dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis All experiments were performed in triplicate, and the data were reported as the mean ± standard deviation (SD). The results obtained for subsequent groups were analysed using one-way analysis of variance (ANOVA) followed by a post hoc Tukey's test. The differences among the groups were considered statistically significant when the p values were 0. 05 or less. This part of statistical analyses was done using STATISTICA v. 13. 3. (TIBCO Software Inc. , Palo Alto, CA, USA). The mixed hierarchical approach, including principal component analysis (PCA) and Partial Least Square (PLS) models, was used to reveal the correlation structure between the investigated parameters and to improve the interpretability of results  [20, 21]. Firstly, new variables were created by blocking original data (concerning selenium and its compounds' concentrations in the evaluated kale sprouts (see the comments in discussion), their water-soluble fractions and in proteolytic extracts  [14]  into meaningful subgroup, for which PCA model was built.
Chemometric assay In this paper we evaluated influence of six new organic selenium compounds on the synthesis of active molecules and on biological activity of the fortified kale sprouts. To better explained the observed effects of those newly synthesized compounds on the above-mentioned activities, we decided to additionally include the results published in our recent study  [14] , together with the data presented in this paper, in one chemometric analysis. The block of selenium and its compounds concentrations in the evaluated kale sprouts contained following parameters: total selenium concentration in sprouts (TSe), selenium concentration in proteolytic extracts (SePE), the concentrations of selenomethionine (SeMet), Se(IV) and Se-methylselenocysteine (SeMetSeCys) in sprouts -expressed as  The PLS model fulfilling leave-one-out cross-validation criteria was constructed for parameters listed in Statistical approach section.
A 1D global twin-screw extrusion model, implemented in numerical software, Ludovic ® , was applied to predict extrusion variables and, therefore, to design various starchy products with targeted structure and properties. An experimental database was built with seven starchy food formulations for manufacturing dense and expanded foods made from starches, starch blends, breakfast cereals, pulse crop ingredients such as pea flour, fava bean flour, and fava bean starch concentrated, and wheat flour enriched with wheat bran. This database includes the thermal and physical properties of the formulations at solid and molten states, melt viscosity model, extruder configurations and operating parameters, and extruded foods properties. Using extrusion and viscosity models, melt temperature (T) and specific mechanical energy (SME) were satisfactorily predicted.
Computation of the flow parameters is done separately for each type of screw element and for the die components. The elementary models are linked together to obtain a global description of the flow along the extruder. This 1D global model has been implemented into a software called Ludovic ® v7. 0. 0 Classic Edition (Sciences Computers Consultants, Saint Etienne, France). It is quite straightforward to provide predictions of flow variables with simple computing resources and time: about a few seconds per simulation using a laptop configured with Intel ® Core™ i7-7600U processor (dual core). A fairly good agreement was obtained between simulation results from this 1D global model and a full 3D FEM model  [18]. Despite significant progress in modeling and simulation, the design of extruded products at the industrial level is still based on a trial-and-error approach.
A detailed experimental database was built, including input and output data for extrusion simulation and extruded product structural and functional features. After describing the 1D extrusion model, virtual extrusion trials were performed, and validation steps were systematically detailed. The extruder operating curves representing a variety of predicted extrusion variables in the function of operating parameters were built. Then we strived to show how products with targeted structural and functional properties can be manufactured using the relations between extruder operating charts, predicted extrusion variables, and extruded product features, including biopolymer structural changes.
Building Database Database for twin-screw extrusion simulation has been collected from literature and our own experimental results  [31] [32] [33] [34] [35] [36]. Due to the large number of experimental works, case studies were selected by applying several criteria: (1) presence of melt viscosity model and melting transition data as input, (2) existence of experimental data of melt temperature and specific mechanical energy (SME), and (3) properties of final products exhibiting clear relations with extrusion variables.
