dataesr/scientific-paragraphs-categorization huggingface dataset:
A C C E P T E D M A N U S C R I P T do not consider any feedback-loop between learning and self-efficacy beliefs. This seems reasonable given the short duration of the experiment. Finally, subjects participated in the experimental game which followed a within-subject design. 7 Our game is divided into 2 treatments whose order was not randomised and which differ from one another mainly in terms of the information participants received. In the baseline treatment, subjects sequentially chose between two alternative colours (1 and 2) for three sets of twenty rounds, thus making 60 binary choices. We will call a set of 20 rounds a sub-setting. The number of periods and sub-settings played were set to reduce the likelihood of participants growing bored and to have enough variation in the data to estimate learning patterns.
Baseline treatment: Individual learning We use data from the baseline treatment to detect patterns of individual learning. This situation can be considered "a black box"  [58]. Players take actions and receive payoffs. 10  No information apart from the result of the individual performance is provided to the players. Consequently, learning is the result of an asocial process. In Table  2  we report descriptive statistics concerning self-efficacy, math abilities, payoffs and number of switches between colours. In addition we count,.
Population density map We used rectangular excerpts from the Landscan dataset  [34]  with the lower left corner of the rectangle located on the center of the city of Guangzhou, China. The rectangle is 55km from east to west and 33km from north to south, and a 4km boundary area was excluded after simulation. The boundary area was chosen according to the following rationale: when population density data for large suburban areas is truncated for the purpose of simulation, it is equivalent to imposing empty space outside of the boundary, and this modification may effect the attack rates calculated in pixels close to that boundary. We ran simulations on a large area of 1km by 1km pixels, and on smaller areas contained within this larger area. We found that attack rates agree on all pixels on the interior of the smaller area once a 4km perimeter is removed.
In the fourth step, expert knowledge is collected through two kinds of interviews: on the one hand, individual interviews, and on the other hand, collective and contradictory ones. Collective interviews are organized in order to resolve potential contradictions detected when pooling the data from individual interviews in order to obtain a consensus. The expert knowledge resulting from these interviews is then represented in the fifth step as a tree structure using mind mapping software. As mind map tools are only equipped with standard scripting mechanisms, our approach in the sixth step involves automatically translating the knowledge from the mind map software into the conceptual graph formalism  [Chein and Mugnier, 2009] , which allows specific automatic reasoning tasks to be performed.
The tool runs on CoGui software, which is a conceptual graph editor that, firstly, permits the terminology, facts, rules and constraints of an application domain in a knowledge base to be managed, and secondly, allows this knowledge base to be queried and reasoned. Finally, the DSS designed in the seventh step is an end-user interface with associated programs based on CoGui API, ensuring that end users of the application can easily use it without knowing anything about conceptual graph formalism. This seven-step workflow is an iterative one, as the processing process and/or the expert knowledge on it may evolve. which is being controlled. For instance, temperature is a common controlled parameter.
Some mind mapping tools provide script mechanisms (e. g. Freeplane), but they do not fit well with our needs. In the next section, we will define the notion of ontology, which is well suited to overcoming the weaknesses of mind mapping tools mentioned above. We will then explain why we have chosen the conceptual graph (CG) model as a specific ontology model, and we will review briefly its main principles. Afterwards, we will present the new core ontology, dedicated to the representation of explanation relations between situations, used in this DSS. This core ontology has been published on the INRA Dataverse repository to be shared with the food processing community (https://doi. org/10. 15454/9Z4PS3). We will also describe the algorithm we have developed to automatically translate mind map explanation trees into the CG formalism using this core ontology.
The conceptual graph formalism In order to encode ontologies, we chose to use conceptual graphs (CGs)  [Sowa, 1984]  with CoGui, a software tool which allows CGs to be managed. We chose CGs for several reasons: (i) their terminological support described below allows ontologies to be defined; (ii) CG models provide querying and reasoning mechanisms to retrieve knowledge; and finally, (iii) while CGs have a logical translation in first-order logic as described in  [Chein and Mugnier, 2009] , they are usually expressed graphically, which is very important so that end users can easily interact with them. Finally, CG models created in CoGui offer a query mechanism that allows us to retrieve situations using their relationships with other concepts of the ontology.
