dataesr/scientific-paragraphs-categorization huggingface dataset:
The longer bundles thereby have a larger contribution to the global metrics than the shorter bundles, which could lead to an overestimation of the fiber bundles. As proposed in  [75] , we compared the global metrics when seeding from the gray/white matter interface for CSD and one specific set of enhancement parameters. The global metrics for that seeding strategy were slightly lower for CSD and comparable when including enhancements. For the sake of comparing our enhancement method with CSD, we therefore believe it is fair to use seeding from the white matter mask. The convincing improvement in the global metrics is supported by Fig. 9 , that shows a selection of the fiber bundles in the dataset. It can be seen that after enhancements, there are more valid connections in the green bundle and less wrong exits in the red bundle, leading to a higher (VCCR) and a better bundle coverage.
Evaluation and Comparison on DW-MRI Data In this experiment we consider a DW-MRI dataset of a part of a human brain, previously used in  [27]. The study was approved by the local ethical commitee of Maastricht University, and informed written consent was obtained from the subject. Although the dataset consists of only 10 axial slices, the corpus callosum, corona radiata and superior longitudinal fasciculus are (partly) present in the data. We show that the combination of CSD and enhancement is well-suited for different combinations of the b-value and the number of gradient directions used in the acquisition. Furthermore, we make a qualitative comparison with the DTI-based method of  [27]  on this dataset and conclude with a brief quantitative comparison with this method on the dataset of 3.
Comparison with a DTI-based FOD In the next experiment we compare the performance of our combination of CSD with enhancements with the method in  [27]  which proposed to combine DTI with non-linear PDE-based enhancement obtained from successively applying erosions and diffusions. Let us briefly describe this method, for details we refer to  [27] , and an implementation of the PDE enhancements can be found in the HARDI package for Mathematica available at (http://bmia. bmt. tue. nl/people/RDuits/HARDIAlgorithms. zip). First an FOD on positions and orientations that we call U DT I was constructed via a transformation of the tensor field D fitted to the data  [2] , according to the following definition  [27, 76] : U DT I (y, n) = 1 4π Ω det(D(x))dx. (n T D -1 (y)n) -3 2. ( 23 ) This FOD is then sharpened with PDE erosions, a type of morphological enhancement adapted from  [15] , on R 3 S 2 and regularized with nonlinear diffusions to find crossing structures from DTI.
Previously in  [27] , the same dataset as in Fig. 10  for acquisition parameters b = 1000 s/mm 2 and N o = 49 was processed. Here we compare the FOD obtained with CSD, that we call U CSD here, with U DT I in the top and bottom figures, respectively, of Fig. 11. Unlike DTI, which is limited by the Gaussian assumption of the diffusion profile, CSD can estimate multiple fiber orientations within a voxel. Furthermore, we see that the large glyphs in the Centrum Semiovale in the bottom figure are not apparent in U CSD. Applying (linear) enhancements, as explained in Section 2. 2, to U CSD gives the second figure, and the approach in  [27]  using erosions/(nonlinear) enhancements applied to U DT I gives the second figure from below. It can be seen that also the enhanced DTI glyphs supports multiple fiber directions within voxels via extrapolation  [27, 38] , but at the cost of high regularization.
Another noticeable difference is the fact that the glyphs in the CSD case are slimmer and crossings are more clearly defined. Whether two separate maxima are visible at a crossing is less dependent on the diffusion parameters in the PDE diffusion. Besides the visual comparison of the FOD glyphs, we provide deterministic tractography results for both procedures in the middle of Fig. 11. It can be observed that both methods produce reasonable results, although the one obtained from the enhanced DTI dataset seems oversmoothed and outliers (indicated with the yellow arrow) can occur. This is due to the extreme diffusion parameters needed to perform the FOD extrapolation. We find that visually the Erosions and nonlinear diffusions for the DTI-based method are done with parameters as in  [27]. The tractographies corresponding to the two methods are shown in the middle.
