dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The final study investigated children's ability to respond to contextual task demands and select the appropriate conceptual relation when given a conflicting choice between a meal script and a thematic associate. As expected, age significantly improved children's performance for the task in both thematic and script scenarios. Reiterating the findings of Studies 1 and 2, children performed significantly better in the thematic trials than in the script trials. In addition, performance in the script scenarios was better for children who had completed the task with the thematic scenarios first followed by the script scenarios. Crucially, food neophobia was determined to reduce the selection of the most appropriate relation for both meal scripts and thematic associates in the food domain.
In response to the first line of inquiry, Studies 1 and 2 investigated children's knowledge of four distinct thematic and script relations; these were defined as follows: Event scripts: Foods that belong to a spatiotemporal concept denoting a place, time, or event in which the target food typically occurs (e. g. , cake-celebration). Food-food associates: Conventional associations in which two objects form a part-whole (e. g. , ice cream-cone) or mere association (e. g. , fish-lemon). Food-utensil associates: Functional associates denoting a characteristic function between food and an object (e. g. , knife-to-cut bread, spoon-to-scoop ice cream). Meal scripts: Slot-filler concepts of foods that are exemplars of specific meals and can be substituted for one another in a spatiotemporal context (e.
The post hoc analyses reveal that functional thematic associates (food-utensils) are mastered earliest, followed by knowledge of conventional food pairs (food-food) and event scripts. Knowledge of slot-filler associates (meal scripts) developed significantly later but still showed a significant developmental improvement. Once again, this finding not only replicates previous research but also aligns with the argument that the computation of script associates is more cognitively demanding than the computation of thematic associates  (Berger & Donnadieu, 2006). Thematic associates must satisfy the requirement of complementarity, convention, or functional affordance  (Estes et al. , 2011). Whereas finding two appropriate meal script associates (e. g. , oatmeal and pancakes at breakfast) requires inhibiting an initial salient script associate (e.
Studies have witnessed that both socioeconomic status and educational level also appear to modulate the expression of the disposition to reject fruits and vegetables  (Flight et al. , 2003; Giskes et al. , 2002; Lien et al. , 2002). Furthermore, as indicated from the stimuli sets in Studies 1 and 2, there is notable cultural diversity in script and thematic concepts in the food domain. Given apparent differences in French and American approaches to food, it would have been interesting to investigate differences in how French and American children conceive food associations or reject food. The literature appears to demonstrate that in older children and adults the rigidity and variety of food scripts varies significantly across cultures, with Americans holding a reduced number of breakfast exemplars compared with Chinese adults  (Bian & Markman, 2020b).
It was beyond the scope of this investigation to determine the effects of culture and socioeconomic status on children's conceptual knowledge in the food domain, but future evidence should consider such potential. A novel limitation that must be considered in future research is the impact of the COVID-19 pandemic on children's food representations and conceptual knowledge. Studies 1 and 2 were conducted pre-pandemic, whereas Study 3 was conducted post-pandemic restrictions. During the national lockdown period in France, the majority of the students participating in our study would have spent prolonged periods eating at home. This may have affected the children's exposure to meals, with most children normally attending the school cafeteria at lunchtime. It would be of great insight into understanding how exposure to different meals shapes children's knowledge of food scripts and thematic associations by investigating their eating habits during the pandemic versus post-pandemic.
Conclusion Previous research on thematic and taxonomic knowledge in the food domain indicates that impoverished conceptual knowledge in young children is linked with greater food rejection tendencies. However, food is a domain susceptible to many conceptual relations and categories across different contexts and situations. These three studies examined the developmental acquisition of distinct thematic and script food relations in children aged 3-7 years. The results indicate that knowledge of event and meal scripts develops slightly later than knowledge of thematic associations based on cooccurrence and functional affordance. These findings are fundamental in understanding that at younger ages children may place greater importance on what foods and objects belong together, whereas at older ages children may place more emphasis on what context is appropriate for food.