Using the CoGui graphical user interface (GUI), Figure  6  shows the core ontology's hierarchy of concept types on the left and its hierarchy of relations on the right. The core ontology allows the explanation tree associated with a given situation of interest to be represented. Figure  7  shows a section of an expla-nation tree expressed using the core ontology. Both the core ontology and section of an explanation tree shown in Figure  7  can be downloaded from the INRA Dataverse repository (https://doi. org/10. 15454/9Z4PS3). Table  1  shows some statistics about knowledge bases for three different domains produced using the core ontology. Table  1 : Statistics about knowledge bases produced for three different domains Finally, it may be noted that the CG model ensures that two nodes with the same marker are equivalent.
Importation from mind mapping tool In order to reduce the business user's workload, we developed an automatic import module which takes as its input the explanatory tree edited using mind mapping tool, and generates as its output a knowledge base using the core ontology described in the previous section. This importation module makes use of mind mapping tool's plain text export feature and the CoGui Core library. By reading the mind mapping tool text export, each mind map node can be processed by creating a concept vertex whose marker is the text associated with the mind map node and whose concept type is a core ontology concept which depends on the mind map node's position in the tree or its specific prefix in the mind map node text. At the same time, the import module links these nodes together with corresponding relations defined by the core ontology.
Intermediate nodes of the trees allow some kinds of logical gates to be modelled in order to express how several failures have an impact on a global failure. Several aggregation nodes are possible, for example, at least one or at least k from n. A whole fault tree can be seen as a Boolean function that provides the global failure state of the system based on the failure of its subsystems. Our core ontology is rather similar to the tree structure used in FTA. Our contribution consists mainly in proposing a semantic representation of the tree structure using the conceptual graph model. This allows the knowledge base to be queried thanks to the core ontology and the domain ontology, which can be specifically defined for each application. Moreover, it should also be noted that FTA focuses mainly on probabilistic risk assessment of failures, which requires a huge amount of numerical data to be implemented, whereas our approach is mainly focused on gathering the collective qualitative technical expertise available for a given domain  (company, factory, etc.
Acknowledgments The research leading to these results has received partial funding from the  CASDAR Docamex Programme  from the  French Ministry of Agriculture  ( 2016 )(2017)(2018)(2019)(2020). It has also benefited from preliminary works performed during one project with the  Technical Centre of Cheeses in Poligny (CTFC)  and two projects with French industrial partners. The authors would also like to thank the CTFC, who authorized us to present in this paper some of 36 the knowledge provided by their technical experts and extracted from their knowledge base.
Background: It is well established that estrogens and other hormonal factors influence breast cancer susceptibility. We hypothesized that a woman's total lifetime estrogen exposure accumulates changes in DNA methylation, detectable in the blood, which could be used in risk assessment for breast cancer. Methods: An estimated lifetime estrogen exposure (ELEE) model was defined using epidemiological data from EPIC-Italy (n = 31,864). An epigenome-wide association study (EWAS) of ELEE was performed using existing Illumina HumanMethylation450K Beadchip (HM450K) methylation data obtained from EPIC-Italy blood DNA samples (n = 216). A methylation index (MI) of ELEE based on 31 CpG sites was developed using HM450K data from EPIC-Italy and the Generations Study and evaluated for association with breast cancer risk in an independent dataset from the Generations Study (n = 440 incident breast cancer cases matched to 440 healthy controls) using targeted bisulfite sequencing.
Results: We observed an estimated 5% increase in breast cancer risk per 1-year longer ELEE (OR = 1. 05, 95% CI 1. 04-1. 07, P = 3 × 10 -12 ) in EPIC-Italy. The EWAS identified 694 CpG sites associated with ELEE (FDR Q < 0. 05). We report a DNA methylation index (MI) associated with breast cancer risk that is validated in the Generations Study targeted bisulfite sequencing data (OR Q4_vs_Q1 = 1. 77, 95% CI 1. 07-2. 93, P = 0. 027) and in the meta-analysis (OR Q4_vs_Q1 = 1. 43, 95% CI 1. 05-2. 00, P = 0. 024); however, the correlation between the MI and ELEE was not validated across study cohorts.
