method where only orthogonal components of the Earth's magnetic field variations (H x , H y , H z ) are measured. In the frequency domain, the relationship between the vertical component and the orthogonal horizontal components of the magnetic field reflect the conductivity of the medium. In the EM community, the MVS technique is also known as the geomagnetic deep sounding (GDS) method. The data generated from this method are the magnetic transfer functions, presented as magnetic induction vectors or induction arrows  (Lilley and Arora 1982; Hobbs 1992).
Important studies using this method are mainly at the crustal scale, due to the deep investigation depths and lateral integrating effect of the magnetometer array recordings at long periods or low frequencies (e. g. ,  Heinson and Lilley 1993; Armadillo et al. 2001; Gurk and Schnegg 2001). With the availability of threedimensional (3D) EM modeling, MVS data can also be interpreted as the 3D resistivity distribution of the subsurface (e. g. ,  Kanda and Ogawa 2014; Wang et al. 2014). This paper describes the application of thin-sheet modeling of MVS data from a regional-scale EM study of Finland. We consider that the so-called thin-sheet approximation is appropriate for modeling MVS data due to the relatively limited frequency band of MVS data in general. In this study, 3D subsurface conductivity variations were assumed to be confined to a thin layer.
With similar homogeneous model parameterization involving only the physical property, while the geometry is held fixed, the extension of the MCMC algorithm to thin-sheet inversion modeling was deemed straightforward. The application of the MCMC technique to invert both synthetic and field data with thin-sheet models has led to encouraging results  (Grandis et al. 2002; 2013). We present the modeling of MVS data from the Finland part of the Fennoscandian Shield and discuss the results.
Inversion of MVS data The MVS dataset used in this study was taken from the Finland sector of a regional-scale EM study of the Fennoscandian Shield. Several field measurement campaigns were conducted, in part by the Department of Physics, University of Oulu, Finland, from which the data were obtained by the UFR Physique de la Terre et des Planètes, Université Paris Sud, France (Menvielle, pers. comm. ). The available data were magnetic transfer functions at 143 stations for periods of 100, 300, and 1000 s. However, only data for periods of 300 and 1000 s were used in the inversion in order to conform to the validity of the thin-sheet approximation. The observation stations were randomly scattered over Finland. Details of the data processing and preliminary analyses can be found in  Pajunpaa (1987) ,  Korja and Hjelt (1993) , and others.
We also observed abrupt changes in the direction over very limited or narrow areas, which may be associated with very conductive anomalies (e. g. , at the northern part of Bothnian Bay and in the south-eastern part of the study area). However, from visual observations alone, it is difficult to qualitatively delineate conductivity anomalies based solely on the induction arrow map. For the inversion, the induction vector most representative of each 50 by 50 km block was selected. We evaluated the difference of each vector with its neighbors in the same block using a least-squares approach. Hence, we obtained the most coherent induction vector for each of the blocks in the thin sheet. In this way, spurious effects caused by the interpolation of the MVS data into a more regular grid were avoided.
The available 300-s MVS data used in this modeling did not strictly meet all three conditions. The skin depth of the 300-s variation in the 100-Ohm underlying layer was ~87 km. As the 20-km thickness of the thin sheet was significantly less than this skin depth, condition (a) was met. The 50-km dimension of the grid spacing was only a little smaller than this skin depth, so condition (c) was marginal. In the 10-Ohm thin-sheet layer, the skin depth was ~27 km, which was comparable to the 20-km thickness of the thin sheet, so condition (b) was not met. However, while the 300-s data do not strictly meet all of the conditions set out by  Weaver (1994) , we consider that overall the thin-sheet approximation still holds since we are more interested in large-scale variations. We used a priori conductance values from 4 to 10,000 Ohm -1 (or Siemens/m), which were quasilogarithmically discretized to represent resistive to conductive anomalies in the region.
Conclusions We have presented thin-sheet modeling of MVS data using the MCMC inversion algorithm. In thin-sheet modeling, lateral variations in the subsurface conductivity structure are expressed as integrated conductivity over the thickness of the sheet (i. e. , conductance), such that vertical conductivity variations within each layer are unknown. This type of approximation is in agreement with the physics of EM induction, which is more sensitive to the conductivity-thickness product than to conductivity variation with depth. However, by assuming the thickness of the thin sheet is fixed, we obtained a model with only lateral conductivity variation. The necessity to comply with the thin-sheet approximation and limitations of the MCMC method led us to use blocks with relatively large horizontal dimensions.