Acknowledgments The research leading to these results received funding from the  France-Stanford Center for Interdisciplinary Studies  and the  European Union 's  Horizon 2020 research and innovation program  under a  Marie Sklodowska-Curie grant  ( 764985 ). The authors are incredibly grateful to the school's faculty, children, and caregivers for their participation and collaboration. The authors also thank  Clara Lakritz  and the Lyon municipality for their help and support in running the research. An enormous thank you goes to  Ellen Markman  and  Lin Bian  for their valuable insight into the design, implementation, and interpretation of the first study as well as to the  France-Stanford Center  for creating the opportunity to complete the U. S. research.
In this research paper, we propose a generic fully dynamicallycoupled THMC approach to predict the appearance and propagation of cracks in deformable porous materials. This approach is suited to describe the generic reactive transport processes (i. e. mineral dissolution and precipitation, drying, porosity evolution, gas transport, heat transfer) and the mechanical damage they exert (i. e. shrinkage, swelling, cracking) under various thermo-hydro-chemomechanical loadings. This approach is based on the numerical coupling of Hytec, a reactive transport simulator  34  and Cast3m, a mechanical Finite Element (FE) code  35  as depicted in Fig. 1. We focus our developments on the specific case of the accelerated atmospheric carbonation of cementitious materials, which is a ubiquitous process responsible for important CO 2 uptake  36.
Model formulation Reactive flow processes include the migration of water, transport of solutes and gaseous species within a porous medium while accounting for the solid-liquid-gas equilibrium. We use the fully coupled reactive transport approach of Hytec  34, 37, 77. Release and consumption of water by chemical reactions are included and coupled with the unsaturated flow, as well as water evaporation, controlled by the sorption isotherm  43  , described by the Van Genuchten empirical relation  78. To represent the appearance and local effect of cracks, a double porosity approach has been used  [79] [80] [81] [82]. It represents that each gridcell is a combination of two interconnected domains, the matrix (superscript m) with a porosity ω m and the cracks (superscript c) with a porosity ω c.
Simulations are parametrized with the Thermodem thermodynamic database  95  , and dedicated measurements on the C 3 S sample including mineralogy, porosity, initial gas diffusion coefficient, Van Genuchten parameters and water content. More details about the hydrogeochemical description of our materials are given in  60. Based on the initial microstructure and phase assemblage, the poroelastic properties estimated using the Mori-Tanaka scheme yields an initial Young Modulus of 14. 47 GPa, in agreement with the measured value 15 ± 1 from Kangni-Foli et al. 66  (see "Governing equations"). The other initial poroelastic properties are given in Table  1. The progressive decalcification of the C-S-H phase is modelled using a discretized set of dissolutionprecipitation reactions towards hydrates with lower Ca/Si atomic ratios  60  , ranging from 1.
Solutes and gasesous species migrate by advection (following Darcy velocity u l ) and diffusion in the fluid phases following the traditional reactive transport equations  77. ∂ω ν S l ν T ν i;l ∂t þ ∂ω ν S g ν T ν i;g ∂t ¼ div D ν l ∇T ν i;l þ D ν g ∇T ν i;g À T ν i;l u l þ J ν dual T c;m i;l þ ϕ ν α þ R ν i (5) where T ν i;α is the total concentration of species i within fluid phase α in continuum ν (mol. L -1 of phase α). R ν i is the chemical source term for species i in domain ν. The solid-liquid-gas equilibrium is solved with a thermodynamic database under the assumption of a thermodynamic equilibrium between the three phases. ϕ ν α is the cross-domain diffusive flux of species i towards ν ϕ m α ¼ ÀD c;m α A T c i;α À T i;α m D (6) where 28 D c;m α ¼ max 1; u n u ref D eff;c α (7) Equation (  7 ) is based on the experimental work of Djerbi et al.
For these reasons, we have chosen a continuous porous medium approach where the mechanical fracture is simulated using a damage model, which has already yielded satisfactory results for leaching  12  and corrosion  39. Using a damage behaviour requires a dedicated regularization method in order to avoid a mesh dependency. The damage is modelled through Fichant-Laborderie model  91, 92, 111  and its regularization is performed using Hillerborg's energetic method  91, 112. The associated equations are depicted in Table  1. The mechanical system is solved using the finite element Cast3m software  35  and the behaviour law is implemented in the code generator MFront  111, 113, 114. The crack openings are estimated from the anelastic strain tensor ε ane defined by: ε ane ¼ C À1 : σ ane ¼ C À1 : σ À σ e (9) where σ e is the elastic part of the stress tensor.