Study cohorts Data from two independent prospective cohort studies were used for the primary analysis: the Italian cohort from the European Prospective Investigation into Cancer and Nutrition study (EPIC-Italy) and the UK-based Generations Study. Questionnaire data and blood samples were collected at the time of study enrollment. EPIC-Italy included epidemiological questionnaire data from 32,059 women (dataset 1) and peripheral blood DNA methylation data measured using the HM450K array for 162 matched pairs of incident breast cancer cases and controls (dataset 2)  [24]. The Generations Study cohort included HM450K peripheral blood DNA array data for a subset of 92 healthy women (dataset 3)  [40]  and 440 matched pairs of incident breast cancer cases and controls (independent from the HM450K dataset), who provided blood DNA samples used for targeted bisulfite sequencing (dataset 4).
Inclusion criteria for incident breast cancer cases in the Generations Study, with blood samples taken prior to diagnosis, were the following: invasive ER-positive breast cancer with no previous history of (non-breast) cancer, white ethnicity, and completeness of epidemiological data. Controls were individually matched to cases on age at blood draw ± 5 years. Additional replication cohorts used in the meta-analysis included an additional 118 case-control pairs from EPIC-Italy (dataset 5), 435 case-control pairs from EPIC-IARC (dataset 6)  [41] , and 310 case-control pairs from the MCCS (dataset 7)  [42]. Further information for these study cohorts is provided in Additional file 1: Supplementary material and methods.
Laboratory analysis HM450K array data generation has been described previously  [24, [40] [41] [42]. For validation, targeted bisulfite sequencing was conducted in the Generations Study (n = 880) using the Fluidigm 48. 48 Access Array. Forty-two CpG sites for validation were selected from the EWAS based on the magnitude of change in DNA methylation (> 0. 1%) per 1-year longer ELEE (mixed-effects linear regression model coefficient) and statistical significance of association with ELEE (beta regression model P value < 7 × 10 -5 ). To estimate WBC composition, five HM450K CpG probes that showed independent correlation with five different WBC types were included in the target panel (Additional file 3: Figure  S1 ). The 880 Generations Study samples were sequenced on the Illumina MiSeq in 20 batches in four sequencing pools (6, 5, 5, and 4 batches).
Statistical analysis Quality control (QC) of the targeted sequencing data was conducted to exclude CpG sites (n = 42) with low coverage or with large difference in DNA methylation levels between duplicated pairs (n = 31 CpG sites passing quality control). A methylation index (MI) of ELEE was developed using the same 31 CpG sites in the HM450K data with complete information for ELEE from both EPIC-Italy (dataset 2, n = 237) and the Generations Study (dataset 3, n = 65, Additional file 2: Table  S2 ). To develop the MI, ridge regression was conducted in a 10-fold cross-validation repeated 100 times, using the R package "glmnet" in "train" in the R package "caret" to estimate the following parameters: penalty coefficient (lambda) for ridge regression and regression coefficients for the model.
The final MI model for predicted ELEE was calculated as a linear function, i. e. , intercept plus the sum of the DNA methylation levels at the CpG sites included in the model weighted by their coefficient. The correlation between the MI and ELEE was evaluated in the Generations Study targeted sequencing data (dataset 4, pairs with coverage > 30 sequence reads for at least 10% of the remaining CpG sites) using the Pearson's correlation coefficient. The association between the MI and risk of breast cancer was assessed using conditional logistic regression (R function "clogit" in the package "survival") for matched case-control pairs in EPIC-Italy HM450K development data (dataset 2, n = 162 pairs), the Generations Study targeted sequencing validation data (dataset 4), and in each of the additional cohorts for the meta-analysis including EPIC-Italy (dataset 5, n = 118 pairs), EPIC-IARC (dataset 6, n = 420 pairs), and MCCS (dataset 7, n = 310 pairs).
The meta-analysis of log odds ratios and standard errors was conducted using a weighted random-effects model, applying the restricted-maximum likelihood method (function "rma. uni" in the R package "metafor"). The Cochran's Q statistics and I 2 statistics were used to estimate heterogeneity between the studies; Q < 0. 05 and I 2 > 50% were defined as heterogeneous estimations. If the heterogeneity I 2 was equal to 0, the meta-analysis behaved as a fixed-effects model. The ORs for all models were adjusted for baseline age, BMI, alcohol consumption, smoking duration, and estimated WBC composition in a multivariable model. Additionally, for each of the target CpG sites passing QC, the association with breast cancer risk was investigated using conditional logistic regression. Further details on methods and workflow are described in Additional file 1: Supplementary material and methods and Additional file 3: Figure  S2.