All subjects gave written informed consent; the study was approved by the Medical Ethics Committee of Maastricht University Medical Center (N 43386. 068). The data is acquired from healthy volunteers, and ground-truth ML-TP distance is not known. Therefore accuracy of this measure of our methods cannot be checked, instead we focus on consistency and reproducibility. We apply CSD to the data to construct the FOD, with spherical harmonics up to order 6 requiring the estimation of 28 coefficients (as 32 directions are insufficient to estimate the 45 coefficients when a spherical harmonic order 8 is used, when not using super-resolution as in  [59] ). We seed from the LGN and include all fibers that reach the primary visual cortex. Both regions of interest are selected manually on a T1-weighted image.
Effect of the Enhancement of CSD on Tractography of the OR In this section, we apply the PDE enhancement (step A) to the CSD FOD as before, with parameter settings D 33 = 1, D 44 = 0. 01 and t = 2. After the enhancement we apply the sharpening deconvolution transform  [18]  and probabilistic tractography with 10000 streamlines. We compare the results of the tractography on the subjects both before and after the enhancement in Fig. 13. We see that the tracking on enhanced data generally shows less spurious fibers, and has a better pronounced tip of the Meyer's loop. However, the optic radiation is a highly curved structure, where the advantage of the enhancement of elongated structures cannot be fully exploited. To further reduce the spurious fibers, we explore our other approach in the next section.
Conclusions and Discussion We have proposed two new tools to improve alignment of fibers in tractography results: (A) the combination of CSD with contextual PDE enhancements and (B) a fiber to bundle coherence measure to classify spurious fibers. Both approaches rely on the same contextual processing via PDEs on the space of coupled positions and orientations. We validate our methodology with a variety of experiments on synthetic and human data. In the first experiment we consider a digital phantom  [66]  that simulates DW-MRI data of a challenging configuration of multiple neural-like fiber bundles for different noise levels, see Fig. 7. The combination of CSD with enhancements and subsequent deterministic tracking was extensively tested for varying enhancement parameters, see Fig. 8. The enhanced FOD peaks were compared with the ground truth fiber orientations, showing for all SNRs that the maxima of the enhanced FOD coincide better with the ground truth peaks than without application of enhancement.
We evaluate our combination of CSD and enhancement for three different (single-shell) acquisition protocols, corre- sponding to different b-values and number of gradient directions. We observed, see Fig. 10 , that whereas tractography on CSD without enhancement showed notable differences between the three acquisition protocols, tractography after our enhancement lead to a qualitatively similar reconstruction in all cases. This implies that the application of enhancement in the processing pipeline makes the tractography results less dependent on the scanning protocol used. We use the same dataset and the phantom dataset to compare our method qualitatively and quantitatively with previous work  [23, 27]  in which sharpening methods and nonlinear enhancement PDEs are applied to DTI. We observed qualitatively on real data in Fig.
12. We demonstrated in Figs. 13, 14 and 15 that either by enhancement of the CSD FOD, or by removing the most spurious fibers using the FBC measure leads to a robust probabilistic tractography. In particular, the combination of both methods in one pipeline allows for a more stable localization of the tip of the Meyer's loop and a more stable determination of the Meyer's loop to Temporal Pole distance. Our experiments show that our PDE enhancement methods for contextual processing are an effective and widely applicable tool to both enhance CSD data and to remove spurious fibers from tractographies. While we used CSD to construct an FOD, the PDE enhancement can be applied to an FOD obtained with any other method. We have seen that both our methods improve fiber alignment in tractography results and hence provide information on structural connectivity of the brain white matter more robustly.
Acknowledgments We would like to thank  Dr. A. Roebroeck  from the  Faculty of Psychology & Neuroscience, Maastricht University  for providing us with the human data used in Section 3. 2. The study was approved by the  local ethical commitee of Maastricht University. We gratefully acknowledge  Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+  for providing us with the healthy volunteer data used in Section 3. 3. The study was approved by the  Medical Ethics Committee of Maastricht University Medical Center  ( N 43386. 068 ). Informed written consent was obtained from all subjects. The research leading to the results of this paper has received funding from the  European Research Council  under the European Community's  7th Framework Programme  ( FP7/2007-2014 )/ ERC  grant agreement No.
