dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Dominique Dubois ,  Olivier Proux ,  Régine Leroux ,  Kerstin Hund-Rinke  and  Claire Gassie  for their technical help in this work and  Ana Pradas del Real  and  Géraldine Sarret  for providing part of the XAS spectra for model compounds that were used in this study. We are also grateful to the  AME Platforme , the LEM and the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing computing and storage resources. We thank you for taking time to consider this study and we look forward to hearing from you in the earliest convenience.
Acknowledgements We acknowledge the  ESRF  for the provision of the synchrotron radiation facilities at the  Nuclear Resonance beamline ID18 , and we thank  J. -P. Celse  for his help in the implementation of the experimental set-up. We also thank  Prof. Kia L. Ngai  ( CNR-IPCF, Pisa, IT ) for interesting discussions on the nature of the β JG relaxation,  Prof. G. P. Johari  ( McMaster University, Hamilton, CA ) for his comments to the manuscript and  Dr. D. T. Bowron  ( STFC, ISIS Neutron and Muon Source, UK ) for sharing the intermolecular pair correlation function data of 1-propanol. S. C. and S. V. thank  CISUP  for the access to dielectric spectroscopy laboratory facilities. The research at the  University of Pisa  was funded by the project  PRA-2018-34  " ANISE ".
X-ray Diffraction Analysis The XRD patterns were collected using a PANalyticalX'Pert Powder X-ray diffractometer (Malvern Panalytical, Malvern, UK) equipped with a Cu-radiation and an X'celerator detector. The analyses were conducted on homogenized dry bulk samples prepared on randomly oriented powder mounts and were scanned in the 2 to 60 • 2Өangular range. The XRD patterns have been interpreted using the EVA© software (Bruker DIFFRAC Plus 2007; for more details see Giencke  [16] ). Reference files from the International Centre for Diffraction Data (ICDD PDF2 data files) have been used for mineral phase identification. (Semi) quantification of the present mineral phases was made using the normalized intensity ratio (RIR) method with an uncertainty of ± 5% (e. g. ,  Chung et al. [17] ).
The spectral subtractions were made with the Omnic software (ThermoFisher Scientific, MA, Waltham, USA). FTIR spectra were also measured in Diffuse Reflection Infrared Spectroscopy (DRIFTS) mode with an Agilent 4100 ExoScan spectrometer (Santa Clara, CA, USA). This FTIR spectrometer equipped with an external reflectance probe allows measuring diffuse reflectance spectra of powders as well as of spherical beads without any specific preparation. In DRIFTS, when coming in to contact with the sample, the infrared light passes through the top surface of the sample before being reflected back out of the sample and into the detector of the spectrometer. Even if distortions from specular reflectance artifacts cannot be excluded, qualitative spectral comparisons could be done. The background was performed on a silicium wafer reference, the acquisition time was 30 s and the resolution was 4 cm -1.
ICP-AES Analysis The chemical nature of the 4A samples was determined through Induced Coupled Plasma-Atom Emission Spectroscopy (ICP-AES) on a Perkin Elmer Optima 2000 DV apparatus (Waltham, MA, USA). From Figure  2 , it can be inferred that the 4A zeolite sample does not allow for the uptake of argon in its micropores, as significant diffusional limitations and molecular crowding phenomena are encountered for pore sizes lower than 4. 5 Å for argon at 87 K  [14, 18]. From Figure  2 , it can be inferred that the 4A zeolite sample does not allow for the uptake of argon in its micropores, as significant diffusional limitations and molecular crowding phenomena are encountered for pore sizes lower than 4. 5 Å for argon at 87 K  [14, 18]. The Pore Size Distribution (PSD) and the micropore volume of each sample were obtained by applying the NLDFT model for argon adsorption on cylindrical/spherical pores of zeolites available in the Autosorb software.
