dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the two resulting datasets were separately corrected for eddy current distortions and head motion as implemented in FSL  [75]. A subset of 64 directions with nearly uniform coverage on the sphere was selected from the full set of 256 gradients directions, and measurements for this subset were used to 'create' an undersampled version of the data, which also included 3 b=0 volumes. The resulting HARDI sequence based on 64 directions is similar to those widely employed in clinical studies, thus results from this dataset are useful to evaluate the impact of the new technique on standard clinical data.
Results 1 Gaussian versus non-Gaussian noise models The angular and volume fraction errors from the dRL-SD and RUMBA-SD reconstructions in the 90 synthetic phantoms with different inter-fiber angles are depicted in  A set of patterns can be drawn from these results. First, RUMBA-SD was able to resolve fiber crossings with smaller inter-fiber angles (around 5 degrees and 10 degrees for datasets corrupted with Rician and nc-χ noise respectively). Second, RUMBA-SD produced volume fraction estimates with a higher precision (lower variance), even in phantoms where the fiber configuration was well-resolved by both methods. Third, although dRL-SD produced a relatively lower proportion of spurious fibers ( n + ), RUMBA-SD produced a lower proportion of undetected fibers ( n -) leading to a higher success rate (SR).
After correcting for this factor, a noticeable advantage was still observed for RUMBA-SD (see left lower panel of Fig  2 ). As before, the above patterns were also observed in the analyses obtained with datasets based on other SNRs and with different number of iterations. In all cases RUMBA-SD+TV detected fiber crossings at lower inter-fiber angles. On the one hand, RUMBA-SD was able to resolve some fiber configurations that were not detected by dRL-SD, especially in voxels involving small inter-fiber angles or fiber crossings with a non-dominant tract. On the other hand, the spatially-regularized algorithms have substantially improved the performance of the original methods. Moreover, RUMBA-SD+TV was the method providing better reconstructions. These findings are in line with results from previous sections and remained valid also when different dictionaries, number of iterations and noise levels were employed.
"HARDI Reconstruction Challenge 2013" phantom data Additional complementary results about the performance of RUMBA-SD in relation to several other reconstruction methods can be found in the website of the 'HARDI Reconstruction Challenge 2013': http://hardi. epfl. ch/static/events/2013_ISBI/workshop. html#results. An earlier version of RUMBA-SD took part in that Challenge, ranking number one in the 'HARDI-like' category (team name: 'Capablanca'). In the discussion section we provide additional information.
Real brain data Fiber ODFs were estimated separately for each different SMF-and SoS-based dataset, including the original measured data with the full set of 256 gradient directions and its reduced form including a subset of 64 directions. In all cases, both RUMBA-SD and dRL-SD methods were implemented using the same dictionary, which was created assuming a sharp fiber response model with diffusivities equal to 1 λ = 1. 7 10 -3 mm 2 /s and 2 3 λ = λ = 0. 3 10 -3 mm 2 /s, and two isotropic terms with diffusivities equal to 0. 7 10 -3 mm 2 /s and 2. 5 10 -3 mm 2 /s. the full and reduced SMF-based datasets. This region contains complex fiber geometries, including the mixture of the anterior limb of internal capsule (alic), the external capsule (ec) and part of the superior longitudinal fasciculus (slf) on the left brain hemisphere.
Although in both cases multiple fibers were detected in the area of intersection, RUMBA-SD+TV has provided multi-directional fiber ODFs with a higher number of lobes, which may represent fiber crossings as well as intra-voxel fiber dispersion. The similarity of the reconstructions from the full and reduced datasets suggests that the method is robust with respect to the number of measurements, with the regularized version being the most robust. In a subsequent analysis we examined the statistical properties of inter-fiber angles as estimated by all methods. with inter-fiber angles in single fiber voxels assumed to be zero. Points on the main diagonal line characterize voxels where both methods gave identical inter-fiber angle estimates, whereas points above and below the main diagonal correspond to voxels where the two methods detected two fibers with different inter-fiber angle.
