High Validation Rate Research Articles

A new Q-matrix validation method based on signal detection theory.

The Q-matrix is a crucial component of cognitive diagnostic theory and an important basis for the research and practical application of cognitive diagnosis. In practice, the Q-matrix is typically developed by domain experts and may contain some misspecifications, so it needs to be refined using Q-matrix validation methods. Based on signal detection theory, this paper puts forward a new Q-matrix validation method (i.e., method) and then conducts a simulation study to compare the new method with existing methods. The results show that when the model is DINA (deterministic inputs, noisy 'and' gate), the method outperforms the existing methods under all conditions; under the generalized DINA (G-DINA) model, the method still has the highest validation rate when the sample size is small, and the item quality is high or the rate of Q-matrix misspecification is ≥.4. Finally, a sub-dataset of the PISA 2000 reading assessment is analysed to evaluate the reliability of the method.

Integrating μCT imaging of core plugs and transfer learning for automated reservoir rock characterization and tomofacies identification

Traditional methods of core characterization are time-consuming and require the considerable effort of a large number of experts. At the same time, it is quite difficult to readily detect small but important elements in reservoir rocks, especially inside the core volume, that can have a strong impact on assessing reservoir potential and hydrodynamic properties. This research explores the integration of high-resolution micro-computed tomography (μCT) imaging and transfer learning for the automated characterization of reservoir rocks and tomofacies identification. The study involved collecting and marking a dataset containing 66,560 μCT images of 130 standard core plugs containing all major reservoir rock types. Using this dataset of 2D μCT images of core plugs, we applied convolutional neural networks (CNNs) pre-trained on ImageNet and fine-tuned them for rock classification tasks. Applying a transfer learning approach, we trained seven ResNet-50 models, which were able to identify and classify reservoir rock features in μCT images. These features included basic characteristics such as rock type, general texture, the presence of fractures and sulfidization that are common for all reservoir rocks. Further, depending on a specific rock type, individual features such as grain size, individual sedimentological texture and the presence of organic matter zones in the sample could be determined by the specialized model. The results show an average classification accuracy of over 94%, indicating the effectiveness of transfer learning in enhancing rock characterization. The obtained models demonstrated high validation rates and were organized into a workflow for automated rock feature characterization. A case study conducted on a carbonate core from a real oilfield provided a practical demonstration of the automated system's work, whose predictions were validated by traditional analyses such as X-ray diffraction and thin-section petrography. The produced models demonstrated the ability for rapid detection of subtle details that might otherwise have been missed during manual inspection. In addition, the concept of tomofacies resulting from the integration of μCT images and machine learning predictions has proven useful in highlighting different reservoir zones, allowing us to understand their heterogeneity and the ability to separate rock sections despite their apparent monotony. These findings suggest that the integration of μCT imaging and transfer learning is a promising approach for improving the automation and accuracy of reservoir rock characterization. The developed technique represents a significant advance in reservoir characterization, offering rapid and accurate identification of rock properties that can improve exploration and development strategies.

How Development of Assessment Instruments with Quizizz Paper Mode in Procedure Texts: a Discourse Analysis Study

This study addresses the need for more engaging assessment tools in Indonesian language lessons for Class VII students at SMP Negeri 2 Sibolangit. It aims to (1) Explore the process of developing assessment instruments using Quizizz paper mode for procedure text materials, (2) Assess the validity of the Quizizz-assisted assessment instrument, and (3) Evaluate its practicality in classroom settings. Following the Borg and Gall research and development model, the study involves eight stages, from data collection to dissemination. Data analysis combines quantitative insights from questionnaires with qualitative feedback from validators and users. Results indicate high validity ratings from assessment and design experts, alongside strong practicality ratings from educators and students, highlighting the effectiveness of the developed assessment tool. Moreover, the discourse analysis approach provides valuable insights into how the assessment instruments facilitate meaningful interactions and learning experiences in the classroom, suggesting broader implications for curriculum design and pedagogical practices.