Most results on the melting temperature (T m ) as a function of moisture content of studied materials can be retrieved from the literature [32,  34, 42, [44] [45] [46] [47]  (Appendix A, Figure  A3 ). The melting point of maize starches, having different amylose content (5%, 23%, 47%, and 70%), were obtained from the data set of waxy maize (amylose content of 5%); wheat (25%), smooth pea (35%), and wrinkled pea (75%) starches. The melting temperature dataset of breakfast cereal formulations (HB, CB), and fava bean flour and starch concentrate were determined experimentally as the peak of the DSC melting endotherm using standard procedure [32,48] (Appendix A, Figure  A3 ). (2) Rheological properties of molten starchy foods The variation of starchy melt viscosity (η) with shear rate. γ is generally described by the Ostwald-de Waele power law  [25] : η = K.
Conclusions Twin-screw extrusion processing of starchy products was simulated using the 1D global extrusion model (Ludovic ® software v7. 0. 0 Classic Edition (Sciences Computers Consultants, Saint Etienne, France)), and melt rheological model as input. A large data set has been built, including results for extruded products' properties and extruder variables (temperature and specific mechanical energy), taken as output variables. Satisfactory correlations between process parameters predicted extrusion variables and features of extruded foods, such as structural and functional ones, justify the use of the extrusion model as a computer-aided tool for designing starchy food by extrusion. Moreover, the capacity for predicting these features is extremely vital to reducing time and labor costs in industrial foods R&D.
Further possible extension includes the processing of plant proteins for meat analogues. Besides classical structural and functional properties, the textural and nutritional features, such as starch and protein digestibility, inactivation of anti-nutritional factors, and destruction of pathogenic microbes and vitamins, will be tackled. The rheology database will be updated with melt viscosity models encompassing a wide range of moisture content, temperatures, and SME, taking into account protein and fiber content, as well as protein structural changes and starch-protein morphology.
The calibration of the instrument was systematically performed before any new series of analysis, with galactomannans (DP3 to DP13) for the reflector mode and IMOS (DP 3 to DP 50) for linear mode. The mass spectra were automatically processed with Flex Analysis 3. 0 software (Bruker Daltonics, Bremen, Germany) and only the ions with a signal-to-noise (S/N) ratio above 5 were considered. The results were analyzed and represented using the Flex Analysis 3. 0 analysis software (Bruker Daltonics, Bremen, Germany). For ESI-qTOF experiments, a fraction of DP6 IMOS obtained by liquid chromatography (see Section 2. 4) was infused into the ESI system at a concentration of 50 µg/mL. The sample was introduced into a Synapt G2 Si HDMS system (Thermo Fischer Scientific, Waltham, MA, USA) at a flow rate of 5 µL/min with methanol/water 1:1 (v/v) as the solvent.
Measurements were performed in the positive and negative ionization modes. The fragments were produced and measured in the collision cell/transfer unit. Spectra were recorded for 30 s (low DIC1 energy plan). Collision energy was set based on the signal/noise ratio observed for the obtained fragments. The acquired data were stored and processed using MassLynx 4. 1 software (Waters, Milford, MA, USA). Spectra annotations were made using Microsoft PowerPoint Professional Plus 2019.
The scanning of 2D-PAGE gels was conducted using Scanner II (Cytiva) coupled with Labscan software from the same provider. Image and statistical analyses were performed with Progenesis Samespot software from NonLinear Dynamics (Newcastle, UK). Images of the IMOS condition were compared with the glucose condition as control. Spot intensity was normalized to the entire spot's intensity to overcome experimental variability. Final values for the expression ratios of specific protein spots between IMOS condition and control were determined for >1. 3-or <1. 3-fold differences. The statistical significance of each expression level was calculated using Student's t-test on the logged ratio.
Peptide Identification For protein identification, the Nano-LC MS/MS results were analyzed by Protein Pilot software (version 4. 5, Sciex, Framingham, MA, USA) to be search for in the UniProt database (release 2014_09) via the Mascot software (version 2. 2, Matrix Science, London, UK) with the following criteria: species Bacillus subtilis, 0. 5 Da tolerance for peptide and peptide fragment mass, a single missed cleavage site allowed during trypsin digestion, and carbamidomethylation of cysteine residues (due to the alkylation of -SH groups by iodoacetamide) and oxidation of methionine as variable modifications. Protein identification was validated if at least 2 peptides had a score of greater than 25, or 1 peptide had a score of greater than 50 at a confidence level of at least 95%. A figure of the proposed model based on the proteomic analysis was produced with Microsoft PowerPoint Professional Plus 2019.
