dataesr/scientific-paragraphs-categorization huggingface dataset:
Scoring by non-local image patch estimator (SNIPE) The second approach is based on the Scoring by Non-local Image Patch Estimator (SNIPE) proposed in  ( Coupé et al. , 2012 ). In order to classify images, SNIPE uses a grading measure based on a nonlocal patch-based framework. This measure estimates the similarity of the patches surrounding the voxels in the image under study with all the patches present in different training populations. In this study, the training database contains two "populations ": hemispheres with the patternof-interest and those without it ( Fig. 7  ). Region Of Interest (ROI) delimitation For this approach, the hemispheres to be classified are represented by binary volumes of 2 * 2 * 2 mm resolution, containing the fold skeleton. Although the SNIPE method was originally developed to search for matching patches not locally but throughout an entire image, here the patterns-of-interest are specific to local areas of the brain.
Thus, it is possible to search for them only on a predefined ROI. This avoids false positives and limits the computing time. Thus, a masking is performed on these volumes in order to extract the ROI. The mask is calculated from the location of the patterns-ofinterest in the training database. To avoid omitting a pattern-of-interest whose position would be slightly different from those in the training database, all voxels within 1cm of one of the patterns-of-interest in the training database belong to the ROI. Note that the calculation of the mask is based on the location of the patterns-of-interest in the training database, so it is recalculated for each cross-validation fold, but on average it includes about 2100 voxels of the fold skeleton for the PBS, and 1700 for the ACC patterns. Optimized PatchMatch algorithm Once the ROI has been extracted, patches with central voxels belonging to the fold skeleton are associated Fig.
7. Illustration of the SNIPE-based method. The column on the left represents the classification of a hemisphere with the pattern-of-interest (ACC study), while the column on the right represents the classification of a hemisphere without the pattern-of-interest (PBS study). For each voxel in the Region Of Interest (ROI) of the hemisphere to be classified, its patch is associated with several similar patches (here 3) in the training database by using the OptimizedPatchMatch (OPM) algorithm. The distance of the similar patches to the patch and their location in a hemisphere with the pattern-ofinterest (+) or without it (-), are used to calculate the grade. All grades associated with ROI voxels are averaged to obtain the grade of the hemisphere. A positive grade predicts the presence of the pattern-of-interest and vice versa for a negative grade.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) with similar patches in the training database. To do this, the Optimized Patch Match algorithm  ( Giraud et al. , 2016; Ta et al. , 2014 )  is used. This algorithm searches for similar patches in an atlas library using a cooperative and random strategy resulting in a very low computational burden. Compared to the PatchMatch algorithm  ( Barnes et al. , 2009 )  from which it is inspired, Optimized PatchMatch is adapted to 3D anatomical atlases by taking into account the rough alignment of images. Here, the adapted version of the Optimized PatchMatch algorithm implemented in  ( Borne et al. , 2020 )  is used. This adapted version has been developed specifically for the study of cortical sulci and makes it possible to take advantage of their organisation in the form of meshes.
In addition, to manage the unbalanced presence of populations in the training database, the under-represented population is made proportionately more probable to be selected during the initialization by weighting their probabilities. Thus, the probabilities  ( ℎ  ) of selecting the hemisphere ℎ  among the hemispheres { ℎ 1 , … , ℎ  } of the training set during the initialization is: 𝑝 ( ℎ 𝑖 ) = 𝑤 ( ℎ 𝑖 ) ∑ 𝑗∈{1 , …,𝑛 } 𝑤 ( ℎ 𝑗 ) , with 𝑤 ( ℎ 𝑘 ) = max (1 , 𝑛 𝑘 ( 𝑛 -𝑛 𝑘 ) ) and   being the number of hemispheres belonging to the class of ℎ  in the training set. To obtain several similar patches, the Optimized PatchMatch algorithm is run several times. The patch sizes and the number of similar patches selected are determined by inner cross-validation. Grading calculation As in  ( Coupé et al.