The use of smaller blocks to achieve a better lateral resolution may have led to difficulties in the intensive exploration of a high-dimensional model space, as necessitated in the MCMC inversion method (e. g. ,  Rosas-Carbajal et al. 2014). Magnetometer array studies are usually performed with a large spatial sampling distance (several to tens of kilometers); hence, the lateral resolution of the MVS data is limited. However, information from subsurface large-scale conductivity structures is useful in understanding the development of the crustal-scale tectonics of a study area. We applied thin-sheet modeling with the MCMC inversion algorithm to MVS data from the Finland part of the Fennoscandian Shield and generated satisfactory results. The main features of the conductivity structures from our study correlate well with the regional geology and tectonics of the area.
A completely exclusive experiment was performed by the INDRA collaboration to study the isospin dependence of the level density parameter. Over a large N/Z range, the fusion-evaporation charged products of 34,36,40 Ar+ 58,60,64 Ni reactions were measured and identified both in charge and mass by coupling INDRA and VAMOS spectrometer. Preliminary results obtained by combining data of both detectors are presented for the 36 Ar+ 58 Ni at 13. 3 A MeV. The analysis method of relevant observables for such an ambitious investigation are discussed and the progress of the data analysis are reviewed.
Experimental data far from the valley of stability are very scarce. Up to now, as only stable beams were available, level densities were studied for nuclei close to the valley of stability, on the neutron-poor side. Moreover, experiments consisted in inclusive measurements, determining either mass (charge) distribution of evaporation residues, and eventually fission products, or multiplicities of light charged particles associated with fusion. At the dawn of this century, some experiments measured evaporation residues (ER) in coincidence with one or two light charged particles  [5, 9]. A first completely exclusive experiment was performed recently by the INDRA collaboration who studied the 78,82 Kr+ 40 Ca at 5. 5 AMeV. The fission channel is 25% higher for the neutron deficient system, including very asymmetric fission configurations  [10]  while the extrapolated evaporation residue crosssection for both systems are similar within the error bars.
Present status of the project Before the data can be analysed for physics, it is necessary to have a good calibration for both INDRA and VAMOS detectors. Presently identifications and calibrations of the INDRA array are nearly ready for all the systems. The Z and A identification of LCPs is achieved. The data reduction of the VAMOS part is more delicate. We are in the final process of getting the mass and charge of the evaporation residues, after reconstruction of the trajectories in the spectrometer and the calibration of ToFs. The obtained mass separation in VAMOS for the ERs in the 36 Ar+ 58 Ni reaction is shown in fig 1. A preliminary selection of ERs is realized by taking into account only complete events with Z tot > 40 and M f 5 = 1 where Z tot = events i Z i and M f 5 is the multiplicity of fragments with Z ≥5.
Conclusion In summary, the E494S INDRA-VAMOS experiment represents a great opportunity to explore the isospin dependence of the level density parameter by coupling a 4π charged particle multi-detector to a high resolution magnetic spectrometer. At present, both INDRA and VAMOS data sets are being combined to give the full information about each decay channel of evaporation residues and the current physics analysis is on the threshold of releasing the new expected constraints on the level density parameter.
Indeed, the accuracy reported for levoglucosan analysis is only slightly lower than the results reported for analysis of SO 2- 4 on filter samples during the latest intercomparison (intercomparison number 30) organised by EMEP (Co-operative programme for monitoring and evaluation of the long-range transmissions of air pollutants in Europe) (http://www. nilu. no/projects/ccc/ intercomparison/DQO-luft-30. pdf); i. e. an accuracy ranging from -23 to 40 % for the entire data set of 64 laboratories, and from -6 to 5 % for the subselection accounting for 62 % of the laboratories. SO 2- 4 in aerosol filter samples has been analysed by numerous laboratories for several decades, typically by ion chromatography, and should be considered as a fairly easy constituent to analyse. Hence, the comparable results obtained for levoglucosan with respect to accuracy are encouraging.
The levoglucosan to mannosan ratio The levoglucosan to mannosan ratio is occasionally used to apportion the contribution of residential wood burning emissions to burning of either softwood or hardwood  (Caseiro et al. , 2009; Favez et al. , 2010; Piazzalunga et al. , 2011; Maenhaut et al. , 2012) , with softwood combustion giving rise to low ratios (< 4) and hardwood to high ratios (14-15)  (Schmidl et al. , 2008). The latter authors proposed the following equation to derive the % spruce (or softwood) burnt (relative to the total amount of softwood + hardwood burnt): %spruce = (14. 8 -levoglucosan / mannosan)/0. 112, where levoglucosan / mannosan is the levoglucosan to mannosan ratio in the ambient aerosol. This equation was derived from data that were obtained for the combustion of common hardwood (beech and oak) and softwood species (spruce and larch) in wood stoves in Austria.
