dataesr/scientific-paragraphs-categorization huggingface dataset:
| Statistical analysis Results were analyzed using SPSS version 28. 0 (IBM, Armonk, NY, USA). We compared the baseline intensity and pleasantness separately using within-subject analysis of variance for repeated measures (rm-ANOVA) with the factors "odor" (mix-P, PEA, mix-NP, and H 2 S) and "concentration" (12%, 24%, 36%, and 48% v/v), followed by the Least Significant Difference (LSD) adjusted post hoc analysis to compare variables pairwise. A series of paired-sample t-tests were used to test each concentration of the odors to investigate the desensitization effect by comparing the intensity and pleasantness between baseline and desensitization sessions. To look into the relationship between the desensitization effect (measured by desensitization minus baseline) and concentration, rm-ANOVA was made with the factors "odor" and "concentration," separately for intensity and pleasantness.
Methods The LFPQ-fr was developed and validated following a recommended procedure previously detailed  [28]. First, a culturally appropriate database of food images was selected and validated based on familiarity, liking, intake frequency, and recognition of nutritional or sensory properties by a sample of healthy-weight French adults. Second, a French translation of the liking and wanting constructs was developed and tested for recognition in the same population. Finally, the LFPQ-fr, comprised of these validated components, was then tested for its sensitivity to different appetitive states (i. e. , fasted versus after intake of a standardized meal) and reliability over two lab visits on different test days in subset of the initial sample. This study received an ethical agreement (CPP Ile-de France III, 2019-A00853-54) and has been registered as a clinical trial (clinicaltrial.
i) Food database creation To create a food database appropriate for the French culture, 36 common French foods were selected, prepared and photographed. The food database was pilot tested to contain: 1) ready to eat and familiar foods within the French culture, 2) an acceptable number of food items to complete the LFPQ tasks, 3) a typical portion size for each food, 4) foods appropriate to the time of day of the measurements, 5) foods appropriate to the culture and habits of the French population, 6) a diversity of foods within each category, and 7) food images of homogenous quality and size. Selected food images in the database were then allocated to an appropriate category based on two nutrient-and sensory-based criteria: To account for the effect of other macronutrients on appetite, protein and carbohydrate levels were matched between high and low fat categories.
i) Food database validation An online questionnaire was created and disseminated by email to 3 areas in France (Dijon, Clermont-Ferrand and Paris) to undertake the validation of the food database. These 3 areas were either university or clinical sites but most of the respondents were working professionals (e. g. , researchers, doctors, technicians, nurses, or secretaries). Respondents that did not live in France (n = 7), were nutritionists/dieticians (n = 3), or reported allergies to any of the foods presented (n = 10) were excluded from analyses. Among the 570 respondents that completed the questionnaire, 430 had responses to all the food questions, thus were included in analyses. The survey aimed to identify food pictures that were familiar, frequently eaten, liked, accurately perceived as sweet or savory, and low or high in fat, and consistent with lunchtime consumption.
The food database was created in collaboration with researchers from Leeds, Dijon and Clermont universities. Food images were considered appropriate if they satisfied the following conditions: 1) correctly recognized (defined by > 80% of the participants naming the food correctly); 2) habitually consumed (defined by a mean score greater than 2. 5 out of 5, which was considered to be representative of frequent contact with the food); 3) liked (defined by mean liking >70 mm), 4) correctly recognized as sweet versus savory and low fat versus high fat (sweet and low fat if the mean value was 〈 40 mm and savory and high fat if 〉 60 mm, respectively); and 5) time appropriate (defined by mean >70 mm for the specific time of day).
The distribution of dependent variables and model residuals were tested for normality using Shapiro-Wilk test. Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, US). All statistical tests were carried out using a two-sided type I error rate at 5%. For the primary objective (i. e. , to evaluate agreement), specific attention was paid to the magnitude of results reported with 95% limits of agreement and interpreted according to the conventions reported by Altman ([-0. 7; -0. 4] low agreement; [-0. 4, 0. 4] negligible agreement; [0. 4, 0. 7] mild agreement; and [>0. 7] high agreement). No correction for multiple testing was applied in the analysis of secondary objectives. Findings from these analyses were interpreted as exploratory.
