dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the biological question could be broadened by studying whether the inhibition effect is conserved within a serotype. Thus, a label-free proteomic study was performed to identify the STEC surface proteins associated with the MFGM fraction after natural creaming. The major MFGMPs, such as xanthine dehydrogenase/oxidase, butyrophilin, and lactadherin  (Ortega-Anaya et al. , 2022)  were identified in all samples. In addition, no proteins from the aqueous phase of raw milk such as casein or β-lactoglobulin were identified. These data confirm that the method used for MFGMP extraction was effective and therefore, the STEC proteins identified are likely to be strongly associated with the MFGM protein fraction. Among the identified STEC membrane-associated proteins, the Outer membrane Accession  protein A (OmpA) and the flagellin (FliC) warranted further investigation (explained below).
, 2015). Our results suggest that OmpA may be more involved in physiological interactions than in adhesive interactions. The flagellin (FliC) was identified only in samples supplemented with surface proteins from O26:H11 str. 21765 (Supplementary Table  S2 ). Regardless of the sample, only one peptide (FDSAITNLGNTVNNLSSAR) related to the FliC protein sequence was identified by MS/MS. This peptide is present in the FliC protein sequences of O26:H11 str. 21765 and O103:H2 str. PMK5 (data not shown). Interestingly, in our previous study, FliC protein was relatively more present in the protein extracts of other strains than in O26:H11 str. 21765  (Bagel et al. , 2022b). We also found that the STEC-MFG association is not always stable and that bacteria can move around and between MFGs, suggesting the involvement of bacterial mobility in the affinity of STEC for MFGs (data not shown).
Acknowledgments The authors thank the staff of the  French National Reference Laboratory  on pathogenic E. coli (including STEC),  Grégory Jubelin  for bacterial mutagenic tools,  Adeline Page  and  Cécile Hilpert  from  Protein Science Facility, SFR Biosciences , for proteomics analysis ( SFR BioSciences ,  CNRS UAR3444 ,  Inserm US8, UCBL, ENS de Lyon, Lyon, France ), and the financial support from  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé ,  National Alliance for Life Sciences and Health)  within the framework of the cancer plan for Orbitrap mass spectrometer founding. We also thank  Leah Cannon  for the English language editing of this manuscript.
This dependence on microbiome databases derived from high-income settings therefore limits the interpretability of the malnourished microbiomes generated from individuals in low-and middle-income countries (LMIC) where gut microbiomes, and the species/strains within, are less well characterized  (96). However, growing evidence from cross-sectional and short-term longitudinal studies in LMIC reveals microbiome differences that differentiate undernutrition from healthy growth.
No barcoding was used All sequences retrieved from the Illumina reactions were trimmed to eliminate those reads with quality scores less than 0. 05 and/or sequences with ambiguous nucleotides. 14, 15 Data were analyzed using the RNA-seq module of the Bioinformatics Workbench software package (CLC) (http://www. clcbio. com/index. php?id=1240). Circos figures in the main text (Figs. 1 2 3 ) and in the supporting information figures (Figure  S7 ) were drawn following the instructions provided at http://www. circos. ca/. For each Tn-insert, the significance of the difference in the proportions of the RPKM from one environment to the other was estimated using the statistical test defined by Kal et al. 16  All P-values were adjusted for multiple comparisons using Bonferroni corrections. Analysis by operon: for this analysis, we took into account possible polar effects created by the Tn-insertions into genes transcribed from a single promoter and considered that fitness is attributable to the inactivation of operons rather than individual genes.
Statistical analysis Nonparametric data were evaluated by the Mann Whitney U test for 2-group comparisons and multi-group comparisons by the Kruskal Wallis test with Dunn's multiple comparison test for pairwise comparisons. Parametric data were analyzed by t-tests (for 2-group comparisons) or ANOVA with Tukey's multiple comparison test for pair-wise comparisons. Survival analysis utilized the Kaplan-Meier method. All analyses, apart from the Tnseq analyses, used GraphPad Prism 6. 0 (GraphPad Software, San Diego, CA).