The ELEE is associated with breast cancer risk in EPIC-Italy The estimated lifetime estrogen exposure (ELEE) was calculated as a woman's reproductive span (age at menopause or, for premenopausal women, age at recruitment, minus age at menarche) minus 1 year per pregnancy and breastfeeding duration before recruitment in years. For EPIC-Italy (dataset 1, n = 1193 cases and 30,671 controls, Additional file 2: Table  S1 ), a 1-year longer ELEE, ranging from 5 to 49, was associated with a 5% increase in breast cancer risk (hazard ratio (HR) = 1. 05, 95% confidence interval (CI) 1. 04-1. 07, P = 3 × 10 -12 , Additional file 2: Table  S3 ) and was associated with breast cancer risk for both pre-and postmenopausal women (HR = 1. 09, 95% CI 1. 03-1. 15, P = 0. 002, and HR = 1. 03, 95% CI 1. 01-1. 05, P = 6 × 10 -4 , Additional file 2: Table  S3 ).
EWAS identifies CpG sites associated with ELEE in EPIC-Italy The EWAS of ELEE conducted in EPIC-Italy (dataset 2, n = 216, Additional file 2: Table  S2 ) gave P values that were moderately inflated (Additional file 3: Figure  S3 ), with a genomic inflation factor lambda of 1. 13. After correction for multiple testing, the EWAS identified 694 CpG probes associated with ELEE (FDR Q < 0. 05, Fig. 1a ) with a mix of hypo-and hypermethylated CpG probes (Fig. 1b ). Two sensitivity analyses were conducted firstly, including all cases and controls with complete information of ELEE (n = 237), and secondly for controls only (n = 119), and estimates from each analysis were highly correlated (r = 0. 997 and r = 0. 963, respectively, Additional file 3: Figure  S4 ). All CpG probes were associated with ELEE (Q < 0.
Quality control of targeted sequencing data prior to model development Targeted bisulfite sequencing of 42 target regions using the Fluidigm 48. 48 Access Array was conducted for 880 samples (440 matched case-control pairs) from the Generations Study (dataset 4). The targeted sequencing data was of high quality: average sequencing depth per CpG site per sample was 1740, > 97% of the reads were assigned to a sample (i. e. , had a barcode sequence), and > 99% of the paired reads were aligned to target regions (Additional file 2: Table  S4 ). Additionally, a high correlation in DNA methylation values was observed between batches, but with some variability in some of the assays (mean r = 0. 88). Eleven CpG sites were excluded from the analysis due either to low coverage across batches or to high variation in DNA methylation levels between duplicated pairs.
The MI is associated with breast cancer risk A methylation index (MI) to predict ELEE was developed in HM450K data from EPIC-Italy (dataset 2, n = 237) and the Generations Study (dataset 3, n = 65, Additional file 2: Table  S2 ) using ridge regression on the 31 target CpG sites passing QC in the targeted bisulfite sequencing data. As expected, the MI correlated with ELEE in the development data; a high correlation was observed in EPIC-Italy (r = 0. 60, P = 6 × 10 -25 ) and moderately correlated in the Generations Study HM450K data (r = 0. 27, P = 0. 027, Fig. 2a ). The correlation between the MI and ELEE, however, was not replicated in the Generations Study targeted sequencing data (n = 678, r = -0. 04, P = 0. 340, Fig. 2b ). We also observed no association between the ELEE and breast cancer risk in the Generations Study (n = 339 matched case-control pairs, age-adjusted odds ratio (OR) = 1.
01, 95% CI 0. 98-1. 04, P = 0. 562), in contrast to EPIC-Italy subjects from the HM450K dataset (n = 162 matched case-control pairs, age-adjusted OR = 1. 10, 95% CI 1. 03-1. 17, P = 0. 007). The correlations between the MI and ELEE were similar for pre-and postmenopausal women in the Generations Study, but stronger for postmenopausal women (r = 0. 72, P = 2 × 10 -22 ) than for premenopausal women (r = 0. 53, P = 7 × 10 -7 ) in EPIC-Italy (test for heterogeneity between pre-and postmenopausal women in EPIC-Italy: Q = 0. 79, I 2 = 0 (no heterogeneity); Generations Study: Q = 0. 42, I 2 = 0 (no heterogeneity)). The association between the MI and breast cancer risk was tested using matched case-control pairs from EPIC-Italy HM450K data and the Generations Study targeted sequencing data (n = 162 and 339 pairs respectively, Table  2 ).