Food database validation Participants involved in this stage of the validation process (N = 430) ranged in age between 19 and 82 years (M = 42. 2, SD=12. 7) and were predominately female (81%). 16 out of 36 food pictures matched the initial validation criteria and were selected for the experimental validation of task reliability (third phase). Results from the food database validation and nutritional characteristics of the food are presented in Table  1  and Table  S1  in the supplementary materials, respectively. All the food matched the selection criteria. However, it was difficult to find a diversity of foods within a category that are all liked and eaten at same time of day. This resulted in the LFSW category mainly being comprised of fruits, for example. Moreover, it is worth noting that yogurt was perceived as high fat and sweets were not considered appropriate for lunchtime in the French sample, contrary to the British population on which the original LFPQ was validated.
However, the concordance among these studies could be higher, highlighting the difficulty in identifying reliable and reproducible biomarkers for mastitis detection and mastitis resistance. In this context, integrating transcriptomic with phenomic information might represent the ultimate step not only to strengthen the information on the complexity of the molecular system by reinforcing complementary levels of knowledge but also to create more reliable prediction models  [20]. Therefore, this study aimed to i) evaluate the milk SC transcriptomic signatures upon natural infection of S. agalactiae and Prototheca spp. , ii) integrate transcriptomic and phenomic information to explain better the complexity underlying the molecular mechanisms of mastitis, and identify hub variables for early mastitis detection and prediction and, iii) perform a meta-analysis using three publicly available datasets to confirm the reproducibility of our results.
The data were analyzed with the FlowMax software version 2. 82 (Sysmex Partec GmbH, Norderstedt, Germany). The morphology and complexity of the cells were evaluated in an FSC vs. SSC dot plot; total white blood cells were identified as CD45 and CD18 positive events; polymorphonuclear cells as CD11b positive CD14 negative events; macrophages as CD11b and CD14 positive events; T-helper lymphocytes as CD4 positive and CD8 negative events; T cytotoxic lymphocytes as CD8 positive and CD4 negative events; B lymphocytes as CD45, CD21, and CD18 positive events. In this study, we considered for the statistical analyses only animals subjected to RNA-seq analyses which are part of the broader cohort of animals previously analyzed  [11]. The Kruskal Wallis and Dunn test assessed significance among the experimental groups for pairwise comparison of milk production, composition, and flow cytometry variables.
RNA-seq data processing and analysis Data pre-processing was made following the consensus pipeline built by Overbey et al. [22]. First, the quality control of RNA sequences was assessed with the FastQC software (v. 0. 11. 9). Clean reads were obtained by removing low-quality bases and adaptors with the TrimGalore software (v. 0. 6. 4) [23]. FastQC was used again on the trimmed sequences to check the quality of the reads. MultiQC package (v. 1. 8) [24]  was run to create summary statistics reports that included the sample quality control result categories from FastQC across all experiment samples. The sample information of clean data is shown in Table  S2. The paired-end clean reads were aligned against the Bos taurus DNA reference genome (ARS-UCD1. 2) from the USDA's Agricultural Research Service with the spliceaware STAR (v.
1. 34. 0) [28]. Differentially expressed gene (DEG) analysis was performed pairwise using DESeq2 and edgeR packages: i) negative animals vs. S. agalactiae naturally infected animals; ii) uninfected subjects vs. Prototheca infected animals; and iii) S. agalactiae vs. Prototheca infected individuals. Then, the voom() function from the limma R package (v. 3. 50. 0) was used to fit a generalized linear regression model to correct the data with the group as a fixed effect. The group factor and the cow dependency were included in the generalized linear model using the nbinomWaldTest() function, which estimates and tests the significance of regression coefficients with the following explicit parameter settings: betaPrior = FALSE, maxit = 5,000, useOptim = TRUE, useT = FALSE, useQR = TRUE, minmu = 0.