Growth Ability of B. subtilis Probiotic Strains on Prebiotics We selected five Bacillus strains marketed as probiotics; then, we first determined their ability to use prebiotic oligosaccharides as a carbon source for their growth. The tested prebiotics and suppliers are presented in Table  S1  (see Supplementary Information). The growth measurements of the Bacillus strains on each oligosaccharide was performed on agar minimum medium containing a single prebiotic source. After 36 h of growth, the analysis was qualitatively performed by the simple observation of colony sizes in comparison to a glucose reference, and the results are reported in Table  1. Excepted with lactulose, all the prebiotics tested seemed to allow the growth of the studied strains, with the exception of B. cereus CH. Among all of these oligosaccharides, galactooligosaccharides (GOSs), soy oligosaccharides (SOSs) and isomaltooligosaccharides (IMOSs) appear to be the most effective substrates on Bacillus strains, and seem to promote the production of exopolysaccharides or EPSs (larger halo observed around the colonies, data not shown).
Study Population This study was conducted within the EDEN ("Étude des Déterminants pre-et post-natals de la santé de l'ENfant") study, an ongoing bicentric French mother-child cohort. The detailed study design has been previously described  [28]. Briefly, in two maternity hospital units, in Nancy and Poitiers, 2002 pregnant women were recruited during their hospital visit before 24 weeks of gestation, between 2003 and 2006. Women were eligible if they had a singleton pregnancy, no diabetes prior to pregnancy, no intention of moving out of the city within the following 3 years and were capable of reading and writing in French. For the present analyses, study populations were restricted to women with complete data for DP consumption during pregnancy, who provided perinatal biological samples afterwards and whom biological samples had a valid fatty acid composition, i.
Significance level was set at alpha = 0. 05 for all tests except for interaction test (alpha = 0. 10). No imputation on the outcomes, the exposures, the confounders and the moderator (missing data = 1-2% for covariates) was performed. As all our tested associations were hypothesis driven and as the studied fatty acids are not independent of each other (% of total fat level in a given biofluid), we did not perform any correction for multiple testing. Analyses were performed using SAS (version 9. 4; SAS Institute, Cary, NC, USA) and forests plots were obtained using R software (version 4. 1. 1; R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/, accessed on 16 March 2022). The analysis plan was pre-registered online on OSF (Open Science Framework)  [37].
Acknowledgments: We thank the  CNIEL scientific steering committee  for their valuable inputs, whose members are  C. Boyer ,  C. Marmonier ,  M. Guerville ,  M. Lemaire ,  C. Puel ,  A. Boulier. The authors thank the EDEN mother-child cohort study group, whose members are I. Annesi-Maesano ,  J. Y. Bernard ,  J. Botton ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A. Forhan ,  X. Fritel ,  A. Germa ,  V. Goua ,  R. Hankard ,  B. Heude ,  M. Kaminski ,  B. Larroque ,  N. Lelong ,  J. Lepeule ,  G. Magnin ,  L. Marchand ,  C. Nabet ,  F. Pierre ,  R. Slama ,  M. J. Saurel-Cubizolles ,  M. Schweitzer ,  O. Thiebaugeorges.
A C T Pepsin diffusion in food particles during gastric digestion is one of the main factors limiting proteolysis kinetics. Diffusion coefficients of pepsin are needed as input parameters for in silico models of digestion, but no values are currently available in real foods. The challenge of this study was to apply the Fluorescent Recovery After Photobleaching (FRAP) technique to determine diffusion coefficients of fluorescently labeled (FITC)-pepsin in four realistic food matrices with complex and heterogeneous structures (Custard, Pudding, Sponge cake and Biscuit), but an identical composition on a dry matter basis. The effective diffusion coefficients determined for FITCpepsin at 37 • C ranged from 48 ± 14 to 2 ± 1 μm 2. s -1 for Custard and Biscuit, respectively. A modelling approach based on the stretched exponential equation generated a very good fit of the experimental dataset as a function of dry matter content of the matrix.
