dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Investigators involved in the Alzheimer Cohorts Consortium collaboration include  Roy Anderson ,  Alexa S. Beiser ,  Joshua C. Bis ,  Eric Boerwinkle ,  Carol Brayne ,  Daniel Bos ,  Lori B. Chibnik ,  Jean-Francois Dartigues ,  Sirwan K. Darweesh ,  Kendra Davis-Plourde ,  Stephanie Debette ,  Carole Dufouil ,  Stephanie Evans ,  Myriam Fornage ,  Jaap Goudsmit ,  Leslie Grasset ,  Vilmundur Gudnason ,  Christoforos Hadjichrysanthou ,  Catherine Helmer ,  Jayandra J. Himali ,  Albert Hofman ,  M. Arfan Ikram ,  M. Kamran Ikram ,  Kevin McRae-McKee ,  Silke Kern ,  Lewis H. Kuller ,  Lenore J. Launer ,  Oscar L. Lopez ,  Fiona Matthews ,  Osorio Meirelles ,  Thomas H. Mosley Jr. ,  Matthew P. Pase ,  Bruce M. Psaty ,  Claudia L. Satizabal ,  Sudha Seshadri ,  Ingmar Skoog ,  Blossom C.
They also studied a possible relationship between these variables and the neonate postconceptional age. However, the population of neonates they considered was undergoing neonatal intensive care. In this paper, we report about a study, where TD-NIRS is employed on healthy full-term neonates, in order to determine the brain optical properties μ a and μ 0 s , and hemodynamic parameters c O 2 Hb , c HHb , c tHb , and sO 2. The ultimate goal of this study is to provide the scientific community with a further and numerically consistent dataset concerning the characterization of the neonate healthy brain from the optical, i. e. , in the visible and near-infrared spectral range, point of view.
Even if the intrasubject variability, i. e. , the variability among the 24 registered channels for each neonate, is important, then the intersubject variability appears prominent. In order to explain the observed intrasubject variability in the measured data, we can note that, besides the physiological variations among different positions of the neonates' heads, an important role is probably played by the head curvature, which also varies and could affect the recovered   optical properties in different ways. Furthermore, the fact that the probe has been held in position by the operator's hands could have had some influence, too. In order to make the dataset on neonates' brains more usable, the values of optical properties μ a and μ 0 s at two wavelengths, and of the hemodynamic parameters c O 2 Hb , c HHb , c tHb , and sO 2 , shown in Figs.
4  and 5 , are reported in the Appendix (see Table  3 ). Here, we report the same parameters for the overall neonates' population. In the format, "median [25th-75th percentile]," we obtained: 0. 28 ½0. 22 -0. 34 cm -1 and 0. 31 ½0. 25 -0. 40 cm -1 for μ a at 690 and 820 nm, respectively; 5. 8 [4. 6-7. 0] and 5. 3 ½4. 2 -6. 6 cm -1 for μ 0 s at 690 and 820 nm, respectively; 103 ½80. 2 -139 μM for c O 2 Hb ; 42. 6 ½33. 4 -53. 6 μM for the c HHb ; 146 ½119 -188 μM for c tHb ; 72% [68%-75%] for sO 2. Now, we compare our dataset about optical properties and hemodynamic parameters of neonates' brains with similar datasets present in scientific literature. First, we have to say that there is little about this issue. In particular, as we stated in Sec. 1, to our knowledge, a TD-NIRS instrumentation has been exploited in only one study about the baseline characterization of neonates' brains.
We want to stress that literature data are in general few and the population of neonates considered in the different studies sometimes are not comparable: for example, in this paper, we considered only healthy neonates, while in the cited papers are reported results involving also neonates with some pathology. If we add that the majority of data on neonates' brains have been obtained with a different technique, i. e. , the FD-NIRS, we think that there is room for reporting also the dataset presented in this paper and obtained by exploiting a TD-NIRS instrumentation. We hope that further studies on this important issue will be performed also with different techniques, in such a way as to arrive at a reliable, consolidated, and widely accepted dataset about the optical properties and hemodynamic parameters of neonates' brains.