In contrast, the high density of points on the Y axis in the same range of inter-fiber angles indicates that in many voxels RUMBA-SD detected two fibers whereas dRL-SD detected a single one. A similar but attenuated effect can be noticed in panel B, suggesting that TV regularization contributes to reduce the differences between both methods. in the same voxels. Results are based on reconstructions in the 64-direction SMF dataset. Only voxels in white matter where both methods detected one or two fibers are included. The inter-fiber angle in voxels with a single fiber was assumed to be equal to zero. Points on the main diagonal line are those voxels where the inter-fiber angle estimated from both methods was identical, whereas points above and below the main diagonal correspond to voxels where the two methods detected two fibers but with different inter-fiber angle.
Points located on the X and Y axes are voxels where one method detected two fibers whereas the other detected a single fiber. were implemented using the same dictionary of basis signals and similar reconstruction methods based on Richardson-Lucy algorithms adapted to Gaussian, Rician and noncentral Chi noise models, results should be considered comparable. Taken together, findings from all synthetic datasets demonstrate the benefits of an adequate modelling of the noise distribution in the context of spherical deconvolution, and of the inclusion of TV regularization. Interestingly, RUMBA-SD resolved fiber crossings with smaller inter-fiber angles and smaller non-dominant fibers. Likewise, RUMBA-SD produced volume fraction estimates with higher accuracies and precision and produced, as well, a lower proportion of undetected fibers, resulting in a higher success rate (see As previously mentioned, an earlier version of RUMBA-SD took part in the HARDI Reconstruction Challenge 2013, ranking number one in the 'HARDI-like' category.
Notably, this position was shared with a reconstruction based on the CSD method included in Dipy software  [81]  (http://nipy. org/dipy/), which had applied a Rician denoising algorithm  [82]  to the raw diffusion MRI data prior to the actual CSD reconstruction. The superior performance of these two approaches strengthens the importance of taking into account the non-Gaussian nature of the noise. Moreover, it opens new questions on the optimal strategy to be followed. Should we divide the deconvolution process into to two disjoint steps (first denoising and then estimation) or it is more adequate to follow the unified approach proposed here?. The main advantage of the former is that it may benefit from including state-of-art denoising algorithms like the adaptative non-local means denoising method proposed in  [82].
Conversely, the main advantage of the unified approach is that it provides a precise model to distinguish real signals from noise throughout all the 4D diffusion MRI data. Many of the advanced denoising algorithms that are currently applied in isolation were developed to filter volumetric (3D) data. Since each 3D image is processed individually, their mutual dependence in terms of orientation is ignored. In contrast, the unified approach described here provides a more general estimation framework that may be extended to include advanced similarity measures like those employed in  [82] , merging the benefits of both strategies. A new manuscript on the 'Challenge' phantom (currently under preparation) will provide additional information on the performance of RUMBA-SD in relation to several reconstruction methods and in terms of connectivity metrics derived from fiber tracking analyses.
When applied to human brain data, RUMBA-SD has also achieved the best results, with its reconstructions showing the highest ability to detect fiber crossings (see Figs  7 8 9 ). And although any conclusion derived from real data is hampered by the unknown anatomy at the voxel level, all previous results on synthetic data seem to support the validity of RUMBA-SD for real data. Our findings can also be contrasted with those reported in  [55]. In that study, the authors show that the SoS approach produces a signal-dependent bias that reduces the signal dynamic range and may subsequently lead to decreased precision and accuracy in fiber orientation estimates. Our study, however, suggests that the noncentral Chi noise in SoS-based data is not a major concern for the SD methods considered. Thus, heavier squashing of fiber ODFs when SoS reconstruction is used  [55]  is not that prominent with the SD if compared to diffusion ODF estimation methods  [11].