Functional pathway analysis The shared list of DEGs for each comparison was fed to the Cytoscape (v. 3. 9. 1, http:// cytos cape. org) ClueGo plugin (v. 2. 5. 8) [29]  software to identify relevant biological processes and immune systems networks. A minimum of 10 genes were needed to be associated with a term. These genes would represent at least 4% of the total number of related genes. Only pathways with a P-value < 0. 05 (Bonferroni step-down correction) were retained. Results were illustrated as a functionally grouped network of terms, having the most significant one as a leading term. The edges that show term-to-term interactions were obtained using a Kappa score of 0. 4. Then, coupled with DEG-driven approaches, we used the pathifier algorithm from the pathifier R package (v. 1. 32. 0)  [30] , which by transforming the whole transcriptome expression data into pathway-level information, infers the pathway deregulation scores by measuring how much the gene expression of a sample deviates from normal behavior.
Phenomics: complementary data integration approaches A global non-metric multidimensional scaling (NMDS) ordination was used to extract, visualize and summarize the variation in the transcriptome (the "response variable") using the vegan R package (v. 2. 5. 7)  [31]. Stress values were calculated to determine the number of dimensions for each NMDS. Stress values measure how much the distances in the reduced ordination space depart from those in the original p-dimensional space. High-stress values indicate a greater possibility that structuring observations in the ordination space is entirely unrelated to the actual full-dimensional space. Then, the explanatory variables related to milk composition and quality traits, cytometry cell profiles, and host morphometric parameters were fitted to the ordination plots using the envfit() function in the vegan R package  [32]  with 10,000 permutations.
The envfit() function performs multivariate analysis of variance (MANOVA) and linear correlations for categorical and continuous variables. The effect size and significance of each covariate were determined by comparing the difference in the centroids of each group relative to the total variation, and all P-values derived from the envfit() function were Benjamini-Hochberg adjusted. The obtained r 2 gives the proportion of variability (that is, the main dimensions of the ordination) that can be attributed to the explanatory variables. As a second integrative approach, the N-integration algorithm DIABLO (Data Integration Analysis for Biomarker discovery using Latent Components) of the mix-Omics R package  [33]  (http:// mixom ics. org/, v. 6. 18. 1) was used. We combined host-centered transcriptomics with phenomics data to achieve this integrated perspective, coined holo-omics  [34].
It is to be noted that, in the case of the N-integration algorithm DIABLO, the variables of all the data sets were also centered and scaled to unit variance before integration. In this case, the relationships among all data sets were studied by adding a different categorical variable, e. g. , the infection status of cows. Healthy cows (n = 9) were compared to infected individuals (n = 22). DIABLO seeks to estimate latent components by modeling and maximizing the correlation between pairs of pre-specified datasets to unravel similar functional relationships  [35]. The model was first fine-tuned using leave-one-out cross-validation by splitting the data into training and testing. Then, classification error rates were calculated using balanced error rates (BERs) between the predicted latent variables and the class labels' centroid.
Identification and validation of hub variables To visualize the high-confidence variable co-associations, only those with |r| ≥ 0. 90 and more than 15 connections were automatically visualized using the organic layout algorithm in Cytoscape (version 3. 9. 1). The Molecular Complex Detection (MCODE) Cytoscape plug-in (version 2. 01,  [36] ) was adopted to detect densely connected modules within the interaction network. MCODE scores ≥ 3 were set as a cut-off criterion with default parameters. Finally, cytoHubba (version 0. 1)  [37] , a Cytoscape plugin, was used to explore the network modules for identifying hub genes, defined as genes having high correlation in candidate modules. The top 20 variables were identified and ranked using the Maximal Clique Centrality (MCC) method. To validate the abovementioned hub genes as putative markers for mastitis infection, we performed the receiver operating characteristics (ROC) and precision-recall analyses using the R package pROC (v.
The meta-analysis cohort To confirm the reproducibility of our prediction results in healthy and infected individuals, studies on the transcriptome of the milk somatic cell in dairy cows with high-throughput RNA sequence data in. fastq format deposited in publicly accessible databases and available metadata were retrieved. We obtained 81 somatic cell transcriptomic samples from three independently published studies as an orthogonal dataset. The three studies were labeled as Seo  [38] , Asselstine  [14] , and Niedziela  [15]. Data included acute and subclinical infection regarding both naturally and experimentally infected animals. Raw sequence data and metadata from the Seo study were available at GSE60575 in the GEO database. In contrast, Asselstine study raw fastq files and metadata were retrieved from the NCBI under PRJNA544129 Bioproject accession number.