Conclusions In this paper, we determined optical properties and hemodynamic parameters of the brain of 33 full-term healthy neonates. An instrument for multichannel TD-NIRS was employed. By adopting fair exclusion criteria for the performed measurements, we obtained a reliable dataset that we compared with a similar dataset already reported in literature that used a different population (newborns in intensive care compared to healthy newborns in our dataset) and a different instrumental approach to NIRS (FD-NIRS as compared to TD-NIRS used here). We found consistencies in the measures of blood oxygenation but variations in the absorption coefficients. However, data concerning the optical characterization of the healthy neonates' brains in the visible and near-infrared spectral range are few and, for this reason, not conclusive.
Appendix: Neonates' Dataset Details In Table  3 , we explicitly report the values of optical properties μ a and μ 0 s at two wavelengths, and of the hemodynamic parameters c O 2 Hb , c HHb , c tHb , and sO 2 , already shown in Figs. 4  and 5. In this way, the neonates' dataset will be more usable and comparisons with other similar datasets, already present in literature or that will be assessed in the future, more straightforward.
The detection and analysis of oscillations in binary star systems is critical in understanding stellar structure and evolution. This is partly because such systems have the same initial chemical composition and age. Solar-like oscillations have been detected by Kepler in both components of the asteroseismic binary HD 176465. We present an independent modelling of each star in this binary system. Stellar models generated using MESA (Modules for Experiments in Stellar Astrophysics) were fitted to both the observed individual frequencies and complementary spectroscopic parameters. The individual theoretical oscillation frequencies for the corresponding stellar models were obtained using GYRE as the pulsation code. A Bayesian approach was applied to find the probability distribution functions of the stellar parameters using AIMS (Asteroseismic Inference on a Massive Scale) as the optimisation code.
Target and data HD 176465 is a nearby binary system displaying solar-like oscillations in both components. The Kepler space tele-scope observed the binary system in short-cadence mode (SC; 58. 85s sampling), for 30 days during the asteroseismic survey phase (20-07-2009 to 19-08-2009, i. e. , Quarter 2. 2), and continuously from the end of the survey (37 months, Quarters 5 -17). Refer to  [9]  and  [10]  for details on how the SC time series were prepared from raw observations and corrected to remove outliers and jumps. Both components in the binary system have overlapping frequencies which have been extracted through detailed modelling of the power spectrum and analysis of the échelle diagram (see  [8]  for details). In order to increase the precision of asteroseismic inference, we adopted both the spectroscopic parameters (i.
The stellar grid contained a mass range M ∈ [0. 7, 1. 15] in steps of 0. 05 M , a mixing length parameter range α ∈ [1. 5, 2. 2] in steps of 0. 1 and a metal abundance range Z ∈ [0. 005, 0. 021] in steps of 0. 001. For every combination of M, Z, and α, stellar evolution tracks were evolved by MESA from the pre-main sequence to the end of the main sequence. Over 60 models at different ages along each evolution track were generated. The oscillation frequencies for each model were calculated using GYRE 3  [23]. The surface effects in the model frequencies were 3 https://bitbucket. org/rhdtownsend/gyre/ corrected using the two-term correction described by  [24]  and implemented in AIMS. AIMS uses a Bayesian approach to find the probability distribution functions (PDFs) of the stellar parameters.
Discussion Our results are compared with findings obtained using other modelling procedures and tools. These include the Figure  1 : Comparison of our results with those obtained by several pipelines in  [8]. The red box corresponds to 1σ-error interval in our results. Asteroseismic Modelling Portal (AMP), the Aarhus STellar Evolution Code (ASTEC) Fitting method (ASTFIT), the Bayesian Stellar Algorithm (BASTA), and MESA code. Refer to  [8]  and references therein for details on the different tools. MESA can also be used for optimisation purposes because it contains a module capable of fitting a set of asteroseismic constraints (results in Tables 2 and   Note: --The surface helium abundance (Y i ) is not determined in AIMS. 3, under column MESA). In this study, MESA was only used to generate a grid of stellar models as described in Section 3.
Acknowledgements: We thank the  MESA community  for the engaging conversations about the  MESA  project. B. Nsamba acknowledges support from  Fundação para a Ciência e a Tecnologia (FCT, Portugal)  under the Grant  ID: PD/BD/113744/2015. TLC acknowledges the support of the  UK Science and Technology Facilities Council (STFC). Furthermore ,  A. García Hernández  acknowledges support from  Fundação para a Ciência e a Tecnologia (FCT, Portugal)  through the fellowship  SFRH/BPD/80619/2011  from the  EC  Project  SPACEINN  ( FP7-SPACE-2012-312844 ). GYRE has been developed with support from the  Astronomy  and  Astrophysics Grants  ( AAG) program  of the  National Science Foundation , through awards  AST-0908688  and  AST-0904607. The " Asteroseismic Inference on a Massive Scale" (AIMS ) project was developed at the  University of Birmingham  by  Daniel R.