1% Tween) then stained with NBT-BCIP (Roche, tablets 11697471001) for 10 min to 2 h in the dark, depending upon the probe used. Embryos were washed in PBT and post-fixed with 4% PFA in PBT, 1 h at RT. They are cleared through 50% glycerol/PBT and mounted to coverslips in Mowiol mounting medium. Embryos were imaged on a Leitz Diaplan microscope using a Leica DC300F camera. Database analysis E(z) protein sequences from D. melanogaster and H. sapiens were found on NCBI (http://www. ncbi. nlm. nih. gov/). For each species, when several proteins were available for the same gene the longest was retained. Each selected sequence was then used to conduct TBLASTN searches in Ciona intestinalis genomic (http://genome. jgi. doe. gov/Cioin2. home. html) and cDNA (http://ghost. zool. kyoto-u. ac. jp/blast_kh.
html) databases. Among hit sequences, we selectively kept those that were indifferently found with both reference species. As expected, the closer the reference species is to Ciona intestinalis, the more the e-value is significant (data not shown). This step gave clusters of sequences that we validated with Aniseed website (http://www. aniseed. cnrs. fr/) and for which we searched for functional domains required for activity with a CD-search on NCBI (http://www. ncbi. nlm. nih. gov/cdd) against CDD-43212 PSSMs database.
Acknowledgements We are very grateful to  Dr Patrick Lemaire  ( CRBM, Montpellier, France ) for the gift of cDNA Unigene clones, to  Prof. Guy Charmentier  ( MARBEC, Montpellier, France ) for ISH and semi-thin sections colour microscopy acquisitions, to  Dr Marie-Christine Lebart  for help with antibody purification (MMDN, Montpellier, France), to  Dr Hitoyoshi Yasuo  (LBDV, Villefranche-sur-mer, France) and  Dr Bernd Schüttengruber  ( IGH, Montpellier, France ) for helpful discussions. Data used in this study were ( partly) produced using the molecular genetic analysis technical facilities of the  CeMEB GenSeq  platform (sequencing), the Montpellier Prorec platform (antibody production), the UM2 TEM platform (electronic microscopy), the qPHD UM2/GenomiX Platform (qPCR experiments) and the  Montpellier RIO Imaging platform  (confocal microscopy) (Montpellier, France).
Understanding how plants are constructed-i. e. , how key size dimensions and the amount of mass invested in different tissues varies among individuals-is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species.
Thus, these concerns highlight the need for methods that allow the long-term reconstruction of stable isotopes in precipitation from existing data sets  [Bowen et al. , 2014]. IsoMAP cyber-GIS system that provides several unique capabilities related to generating space-and time-specific water isoscapes is a good framework for such prediction, but the functionality of the existing toolkit is limited in scope  [Bowen et al. , 2014]. The purpose of this paper is to reconstruct the TP spatiotemporal variations in annual precipitation δ 18 O back to 1910. The spectral optimal gridding (SOG) method can fully utilize the information provided by general circulation models (GCMs) and observed data and yield an error estimate  [Shen et al. , 2004 [Shen et al. , , 2014;; Smith et al. , 1998]. The GCM isotope output can help simulate coherent spatiotemporal patterns of the isotope field based on climate dynamics, while the ice core data can anchor these patterns to form gridded data.
Model-Data Comparison We compare the ice core data with LMDZiso output from the grid box, which includes the ice core. When a model grid box has more than one ice core, the grid box ice core data are an average of the ice core data. In this way, the 10 ice cores fall into eight grid boxes. LMDZiso simulates δ 18 O anomalies well for some years, such as 1978, 1987, and 1991 at low ebb of δ 18 O (Figures  2a  and 2b ), but it performs poorly in representing interannual variations in some southern grid points. Figure  3   The LMDZiso data have demonstrated a slight increasing trend since the late 1970s and noticeably underestimate the variances at Muztagata, Guliya, and Pruogangri (Figure  2b ). The model simulation with the least variance of δ 18 O anomalies shows at Dasuopu, where observed ice core δ 18 O anomalies also show the smallest variance among the 10 ice cores.