A3. 5 Lab. 12 Analyses were carried out using in situ derivatisation thermal desorption gas chromatography time-of-flight mass spectrometry (IDTD-GC-TOFMS). Filter punches (0. 12-0. 28 cm 2 ) were placed into a GC liner, internal standard ( 13 C 6 -levoglucosan) was added, and liners were closed. For derivatisation filter punches were soaked with 10 µL N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). Closed liners were placed in an oven at 80 • C for 1h. After this derivatisation step the liners were put into the cold injector. For desorption, the temperature was raised to 300 • C and kept there for 16 min. During desorption MSTFA was added to the carrier gas. Analytes were focused on a retention gap at 70 • C followed by GC separation (Agilent 6890). The TOFMS instrument (Leco Pegasus III) was operated at a data acquisition frequency of 25 scans s -1.
N2O concentration within the chamber was measured at a time step of 1 s (ppb s -1 ), and the increase in the headspace was checked for linearity for a period of 2-3 min. Data were recorded by a palmtop connected via Bluetooth ®. N2O flux was measured in all the experimental fields bimonthly, and samplings were intensified immediately after N fertilisation events and after residue incorporation with tillage, when measurements were carried out twice a week for two/three consecutive weeks. Depending on the row spacing, the collar was placed in the interrow space or the crop was left uncut within the collar. At every measurement soil temperature and volumetric water content were recorded in the proximity of each collar by a dielectric probe (GS3; Decagon Devices, Inc. , Pullman, WA, USA) inserted into the soil at a depth of 5 cm and linked to the prototype by a Bluetooth ® connection.
Data analysis N2O flux was calculated by performing a linear regression on the logged N2O concentration data, which was corrected for atmospheric pressure and air temperature. A linear mixed-effects model was used to analyse N2O log transformed flux data using the R lme4 package  (Bates et al. , 2014). To analyse the whole dataset, tillage, N fertilisation rate and date were considered as fixed variables, while collar and blocks were considered as random factors. Significance was determined using the R LMERConvenienceFunctions package  (Tremblay and Ransijn, 2013). Significance was tested also analysing each sampling date separately. Cumulative N2O emissions over the period were calculated by linear interpolation of two neighbouring sampling dates and the numerical integration over time. A linear mixed effects model was used for cumulative values.
N level Cumulative N2O emissions SE P value (N2O-N g ha -1 period -1 ) (N2O-N g ha -1 period -1 ) was very user-friendly. Moreover, data visualisation on the palmtop in real time allows evaluating the goodness of the measure. Some considerations need to take into account concerning the IPNOA system, related to the prototype cost, due mainly to the value of the two detectors, and to the prototype dimension and weight. In fact, an adequate van is needed to transport the instrument close to the field.
Conclusions The IPNOA mobile prototype performed well in field conditions, allowing recording a wide range of N2O flux, including very low emissions. Preliminary results on durum wheat showed a decisive influence of nitrogen rate on N2O production, while conventional and minimum tillage did not produce differences in N2O flux. However, these firstyear findings will be endorsed by data from the other site of study (CATES) and by further years of investigation. Data on N2O flux will be used to calibrate and to validate a processbased model for the assessment of emissions at regional scale and to develop mitigation scenarios based on alternative crop managements. Finally, the results will contribute to the drafting of the BMP for the N2O emissions reduction in Tuscany.
FUNDING This work was supported by the  National Institutes of Health  [grant number  R01 AR055225 ]; the  Hungarian Scientific Research Fund  [grant number  K104586 ]; the  Hungarian Academy of Sciences  ( Momentum Grant  to G. S. and G. E. T. ) the  Austrian Science Fund  [grant number  SFB35 ]; and the  France-BioImaging infrastructure  was supported by the  French National Research Agency  [grant number  ANR-10-INSB-04 ].
Magnetic Resonance Image data Volumes of cerebral white matter hyperintensities (WMH) normalized by intracranial volume were estimated from the MRI data in a subset of 25 MBS participants who completed MRI substudy. Eligible and willing participants were imaged using a Siemens Trio 3 Tesla system (Erlangen, Germany) employing a 12-channel phased-array head coil for reception and body coil for transmission. Cerebral WMH were measured with Free Surfer (http://surfer. nmr. mgh. harvard. edu) using a multispectral procedure that classifies white matter as normal or abnormal from signal intensities from the T1, PD, and T2 images at each voxel. The Free Surfer procedure for lesion segmentation combines the initial standard segmentation with an extension of the subcortical segmentation procedure that incorporates information from a co registered T2 and PD image for the segmentation of signal abnormalities within the white matter.