Guelph permeameter principles rely on the measurement of water flux from a cylindrical hole (5 cm in diameter) into a homogeneous, non-saturated porous media. A Mariotte device ensures constant pressure in the cylindrical hole and enables the infiltration rate to be monitored. This device can estimate Kf s and soil sorptivity (characterizing infiltration capacity as a function of time and initial soil moisture). In our study, Kf s data were derived from GP Author-produced version of the article published in Journal of  Hydrology and Hydromechanics, 2017, vol. 65, iss. 3  The original publication is available at https://www. degruyter. com doi : 10. 1515/johh-2017-0017 measurements at a depth of 15 to 20 cm, using a constant head of 5-10 cm above the hole bottom. The double ring infiltrometer was composed of two concentric cylinders (40 cm diameter for the inner cylinder) that were inserted into the soil to a depth of 5 cm.
The water level was maintained constant in both cylinders and the infiltration rate was measured in the inner cylinder where the lateral diffusion effect was minimized. Kf s was directly deduced from the surface infiltration volume divided by the cylinder area when a constant flux was reached, assuming that deeper horizons did not perturb the water flux and that the gradient governing water flux was vertical. For the GP and DR data sets, additional available information was the location, geology and land cover at the sampling locations. The third data set is the result of single ring (SR) or Beerkan infiltration measurements (Figure  2c ) performed in the Mercier sub-catchment of the Yzeron catchment, close to Lyon, France in 2008 (see description in Gonzalez et al. (  2010 )) (57 sampling points) and from a similar data set collected in the Claduègne sub-catchment, in the Ardèche catchment (Figure  1 , 50 sampling points) in 2012 (described in  Braud and Vandervaere (2015) ).
Another method to compute Kf s was used for the TI measurements performed with the infiltrometer in Figure  2d. As the disk was not directly connected to the water column, the precise adjustment between the disk and water column heights caused some technical difficulties, easily leading to a few mm of uncertainty in the estimation of the applied tension. To overcome the problem, experiments were conducted until a positive soil water head was obtained at the soil surface that could be detected when leakage occurred at the bottom of the disk. The last measured tension was assumed to be -0. 1 mm of applied water potential and the hydraulic conductivity estimated at this tension was assumed to be Kf s. This data set will be referred to as the TI data set. For the TI and SR data sets, additional information at each sampling site was location, soil texture, organic matter content, dry bulk density, geology and land cover.
Building a common pooled Kf s data set Figure  3  shows a box plot of Kf s as a function of the infiltration method. The Kruskal-Wallis test shows that the differences between infiltration methods are significant (p<0. 0001). The median DR is about one order of magnitude larger than that of GP, whereas the medians of DR, SR and TI are closer (about a 0. 5 order of magnitude difference). Individual values span three to four orders of magnitudes. Several studies have compared different techniques for estimating soil saturated hydraulic conductivity (e. g. Mohanty et al. , 1994; Ronayne et al. , 2012; Verbist et al. , 2013 , Bagarello et al. , 2014). Mohanty et al. (1994)  reported systematically lower values with GP than with TI, but they were dealing with infiltration measurements performed at a depth of 15, 30 or 90 cm and not in the topsoil.
(2010). The advantage of our solution is that it fits a linear relationship rather than a non-linear one which allows uncertainty on the regression coefficients to be easily computed. Working with log(Kf s ) also allowed us to Author-produced version of the article published in Journal of  Hydrology and Hydromechanics, 2017, vol. 65, iss. 3  The original publication is available at https://www. degruyter. com doi : 10. 1515/johh-2017-0017 reduce the differences in orders of magnitude among the data sets. Furthermore, the tests described in the Results section were performed to choose the method (DR or GP) used to compute the "equivalent" Kf s used in the subsequent analysis. The DR method was chosen after those tests which allowed us to reduce the data sets to three infiltration methods. In the second step, Kruskal-Wallis tests were conducted to determine wheather or not the three remaining types of infiltration methods were significantly different.