Raw fastq files and metadata of the Niedziela study were available in the European Nucleotide Archive (ENA) repository with the project number PRJEB43443. The published data was pre-processed and annotated as described above. In this validation set, "healthy" subjects were defined as those reported as not being infected in the original research; alternatively, "mastitis" subjects were defined as those diagnosed with mastitis infection either by the California Mastitis Test  [14]  or after 24 h from the disease onset with two different strains of Staphylococcus aureus  [15]. Ten-fold cross-validation sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was employed to evaluate the prediction model's performance and validate the essential genes responsible for the differences between groups using the mixOmics R package.
Animals and data The 31 Holstein cows enrolled in this study were, on average, 4 years of age. The mean DIM at enrollment for all dairy cattle was 235 d, and the mean parity was 2. 5, ranging from 2 to 5. The average milk yield was 26. 97 (± 9. 01) kg/d. Milk had 2. 19 ± 0. 74% of fat, 3. 49 ± 0. 29% of protein, 2. 73 ± 0. 27% of casein, and 4. 51 ± 0. 44% of lactose. Milk pH and conductivity were 6. 46 ± 0. 08 and 9. 94 ± 1. 30 mS/ cm, respectively (Table  S1 ). A schematic summary of the experimental design and the conducted analyses is reported in Fig. 1.
Identification of mastitis hub variables The co-association of gene expression and phenomic data obtained from CIRCOS (Fig. 4e ) resulted in a network construction consisting of 116 interactors (nodes) and 4,430 interactions (edges). This network was further analyzed using the Molecular Complex Detection (MCODE) Cytoscape plug-in, which identified four densely connected modules. The two most significant modules showed MCODE scores of 54. 732 and 6. 667, respectively. Seven variables (AGFG1, CEMIP2, ITGB7, RRAD, Urea, T killer cells, and leucocytes) were not assigned to any module (Fig. S4 ). Lastly, with cytoHubba, we identified the top 20 hub variables, which are displayed in Fig. 5  and ordered as follows according to the MCC ranking method: milk conductivity, lactose, P2RY6, SPTBN5, BoLA-DOA, ENSBTAG00000053850, CIITA, GNA11, ENS-BTAG0000003367, casein index, ENSBTAG0000003408, HIP1, CLMN, RESEF, EFHD1, LAPTM4A1, FCRL5, milk yield, PAH, ELF5 (Full MCC ranking in Table  S5 ).
The genes that were commonly shared by both DIABLO and cytoHubba (P2RY6, SPTBN5, BoLA-DOA, CIITA, GNA11, ENSBTAG0000003367, ENSBTAG0000003408, HIP1, LAPTM4A, FCRL5) were then submitted to a ROC analysis resulted in having excellent prediction performances in terms of discriminating healthy and mastitis animals with sensitivity > 0. 89, specificity > 0. 81, accuracy > 0. 87 and precision > 0. 69. The detailed predictive performances of the hub genes are presented in Table  S6.
Meta-analysis of subclinical mastitis To validate the core mastitis response genes, we additionally gather publicly available somatic cell transcriptome RNA-seq datasets (n = 81) derived from three independent studies in dairy cows: i) pooled milk sampled from 12 healthy cows  [38] , ii) quarter samples from 6 cows (n = 12)  [14] , and iii) 14 cows sampled at five different times (n = 48)  [15]. As with the study cohort, the transcriptome profiles could be distinguished based on the infection status (Fig. 6a ), with higher transcriptome dispersion in infected individuals. The first principal component accounted for 51% of the total variance, and the first two accounted for 74%. with the training set, many genes (n = 7,226) were differentially expressed between the healthy and infected individuals, including 3,912 up-and 3,314 down-regulated.
We then intersected the list of core mastitis-response genes and the list of DEGs from these published datasets (Fig. 6b ). We found that 53% of these core-mastitis response genes were DEG in the validation set, strengthening the shared transcriptional response to infection, independently on the pathogen, regional, and other potential differences, such as diet, medication, energy expenditure, age, and DIM of the dairy cows. To further assess the feasibility of the previously selected hub genes for discriminating healthy and infected animals, we re-performed a ROC analysis on the unified training-validation dataset (n = 112). The class II transactivator (CIITA) had the best prediction performances, having sensitivity, specificity, and accuracy > 0. 7 and precision equal to 0. 8.