Acknowledgements This research was supported by grants  NS085002 ,  P01 AG04390  and  R37 AG25037  from the  National Institute on Aging  and by grants  R01-ES020871  and  R00-ES015774  from the  National Institute for Environmental Health Sciences. Dr. Wellenius  has received consulting fees from  Environmental Health and Engineering, Inc. , for work unrelated to this manuscript. The authors declare no competing financial interests. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr. Tchalla  was supported by the  Limoges University, Universitary Hospital Center of Limoges (CHU de Limoges)  and  Regional Council of Limousin from France. Dr. Lipsitz  holds the Irving and  Edyth S. Usen  and Family Chair in Geriatric Medicine at Hebrew SeniorLife.
NOESY-HSQC, all other proton side chain assignments from 3D hCCH-TOCSY (15. 6 ms spin lock) and 2D H-H NOESY spectra. The NOESY mixing time was 80 ms. H chemical shifts were referenced to DSS. Spectra were processed with Topspin 3. 1 (Bruker Biospin) or NMRPipe  (Delaglio et al. 1995 )  and analyzed with CCPNMR 2. 2 software  (Vranken et al. 2005 ). The N-HSQC spectrum and amide assignments of Crl at pH 8. 0 and 293 K are shown in Fig. 1. Assignment completeness for Crl was 82. 0 % for amide resonances, 90. 9 % for backbone resonances ( C , C , C′, H ) and 91. 1 % for side chain protons. Taking the His-tag into account, completeness amounted to 81. 0, 83. 2 and 82. 6 %. Exchangeable arginine and lysine side chain protons were not assigned. Assignments are completely missing for Asp30 and Glu89.
Crl is a 133 residue protein containing 20 aromatic residues (4 tryptophans, 4 tyrosines, 10 phenyalanines and 2 histidines) that contribute to the core structure of the protein by stacking the two main helices α1 and α3 onto the central five stranded β-sheet (the topology of Crl is indicated in Fig. 2  ). A high proportion of H chemical shift outliers was found in the vicinity of these aromatic moieties, ranging from backbone atoms (Gly55-H , Thr68-H , Gly80-H , His104-H , Leu38-H , L107-H ) to methyl (  Val42-H , Leu60-H , L100-H  ) and aromatic protons (Phe76-H ). Conversely the high amount of aromatic residues may explain the difficulty to assist backbone chemical shift assignment of Crl by prediction from homology models based on the X-ray structures of P. mirabilis Crl (47 % sequence identity, PDB IDs 4Q11 and 3RPJ).
The general scheme, however, where an unstable electron distribution at medium latitudes creates locally seed electrostatic emissions converted into electromagnetic waves is still an obviously plausible scenario. It has been examined by  Menietti and Yoon (2006)  in the context of small-scale density structures, and by  Grimald and Santolik (2010)  who anal-ysed wave and particle properties observed by Cluster during a wide-banded NTC event. This paper does not deal with NTC generation mechanism. It is devoted entirely to present and discusses observations of electric field waves measured during wide-banded NTC events by the unique tool offered by the multi-point Cluster mission. The underlying idea is to examine what is the topology of NTC sources and ray paths leading to the observations, such as to be able to better decipher the large number of signatures accumulated over years in Cluster data set, and to try to examine how a given local observation could be used as a clue to inform about the more global shape and state of the plasmasphere at that time.
Indeed, the two satellites share the same orbit and their separation distance is easily adjustable. When this distance is significant, the Cluster constellation offers a large receiving mesh, which can be illuminated at its different measuring nodes, by waves radiated from various parts of the PBL. When this distance is very small (∼ 50 km) another approach is made possible, whereby the pair of close spacecraft forms a single observatory. During one such configuration, in May 2008, the spin axis of C3 had been tilted by an angle of about 45 •. For the first time, electric field measurements at the sensitivity available with long boom spinning antennas were accessible in 3-D. This new context opened the path to a new way of measuring electric field polarization properties. We concentrate here on wave and density data obtained by the WHISPER (Waves of HIgh frequency and Sounder for Probing of Electron density by Relaxation) instrument  (Décréau et al.