The BET surface area was calculated according to the procedure defined for microporous adsorbents by Rouquerol et al. [19]. These results are shown in Figure  3  and Table  2 , respectively. The Pore Size Distribution (PSD) and the micropore volume of each sample were obtained by applying the NLDFT model for argon adsorption on cylindrical/spherical pores of zeolites available in the Autosorb software. The BET surface area was calculated according to the procedure defined for microporous adsorbents by Rouquerol et al. [19]. These results are shown in Figure  3  and Table  2 , respectively. The Pore Size Distribution (PSD) and the micropore volume of each sample were obtained by applying the NLDFT model for argon adsorption on cylindrical/spherical pores of zeolites available in the Autosorb software.
Given the inexistence of available NLDFT models for zeolites in the Autosorb software, PSD was obtained by applying a NLDFT model for CO 2 adsorption on carbon. Indeed, this model allows performing a qualitative analysis of the effect of the binder. From Figure  5b , one can conclude that the binder blocks part of the microporosity and induces an additional pore size, which leads to the observed shift of the filling pressure. To verify if the irreversibly of CO 2 adsorption isotherm on 4A zeolite spherical beads samples was due to the pretreatment conditions, several isotherms were recorded with a pretreatment at different temperatures (523 K, 593 K, and 723 K) under secondary vacuum during 12 h. These results are shown in Figure  6a ) and confirm that insufficient purification is not the cause of the irreversibility.
Mechanical characterization of dendron OEG monolayers We evaluated the mechanical properties of the dendron monolayers using AFM nanoindentation with a cantilever coupled to a 10 mm SiO 2 particle. 24 It is well established that AFM indentation data for a thin lm on a stiff substrate cannot be treated with a conventional Hertz model with a spherical indenter  24  as the lm deformation is limited under a high load. 25, 26 In this study, we assumed a transition function that linearly connects the inuence of two elastic layers, the monolayer and the substrate, 27,28 1 E ¼ À 1 À e ad=d Á E M þ e ad=d E S (2) where E is the composite Young's modulus, and E M and E S are the Young's moduli of the monolayer and substrate, respectively. d is the indentation depth, and d is the thickness. a is an empirically determined parameter that characterizes the sharpness of the transition.
56 Å),  17, 18  and the signal was recorded with a linear position sensitive detector (Mythen 1K, Dectris, Switzerland). The reectivity data measured in air were analyzed using a genetic minimization algorithm implemented in the MOTOFIT/Igor package (Wavemetrics, OR, USA). As the data measured in buffer could not be analyzed with the standard slab model owing to the diffusive nature of the interface, we modeled the SLD proles near the interface using a hyperbolic tangent function. 21 The analytical SLD prole was split into slabs with 1 Å thickness. Since this is close to the instrument resolution, it was not necessary to include slab roughness in these calculations. 40 The reectivity curve was computed as a function of q z using the Abelès formalism,  22, 23  using a self-written tting algorithm in Igor (Wavemetrics, OR, USA).
Acknowledgements D. F. -F. and M. T. thank the support from the  INTERREG V Upper Rhine Program (NANOTRANSMED). T. H. and M. T. thank  JSPS (KAKENHI  grant numbers  JP20H05210  to T. H. ,  JP19H05719  to M. T. ) for support. T. H. is thankful to  Heidelberg Alumni Network , and M. T. to the  German Science Foundation  ( SPP2171 ) and  Nakatani Foundation  for support. J. C. thanks  R. Chang  for experimental assistance, and M. T. thanks  R. Dahint  for helpful comments. We thank  S. Dodds  from  Edanz Group  (https://enauthor-services. edanz. com/ac)for editing a dra of this manuscript.
, UK) at the University of Cambridge Zoology museum CT lab. Voxel resolution was 10, 47 and 11 (micron) for Heloderma, Ophisaurus and Corucia respectively. MicroCT images were imported into an image processing software and were manually segmented (Avizo 9. 2. 0, Thermo Fisher Scientific Inc. , USA). 3D solid models of single OD were developed for Heloderma (n = 8), Ophisaurus (n = 4) and Corucia (n = 8). The 3D models were exported to a computer aided design (CAD) software (Solidwork 2018, Dassault Systèmes SOLIDWORKS Corp. , USA) for further measurements. Various measurements were carried out to characterise the morphology of the ODs. See figure  2  for details of the measured parameters. Biomimetic development and manufacturing. Biomimetic 3D CAD models of ODs were developed based on the results of structural analysis mimicking the main morphological features of the OD sheets.