ACKNOWLEDGEMENTS The project leading to this work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  n847593 , in particular from the EURAD/MAGIC workpackage. We want to thank the  Institut de Radioprotection et de Sureté Nucléaire (IRSN, France)  and in particular  Alexandre Dauzères  for their participation in the data acquisition.
DATA AVAILABILITY All data generated or analysed this study are included in this published article. More details about the experimental data and numerical methods can be found in cited references, mainly Kangni-Foli et al. 2021  66  , Seigneur et al. 2018, 2020 and 2022  37, 60, 77. Cast3m is a publicly available mechanical code. Hytec software for this research is not publicly available due to intellectual and industrial property.
Six also showed a progressive difference in expression between control, TSC without epilepsy, and TSC with epilepsy groups. A multivariate approach using enrollment samples identified multiple 3-variable predictors of epilepsy, with the best having a positive predictive value of 0. 987. This rich dataset will enable further discovery and analysis of developmental effects, and associations with seizure development in TSC. Tuberous sclerosis complex (TSC) is a multisystem disorder caused by inactivating mutations in either TSC1 or TSC2 1,2. Tumors form in many organs in TSC, following the two hit Knudson mechanism, including the heart, brain, kidneys, skin, and lungs 1,2. However, the predominant morbidity in TSC infants and young children is due to severe and often drug-resistant epilepsy, with infantile spasms and other seizures occurring in 70-90% of TSC infants  3, 4.
Neurodevelopmental comorbidities including intellectual disability and autism are also common in TSC, are collectively called TSC-associated neuropsychiatric disorders (TAND)  5, 6  , and correlate with seizure severity and lack of effective seizure control. TSC is often diagnosed prenatally, because cardiac rhabdomyomas are a cardinal feature of TSC, and are commonly observed on fetal ultrasound 2. This situation has enabled consideration of presymptomatic antiseizure medication (ASM) for TSC infants, prior to seizure onset, with the goal of prevention or at least diminution of seizure severity, ideally leading to long-term seizure control and improved neurocognitive outcome  7. EPISTOP (Long-Term, Prospective Study Evaluating Clinical and Molecular Biomarkers of Epileptogenesis in a Genetic Model of Epilepsy-Tuberous Sclerosis Complex, NCT02098759) was a controlled multicenter study, in which the safety and efficacy of presymptomatic ASM treatment with vigabatrin, introduced when focal discharges more than 10% of the time or multifocal discharges were seen on video-EEG, was compared with conventional ASM started after the onset of seizures 8.
RNA was extracted from total blood samples, and analyzed by RNA-Seq (Supplementary Fig. 1b ). Whole genome sequencing was performed to permit analysis of single nucleotide polymorphisms (SNPs) potentially associated with epilepsy. TSC1 and TSC2 deep sequencing in concert with whole genome sequencing enabled detection of heterozygous and mosaic pathogenic variants  9. Four hundred and eighty-one protein groups were identified in the proteomics analysis (at false discovery rate (FDR) < 1%). 63,677 RNAs were identified by RNA-Seq (Supplementary Data 1). For the univariate analyses, RNAs and metabolites were retained for further analysis only if non-zero values were seen in ≥ 50% samples; protein groups if seen in ≥ 70%. 20,579 RNAs (13854 protein coding genes, 3221 long noncoding, 2598 of other biotypes, including miRNAs, snRNAs, pseudogenes, etc.