This result may have different explanations for each technique. The robustness of dRL-SD may be explained, in part, by its lower over-all sensitivity to selection of the response function  [50] , which make it robust to the use of dictionaries estimated from either biased or unbiased signals. This behaviour may be additionally boosted by the inclusion of the damping factor in the RL algorithm. In contrast, the robustness of RUMBA-SD can be explained by the use of proper likelihood models that explicitly consider the bias as a function of the noise corrupting the data. To finish, some limitations and future extensions of the study should be acknowledged. First, we have not evaluated the proposed method in synthetic data simulating partial Fourier k-space acquisitions and with parallel imaging using various acceleration factors (i.
e. , R>1), yet it may be interesting to consider it. Second, the RUMBA-SD estimation framework is based on a discrete approximation of the fiber ODF, which may be potentially extended to continuous functions on the sphere, like the spherical harmonics and the wavelets. Third, the TV regularization implemented in this study is based on a channel-by-channel first order scheme. New studies may be designed to compare different regularization techniques such as higher order TV, vectorial TV and the fiber continuity approach introduced in  [56] , to mention only a few examples. Fourth, different strategies for creating the signal dictionary could be explored, like using mixtures of intra-compartment models to capture different diffusion profiles, or applying more appropriate models to fit multi-shell data  [31, 83].
where v denotes the local fiber orientation. Signal from the intra-axonal compartment int s was created following the theoretical model of a restricted diffusion process inside a cylinder of length 5 time τ =20. 8 s [19, 71]. The extra-axonal signal ext s was generated using a diffusion tensor model with cylindrical symmetry (i. e. , 1 λ = 1. 7 10 -3 mm 2 /s, 2 3 λ = λ = 0. 2 10 -3 mm 2 /s). Mixture fractions were fixed to int f = 0. 6 and ext f = 0. 4. The noiseless dataset can be freely downloaded from the Webpage of the site: http://hardi. epfl. ch/static/events/2013_ISBI/.
as well as in Fig C in S2 File. Fig 1 shows results using a dictionary created with the same diffusivities applied to generate the data (i. e. , 1 λ = 1. 7 10 -3 mm 2 /s and 2 3 λ = λ = 0. 3 10 -3 mm 2 /s), whereas Fig C in S2 File displays results using tensor diffusivities estimated from the noisy data ( 1 λ = 1. 1 10 -3 mm 2 /s and 2 3 λ = λ = 0. 35 10 -3 mm 2 /s). In both dictionaries two isotropic compartments with diffusivities equal to 0. 1 10 -3 mm 2 /s and 2. 5 10 -3 mm 2 /s were included. Average values of SR, n + and n -are also reported in Figs D and E in S2 File. Results shown come from the reconstructions employing 200 iterations and the datasets with a SNR=15.
Discussion and Conclusions In this study we propose a new model-based spherical deconvolution method, RUMBA-SD. In contrast to previous methods, usually based on Gaussian noise with zero mean, RUMBA-SD considers Rician and noncentral Chi noise models, which are more adequate for characterizing the non-linear bias introduced in the diffusion images measured in current 1. 5T and 3T multichannel MRI scanners. Although recent progress has been made in new SD methods adapted to corrupted Rician data, e. g. , see  [41, 76]  to the best of our knowledge, our study provides the first SD extension to noncentral Chi noise. Furthermore, RUMBA-SD offers a very general estimation framework applicable to different datasets, with its flexibility emanating from two features: i) the explicit dependence between the likelihood model and the number of coils in the scanner and ii) the specific methodology employed to combine multichannel signals.
In addition, the voxel-wise estimation of the noise variance adequately deals with potential deviations in the noise distribution, due, for instance, to accelerated MRI techniques or to preprocessing effects. We hope that the proposed technique will help extend SD methods to a wide range of datasets taken from different scanners and using different protocols. This study adds to previous diffusion MRI studies trying to overcome the signal-dependent bias introduced by Rician and noncentral Chi noise. Apart from the robust DTI estimation methods in  [77]  and the earlier DTI study conducted by  [78] , the noise filtering techniques recently described in  [79, 80]  are specially relevant. These techniques can be applied in the preprocessing steps prior to HARDI data estimation. The performance of RUMBA-SD has been evaluated exhaustively against the state-of-the-art dRL-SD technique.