Acetylation of histone H2B marks active enhancers and predicts CBP/p300 target genes

Chromatin features are widely used for genome-scale mapping of enhancers. However, discriminating active enhancers from other cis-regulatory elements, predicting enhancer strength and identifying their target genes is challenging. Here we establish histone H2B N-terminus multisite lysine acetylation (H2BNTac) as a signature of active enhancers. H2BNTac prominently marks candidate active enhancers and a subset of promoters and discriminates them from ubiquitously active promoters. Two mechanisms underlie the distinct H2BNTac specificity: (1) unlike H3K27ac, H2BNTac is specifically catalyzed by CBP/p300; (2) H2A–H2B, but not H3–H4, are rapidly exchanged through transcription-induced nucleosome remodeling. H2BNTac-positive candidate enhancers show a high validation rate in orthogonal enhancer activity assays and a vast majority of endogenously active enhancers are marked by H2BNTac and H3K27ac. Notably, H2BNTac intensity predicts enhancer strength and outperforms current state-of-the-art models in predicting CBP/p300 target genes. These findings have broad implications for generating fine-grained enhancer maps and modeling CBP/p300-dependent gene regulation.

Extru-seq: a method for predicting genome-wide Cas9 off-target sites with advantages of both cell-based and in vitro approaches

We present a novel genome-wide off-target prediction method named Extru-seq and compare it with cell-based (GUIDE-seq), in vitro (Digenome-seq), and in silico methods using promiscuous guide RNAs with large numbers of valid off-target sites. Extru-seq demonstrates a high validation rate and retention of information about the intracellular environment, both beneficial characteristics of cell-based methods. Extru-seq also shows a low miss rate and could easily be performed in clinically relevant cell types with little optimization, which are major positive features of the in vitro methods. In summary, Extru-seq shows beneficial features of cell-based and in vitro methods.

An Acceptance and Commitment Training (ACT) Framework for Teaching Cultural Humility: A Guide for Translating ACT from a Therapeutic Context into a Medical Education Curriculum.

The objective of this project was to train future physicians to work effectively and thoughtfully with diverse populations by teaching them to employ Acceptance and Commitment Training (ACT) skills to increase cultural humility, with the goal of improving attitudes, knowledge, and beliefs about working with diverse patients. We developed ACT for cultural humility online interactive modules as part of an elective course to teach Medical Spanish to 4th-year medical students. Pre- and post-pilot data pertaining to the cultural humility training modules on the Work-Related Acceptance and Action questionnaire, Multidimensional Cultural Humility Scale, knowledge, attitudes, and beliefs were analyzed using paired samples t-tests and Wilcoxon signed-rank tests. We also included descriptive data pertaining to overall satisfaction with the cultural humility modules and intent to apply the material learned to patient care. Our data showed a significant increase in the cultural humility of our participants as well as an increase in psychological flexibility, a higher favorability rating toward various ethnicities, improvements in attitude, and positive changes in beliefs and knowledge following completion of the modules. The modules were well received by the medical students, with high social validity ratings. The ACT for cultural humility curriculum has great potential to enhance medical education in diversity, equity, and inclusion by increasing both the understanding and the cultural humility of medical students and future professionals to work with diverse populations. The current paper provides a framework that can be used by other programs to shape the education of the future medical workforce to help promote culturally humble care.

Exome sequencing in multiplex families with left-sided cardiac defects has high yield for disease gene discovery.

Congenital heart disease (CHD) is a common group of birth defects with a strong genetic contribution to their etiology, but historically the diagnostic yield from exome studies of isolated CHD has been low. Pleiotropy, variable expressivity, and the difficulty of accurately phenotyping newborns contribute to this problem. We hypothesized that performing exome sequencing on selected individuals in families with multiple members affected by left-sided CHD, then filtering variants by population frequency, in silico predictive algorithms, and phenotypic annotations from publicly available databases would increase this yield and generate a list of candidate disease-causing variants that would show a high validation rate. In eight of the nineteen families in our study (42%), we established a well-known gene/phenotype link for a candidate variant or performed confirmation of a candidate variant’s effect on protein function, including variants in genes not previously described or firmly established as disease genes in the body of CHD literature: BMP10, CASZ1, ROCK1 and SMYD1. Two plausible variants in different genes were found to segregate in the same family in two instances suggesting oligogenic inheritance. These results highlight the need for functional validation and demonstrate that in the era of next-generation sequencing, multiplex families with isolated CHD can still bring high yield to the discovery of novel disease genes.