Confounding effects in single dataset analysis Several confounding effects were expected and identified. First, sample processing and analysis via mass spectrometry and sequencing was performed in batches, and batch effects were anticipated. Second, vigabatrin (VGB) is known to impact metabolite levels  10, 11. VGB therapy was the main therapeutic intervention in the EPISTOP trial, such that by age 2 years, 89% of all subjects were taking VGB. Third, we anticipated that multiple analytes would vary naturally from newborn to age 2 years, so that accounting for such effects was quite important to avoid confounding by age. Batch effects. Batch effects were observed in the metabolite dataset (Supplementary Fig. 2a, b ), but not in any of the other analyses. Metabolite batch effects were corrected by a Z-score based correction method (see Methods for details).
3a-c , Supplementary Data 3). To assess if these clusters of age-dependent analyte changes were due to common pathways or developmental processes occurring with age, clusters of analytes were assessed for enrichment in Gene Ontology (GO), KEGG, and Reactome gene sets (Supplementary Data 4a, b). Protein cluster group 2 showed the greatest decline with age, and was enriched for genes involved in extracellular matrix organization and structure in all three gene sets (Supplementary Data 4a). Protein group cluster 3, in which protein levels decreased less strongly over time, was enriched for genes involved in carbohydrate metabolic process by GO gene set analysis (Supplementary Data 4a). Protein group clusters 4 and 6, in which protein levels steadily increased with age, were both enriched for genes involved in complement and coagulation (Supplementary Fig.
3a , Supplementary Data 4a). None of the metabolite age clusters showed enrichment for any of the Reactome terms which we tested (Supplementary Fig. 3b ). In contrast to the protein and metabolite datasets, there was no strong correlation between gene expression and age. Since it is known that there is significant variation in the fraction of white blood cell types in human blood according to age from birth to age 2 years  [14] [15] [16]  , we considered the possibility that some of the variation in expression might be due to variation in the relative numbers of different white blood cell types. However, age appeared to be a minor factor affecting gene expression (Fig. 2c ). Nonetheless, CIBERSORT  17  was used to analyze RNAseq data to determine the relative proportions of white blood cell types in each sample.
05, fold change > 1. 5, Supplementary Data 5, Supplementary Fig. 8 ). Although these differences were statistically significant, no analyte had levels that could reliably distinguish the control group from the TSC group. Gene set enrichment analysis of the 25 genes with higher expression in the TSC subjects identified multiple enriched gene sets using each of the GO, KEGG, and Reactome gene sets, many of which related to ribosome structure and function (Supplementary Data 6). In contrast, the 9 genes whose expression was lower in the TSC subjects than controls were enriched in gene sets related to receptor tyrosine kinase and phosphoinositide 3-kinase activity (Supplementary Data 6). These observations may reflect the consequences of haplo-insufficiency for either TSC1 or TSC2 in the blood cells from which the RNA was derived, as complete loss of either TSC1 or TSC2 is known to strongly increase ribosome biogenesis while suppressing upstream receptor tyrosine kinase and phosphoinositide 3-kinase signaling  18.
05; fold change > 1. 5 comparing group 1 to either group 2 or 3; Table  1 , Fig. 4 ). We repeated this analysis under less stringent conditions, in which group 2 included subjects who never developed seizures but had received pre-symptomatic vigabatrin, per study protocol; and TSC subjects with an abnormal EEG at enrollment were included in both groups 2 and 3. Under these conditions, max group sizes were 34, 24, and 59 respectively. Nine protein groups, five metabolites, and 48 genes showed significant differences in levels (FDR < 0. 05; fold change > 1. 5, comparing group 1 with either group 2 or 3; Kruskal-Wallis Rank Sum test) (Supplementary Data 7). The single protein group, single metabolite, and four RNAs that were identified in the more stringent analysis were also identified in this less stringent analysis.
Eight of nine protein groups were increased in the TSC epilepsy group compared to controls, of which six were also upregulated in the TSC no epilepsy group. These protein groups were enriched in ten GO, nine KEGG, and 11 Reactome gene sets, of diverse types, but showed some consistency with gene sets involving collagen and extracellular matrix structure (Supplementary Data 8a). Only one protein group, Plasma protease C1 inhibitor, was increased in the control group. For the five metabolites, all of which were upregulated in the TSC epilepsy group, there was no significant enrichment in any pathway. Expression of 16 genes was increased in TSC epilepsy subjects compared to controls, while 26 were decreased in that comparison (Supplementary Data 7). There was apparent enrichment of the 16 genes in multiple gene sets for these genes, which showed limited consistency for electron transport, respiratory chain, and mitochondrial activity; while the 26 genes were enriched in diverse pathways without apparent consistency (Supplementary Data S8b).