Three hyperparameters are concerned: number of approximate nearest neighbors selected  (5, 10, 15, 20, 25, 30) , patch sizes (18, 26 or 34mm) and their combinations (all combinations of 1, 2, and 3 different patch sizes are tested). Note that two parameters were set a priori: the size of the search window at 14 * 14 * 14 mm and the number of iterations of the propagation and random search steps at 4. In summary, several adaptations have been made compared to  ( Coupé et al. , 2012  ): • The search for similar patches is performed using the adaptation of the Optimized PatchMatch algorithm proposed in  ( Borne et al. , 2020 )  and not using an exhaustive search. • In order to manage the unbalanced databases, the initialization of the algorithm is weighted to compensate. • The average of the grades is done on the ROI instead of on a simultaneously segmented region.
Which is the best model? When analysing the balanced accuracy by model ( Fig. 9  , Tables  1 , 2  ), we observe that whatever the fold pattern studied, all models are better than chance and that none of them is significantly better than the other   Regarding the distribution of the  score defined in section 3. 3. , the Mann-Whitney test indicates that, except for the CNN-based model, the  scores significantly separate the hemispheres with and without a PBS ( Fig. 10  ). Thus, the CNN-based model does not provide sufficiently reliable scores to assist in the search for PBSs, while the other two models can be used to find PBSs on a new database and enrich the current training database.
Looking for PBSs Concerning the search for PBSs on the Human Connectome Project database, the models identified that 30% (for the CNN-based model) to 75% (for the SVM-based model) of the hemispheres presented PBS, which is largely overestimated given the rarity of this pattern in the healthy population. In order to see if these methods can identify some PBSs on this new database, the 9 hemispheres with the highest  score are examined  ( Figs. 11 , 12 and 13 ). Note that we do not know the proportion of PBSs in the healthy population, so it is difficult to estimate how many PBSs are actually present in the Human Connectome Project database. However, the three models proposed make it possible to find some of them, which is already promising, in particular for SVM and SNIPE-based models that allow us to find more PBSs than the CNN-based  model.
Indeed, the first two models identify respectively four and three PBSs out of the nine hemispheres examined, while the CNN-based model identifies only one. Note that the PBSs identified with the most confidence by the SVM and SNIPE-based models are different and are not always classified as such by the other model. These methods therefore seem to be complementary for PBSs research. Therefore, by illustrating the usefulness of the proposed models in a particular practical case (the search for a rare pattern in a large database of healthy subjects), these results confirm that the models allow us to meet one of the main motivations of this study.
Conclusion In this study, three methods were proposed for the automatic classification of sulcal patterns. The first method uses the distances of the patterns of the training database to the hemisphere to be classified to train a SVM. The second method is based on the SNIPE patch classification approach proposed by  Coupé et al. (2012). The third method uses an 18-layer 3D recurrent neural network. For the recognition of ACC patterns, the three proposed models have equivalent performance in identifying individual pattern, with a balanced accuracy of about 80%. However, the fastest model to apply is the one based on CNN. For PBS recognition, all three models also have equivalent identification performance, with a balanced accuracy of about 60%. Therefore, it appears that these models perform considerably less efficiently when the database is limited and unbalanced, thus preventing their application for large-scale morphological studies.
It is therefore complicated to advise one model over another because it depends on the pattern-of-interest. However, we believe that these methods perform well enough to later help (1) to identify a link between sulcal shapes and functional organization of the cortex (e. g. by studying the distribution of ACC patterns on larger databases) and (2) to understand their role in neurodevelopmental disease (e. g. by helping to identify rare patterns such as PBS in healthy population). Moreover, these methods are generic enough to be applicable to a wide range of other folding patterns that are the subject of numerous studies  ( Germann et al. , 2005; Hotier et al. , 2017; Huntgeburth and Petrides, 2012; Kim et al. , 2008; Ochiai et al. , 2004; Plaze et al. , 2015; 2009; Segal and Petrides, 2012; Snyder et al.
How to define fold patterns automatically? Cortical folds are so variable that manual pattern definition is tricky. Indeed, it is usually the result of long reflections after visualization and study of a large number of subjects by experts. Moreover, the use of a limited database, or of the limited human memory, questions the generalisability of the defined patterns. Thus, many approaches seek to discover folding patterns in an unsupervised manner. These methods define a region of interest and deduce patterns automatically, using clustering algorithms  ( Duan et al. , 2019; Im et al. , 2011; Meng et al. , 2018; Sun et al. , 2009; 2007 ). However, in this study, we observed that the classification of local patterns is sometimes difficult to carry out because of the continuity between the different configurations.