Abstract LB519: Bimodality of gene expression in cancer patients as interpretable biomarkers for drug sensitivity

Abstract Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. We showcased the performance of the approach in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets. These results along with in vivo validation support a better translation of gene expression biomarkers between model systems using bimodal genes. Citation Format: Benjamin Haibe-Kains, Wail Ba-alawi. Bimodality of gene expression in cancer patients as interpretable biomarkers for drug sensitivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB519.

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity.

A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.

Development of a Metric to Detect and Decrease Low-Value Prescribing in Older Adults

Metrics that detect low-value care in common forms of health care data, such as administrative claims or electronic health records, primarily focus on tests and procedures but not on medications, representing a major gap in the ability to systematically measure low-value prescribing. To develop a scalable and broadly applicable metric that contains a set of quality indicators (EVOLV-Rx) for use in health care data to detect and reduce low-value prescribing among older adults and that is informed by diverse stakeholders' perspectives. This qualitative study used an online modified-Delphi method to convene an expert panel of 15 physicians and pharmacists. This panel, comprising clinicians, health system leaders, and researchers, was tasked with rating and discussing candidate low-value prescribing practices that were derived from medication safety criteria; peer-reviewed literature; and qualitative studies of patient, caregiver, and physician perspectives. The RAND ExpertLens online platform was used to conduct the activities of the panel. The panelists were engaged for 3 rounds between January 1 and March 31, 2021. Panelists used a 9-point Likert scale to rate and then discuss the scientific validity and clinical usefulness of the criteria to detect low-value prescribing practices. Candidate low-value prescribing practices were rated as follows: 1 to 3, indicating low validity or usefulness; 3.5 to 6, uncertain validity or usefulness; and 6.5 to 9, high validity or usefulness. Agreement among panelists and the degree of scientific validity and clinical usefulness were assessed using the RAND/UCLA (University of California, Los Angeles) Appropriateness Method. Of the 527 low-value prescribing recommendations identified, 27 discrete candidate low-value prescribing practices were considered for inclusion in EVOLV-Rx. After round 1, 18 candidate practices were rated by the panel as having high scientific validity and clinical usefulness (scores of ≥6.5). After round 2 panel deliberations, the criteria to detect 19 candidate practices were revised. After round 3, 18 candidate practices met the inclusion criteria, receiving final median scores of 6.5 or higher for both scientific validity and clinical usefulness. Of those practices that were not included in the final version of EVOLV-Rx, 3 received high scientific validity (scores ≥6.5) but uncertain clinical usefulness (scores <6.5) ratings, whereas 6 received uncertain scientific validity rating (scores <6.5). This study culminated in the development of EVOLV-Rx and involved a panel of experts who identified the 18 most salient low-value prescribing practices in the care of older adults. Applying EVOLV-Rx may enhance the detection of low-value prescribing practices, reduce polypharmacy, and enable older adults to receive high-value care across the full spectrum of health services.

Analisis Rasio Keuangan Untuk Memprediksi Financial Distress Perusahaan Manufaktur Di BEI

&#x0D; This study aims to examine the prediction of the company's financial difficulties using the Altman Z-score 1968 model and the effect of financial ratios including working capital to total assets, retained earnings to total assets, earnings before interest and tax to total assets, market value equity to book value. of total liabilities, and sales to total assets on financial distress. The sample used in this study is a manufacturing company listed on the Indonesia Stock Exchange (BEI) 2015-2019. Sampling in this study using purposive sampling method and obtained 64 companies.&#x0D; The results showed that the variables Working Capital to Total Assets (X1), Retained Earnings to Total Assets (X2), Earnings Before Interest and Tax to Total Assets (X3), Market Value Equity To Book Value of Total Liabilities (X4), and Sales to Total Assets (X5) has a positive effect on financial distress, and the most significant effect on financial distress is the variable Retained Earnings to Total Assets. From the results of SPSS 17.0 processing, the equation Z = -1,813 + 1,216 X1 + 1,837 X2 + 0.122 X3 + 0.070 X4 + 0.506 X5 is produced. Meanwhile, the discriminant model that was formed had a high enough validation rate, namely 97.6%.&#x0D; Keywords: Financial ratio analysis; Financial distress; Altman Z-score