The DR and TI values were not significantly different whereas the SR values were significantly different from DR and TI. Therefore we tried to establish a relationship between the SR and DR + TI data sets which would enable all the data to be pooled together for subsequent analysis. This analysis consisted mainly in comparing various distributions, according to geology and land cover information, as they were the only available common factors for all the sampling points. The Kruskal-Wallis test was used to compare the distributions and statistical analyses were performed using R software (R Core Team, 2014).
General description of the data sets Table  1  shows the contingency tables of number of sampling for the various infiltration tests (original data set) by infiltration method and geology and land cover. In both cases, the chisquared test had a p-value <0. 0001, showing that the points distribution was far from being homogeneous, in particular in terms of geology. Indeed, two geologies: alluviums and marls were only sampled using DR and GP methods, whereas volcanic rocks were only sampled using the SR and TI methods. Nevertheless, most of the samples were taken for the dominant geology classes in the area: sedimentary, granite and schist. The sampling is somehow more homogeneous for land cover, but TI data are much less abundant, given the longer time required to perform this kind of infiltration tests.
Three points with very low Kf s values were removed from the GP data set for subsequent analysis as they were outliers. For the SR and TI data sets, soil texture was available, so it is possible to show the sampling points in the USDA textural triangle (Figure  4 ). We can see that the data span over a large range of clay and sand contents. Nevertheless, coarse soils (sand and loamy sands) are hardly represented. weight regression was chosen as it resulted in a higher determination coefficient (R 2 = 0. 665, p=0. 0045) than when non-equal weights were used (R 2 = 0. 566, p=0. 012). The corresponding equation was the following:.
Indeed, in Eq. (  1 ), Kf s_DR tends towards a constant value when Kf s_GP tends towards zero, which was not very satisfactory. This problem is avoided with Eq. (2). Once this equation was established, for each sample point where a GP value was measured, an equivalent DR Kf s_DR value was computed using Eq. (  2 ). Similarly for each sample point at which a DR value was measured, an equivalent GP Kf s_GP value was computed using the regression between GP and DR values. These values were used to draw box plots of log(Kf s ) for three infiltration methods (GP, SR and TI) and (DR, SR and TI) (Figure  6a  and Figure  6b , respectively). The Kruskal-Wallis test showed that GP and SR, and GP and TI data were all significantly different (p < 0. 0001) and could not be analyzed together. On the other hand, DR and TI data did not have significantly different distributions (p=0.
25) and could be analyzed together. The SR data were still significantly different from the other two data sets (p<0. 0001). Therefore, similarly to what was done for the DR and GP data sets, we tried to establish a relationship between the SR and DR+TI data sets. For both data sets, geology and Author-produced version of the article published in Journal of  Hydrology and Hydromechanics, 2017, vol. 65, iss. 3  The original publication is available at https://www. degruyter. com doi : 10. 1515/johh-2017-0017 land cover information was used to compute averages for each geology * land cover class, as the Kruskal-Wallis test showed that the log(Kf s ) values were significantly different for geology and land cover classes with p values < 0. 0001. Table  3  shows the averages and standard deviations for both types of infiltration methods and Figure  7  shows the relationships that were fitted using either equal weights (black line, R 2 =0.
25, p=0. 07) or with weights inversely proportional to the variance of each point (blue line, R 2 =0. 32, p=0. 05). This second equation, with the highest determination coefficient and the lowest p value, was retained for further analysis and reads: (  + )) = (0. 33 ± 0. 14) * log( _ )) + (1. 49 ± 0. 50)  2 = 0. 32  = 0. 05 (3) The determination coefficient is much lower than in Eq. (  2 ) but still significant at the 5% level. Table  3  shows that only two geologies could be used to establish this relationship: granite and sedimentary. We must therefore assume that the relationship is also valid for all the other combinations of geology and land cover information, which is a limitation of the results as all available combinations were not used in establishing the relationship. The Kf s values of the SR data sets were converted to an equivalent DR+TI value using Eq.