For this reason, in the study of ACC patterns, some subjects were excluded from the study due to a lack of consensus on their identification. Similarly, different levels of PBS presence were used when tagging the training database. Also, other unsupervised approaches prefer to use manifold based techniques to better represent the continuity between two extreme configurations  ( Sun et al. , 2016; 2012 ). Similarly, it would be interesting to predict a score for the presence of ACC patterns or PBS, rather than attempt to distinguish them into separate groups. Nevertheless, all these unsupervised approaches are based on the prior labeling of the structures of interest, which is sometimes difficult to obtain and may lead to radically different results depending on the region of interest used.
Similarly, by training a CNN to make such classifications, different techniques could help identify areas of the fold skeleton that allow correct classification, such as the use of saliency maps  ( Simonyan et al. , 2014 )  or occlusions  ( Zeiler and Fergus, 2014 ) , already used to understand how neural networks work. Such tools would help neuroanatomy experts to identify fold patterns specific to a given group. Currently, there is no approach to automatically discover folding patterns that gets rid of both a priori definition of region of interest and group of interest. In the future, a fully unsupervised approach would enable the use of the huge databases available today to define patterns generalisable to the whole population, which would greatly facilitate the study and understanding of cortical folds variability.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 785907 & 945539  ( HBP SGA2 & SGA3 ), from the  FRMDIC20161236445 , from the  ANR IFOPASUBA , from the  ANR-14-CE30-0014-01 APEX  and from the  ANR FOLDDICO. Data were provided in part by the  Human Connectome Project, WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research  ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
In addition, by calculating the fourth-derivative of the emission spectra, it is possible to distinguish the partitioning of Trp residues between hydrophobic and hydrophilic environments. 24, 25 ea protein isolates are composed out of legumin, vicilin, and convicilin. Pea protein as most plant storage proteins are diverse in terms of primary structure. 27 Based on the reviewed sequences found in the Uniprot database, legumin, convicilin, and vicilin are expected to contain 12-18, 9-12, and 0 Trp residue per protein chain, respectively; 42-78, 42-45, and 27 Tyr residue per protein chain; and 48-120, 30-60, and 33-66 Phe residue per protein chain. It is expected that these aromatic amino acids are relevant intrinsic probes for the pea protein conformation. In this work, we aimed to investigate the conformational changes induced upon emulsification of proteins present in food ingredients arising from different biological origins and transformation processes: whey protein isolate, commercial pea protein isolate, and mildly purified pea proteins.
S. A. ) at 150 V for 45 min. After electrophoresis the gels were stained with Instant Blue for 1 h. Subsequently, the gels were washed with ultrapure water for 12 h before analysis. Gels were scanned and analyzed using a calibrated densitometer (ImageScanner III LabScan 6. 0 (GE Healthcare, U. S. A. )) and Image Lab software (Bio-Rad laboratories, U. S. A. ). The molecular weights were determined by point to point regression. For WPI solutions, mainly bovine serum albumin (BSA), βlactoglobulin (β-lg), α-lactalbumin (α-lac), and traces of immunoglobulins were found. PPI containing samples mainly consisted of convicilin (∼71 kDa), vicilin subunits (∼30, ∼34, ∼47 and ∼50 kDa), α-legumin (38-40 kDa), β-legumin (19-22 kDa),  34  and vicilin fragments (∼12. 5-19  35 ). The pea proteins dissociated in the buffer containing SDS solution and 2-mercaptoethanol, leading to multiple bands in SDS-PAGE.
The sum of the subunits is reported for the different pea proteins. 2. 4. Absorption Spectra. Absorption spectra of protein solutions were recorded with a UV-visible spectrophotometer (Shimadzu UV1800, Shimadzu corporation, Kyoto, Japan) coupled with the UV probe software (v2. 30). UV-visible spectra were recorded for solutions at 1 g/L for WPI and iePPI and at 0. 25 g/L for cPPI, to have absorbances of ∼1 at 280 nm. The spectra were acquired between 200 and 650 nm with a scan speed of 360 nm/min and a sampling interval of 0. 5 nm. Ultrapure water was used for setting the baseline. The theoretical UV-visible absorbance spectra of each protein dispersion were calculated for 1 g/L of protein. Experimental reference spectra of Trp, Tyr, and Phe were obtained from the PhotochemCAD database. 36, 37 The theoretical spectra were calculated as a linear combination of each reference spectrum based on the respective content of Trp, Tyr, and Phe of the protein isolate.