Abstract PO-070: Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

Abstract Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets. Citation Format: Wail Ba-alawi, Sisira Kadambat Nair, Bo Li, Anthony Mammoliti, Petr Smirnov, Arvind Singh Mer, Linda Penn, Benjamin Haibe-Kains. Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-070.

SeekCRIT: Detecting and characterizing differentially expressed circular RNAs using high-throughput sequencing data.

Over the past two decades, researchers have discovered a special form of alternative splicing that produces a circular form of RNA. Although these circular RNAs (circRNAs) have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of current studies has been on the tissue-specific circRNAs that exist only in one tissue but not in other tissues or on the disease-specific circRNAs that exist in certain disease conditions, such as cancer, but not under normal conditions. This approach was conducted in the relative absence of methods that analyze a group of common circRNAs that exist in both conditions, but are more abundant in one condition relative to another (differentially expressed). Studies of differentially expressed circRNAs (DECs) between two conditions would serve as a significant first step in filling this void. Here, we introduce a novel computational tool, seekCRIT (seek for differentially expressed CircRNAs In Transcriptome), that identifies the DECs between two conditions from high-throughput sequencing data. Using rat retina RNA-seq data from ischemic and normal conditions, we show that over 74% of identifiable circRNAs are expressed in both conditions and over 40 circRNAs are differentially expressed between two conditions. We also obtain a high qPCR validation rate of 90% for DECs with a FDR of < 5%. Our results demonstrate that seekCRIT is a novel and efficient approach to detect DECs using rRNA depleted RNA-seq data. seekCRIT is freely downloadable at https://github.com/UofLBioinformatics/seekCRIT. The source code is licensed under the MIT License. seekCRIT is developed and tested on Linux CentOS-7.

TRiCoLOR: tandem repeat profiling using whole-genome long-read sequencing data.

BackgroundTandem repeat sequences are widespread in the human genome, and their expansions cause multiple repeat-mediated disorders. Genome-wide discovery approaches are needed to fully elucidate their roles in health and disease, but resolving tandem repeat variation accurately remains a challenging task. While traditional mapping-based approaches using short-read data have severe limitations in the size and type of tandem repeats they can resolve, recent third-generation sequencing technologies exhibit substantially higher sequencing error rates, which complicates repeat resolution.ResultsWe developed TRiCoLOR, a freely available tool for tandem repeat profiling using error-prone long reads from third-generation sequencing technologies. The method can identify repetitive regions in sequencing data without a prior knowledge of their motifs or locations and resolve repeat multiplicity and period size in a haplotype-specific manner. The tool includes methods to interactively visualize the identified repeats and to trace their Mendelian consistency in pedigrees.ConclusionsTRiCoLOR demonstrates excellent performance and improved sensitivity and specificity compared with alternative tools on synthetic data. For real human whole-genome sequencing data, TRiCoLOR achieves high validation rates, suggesting its suitability to identify tandem repeat variation in personal genomes.

Reliability Assessment of an Observation Tool to Measure Praise Characteristics

Supported by decades of research on praise and its effect on student behaviors, we developed the Behavior-Specific Praise–Observation Tool (BSP-OT) to measure characteristics of effective praise. We evaluated interrater reliability of the BSP-OT to measure praise specificity, contingency, and variety using intraclass correlation (ICC) and Cohen’s kappa statistics. In addition, we assessed usefulness and practicality of the tool with social validity measures. Four raters with experience in praise research completed a survey and coded videos ( n = 14) of teachers in authentic situations delivering praise. Overall assessment indicated strong reliability between raters with ICC(2, k) of .80: 95% confidence interval (CI) = [0.77, 0.83], F(269, 19906) = 5.1, p &lt; .001, and mean kappa score of .91. Furthermore, high social validity ratings suggest the BSP-OT is a valuable contribution to the field concerning praise research and teacher development. The process of developing the BSP-OT and study findings are presented, with a discussion of implications and suggestions for future research.