In both  outcome scenarios, the antibodies to PNAG provided significant protection (P < 0. 01, unpaired t-test) (Table  2 ). While PNAG was previously described to be expressed by several pathogens,  11  no systematic analysis has been performed to date to find the prevalence of the pga genes among the species where these genes have been reported in some isolates. Therefore, we performed a bioinformatic analysis of the presence of the PNAG encoding genes pga A, B, C, and D in the following bacteria that can be either commensal or pathogens depending of the clinical circumstances: E. coli, Klebsiella pneumoniae, Citrobacter freundii, and Enterobacter cloacae. We used 1294 E. coli genomes published in 41 and all genomes downloaded from NCBI database server that contained metadata about the host and its environment for K.
Acknowledgments Funding: SP received an award from the  Groupe Pasteur Mutualité , DR received an award from the  Hearst Foundation , OD was supported by  AI-026289  from  National Institute of Allergy and Infectious Diseases , CCB received an unrestricted gift from Alopexx Vaccines, DS received an award from the  ANR  ( AAPG2020-Seq-N-Vaq, France ), DS received an award from the  Charles H. Hood Foundation.
Participants A sample of young men (between 21 and 30 years old) were recruited via media advertising and word-of-mouth. Exclusion criteria (established prior to data acquisition) were lefthanded, a history of brain trauma, neurosurgical or psychiatric disorder, olfactory dysfunction, addiction, asthma, nasal polyps, claustrophobia, MRI contraindications, smoker, allergic skin reaction to fragrances and scores at the "Sniffin' Sticks" test  (Kobal et al. , 1996)  falling below the 25th percentile (see below for details). We did not perform a power analysis. The study was approved and carried out in accordance with the recommendations of the ethics committee of the Faculty of Medicine of the University of Li ege. All participants completed a written informed consent in accordance with the Declaration of Helsinki and its later amendments.
ENT exam The standard "Sniffin' Sticks" test  (Kobal et al. , 1996; Hummel, Sekinger, Wolf, Pauli, & Kobal, 1997 ) is a widely used test aiming to evaluate olfactory performance, including three subtests: olfactory threshold, odor discrimination and odor identification. A total score can be calculated by summing the three subscores, ranging from 1 to 48 points. Scores of the individuals are compared to standard values recently updated by Oleszkiewicz, Schriever, Croy, Hahner, and Hummel (2019) (group C including people aged 21e30). These normative data can be used in clinical practice, as well as in scientific quantitative assessment of olfactory performance.
Data pre-processing Data preprocessing was performed using a locally developed pipeline using Statistical Parametric Mapping (SPM) 12 (https://www. fil. ion. ucl. ac. uk/spm/software/spm12/) and Artifact Detection Tools (ART) (http://web. mit. edu/swg/ Fig. 2  e Schema of the diffuser system in the MRI session. PTFE ¼ polytetrafluoroethylene. c o r t e x 1 6 5 ( 2 0 2 3 ) 1 1 9 e1 2 8 software. htm). We performed slice timing correction, followed by the realignment of all the functional volumes to the first volume and, in a second pass, to their average. Estimated motion parameters were then used for artifact detection. An image was defined as an outlier or artifact image if the maximum voxel displacement (composite motion measure in ART) was greater than 2 mm from the previous frame or if the global mean intensity in the image was greater than 3 standard deviations (SDs) from the mean image intensity for the entire scans.
Realigned functional images were registered to the bias-corrected structural image in the participant space (rigid-body transformation with normalized mutual information cost function). After extracting white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) masks, all the data and masks were normalized into the standard stereotaxic Montreal Neurological Institute (MNI) space (MNI152 with 2 mm resolution), and functional data were smoothed using a Gaussian kernel of 6-mm full width at half-maximum. WM and CSF masks were further eroded by one voxel. For noise reduction, we modeled the influence of the noise as a voxel specific linear combination of multiple empirically estimated noise sources by deriving the first five principal components from WM and CSF masked functional data separately.