FF fluorescence emission spectra were recorded from 275 or 290 to 450 nm (step 0. 5 nm, dwell time = 0. 3 s) with excitation wavelengths set at 275 or 290 nm, respectively. The slit widths were set at 2 nm in both excitation and emission pathways. FF emission spectra were normalized to a maximum intensity of 1, and the normalized spectra were used for fourth-derivative calculations. The spectra were smoothed by the Savitsky-Golay procedure (polynomic order 2, width 21 points), and fourth derivatives were calculated according to the Savitzky-Golay procedure (width 40 points). The data sets were analyzed with Unscrambler x10. 2 (CAMO Software, Oslo, Norway). 2. 6. Statistical Analysis. Each measurement was performed in at least independent duplicates. Two independently prepared emulsions were used for the FF measurements and surface load determination.
Small angle X-ray scattering (SAXS) SAXS measurements, using Mo radiation (λ = 0. 071 nm), were performed on a bench built by XENOCS. The scattered beam was recorded using a large online scanner detector (diameter: 345 mm, from MAR Research). A large q-range (0. 2 to 35 nm - 1 ) was covered with an off-center detection. The collimation was applied using a 12:∝ multilayer Xenocs mirror (for Mo radiation) coupled to two sets of scatter less FORVIS slits providing a 0. 8 × 0. 8 mm X-ray beam at the sample position. Pre analysis of data was performed using the FIT2D software. The scattered intensities, Intensity or I(q), are expressed versus the magnitude of scattering vector q = [(4π)/λ]sin(θ/2), where λ is the wavelength of incident radiation and θ the scattering angle. 2 mm quartz capillaries were used as sample containers for the solutions.
Small angle neutron scattering (SANS) All measurements were performed on beamline D33 at the Institut Laue-Langevin (ILL) in Grenoble, France. The measurements were made in time-offlight (TOF) mode. All measurement parameters on D33 are controlled by the NOMAD software on site and were calibrated according to Dewhurst et. al. [26]  Sample-to-detector distances were at 3 and 7 m with collimation at 7. 8 m. This enables to cover an even larger q-range than using solely one detector at a fixed position. Afterwards both parts of the SANS spectra, detected by a set of 4+1 detectors, were merged and treated with the GRASP software on site. Note, that the merging of the spectra of the different panels make some inhomogeneities appear in the spectra at q around 0. 8 nm -1. Water was used as a calibrant in order to obtain absolute intensities.
The resulting spectrum was similar to what was obtained for the gel by MAS NMR. The SEM image of dried P1 shows a three-dimensional cross-linked structure (Fig. 1b ). Rheology measurements were used to assess the mechanical properties of P1-P4. The storage modulus (G 0 ) increases in the order P2 < P1 < P3 < P4. Two factors are expected to contribute to the observed differences: the cross-link density,  10, 11  and the strength of the metal-ligand bond. The latter is related to the basicity of the ligands. An alternative measure for the donor strengths of a ligand is the Huynh Electronic Parameter (HEP). 24  We have determined the HEP parameter for close analogues of L1-L4 (lacking the polymer linker), and the data conrmed that the relative donor capability of the ligands is in line with the ligand basicity (see the ESI †).
[44] T calculations. DFT calculations were performed utilizing Gaussian09. [45] [51]  The optimized geometry was subsequently utilized for the calculation of magnetic shielding and EFG tensors with the GIAO method  [52, 53]  at the same level of theory. Subsequently, a single 1 H nucleus was positioned in the proximity of a 3-O moiety. Geometry optimization allowed the position of this proton to be optimized, while the remaining structure was kept frozen. The resulting geometry was utilized for the calculations of NMR parameters as described above. Only the relevant 3-O site was considered in this latter case. The EFGShield software  [54]  was utilized to extract the calculated NMR parameters. Cartesian coordinates are given in Supplementary Information.