The SASMI, defined as an area-averaged summer (from June to September) dynamical normalized seasonality at 850 hPa within the 5°N-22. 5°N and 35°E-97. 5°E, reflects the interannual variation of the South Asian summer monsoon  [Li and Zeng, 2002, 2005]. Yearly averages are calculated from June to September. The coincident shifts between reconstruction and SASMI exist during 1973-1984 (Figure  6b ) but without significant correlations. This may indicate the coaction of the westerlies and South Asian summer monsoon on TP precipitation δ 18 O, but each plays a dominant role interlacing during different periods. More in-depth analyses are needed to compare with other GCM output and satellite data in the future.
Between April 1925 and June 1927 , the research vessel Meteor cruised the tropical and southern Atlantic Ocean in the framework of the German Atlantic Expedition. One purpose was to systematically explore the vertical structure of the atmosphere. To this end, the ocean was crossed in 14 profiles across parallels of latitude. 801 pilot balloons and 217 kites were launched. The resulting data have been digitised in the framework of the European project ERA-CLIM. Here, they are compared to the Twentieth Century (20CR) and ERA-20C reanalyses, independent datasets based on the assimilation of synoptic pressure and hurricane pressure records, and marine surface winds for the latter, using monthly sea surface temperature and sea ice as boundary conditions. Both reanalyses display similar patterns of systematic differences relative to the observations for temperature, specific humidity, and wind.
In all regions, both reanalyses strongly underestimate the observed range of zonal and particularly meridional wind variability. Even though remaining errors in the observational data cannot be excluded, we estimate that the inherent observational uncertainties do not alter our conclusions. Vice versa, two pieces of evidence support the credibility of the early upper-air data: the robust regressions of both 20CR and ERA-20C against observed pressure and temperature over a large spatial and temporal range, and the similarity between the uncertainties predicted by 20CR and the actual uncertainties determined from the root mean square difference of reanalysis and observation values.
In this latitudinal range, the Atlantic Ocean was crossed in 14 horizontal transects with latitudinal distances of approximately 7°, stretching more or less along latitude parallels. A distance of 67,535 nautical miles (≈ 125, 075 km) was covered during the whole journey, more than three times the Earth's circumference. This probably represents the spatially most comprehensive upper-air probing above the Atlantic to the present day. The Meteor data are part of a larger, historical upperair dataset that contains significant amounts of pre-1957 upper-air data (> 1. 3 million station days catalogued, > 200, 000 images taken, > 700, 000 station days digitised), spanning large parts of the globe and focussing on so far poorly represented regions such as the Tropics, the polar regions and the Oceans, and on very early 20 th century upper-air data from Europe and the US.
These data have been made available in digital form by the EU FP7 project ERA-CLIM (European ReAnalysis of global CLIMate observations; http://www. era-clim. eu, Stickler et al. , 2014ab). They can help to assess new, totally independent datasets such as the Twentieth Century Reanalysis (20CR;  Compo et al. , 2011)  and ERA-20C  (Poli et al. , 2013) , reanalyses that assimilate exclusively surface pressure observations and hurricane pressure reports (and additionally marine surface winds in the case of ERA-20C), and prescribe monthly sea surface temperature and sea ice as boundary conditions. Eventually, the data will be assimilated into full reanalyses that make use of historical upper-air data. Earlier studies  (Compo et al. , 2011; Brönnimann et al. , 2011; Zhang et al. , 2013)  have found surprisingly high correlations of 20CR with observations of temperature and geopotential height in the southern subtropics and midlatitudes for the early 20 th century, in spite of the very low input observational density.
On the other hand, some studies have also shown that the correlations are generally lower in the Tropics (e. g. Compo et al. , 2011)  and that large biases exist in this region, e. g. , in the wind field above the African monsoon region in the 1940s and 1950s  (Stickler and Brönnimann, 2011). A possible conclusion of these results is that a purely pressure-driven reanalysis might be of lower quality over the Tropics compared to midlatitudes, and that the additional assimilation of wind (such as in ERA-20C, albeit only for the surface level) might help to improve the results. Here, we would like to test this hypothesis and assess the quality of the datasets by comparing 20CR and ERA-20C with the observational data from the Meteor and with each other. Section 2 describes the observational and reanalysis datasets, the methods used to interpolate the reanalysis data to the locations and times of the observations, and the methods used to calculate anomalies for the correlation analysis.