These nuisance regressors together with detected outlier volumes, effect of rest/task period, motion parameters and their firstorder derivative were regressed out from the voxel-level time series and the denoised time series were temporally band-pass filtered (. 008e. 09 Hz) using CONN's denoising procedure. We have regressed out task effect in CONN denoising as per standard recommendation of CONN toolbox. To assess the brain connectivity in a data driven approach, voxel-wise whole-brain global functional connectivity (GFC) was conducted using the CONN-functional connectivity toolbox. The GFC is a measure of global coherence that computes the average connectivity of every seed voxel to the entire brain. Constant and linear trend regressors were used to create a design matrix in the first-level general linear model (GLM).
Acknowledgments The authors thank the whole staff from the  MRI department at University Hospital of Li ege. This work was supported by  Valeo , the  BIAL Foundation , the  Belgian National Funds for Scientific Research (FRS-FNRS) , the  University  and  University Hospital of Liege , the fund L eon Fredericq, the  Society for Psychical Research, the Fund Generet , the  King Baudouin Foundation ,  DOCMA  project ( EU-H2020-MSCAeRISEe778234 ), the  AstraZeneca Foundation , the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ), the  European Space Agency (ESA)  and the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Programme , the  Center-TBI  project ( FP7-HEALTH-602150 ), the  Public Utility Foundation 'Universit e Europ eenne du Travail', "Fondazione Europea di Ricerca Biomedica", the Mind-Care Foundation , the  Mind Science Foundation  and the  European Commission.
Another form of sensory evaluation data describes the similarity between observations based on their similarities or dissimilarities (or distance); one example of such data is obtained from a sorting task where the participants are asked to sort the observations into groups. This type of multiblock data also includes multiple data tables associated with the participants and are often represented by symmetric tables that describe pairwise relationships between the observations. To analyze such data sets, two other methods called DiSTATIS and CovSTATIS extend STATIS to extract orthogonal components from, respectively, distance and variance-covariance matrices. 2 DiSTATIS and CovSTATIS compute a weighted sum of all the tables to obtain an optimal table that best represents the general pattern.
Just like in STATIS, the optimal table is computed by weighting the tables with a common pattern more and those with a rarer pattern less. Because this optimal table is symmetric with matching rows and columns, instead of using the SVD that analyzes rectangular matrices, DiSTATIS and CovSTATIS use EVD to decompose it. Current data sets now routinely include a large number of items such as participants, observations, and attributes. Often, the analysis of these large data sets gives results that are difficult to interpret because (1) they give complex components that involve a large proportion of the items under scrutiny, and (2) the data include heterogeneous data tables which are poorly represented by a single pattern. The first issue can be solved by obtaining components that have a simple structure.
Different from sMFA and sSTATIS, DiSTATIS/ CovSTATIS are sparsified by extending sEVD because the data tables they analyzed are square and symmetric with rows and columns that match each other and should, therefore, be kept or eliminated in pairs. In sparse DiSTATIS (sDiSTATIS) and sparse CovSTATIS (sCovSTATIS), the algorithm also aims to identify homogeneous subsets of tables. To demonstrate these sparse methods, we apply them to two data sets in this paper: One gives the results of a sensory evaluation experiment on food, and the other gives sensory evaluation results of experts and novices on wine tasting. We show how introducing sparsity and orthogonality constraints complements regular methods and allows users to have a rich interpretation of the data structure. We also identify the properties that are preserved or lost after imposing these constraints.