High-Resolution Imaging Analyses. High-resolution fundus images were obtained with an infrared AO retinal camera with a transverse resolution of 2. 4 m (rtx1, Imagine Eyes, Orsay, France)  [35]. This system has been used to image individual cone photoreceptors  [21, [36] [37] [38] [39]    structures  [18, 36, 40]. Successive AO images are taken at adjacent retinal locations with an angular spacing of 2 degrees in the horizontal and vertical directions. This procedure allows an overlap of horizontal and vertical images of at least 2 degrees. We had acquired AO images of the photoreceptor mosaic at the depth of maximum cone image intensity. We had acquired AO images around 6 degrees to both nasal and temporal sides horizontally from the fovea. The resulting images were stitched together by superimposing retinal vessel landmarks with an image editing software (GIMP, The GIMP Development Team; Image J, National Institute of Health, Bethesda, MD).
The size of each pixel was typically 0. 8 m when calculated at the retinal plane, and the values were adjusted for variations in the axial length of the eye  [41]. To evaluate the cone patterns of normal controls and Case III-2, we used the automated cone labeling analysis software (AOdetect; Imagine Eyes). AOdetect was developed by Imagine Eyes. The positions of the photoreceptors are computed by automatically detecting the central coordinates of small circular spots where the brightness is higher than the surrounding background level. First, the averaged image, without contrast adjustment, was filtered so that the local maxima of the image were detected. The spatial distribution of these points was analyzed using Voronoi diagrams where the detected points served as generators. After automated cone labeling, the estimated cone labeling was manually verified by three investigators to minimize any potential cone under-or oversampling made by the automated software.
Molecular Genetic Findings. Mutation analysis of RP1L1 of Case II-2 identified a novel homozygous mutation (Figure  6 (a), Table  2 ). The homozygous mutation was c. 3628T>C in exon 4 which resulted in the substitution of proline for serine at amino acid position 1210. Parental consanguinity was denied by the patient. This mutation has not been reported in the single nucleotide polymorphisms (SNP) database (http://www. ncbi. nlm. nih. gov/projects/SNP/), 1,000 gene project database, Japanese 1,500 exome database, the European genome database, and Exome Variant Server (http://evs. gs. washington. edu/EVS/) or in earlier reports  [3, 4, 42]. This mutation was also not present in 460 ethnically matched control alleles. The serine at position 1210 is well conserved among the RP1L1 family in other species (Figure  6 (b)).
However, we could recognize that the arrangement of cone mosaic was not normal throughout the investigated region of the eyes in Case II-2. By comparing the ellipsoid and interdigitation Regression line Upper limit of 95% confidence intervals Lower limit of 95% confidence intervals Upper limit of 95% prediction intervals Lower limit of 95% prediction intervals Cone density (×100 cones/mm 2 ) Figure  8 : Statistical data of adaptive optics analyses. Relationship between cone density and axial length was obtained from 26 normal control samples. There was a statistically significant negative correlation between cone density and axial length (R 2 = 0. 6187). Upper and lower limit of 95% confidence intervals, 95% prediction intervals, and regression line of normal controls are shown. The results of estimated cone density and axial length in Patients II-2 and III-2 are plotted as indicated marks.
There have been approximately 1,000 nonsynonymous SNPs reported for the RP1L1 gene with missense and frameshift variants in the SNP database. Almost all of these variants including 11 frameshifts with premature termination of the RP1L1 protein were nonpathogenic. Although none of the frameshift variants reported in the SNP database was found in the population-frequency data, most of them were reported in multiple independent studies. These findings indicate that a loss-of-function effect in RP1L1 gene may not be pathogenic. At present, several putative pathogenic variants of the RP1L1 gene have been reported; however, only a variant, p. R45W, was shown to be in the multiple OMD reports  [1] [2] [3] [4] [5] [6]. Therefore, it is very important to evaluate new RP1L1 variants carefully to determine whether they are pathogenic.
Acknowledgments The authors thank  Dr. Duco Hamsaki  of the  Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, FL , for discussions and editing the paper. This work was supported by  Foundation Voir et Entendre (CZ) ,  Prix Dalloz  (CZ),  Foundation Fighting Blindness (FFB)  ( CD-CL-0808-0466-CHNO ) (IA) and  FFB center (FFB  grant  C-GE-0912-0601-INSERM02 ),  Prix de la Fondation de l'Oeil (IA) ,  Ville de Paris  and  Region Ile de France , and the  French State program  " Investissements d' Avenir " managed by the  Agence Nationale de la Recherche  [ LIFESENSES :  ANR-10-LABX-65 ].