The transmitted impact load that the OD sheets and underlying hard surface were subject to during the drop weight test was measured for the weights. This was carried out using a thin film force sensor RP-S40-ST with 40 × 40 active area (Shenzhen Film Sensor Technology Co. , Ltd. , China) connected with an ELEGOO UNO R3 Board (ELEGOO Inc. , China) and placed under the 3D printed samples and over a hard surface during the drop weight test. Detailed functions for data acquisition were programmed and verified in Arduino IDE (Arduino Software Inc. , Italy).
Nonoptical Characterization X-ray diffractograms (XRD) of both powders were detected using Bruker D2 Phaser powder X-ray diffractometer with CuKα radiation. Processing of the results, the phase analysis of the powders, and lattice parameters refinement were performed using software package DIFFRACplus (TOPAS 4. 2. 0. 2). Samples of type I were studied at the Institute of Physics of the University of Tartu (Estonia) using transmission electron microscopy (TEM) analysis, and those measurements were performed in the scanning mode (STEM) at 200 kV using a Cs-probe-corrected transmission electron microscope (FEI Titan Themis 200, ThermoFisher Scientific, Hillsboro, OR, USA). Powders in solutions were diluted in ethanol and ultrasonicated. The colloid was placed on a TEM copper grid with carbon film and dried for several hours.
Energy dispersive X-ray spectroscopy (EDX) signal of the NPs was collected with SuperX silicon drift detectors (Bruker, Billerica, MA, USA) to measure element concentrations. Quantitative analysis was performed with Cliff-Lorimer method for F with K-line and for La and Nd with L-line using Bruker Esprit software. It provides accurate enough elemental analysis with high spatial resolution. Samples of type II were studied at the GPI RAS (Moscow). The transmission electron microscopy (TEM) and STEM images of the samples of type II were taken with Zeiss Libra 200 FT HR microscope under accelerating voltage 200 kV. The colloid was placed on a TEM copper grid with carbon film and dried for several hours. EDX spectrometer was controlled by EDS Aztec OXFORD software. Processing of TEM images for calculation of size distribution was carried out using the ImageJ program.
Acknowledgements The authors are grateful to the  Chief Executive/Rector, Akanu Ibiam Federal Polytechnic Unwana, Nigeria, Venerable (Dr) Ogbonnia Ibe-Enwo  for granting the authors free access to laboratory facilities in the  Department of Science Laboratory Technology of his institution  for the execution of this work, and to all the Technological staff of the  Department of Science Laboratory Technology  for their cooperation and technical assistance.
• Dynamic Light Scattering A Delsa TM Nano C particle size analyzer (Beckman Coulter) was used to determine the size of the droplets and polydispersity index of starch and chitosan-based emulsions according to the cumulative distribution of intensity. The samples were measured in triplicate with 20 scans each and averaged. The Delsa Nano C uses photon correlation spectroscopy, which determines particle size by measuring the rate of fluctuations of the laser light intensity scattered by particles. The non-negative least-squares (NNLS) algorithm was used to analyze dynamic light scattering data for the droplet size distribution.
The organic upper phase (5 mL) was then transferred into a new glass tube and evaporated under nitrogen flux. The different dry extracts were resolubilized in 400 µL of methanol/water (1:1). The extracted samples were filtrated through a 0. 22 µm filter and then run on an Agilent Technology 1100 liquid chromatography system as already described by  Gauthier et al. and Montibus et al. [53, 63]. All experiments were done with at least three repetitions. Data for TCTB production and fungal growth were reported as mean values ± standard deviation of three to five replications. Mean values were compared at the 5% significance level using Conover-Iman test (control vs. treated) with the XLSTAT software.
a b s t r a c t The study of local cortical folding patterns showed links with psychiatric illnesses as well as cognitive functions. Despite the tools now available to visualize cortical folds in 3D, manually classifying local sulcal patterns is a timeconsuming and tedious task. In fact, 3D visualization of folds helps experts to identify different sulcal patterns but fold variability is so high that the distinction between these patterns sometimes requires the definition of complex criteria, making manual classification difficult and not reliable. However, the assessment of the impact of these patterns on the functional organization of the cortex could benefit from the study of large databases, especially when studying rare patterns. In this paper, several algorithms for the automatic classification of fold patterns are proposed to allow morphological studies to be extended and confirmed on such large databases.