Acknowledgements This work was performed using HPC resources from  GENCI-TGCC  (Grant  2019-AP010910927 ), the  Equipex  grant " Paris en resonance " ( ANR-10-EQPX-09 ) and the  ERC grant "Dilute para-water" (grant agreement   339754 ). SW and CS are grateful to the  ANR  Project  MeaCoPA  ( ANR-17-CE29-0003 ) for financial support. All authors acknowledge support by the  Paris Ile-de-France Region / Respore program.
The position of the whiteline maximum was determined from the first zero-crossing of the first derivative. PyMca software was used to remove the background and normalize the spectra  [43]. Linear combination fitting (LCF) was done using ATHENA software  [44]. The goodness of fit value is represented by the R factor defined as following : 𝑅 = ∑( ) ∑( ) Eq. 1 This equation indicates that the lower the R factor, the better fit.
The U VI , U V and U IV fractions in the glass samples were subsequently determined by fitting the experimental data with a linear combination of the HR-XANES reference spectra. Linear combination fitting (LCF) using UO2, U3O8, and (UO2)O2(H2O)2 over a -20 to 20 eV energy range around the edge energy gives the relative weights of the reference systems. As reference materials are well-known compounds, the fraction of each U oxidation state in the glass samples can then be deduced. The results corresponding to the best fit obtained for each glass are provided in Table  5 , and are consistent with the qualitative analyses of glasses by the XANES spectra. The AU-Ox glass sample exclusively contains 100% U VI. The fits yield 32% U VI , 57% U V , and 11% U IV for the AU-Ar sample and 9% U VI , 17% U V and 74% U IV for the AU-C sample.
These high density HfO2 and UO2 crystals have the same tendency to settle at the bottom of the glass melt. In a previous study, the thermodynamic solubility of hafnium in the same aluminosilicate melts (glasses A and B) was determined  [32]. The experiment protocol consisted of measuring the hafnium content in Hf-saturated glass in equilibrium with Hf-bearing crystals at different temperatures. The new set data for uranium, obtained under highly reducing conditions with U IV as the main species, allows us to accurately compare the behavior of these two elements in a glass melt. Fig. 7  shows a comparison between the solubility limits of UO2 and HfO2 in the glasses A and B melted at 1250 and 1400 °C. Tetravalent hafnium is about three times more soluble than tetravalent uranium but similar trends are observed.
Acknowledgments The authors gratefully acknowledge the synchrotron light source KARA for provision of instrumentation and beamtime. XAS experiments were supported by the  TALISMAN  project (grant agreement number  323300 ). The authors would also like to thank the  Helmholtz Young Investigators Group  ( VH-NG-734 ) for setting up the HR-XANES measurement system. The authors are grateful to  E. Gavilan  and  A. Caleyron  from  CEA Marcoule  for their help with the preparation of some uranium-doped glasses. The authors thank the SEM team ( C. David  and  E. Excoffier ) for access to equipment in a controlled area. Finally, the author acknowledge the reviewers for their valuable suggestions. This research was funded by the  French Alternative Energies and Atomic Energy Commission (CEA) ,  Orano , and within the  Investments for the Future Program  of the French Government and operated by the  French National Radioactive Waste Management Agency (Andra).
Acknowledgment These studies were carried out as a part of the  France-Japan International Collaboration Framework  ( IRL3629 LINK ). The authors wish to thank  Mr. D. Lechevalier  of  Saint-Gobain KK (Tokyo, Japan ) for his significant support involved in LINK, and  Mr. S. Hashida  and  Mr. K. Tanaka  for their significant cooperation in measuring the zeta potential at  Otsuka Electronics Co. , Ltd.
The data obtained were statistically analyzed using the Trimmed Spearman-Karber method and expressed as mean EC 50,48 h ± standard deviation. All treatments were carried out in triplicate. The sensitivity of the culture was measured weekly by exposing neonates to potassium dichromate (K 2 Cr 2 O 7 ) diluted in ISO medium at concentrations of 0. 7, 0. 9, 1. 1 and 1. 3 mg L -1. Organisms were exposed for 24 h and EC 50,24 h values between 0. 6 and 1. 7 mg L -1 indicate the validation of the assays  (Gonçalves et al. , 2018).
Acknowledgments The authors gratefully acknowledge the  Brazilian governmental agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  ( CNPq 473046/2013-0 ) and  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  for their financial support,  LabCeV/ UFSC  for their help with the sample preparation and  LCME/UFSC  for the SEM-EDS and TEM analysis.