Statistical analysis No minimum sample size was considered necessary for this mainly descriptive statistical analysis. Subgroup analyses included groups based on sex, age (< 18, 18-25 and > 25 years) and truncal acne severity, combining data from participants with mild, moderate, severe, and very severe truncal acne, as well as the impact on QoL, grouping participants who reported being not concerned, never or rarely impacted, and those being sometimes, often, very often or constantly impacted. Percentages were calculated excluding missing values. The χ 2 test was used, and significance levels were set at 5%. SAS v9. 4 (SAS Institute, Cary, NC, United States) was used for statistical analyses.
Demographic and general data on truncal acne Overall, 1,001 subjects completed the survey. Mean age was 18. 6 ± 4. 3 years; a large majority (75. 7%) of respondents were female. When grouping truncal acne severity grades, 31. 2% reported mild or moderate, and 68. 8% severe or very severe truncal acne. Ninety percent (90. 0%) of participants with truncal acne also reported past (24. 3%) or ongoing facial acne (65. 7%); 10. 0% reported never having had facial acne. Detailed demographic general truncal and facial acne data are shown in Table  I.
Physicochemical Properties of Fermented and Unfermented Cowpea Smoothies Only pasteurized and fermented smoothies had their physicochemical parameters tested at 0, 2, 24, and 48 h of fermentation. The pH of the samples was determined using the EUTECH pH2700 Instrument (EUTECH Instruments, Illinois, IL, USA), and the total soluble solids (TSS) was determined using the ATAGO PAL-3 pocket refractometer (Atago USA Inc. , Tokyo, Japan). The refractive index values obtained were saved in Brix. The total titratable acidity of the samples was evaluated using the Reddy et al. [5]  technique. The effect of fermentation on the colour characteristics of the smoothies was measured using a CM-3500 d spectrophotometer and spectral magic NX software (Konica Minolta, Konica Minolta Sensing Inc. , Tokyo, Japan).
Statistical Analysis The experiments were performed in triplicate and repeated twice, and the data were analyzed using one-way ANOVA in the statistical program Statistica data analysis software system (10) (Statsoft, Inc. , Tulsa, OK, USA). The Fisher LSD test was performed to discover significant differences at p-values of 0. 05. The linear correlations between phenolics and bioactivities were established using regression correlation coefficients. The nonlinear regression "dose-response inhibition" was used to determine the IC 50 for DPPH and ABTS activities.
Metabolomic and Chemometric Analysis Data processing was performed with the UNIFI software V1. 9. 4 (Waters, Manchester, UK). The multivariate matrix consisted of the ions detected in the samples after the UHPLC-QTOF-MS analysis in the negative (neg) and positive (pos) ionization modes (see Tables  S3  and S4 ) in the retention time range (0. 5-5 min). Only the ions with a response greater than 2000 (peak area) were retained in at least one condition (i. e. , leaves, stem bark and fruit). Among these ions, those correlated with each other (r ě 0. 80) at the same retention time were removed. Consensus orthogonal partial least squares (multiblock OPLS) was used as the chemometric tool to model the information in the rich chemical and biological data elaborated in this study  [52]. This multiblock OPLS analysis was performed using the latest version of MATLAB 2020b.
Intensity Variation of the Metabolites Identified The objective of the univariate analysis was to study the variation of (i) the inhibition of the α-glucosidase enzyme and (ii) the intensity of the metabolites most likely responsible for this inhibition according to the part of the lentisk used and its geographical origin. These statistical analyses were performed by pairwise comparison through a post hoc Kruskal-Wallis test adjusted with Bonferroni correction using the package PMCMRplus version1. 9. 4  [53]  for analysis of the variances and the package MulticompView version 0. 1-8 for analysis of the groups  [54]. Significant differences were considered at the 95% confidence level. These analyses were performed with the R software version R (ě3. 5. 0).