Favourable and Restrictive Factors for Quercus pubescens in the Transylvanian Basin, Evaluated by GIS Techniques

A very simple analysis of the forested areas across the Transylvanian Basin shows that they cover approximately 45.8% of the area, according to data provided by the European Environment Agency. In order to extend the areas covered by forests, especially over badlands specific for the Transylvanian Basin and to increase the economic and environmental value of these lands, a GIS model of spatial analysis has been developed to identify the areas favourable for downy oak (Quercus pubescens) plantations, a forest species which has specific requirements in terms of adaptability conditions. The developed spatial analysis model is based on the unitary analysis of the climatic, soil and geomorphologic components, spatially materialized as raster format databases, and their integration according to spatial analysis equations in order to get a modelled database which represents spatially the favourable areas for the creation of downy oak plantations. The result of this study highlights the territories which provide favourable but also restrictive conditions for Quercus pubescens. The model has a high predictability rate taking as comparative reference the direct monitoring at the level of forest planning units (PUs) within Cluj County, identified from the analysis of forestry plans. The high validation rate of the proposed model was obtained by overlapping the favourability classes which themselves were obtained after modelling with the limits of the forest planning units where the consistency of the downy oak is greater than 0.7. According to the presented validation procedure, a 93% validation rate was obtained, fact which highlights the usefulness of applying the model in areas having similar features and its extrapolation in areas where the environmental conditions present only slight differences.&#x0D; &#x0D; *********&#x0D; In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 4, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue.&#x0D; *********

On the representativeness of pump water samples versus manta sampling in microplastic analysis

To broaden the understanding of sources, pathways and sinks for microplastic pollution in the environment, the exact and representative determination of pollution levels is crucial. Still, sampling techniques differ greatly between studies and the influence of these differences is not fully understood. Thus, we evaluate the representativeness of manta trawling and pump sampling for microplastics in a freshwater lake. While large microplastics are not captured by most pump sampling approaches due to their low abundance, small and fibrous microplastics pass the relatively coarse nets of volume-reduced techniques. Testing different water volumes for pump samples, we show that sample volumes should be large enough to minimize overestimation induced by scaling up results. Moreover, we discuss the influence of sample numbers for microplastic analysis. Finally, we argue that manta trawling and pump sampling are complementary techniques, as they cover different parts of the overall microplastic pollution.

Morphoagronomic and molecular characterization of ornamental passion fruit cultivars

Abstract: The objective of this work was to validate the morphoagronomic descriptors used in the protection processes of plant cultivars in Brazil, by characterizing six cultivars of ornamental passion fruit. The BRS Rubiflora, BRS Rosea Púrpura, BRS Céu do Cerrado, BRS Roseflora, BRS Estrela do Cerrado, and BRS Pérola do Cerrado cultivars, 33 morphoagronomic descriptors, and two molecular markers were used. The categorical morphoagronomic descriptors were analyzed by frequency distribution and multivariate analyses. The quantitative morphoagronomic descriptors were subjected to the analysis of variance and to the comparison of the means of each cultivar. The random amplified polymorphic DNA (RAPD) and the inter-simple sequence repeats (ISSR) markers were used for molecular analysis. A high-validation rate was observed for the morphoagronomic descriptors used in the protection of plant cultivars. The analyses of variance showed significant differences between the quantitative descriptors, and the molecular markers confirmed the genetic differences among the cultivars. There was a high correlation between the calculated distances based on the categorical morphoagronomic descriptors and molecular markers. The morphoagronomic descriptors and molecular markers are useful and complementary for the characterization and differentiation of cultivars.