Observational data We make use of the early, historical upper-air dataset that has been made available by the EU FP7 project ERA-CLIM  (Stickler et al. , 2014a,b ; http:// www. era-clim. eu; data and metadata available from the PANGAEA data repository at http://doi. pangaea. de/10. 1594/PANGAEA. 821222 and from http://www. oeschger-data. unibe. ch/metads). The data have been subject to a raw quality control consisting mainly of range checks. Furthermore, suspicious values were flagged during digitisation and manually re-checked and corrected afterwards, if possible, with the help of digital images of the sources. Finally, the temperature data were additionally screened for large outliers (absolute departures > 30 K) relative to the new, surface only reanalysis ERA-20C  (Poli et al. , 2013) , and those values were again manually re-checked in the sources.
The value of 30 K was chosen globally, based on scatter plots of reanalysis departure against ECMWF collection identifiers, as a cut-off value representative of strong outliers. This process led to the correction and/or flagging of 2,325 temperature values for the whole ERA-CLIM upper-air data collection, corresponding to a relatively small share of 2 ‰ of all temperature values collocated with reanalysis values. Therefore, the observational data can still be regarded as virtually independent of ERA-20C. Further details on the data treatment can be found in  Stickler et al. (2014b). All the new ERA-CLIM upper-air data will be incorporated in future versions of the Comprehensive Historical Upper-Air Network (CHUAN;  Stickler et al. , 2010). For this study, we consider the aerological data that were obtained during the German Atlantic  Expedition 1925-27 (Kuhlbrodt and Reger, 1933)  on board the research vessel Meteor.
The Meteor, originally conceived as a gunboat for the German Imperial Marine, was never finished as such after the start of World War I. Instead, following a modification in 1923/24, the ship was transferred as a survey vessel to the Imperial Dockyards located in Wilhelmshaven (see e. g. http://de. wikipedia. org/wiki/Meteor_ (Schiff,_ 1915) , last accessed 17 January 2014). Its aerological dataset is split into three separate records (ERA-CLIM record IDs 71-73 in the moving upper-air inventory), which correspond to the three observational platforms that were used during the expedition: pilot balloons to determine wind direction and speed; kites to measure air pressure, air temperature, humidity, wind direction and speed; and finally, registering balloons that observed air pressure, air temperature, and humidity.
The majority of the soundings are located between 20 °N and 65 °S. Within this latitudinal range, the Atlantic Ocean was crossed in 14 sections oriented largely along parallels of latitude. A map showing the complete journey is shown in Fig. 1. Fig. 2  shows the locations of all pilot balloon and kite soundings. Fig. 3  illustrates the launch of the pilot balloons and kites on board the Meteor from original photos taken during the expedition  (Kuhlbrodt and Reger, 1933). More comprehensive information about the records can be found in the ERA-CLIM metadatabase (http://www. oeschger-data. unibe. ch/metads) and on http://doi. pangaea. de/10. 1594/ PANGAEA. 821222.
The data assimilation system relies on a tenmember Ensemble of Data Assimilations (EDA)  (Isaksen et al. , 2010). Each member employs a 4D-Var analy- sis scheme with an analysis window of 24 h instead of 12 h as in ERA-Interim  (Dee et al. , 2011)  and in the ECMWF operational forecasts. A variational bias correction (VarBC;  Dee, 2004)  is employed within the IFS 4D-Var to correct for biases in surface pressure observations. The analysed fields we study here are from the deterministic run. Both 20CR and ERA-20C assimilate synoptic pressure from the International Surface Pressure Data Bank (ISPD V2 for 20CR, V3. 2. 6 for ERA-20C; http://rda. ucar. edu/datasets/ds132. 0/) and from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS, version 2. 5; http://icoads. noaa. gov/; Worley et al.