, 2018; Crosson et al. , 1999; Derrfuss et al. , 2012; Grosbras et al. , 1999; Lopez-Persem et al. , 2019; Segal and Petrides, 2013; Watson et al. , 1993; Weiner et al. , 2014; Zlatkina et al. , 2016 ). Such results suggest that each local folding pattern may be the indicator of a specific functional organization. This predictive power would result from the impact of architecture on the dynamics of folding during development  ( Foubet et al. , 2019; Llinares-Benadero and Borrell, 2019; Mangin et al. , 2019; Van Essen, 1997 ). Our goal in this paper is to deliver a tool helping to exploit massively the rare findings of the past, using already annotated databases of patterns. We hope that this will motivate the search for new patterns of interest, and later on the development of new methodologies to automate this quest.
, 2014 ). From a computational point of view, the identification of these two types of sulcal patterns is a challenging task because of their complex structure and great variability. While a template pattern can be drawn or inferred from an averaging process, the actual shapes in individual subjects may correspond to complex distortions of this template. Regarding the sulcal pattern of the ACC, it can be classified with structural MRI  ( Cachia et al. , 2016; 2014 )  in two different types ( Fig. 1  ): a "single " type, with only the cingulate sulcus, and the "double parallel " type, with an additional paracingulate sulcus. Automating the recognition of these patterns is particularly difficult because of their high variability ( Fig. 2  ). For this purpose, we have a database of 348 hemispheres, including 207 with the double parallel pattern, of which the cingulate and paracingulate sulci have been manually labeled, by one of the team investigating the meaning of these alternative patterns.
4  ), the training database is limited (19 controls and 38 patients with dysplasia, i. e. 114 hemispheres) and the proportion of hemispheres with a PBS is particularly unbalanced (22 subjects, including 1 control and 21 patients with dysplasia, have at least one of the two hemispheres with PBS, i. e. 28 hemispheres with PBS in the database). Thus, these two types of patterns allow us to see the performances of the proposed methods in two fundamentally different cases: for one, the training database is large and balanced, for the other, it is small and unbalanced, which is a recurrent problem when studying rare patterns. Many other cortical folding patterns exist  ( 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. , 2019; Sprung-Much and Petrides, 2018; Zlatkina and Petrides, 2010 )  and the methods proposed in this article are generic enough to be applied to them. Note that the two chosen patterns are not considered as more interesting than these other patterns but our previous studies had delivered the two annotated datasets used in this article, providing the opportunity to design an automatic method to use the findings on a larger scale. More importantly, these two patterns have been chosen because they are instances of the two very different kinds of situations faced when exploring the cortical folding patterns: 1) the stratification of the whole population using a small number of patterns, two subgroups in the case of ACC; 2) the detection of a rare pattern, usually considered has the signature of a rare developmental event, in the case of the PBS.
Databases The Morphologist pipeline of the BrainVISA toolbox, freely available at www. brainvisa. info , was used to segment and provide 3D graphical representation from raw MRI data. This 3D reconstruction of cortical folds was used to manually visualize and label brains. Intra-and interrater reproducibility of ACC and PBS pattern labelling have been described previously in  ( Cachia et al. , 2016 )  and  ( Mellerio et al. , 2014 )  respectively. The two databases studied are described below.
The ACC sulcal pattern was visually identified for each individual hemisphere, following the procedure described in  ( Cachia et al. , 2014 )  and previously used to label a part of this study's database, as published in  ( Cachia et al. , 2016 ). The learning base was composed of 348 identified ACC sulcal patterns, including 141 "single " types and 207 "double parallel " types. The cingulate and paracingulate sulci were manually labeled by one of the team investigating the meaning of these patterns. Further details on this database has been described previously  ( Cachia et al. , 2016; Chakravarty et al. , 2015; Giedd et al. , 1999 ). The study was approved by the National Institute of Mental Health review board. Written informed consent was obtained from parents and controls older than 18 years, and written informed assent was obtained from minors.