Note that, when S k is a squared Euclidean distance matrix, S k is psd. For CovSTATIS, Sk denotes a correlation or covariance matrix (which is already a psd matrix). In most cases this matrix is also doubled centered to obtained S k as S k ¼ 1 2 Ξ Sk Ξ > ð53Þ (note that, compared to Equation 52, there is no minus sign). If the double centering step is not considered of interest, then S k ¼ Sk for CovSTATIS. From S k , the weights for the tables are computed as in Equation (  46 ), and the compromise matrix is computed as in Equation (47). Finally, DiSTATIS and CovSTATIS decompose the data according to Equation (48). Because DiSTATIS and CovSTATIS analyze symmetric matrices, the factor scores for rows and columns are identical. The global factor scores that represent the variables (i. e. , the rows/columns) in the compromise space are computed as F ¼ S þ P * Λ À 1 2 , ð54Þ and the partial factor scores that represent the variables in the same space but from the perspective of individual tables are computed as F k ¼ S k P * Λ À 1 2 : ð55Þ 3.
| Data To illustrate MFA, STATIS, and their sparse methods, we used a sensory evaluation data set in which a trained sensory panel of eight panelists rated the same eight turkey breast products from Mexico on the same 12 attributes. These raters had been trained to identify and measure specific attributes of products. These attributes were rated on a 0 to 9 10-point Likert scale and included sweet, nutmeg, spicy, yeast, salty, acid, umami, salivating, white meat, juicy, cured, and fatty. In this example, each table corresponds to a rater and describes the nine products (on the rows) rated by the 12 attributes (on the columns); rows and the columns are organized in the same order across tables. F I G U R E 4 Results from STATIS and sSTATIS when the sparsification is performed with the group constraints.
However, when we sparsify DiSTATIS and CovSTATIS by sparsifying the compromise matrix, we perform an sEVD on it. Thus, the elements in pseudo-eigenvectors correspond to products (i. e. , rows and columns of the compromise) instead of assessors (i. e. , tables). The partial factor scores of each table need to be obtained by projecting each table as an out-of-sample data set. Because the projecting algorithm is non-linear with sparsification, linear properties such as the barycentric property, are not guaranteed. Such outcome could be due to the feature of the algorithm itself or of the specific example that we used, and more research is needed to investigate when and why the barycentric property could be lost. Although not demonstrated, it is also computationally possible to (1) sparsify the products (i.
DATA AVAILABILITY STATEMENT All programs and data are available at https://github. com/juchiyu/sDiSTATISpaper. Proof. Since a and b are non-negative vectors, then for their cross-product to be null, all elements of the cross-product need to be null. Therefore, for each element a k b k of the cross-product needs to be null, which means at least one of a k or b k needs to be null. In other words, since a and b are non-negative vectors, then.
Current weight and height were collected in the computerized medical record and used to calculate BMI. The mid-arm circumference in centimetres (cm) was measured by the GP, with the elbow bent at 90 degrees, at the middle of the patient's left arm, using a measuring tape. The calf circumference (cm) was measured when the patient is seated or bedridden, with the knee bent at 90 degrees, by rolling up the measuring tape at its widest point. The BMI, midarm and calf circumferences were collected during the realization of MNA ®. All the data needed for MNA ® items were collected by the GP for the 93 included patients during the clinical examination, the interrogation of the patient and paramedical staff, or by analysing the computerized medical record. In case the patients were unable to answer the questions relating to food intake, eating habits or general assessment, nursing assistants and nurses were interviewed.
Acknowledgements We deeply thank all the staff at  EHPAD Janzé, Janzé, Ille-et-Vilaine, France , for their help in the performance of the study:  Mrs Véronique Escop ,  Mrs Guérin ,  Mrs Martine Jouzel ,  Dr Gilles Quiboeuf ,  Mr Man-Hung Vuong , and all the nurses and nurse assistants. All these people have given their permission to be named in the acknowledgement section.
96 g/100 g, and ratio of the standard deviation values RPD = 3. 60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. CONCLUSIONS: This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits.
HSI setup The monitoring mode of the HSI system used in this study and all required steps for the acquisition of HSI data are described in the RTBfoods' standard operating protocol (SOP). 39 he system is based on pushbroom imaging equipment. The main components include a spectrograph with prism-gratingprism optical structure (ImSpector, N17E; SPECIM, Oulu, Finland), a 12-bit CCD camera (V-light; Lowel Light Inc. , New York, NY, USA), 150 W tungsten halogen lamps (Fibre-Lite DC950 Illuminator; Dolan Jenner Industries Inc. , Boxborough, MA, USA), and a translation LabScanner with dimension (L × W) of 40 cm × 20 cm by a step motor. The harmonious work of the integral system is assured by using LumoScanner control software (SPECIM). The assembly disperses the incoming line of light into the spectral and spatial matrices and then projects them onto the CCD.