The challenging complexity of biological structures has led to the development of several methods for quantitative analyses of form. Bones are shaped by the interaction of historical (phylogenetic), structural, and functional constrains. Consequently, bone shape has been investigated intensively in an evolutionary context. Geometric morphometric approaches allow the description of the shape of an object in all of its biological complexity. However, when biological objects present only few anatomical landmarks, sliding semi-landmarks may provide good descriptors of shape. The sliding procedure, mandatory for sliding semi-landmarks, requires several steps that may be time-consuming. We here compare the time required by two different software packages ('Edgewarp' and 'Morpho') for the same sliding task, and investigate potential differences in the results and biological interpretation.
'Morpho' is much faster than 'Edgewarp,' notably as a result of the greater computational power of the 'Morpho' software routines and the complexity of the 'Edgewarp' workflow. Morphospaces obtained using both software packages are similar and provide a consistent description of the biological variability. The principal differences between the two software packages are observed in areas characterized by abrupt changes in the bone topography. In summary, both software packages perform equally well in terms of the description of biological structures, yet differ in the simplicity of the workflow and time needed to perform the analyses.
One of the first packages that was developed and that is still used in many studies (e. g. ,  Kulemeyer et al. , 2009; Fabre et al. , 2013a; Fabre et al. , 2013b; Cornette et al. , 2013; Cornette, Tresset & Herrel, 2014)  is 'Edgewarp'  (Bookstein & Green, 1994). The 'EVAN toolbox' (http://evan-society. org) also performs sliding of semi-landmarks amongst other operations. Some authors have developed their own routines such as the Mathematica (Wolfram Inc. , Modesto, California, USA) code used by  Gunz & Mitteroecker (2013). Recently two R packages have been published: geomorph  (Adams & Otárola-Castillo, 2013) , featuring landmark placement, treatment and analysis, and 'Morpho'  (Schlager, 2013b)  featuring landmark importation from several other software packages and performing geometric morphometric treatment and analysis.
Because specimen digitization, the first step of any three dimensional surface analysis, is a time-consuming step that cannot be easily shortened, it could be of great interest to reduce the duration of the second step: the sliding procedure. The aim of the present study is to compare the workflow and the results obtained with two different software packages for the same three dimensional sliding task. We selected 'Edgewarp'  (Bookstein & Green, 1994) , an established reference for these types of analyses and 'Morpho'  (Schlager, 2013b)  a recently published set of R routines. We focused on a practical and biological test comparing a long bone in different mustelids. We followed the sliding procedure detailed in  Gunz, Mitteroecker & Bookstein (2005)  using both software packages. Differences between the results were examined and discussed in the light of the biological structures involved.
3D anatomical landmark and curve digitization Landmarks and curves were digitized on the surfaces of the scans using the Landmark software package  (Wiley et al. , 2005). Twenty-seven 3D homologous anatomical landmarks were chosen and are visible on all specimens (Fig. 1  and Table  2 ). Eighteen 3D curves were defined at the margins of articular surfaces and along crests. All curves are bordered by anatomical landmarks as recommended by  Gunz, Mitteroecker & Bookstein (2005). The curves were digitized with a high density of points (40-160 points per curve depending on the curve length) and then sub-sampled to the number listed in Table  3. This dataset was then used to perform an identical sliding procedure using both 'Edgewarp' and 'Morpho. ' A semi-automatic point placement was used to place surface semi-landmarks on the scans.
A template was modeled using the Blender software (Blender Online Community, 2014) following the procedure described in  Souter et al. (2010). We created a mesh of 598 points representing a simplified form of the humerus. Landmarks and curves were digitized on the template surface as it was done on the actual scans of the specimens. Then in both 'Edgewarp' and 'Morpho,' landmarks and curves were used to compute a thin plate spline (TPS) deformation of the template. Next, surface sliding landmarks were projected from the deformed template onto the bone surface  (Gunz & Mitteroecker, 2013). In summary, the template used in this study contains a total of 817 points including 27 3D homologous anatomical landmarks, 192 sliding semi-landmarks on curves and 598 sliding semi-landmarks on surfaces.