This set of experiments was also conducted three times on each type of matrix, resulting in an average effective diffusion coefficient value for pepsin calculated from 30 replicate data per product (n = 30). Control FRAP experiments were carried out in the same conditions to determine the diffusion coefficient of FITC-pepsin in water using a 0. 5 mg/mL FITC-pepsin solution. Data were analyzed using FIJI software, according to the method described in  Thévenot et al. (2017). Briefly, both a pure isotropic diffusion in a homogeneous medium, and a two-dimensional and Fickian diffusion process were assumed for the FRAP data modelling. Data fitting via nonlinear least squares was conducted using RStudio software, allowing the estimation of the effective diffusion coefficient of pepsin (D eff ).
Statistical analysis and modelling of diffusion data The dataset of effective diffusion coefficients of FITC-pepsin determined in the four different food matrices and the water control sample, was analyzed thanks to RStudio software using a one-way analysis of variance (ANOVA) and a Tukey's paired comparison test, at a 95% confidence level. Then the reduced diffusion coefficients were fitted using the empirical stretched exponential equation, based on a scaling low and considered as a "universal" equation by  Phillies (2016)  as: D r = D eff D 0 = exp ( -α × c ν ) [Eq. 1] where α and ν represent the scaling parameters which should depend on the size of the diffusing solute, and c represents the number concentration of obstacle (the polymer). Data fitting was performed using Excel 2016 and its evolutionary solving method by minimizing the sum of the squared distances between values of the model predictions [Eq.
38 10 -23 J. mol -1. K -1 ), T the absolute temperature (K), η the viscosity of the medium (Pa. s), and R h the hydrodynamic radius of the diffusing molecule (m). Given that water viscosity is 0. 6915 mPa. s at 37 • C (310 K), and 1 mPa. s at 20 • C (293 K)  (Braga et al. , 2004) , the diffusion coefficient of pepsin in water should be theoretically 1. 44 higher at 37 • C than at 20 • C. Somaratne et al. (2020b) used the same FRAP protocol and data analysis as described in the present study. They measured a diffusion coefficient for FITC-pepsin in water equal to 104. 5 ± 10. 7 μm 2. s -1 at a temperature of 20 • C. In the present study, we measured an experimental diffusion coefficient of pepsin in water equal to 138 ± 11 μm 2. s -1 at 37 • C, which is 1. 3 times higher than the value  previously obtained at 20 • C.
Acknowledgment The authors thank  Liot (Pleumartin, France) and Sotexpro (Bermericourt, France)  for providing raw ingredients (egg powders and pea flour respectively). This work was supported by the  French National Research Institute for Agriculture, Food and the Environment (INRAE)  and  Carnot Qualiment. The ZEISS LSM880 confocal microscope was funded by the  European Union (FEDER) , the  French Ministry of Education, Research and Innovation , the  Regional Council of Brittany  and  INRAE.
Materials and methods Horse spleen ferritin and sample preparation were described in a previous report. Its sequence is displayed in Table  S1  of the supporting information. The sample described in this paper is the F-Pu complex with an F:Pu ratio equal to 1 : 550 and described by  Zurita et al. (2021). This ratio ensures that saturation of the protein complexation functions has not been reached. XAS experiments were carried out on the MARS beamline at the SOLEIL synchrotron facility which is dedicated to the study of radioactive materials. All the measurements were recorded in double-layered solution cells (200 ml) specifically designed for radioactive samples at room temperature. XAS data were acquired at the Pu L III edge (18 057 eV). Data were processed using the Athena code of the Demeter package (version 0.