A new discrete dynamic model of ABA-induced stomatal closure predicts key feedback loops.

Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated. We integrated and processed hundreds of disparate observations regarding ABA signal transduction responses underlying stomatal closure into a network of 84 nodes and 156 edges and, as a result, established those relationships, including identification of a 36-node, strongly connected (feedback-rich) component as well as its in- and out-components. The network’s domination by a feedback-rich component may reflect a general feature of rapid signaling events. We developed a discrete dynamic model of this network and elucidated the effects of ABA plus knockout or constitutive activity of 79 nodes on both the outcome of the system (closure) and the status of all internal nodes. The model, with more than 1024 system states, is far from fully determined by the available data, yet model results agree with existing experiments in 82 cases and disagree in only 17 cases, a validation rate of 75%. Our results reveal nodes that could be engineered to impact stomatal closure in a controlled fashion and also provide over 140 novel predictions for which experimental data are currently lacking. Noting the paucity of wet-bench data regarding combinatorial effects of ABA and internal node activation, we experimentally confirmed several predictions of the model with regard to reactive oxygen species, cytosolic Ca2+ (Ca2+c), and heterotrimeric G-protein signaling. We analyzed dynamics-determining positive and negative feedback loops, thereby elucidating the attractor (dynamic behavior) repertoire of the system and the groups of nodes that determine each attractor. Based on this analysis, we predict the likely presence of a previously unrecognized feedback mechanism dependent on Ca2+c. This mechanism would provide model agreement with 10 additional experimental observations, for a validation rate of 85%. Our research underscores the importance of feedback regulation in generating robust and adaptable biological responses. The high validation rate of our model illustrates the advantages of discrete dynamic modeling for complex, nonlinear systems common in biology.

Associations among body size across the life course, adult height and endometriosis.

Are body size across the life course and adult height associated with endometriosis? Endometriosis is associated with lean body size during childhood, adolescence and adulthood; tall total adult height; and tall sitting height. The literature suggests that both adult body size and height are associated with endometriosis risk, but few studies have investigated the role of body size across the life course. Additionally, no study has investigated the relationships between components of height and endometriosis. We used a nested case-control design within E3N (Etude Epidémiologique auprès de femmes de l'Education Nationale), a prospective cohort of French women. Data were updated every 2-3 years through self-administered questionnaires. Odds ratios (ORs) and 95% CIs were computed using logistic regression models adjusted for a priori confounding factors. A total of 2416 endometriosis cases were reported as surgically ascertained among the 61 208 included women. The odds of endometriosis were lower among women who reported having a large versus lean body size at 8 years (P for trend = 0.003), at menarche (P for trend < 0.0001) and at ages 20-25 years (P for trend < 0.0001). Women in the highest quartiles of height had statistically significantly increased odds of endometriosis compared to those in the lowest (<158 cm) (162-164 cm: OR = 1.28, 95% CI = 1.12-1.46; ≥165 cm: OR = 1.33, 95% CI = 1.18-1.49, P for trend < 0.0001). Statistically significantly increased odds were also observed among women with a taller sitting height (OR = 1.24, 95% CI = 1.05-1.47, P for trend = 0.01). Leg length was not statistically significantly associated with endometriosis. Endometriosis cases may be prone to misclassification; however, we restricted our case definition to surgically-confirmed cases, which showed a high validation rate. Body size is based on retrospective self-report, which may be subject to recall bias. The results of this study suggest that endometriosis is positively associated with lean body size across the life course and total adult height. They also suggest that components of height are associated with endometriosis, which should be investigated further. The Mutuelle Générale de l'Education Nationale (MGEN); the European Community; the French League against Cancer (LNCC); Gustave Roussy; the French Institute of Health and Medical Research (Inserm). L.V.F. was supported by a T32 grant (#HD060454) in reproductive, perinatal and pediatric epidemiology from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Cancer Institute (3R25CA057711) National Institutes of Health. M.K. was supported by a Marie Curie Fellowship within the seventh European Community Framework Programme (#PIOF-GA-2011-302078). The authors have no conflicts of interest to declare.