Multivariate data analyses Multivariate analyses were applied on average spectra using R software (version 4. 1. 0)  56 coupled with packages such as prospectr  43  and rchemo. 44 The dataset was split into two sets using the duplex algorithm: a learning set, which corresponds to two thirds of the complete dataset, and a test set, which corresponds to the remaining third of the samples. Before the model development, different combinations of preprocessing were applied to the spectra. The best performances were obtained with Savitzky-Golay smoothing with a parameter width of 15 and polynomial order 2, a baseline correction with the parameters ⊗ = 5 and P = 0. 001, and, finally, normalization reduction using the standard normal variate method. Different chemometric methodspartial least-squares regression (PLSR),  45  K nearest neighbors regression,  46  support vector machine regression,  47  and CovSel multiple linear regression (CovSel_MLR)  48  have been tested in order to obtain the best model for the quantification of quality traits from the spectral information.
Prediction of water absorption and texture (Area) Performances of models of prediction PLSR was applied to average spectra (n = 109) of fresh slices of cassava in order to investigate the potential of HSI to predict WAB and texture. Weak prediction performances were obtained for WAB and texture (Area) with, respectively, coefficient of determination for calibration R 2 cal = 0:53, root-mean-standard error for calibration RMSEC = 5. 68% and R 2 cal =0:51, RMSEC = 40 273 g mm (Fig. 5 ). Other texture parameters (initial gradient, maximum force, etc. ) gave weak predictions too. Hence, no reliable predictive distribution maps of WAB and texture can be made for the slices of cassava roots, probably due to the limited dataset (n = 109).
Visualization of water absorption distribution in contrasting cooking quality of cassava genotypes As the previous models did not present satisfactory performances to predict WAB from fresh cassava, a PCA was applied to the HSI data from boiled cassava by following the procedure described in the Materials and methods section. The first component (PC1) loading explained 96% of total variability (Fig. 6 ). The NIR bands contributing most to PC1 were located at 1131 nm and 1396 nm, corresponding respectively to the first and second overtones of the O H bond from water. 50 Thus, PC1 was related mostly to water content of the samples after boiling, and by inference to WAB and cooking quality. In order to determine variations in water content within the slices, each pixel of each image was projected onto the PC1 axis.
To our knowledge, the present study is the first carried out for the prediction of DMC in cassava roots using HSI, although many studies have been done for DMC prediction in potato tubers. 25 In particular, locally weighted partial least-squares regression (LWPLSR) achieved high accuracy for DMC quantification in potato slices (R 2 p =0:98, RMSEP = 0. 016%). The LWPLSR chemometric method can be used in cases of large databases containing thousands of samples from different locations, years, harvests, and so on. 11 For cassava, further data collection is needed to apply LWPLSR, as the current number of samples is still limited (n = 116). Using HSI for predicting DMC per se may not be practical, as other high-throughput phenotyping approaches, such as portable NIR spectroscopy and the conventional gravimetric method allow for the screening of large numbers of genotypes reliably and inexpensively.
Nevertheless, high performances were obtained using classification approaches for the prediction of physical parameters by HSI in other crops,  27, 28, 32, 35, 52, 53  so further data collection to increase the database of HSI, WAB, and texture data may also improve the prediction models for boiled cassava. The visualization of water content after boiling among contrasting cooking quality cassava genotypes was investigated using the PCA method. The visualization of PC1 scores (Fig. 7 ) highlighted higher water contents after boiling in high WAB genotypes (WAB > 12%, i. e. good cooking) and that water absorption during boiling spreads from the internal part to the periphery of the halfcylinders of cassava roots. HSI may be used for a more in-depth investigation of water absorption, by taking hyperspectral images of root slices at increasing boiling times (e.