9. 25; Ravel & Newville, 2005). The site of Pu3(B) obtained in molecular dynamics (MD) simulation B was used as a model. EXAFS signal was fitted in R space between 1 A ånd 6 A ˚without any additional filtering after Fourier transformation using a Hanning window in k 2 (2. 7-10. 8 A ˚À1 ). In all the fits, only one global amplitude factor S 0 2 and one energy threshold factor e 0 were considered for all contributions. The agreement factor R (%) and reduced quality factor 2 (Q) are both provided as an indication of the fit quality. Simulations of the six Pu centers obtained from simulation B were performed with the Feff9 code using an energy threshold of 8 eV as described by  Zurita et al. (2021). Modeling: MD simulations were performed using the Amber program package  (Case et al. , 2015)  with an ff99SB force field applied to the protein.
Molecular dynamics simulations of the ferritin-Pu complex The F-Pu model was obtained from classical MD simulations and is reported by  Zurita et al. (2021). For this model, only the L subunit has been used (L subunit of cadmium-bound ferritin, PDB entry 1aew;  Hempstead et al. , 1997). The model is established by taking this subunit and starting from Pu 4+ (CO 3 ) 5 6À , the anionic form in which Pu has been experimentally introduced. The results of the MD simulation of the F-Pu complexes are shown in Fig. 1. In simulation A, the L-chain structure of 1aew was used by replacing six Cd atoms initially present with Pu cations. In simulation B, six Pu cations were randomly distributed around the metal-free Lchain (see also Fig. S1 ). Carbonate (CO 3 2À ) and Na + ions were added to mimic the experimental carbonate concentrations and to neutralize the charges.
e. a random distribution of plutonium carbonate in contact with apo-ferritin (with no saturation of the protein complexation functions). But this conclusion at this point is only qualitative, keeping in mind that EXAFS sees averaged configurations and/or an average of complexes. EXAFS data fitting of F-Pu-exp was performed [with the model of Pu 3(B)] as described in our previous report and briefly above. This Pu site interacts with the different environments: water, amino acids and carbonate  (Zurita et al. , 2021). From the fit of the EXAFS data, the total coordination number (CN) is difficult to determine with accuracy better than ca 20% because An(IV) CNs are large, flexible and can range from 8 to 12. We chose to fix the value to an average of 10. Best fit data already shown in our previous report give an average Pu-O distance equal to 2.
1 A (for Th-O and Pu-C). One should keep in mind in this comparison that, in all models, at least one water molecule is included and therefore the average An-O distance results from an average between the water and protein contributions. The presence of a carbonate anion within the An coordination sphere is possible but is difficult to assess according to this dataset. One may propose at this point that Pu(IV), Th(IV) and Fe(II) interact with the protein ring via the carboxylate functions of mainly Asp and Glu amino acids. Fig. S1  of the L subunit shows that the Pu atoms are either directly interacting with at least one Asp and one Glu amino acid (or both) or gravitating close to those residues. Conservation of the glutamate and aspartate residues in both subunits  (Chasteen & Harrison, 1999)  underline their potential role in cation binding.
Introduction and Methods With the development of new computer technologies in the healthcare field, a growing number of healthcare institutions use information systems that gather administrative and medical data on patients. NETSoins, edited by Teranga Software, is one of these systems and is used by more than 3 000 nursing homes (NH). The goal is to use anonymous data from this NH residents record software to develop algorithms able to predict several adverse health events that are potentially preventable by appropriate health interventions. As a first step, the prediction is focused on the occurrence of a resident's first pressure ulcer (PU). A PU is a lesion of the skin related to prolonged compression between a bone and the support on which the patient rests. This condition is painful and a source of infection, and it significantly impairs quality of life  [1].
In this process, we have retrieved information from medical records of more than 100 000 residents free of PU at admission in the facility. Variables with potential interest were determined with expert geriatricians and kept in the analysis based on the completion rate. A training dataset was then set up with the parameters of the residents and two classes were defined: experience the PU or not. An important issue is to take into account the transformation of an event database into data that can be used by statistical learning, while keeping a medical meaning. To elaborate a BN classifier, we learn the structure of the network from estimates on the training base, and then compute the probability of belonging to a class knowing the observations of each variable on an individual. For the evaluation, we compute the F-score.