In addition to the 15 PBS identified by the experts (including one belonging to a control subject), some hemispheres have been identified as having an intermediate pattern when they show a similar pattern which is not sufficiently pronounced to be classified as such. In order to slightly rebalance the number of samples in each class, intermediate patterns are considered as PBSs in this study. Among the 114 hemispheres in the database, 28 have the PBS (including 13 intermediate patterns, all belonging to epileptic subjects). We manually labeled the central sulcus, its ascending branch and the pre-central sulcus forming the PBS. Further details on this database has been described previously  ( Mellerio et al. , 2014 ). This study was found to conform to generally accept scientific principles and ethical standards by the Ethics Review Committee of Ile de France III.
Method In this section, the first part will be devoted to the method used to represent cortical folds from structural MRIs. Then, the second part will describe several algorithms that automatically classify the folding patterns from the folds representation described in the previous part. In this study, three different approaches are proposed: the first one based on a Support Vector Machine (SVM) classifier, the second one on the Scoring by Non-local Image Patch Estimator (SNIPE) proposed in  ( Coupé et al. , 2012 )  and the last one on 3D Convolutional Neural Network (CNN). Finally, the third part will describe how the three obtained models are evaluated and compared. The code implementing the methods described in this section is available here: https://github. com/brainvisa/morpho-deepsulci.
When manually classifying the ACC and PBS patterns, the same morphological criteria were used for the right and left hemispheres. Therefore, for the three approaches presented in this article, the right hemispheres have been flipped in order to be superimposable with the left hemispheres. This significantly increases the size of the training database. However, this manipulation is questionable when applying the methods presented in this article to other folding patterns, as it may actually decrease the performance of the models due to the natural hemispherical asymmetry that can unnecessarily increase the variability of the patterns. With regard to ACC patterns, the single and double parallel types are respectively characterized by the absence and presence of the paracingulate sulcus. Thus, the objective is to detect the presence of the paracingulate to perform the classification.
Classification approaches 3. 2. 1. Support vector machine (SVM) The idea of this first approach is to use the distances from the patterns-of-interest of the training database to the image to be classified to train a SVM classifier ( Fig. 6  ). Registration In order to realign the patterns-of-interest to the hemisphere to be classified, the Iterative Closest Point algorithm  ( Besl and McKay, 1992 ) , with the robust implementation of  Holz et al. (2015)  , is used. The voxels of the fold skeleton, extracted by the Brain-VISA/Morphologist pipeline, are used as point clouds to perform the registration. In order to constrain the registration of the patterns-of-interest, they are registered with their attached sulcus (i. e. cingulate sulcus for paracingulate sulcus, and central sulcus for PBS), which has been manually labeled.
This distance corresponds to the average Euclidean distance from the voxels of the pattern-of-interest to their nearest voxels in the new hemisphere. The distance is low if the hemisphere to be classified also has the pattern-of-interest. Classifier The calculated distances are used to train a Support Vector Machine (SVM) algorithm to classify hemispheres with or without the pattern-of-interest. A vector containing the distances to the hemisphere to be classified is used to train the classifier: each feature corresponds to a pattern-of-interest in the training database. Thus, this vector can contain up to 28 features for the PBS and 207 for the ACC. Note that during cross validations, the patterns-of-interest being in the group of subjects for testing are removed from the vector (e. g. for the 10-fold cross validation, the vector therefore contains 25 or 26 features for the PBS and 187 or 188 for the ACC).
The Radial Basis Function is used as a kernel. Penalty parameter  of the error term and kernel coefficient  are determined by inner cross validation. The SVM algorithm used corresponds to the one implemented in the scikit-learn library  ( Pedregosa et al. , 2011 ). In this implementation, a weight   is assigned to each class  in order to calculate the value of the parameter  of the class  such as   =   *. In order to compensate for the potential imbalance of the classes in the input data, these weights are automatically adjusted to be inversely proportional to class frequencies. Thus, the number of elements   belonging to class  among the  elements of the training database is used to calculate the weight   such that   = ∕(2 *   ). Hyperparameters The hyperparameters are optimized by 3-fold crossvalidated grid-search over a parameter grid.