In a multivariable model, each unit increase in the MI, ranging from 27. 6 to 39. 1, was associated with a 51% increase in breast cancer risk in EPIC-Italy (OR = 1. 51, 95% CI 1. 28-1. 82, P = 1 × 10 -5 , Fig. 2c ). The association was validated in the Generations Study targeted sequencing data: A comparable analysis of the Generations Study data gave an estimated 4% increase in risk per unit increase in MI, which ranged from 20. 3 to 53. 1 (OR = 1. 04, 95% CI 1. 01-1. 08, P = 0. 022, Fig. 2d ). The OR estimate was greater for postmenopausal women in both EPIC-Italy and the Generations Study (OR = 1. 91, 95% CI 1. 29-2. 82, P = 0. 001 and OR = 1. 07, 95% CI 1. 02-1. 12, P = 0. 006, respectively) than for premenopausal women (OR = 1. 61, 95% CI 1. 17-2. 22, P = 0. 004 and OR = 1. 01, 95% CI 0.
94-1. 09, P = 0. 713, respectively). However, the test for heterogeneity between pre-and postmenopausal women in EPIC-Italy, Q = 0. 19 and I 2 = 43, and Generations Study, Q = 0. 24 and I 2 = 27, suggested no significant heterogeneity. Sensitivity analysis on the model development was also conducted using controls only in the combined EPIC-Italy and the Generations Study HM450K data (datasets 2 and 3, n = 184). The control-only MI model showed a very similar association with breast cancer risk as previously when tested on the case-control pairs from the Generations Study (n = 339 pairs, OR = 1. 04 per unit increase in MI, 95% CI 1. 01-1. 08, P = 0. 012).
Meta-analysis of the association between MI and breast cancer risk The association between the MI and breast cancer risk was examined in a meta-analysis, excluding the discovery data from EPIC-Italy. The analysis included 2374 women (1187 matched case-control pairs) from four prospective study cohorts, the Generations Study targeted sequencing data, additional subjects from EPIC-Italy, EPIC-IARC, and MCCS, with mean time to diagnosis of 4. 0, 8. 5, 7. 5, and 7. 9 years, respectively (Additional file 2: Table  S7 ). All estimates were adjusted for baseline age, BMI, smoking duration, alcohol consumption, and WBC composition. The combined meta-analysis for MI as a continuous variable showed low heterogeneity across study cohorts (Q = 0. 45, I 2 = 0%) and an association with breast cancer risk, with 4% increase in risk per one unit increase in the MI (OR = 1.
01-1. 10, P = 0. 021, Fig. 4d ). Therefore, the data do not support reverse causation A B Fig. 3  Meta-analysis of the association between MI and breast risk. association between MI and risk for breast cancer, as a continuous variable (a) or as a categorical variable (b), was estimated in the four studies included in the meta-analysis using conditional logistic regression adjusted for age, BMI, alcohol consumption, and smoking duration (all variables reported at recruitment) and WBC composition. The log odds ratios were combined in a meta-analysis using restricted-maximum likelihood model. The square boxes represent the odds ratios (ORs) and the lines the 95% confidence intervals (CIs). a EPIC-Italy corresponds to the new EPIC-Italy samples, not included in the development of the MI as a mechanism for the association with DNA methylation.
Individual associations between each of the 31 target CpG sites and breast cancer risk were further examined in the Generations Study (dataset 4). Four CpG sites located nearest the genes CTNNA2, GRB10, RPH3AL, and TINCR showed individual associations with breast cancer risk in a multivariable model (P < 0. 05, Additional file 2: Table  S6 ) and were also associated with breast cancer risk in matched case-control pairs in EPIC-Italy (dataset 2).
In the meta-analysis, including three other independent datasets, the highest quartile had a 45% higher risk compared with the lowest quartile (Fig. 4 ). There are several potential explanations why the correlation between the MI and ELEE was only seen in the discovery EPIC-Italy dataset and not in the additional validation cohorts. Firstly, it is possible that the observed association between MI and ELEE in EPIC-Italy is a false positive and that the MI was over fitted in this study cohort. It may also be possible that the measured methylation index could capture the biological effects of unknown confounders that are not included in the calculated ELEE model. Alternatively, population-specific differences between the cohorts (or between sub-cohorts of EPIC-Italy) cannot be excluded. For example, there are more smokers in the EPIC studies compared with the Generations Study and MCCS, which considerably affects DNA methylation.