6 for association with epilepsy development in univariate analysis were retained. In addition, a maximum of 30 analytes, those with highest AUC values, from each data type were included. Thus 126 analytes were considered for predictive classifier analysis. Logistic regression was used to examine all potential combinations of variables, for their ability to predict epilepsy development, using an arbitrary decision boundary for each model. The Matthews Correlation Coefficient (MCC)  19  was used as a metric for the quality of each classifier with the observed data. A maximum of three variables per predictor were considered so that in total ~330,000 1-, 2-and 3-variable models were evaluated. The mean MCC among all classifiers was 0. 4; 295 classifiers had MCC values > 0. 70; 5 had values >0.
873 with PPV of 0. 987 (Fig. 5a ). We recognize the limitation of the size of this patient dataset, in that only 93 subjects with TSC were studied, and only 65 were available for seizure association analysis, since many had received presymptomatic vigabatrin. In addition, additional follow-up beyond 2 years will be valuable, and is ongoing at this time. Hence we view all of these observations as exploratory and tentative, and hope that a replication population may be studied for validation in the near future.
All TSC subjects had a last blood sample drawn at age 24 months, the end of the study. Due to delays in recognition of abnormal EEGs or caregiver's report of seizures, those samples were often collected 1-2 weeks following those clinical events. The metadata available for each sample included but was not limited to: TSC mutation type, family history of TSC, epilepsy in the family, presence of seizures (clinical or subclinical), presence of tubers, presence of radial migration lines, white matter abnormalities, gender, grouping (observational or randomized study arm), treatment type (conservative or preventative), infantile spasms, drug-resistant epilepsy (defined as either: 1) ongoing seizures despite the administration of 2 ASM with appropriate doses; or 2) special treatment (epilepsy surgery, ketogenic diet, ACTH)), treatment sequence, vigabatrin treatment, and presence of autism spectrum disorder (ASD) and/or developmental delay (DD) by psychological examination.
Additionally dynamic exclusion was enabled with a repeat count of 2 in 30 s before exclusion of the precursor (± 5 ppm) for 900 sec. Early expiration was allowed to remove precursors from the exclusion list after an expiration count of 7 full scans with a signal-to-noise ratio of lower than 3. To assure quality over the processing pipeline in each batch of processed samples a serum pool derived from various female and male subjects with and without diagnosed TSC between the age of 3 months and 3 years of age was also processed as described above. These control samples were measured in a randomized manner in between every 5-10 samples. Raw files were processed using MaxQuant software (version 1. 6. 1. 0)  33, 34  with the following settings: carbamidomethyl of cysteine as fixed modification; variable modification of methionine oxidation and deamidation of glutamine and asparagine; match between runs was enabled with a matching window of 2 min and an alignment window of 20 min; peptide spectrum match FDR, as well as peptide and protein FDR were set at 1%, LFQ with a minimum ratio count of 1.
Peptide spectrum matches were analyzed against the MaxQuant contamination database and a fasta file consisting of only reviewed entries from Homo sapiens (Uniprot, downloaded 3 rd September 2018). Downstream processing was performed in Excel, Perseus 35 software and R. Blood samples for transcriptomic analysis and processing were collected and stored in PAXgene tubes. The total RNA was isolated using the PAXgene blood RNA kit in accordance with the manufacturer's guidelines (Qiagen). Library preparation and sequencing was completed at GenomeScan (Leiden, the Netherlands). Sequencing libraries were prepared using the NEBNext Ultra Directional RNA Library prep Kit for Illumina (New England Biolabs) in accordance with the manufacturer's guidelines. Globin mRNA was removed using Invitrogen's Globin clear kit (Waltham) and mRNA was isolated from total RNA using oligo-dT magnetic beads.