Thus, much lower values were obtained with GP than with the other methods which could be expected as the GP measurements were performed between 15 and 20 cm depth, whereas the depth was of less than 10 cm for the other methods. We might expect larger Kf s values with positive head infiltration methods than with TI, since the extrapolation procedure for the latter may give underestimated values. However,  Bagarello et al. (2014)  have reported, for some sampling points, lower Kf s values when a positive head is maintained during the measurements than when a falling head or even tension infiltrometers are used. With a positive pressure head, soil particles may be disturbed and clog the soil surface and top soil. For the SR data set, we only experienced clogging at a few sampling points. In addition, the infiltration measurements were mainly performed on vegetated surfaces and although vegetation was cut before the infiltration test, the root network was still present, which should limit the clogging problem.
The sampling surface is also generally lower with TI than with the other methods. In our data set, TI leads to values of the same order of magnitude as the DR method. Nevertheless, the number of sampling points with TI was much lower (Table  1 ) than with the three other methods. This could reduce the impact of TI data on the final results. The standard deviations of the regression coefficients in Eq. (  2 ) and Eq. (  3 ) are quite large, showing that the fitted regressions have a large uncertainty that is propagated to the final map of Fig. 9a. In addition, only two geologies could be used in establishing Eq. (  3 ): granite and sedimentary. We had to assume that the relationship was also valid for all the other combinations of geology and land covers, which was a limitation of the study.
All those Author-produced version of the article published in Journal of  Hydrology and Hydromechanics, 2017, vol. 65, iss. 3  The original publication is available at https://www. degruyter. com doi : 10. 1515/johh-2017-0017 uncertainties should be kept in mind when considering using the final map in hydrological models for instance. It was not possible to include soil texture, organic carbon and dry bulk density in our analysis because they were only available for the SR and TI data sets. It would have been interesting to perform the kind of analyses proposed by  Jarvis et al. (2013)  and  Jorda et al. (2015)  on those data, but the sample size was too small. Nevertheless, similarly to these studies, our analysis points out the main impact of land cover on Kf s , calling for an inclusion of land cover in soil surface Kf s mapping methods.
, 2013) , a map such as the one proposed in Figure  9b , computed pedotransfer functions for the deeper horizons could be relevant. Infiltration data of deeper horizons are much less abundant as most methods require pedological pits to be dug, and using pedotransfer functions is thus the easiest available method. In this context, the GP method is practical since measurements can easily be made at deeper horizons in replicated auger made by drilling. Author-produced version of the article published in Journal of  Hydrology and Hydromechanics, 2017, vol. 65, iss. 3  The original publication is available at https://www. degruyter. com doi : 10. 1515/johh-2017-0017 The mapping method proposed in the present paper is quite simple and only relies on two easily accessible maps: geology and land cover.
Acknowledgements The  FloodScale  project was funded by the  French National Research Agency (ANR)  under contract no. ANR2011 BS56 027 , which contributes to the  HyMeX program. It also benefited from funding by the  MISTRALS/HyMeX program  (http://www. mistrals-home. org). The authors thank  Stanislas Bonnet ,  Flora Branger ,  Louise Jeandet ,  Mickaël Lagouy ,  Olivier Vannier  for their participation in the field infiltration tests in the Claduègne catchment;  Pascal Brunet ,  Olivier Le Bourgeois  and students from  Ecole des Mines d'Alès  for their help in deploying infiltrometers in the Gardons and Vidourle catchments. BRGM thanks the  SCHAPI  for its support of the infiltration campaigns conducted in the Gardons and Avène catchments. The Cévennes soil data base was built from the  Languedoc-Roussillon  and Ardèche data base provided by the  Chambre d'Agriculture  of both areas in the framework of the  IGCS program (https://www.
PCR Amplification and Analysis Single sized DM1 alleles containing 5, 22, 44, 56 or 200 CTG repeat units were isolated by serial dilution and single molecule PCR amplification of human genomic DNA, as previously described  (Gomes-Pereira et al. , 2004). These alleles were then used as input DNA (0. 1-1 ng) in subsequent standard PCR amplifications, using the previously described oligonucleotide primers DM-A, DM-BR, DM-C, DM-DR, DM-ER, DM-H  (Monckton et al. , 1995 (Monckton et al. , , 1997)). PCR amplifications, agarose gel electrophoresis, Southern ''squash'' blotting and hybridization were performed as previously described  (Gomes-Pereira et al. , 2004). The AmpliSize TM molecular ruler (50-2000 bp ladder; BioRad) or the GeneRuller 100 bp ladder (Thermo Scientific), and the Kodal Digital Science 1D software (Kodak) were used to quantify electrophoretic mobility and DNA sizing.