Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning.

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

Origin of enhanced water oxidation activity in an iridium single atom anchored on NiFe oxyhydroxide catalyst.

The efficiency of the synthesis of renewable fuels and feedstocks from electrical sources is limited, at present, by the sluggish water oxidation reaction. Single-atom catalysts (SACs) with a controllable coordination environment and exceptional atom utilization efficiency open new paradigms toward designing high-performance water oxidation catalysts. Here, using operando X-ray absorption spectroscopy measurements with calculations of spectra and electrochemical activity, we demonstrate that the origin of water oxidation activity of IrNiFe SACs is the presence of highly oxidized Ir single atom (Ir5.3+) in the NiFe oxyhydroxide under operating conditions. We show that the optimal water oxidation catalyst could be achieved by systematically increasing the oxidation state and modulating the coordination environment of the Ir active sites anchored atop the NiFe oxyhydroxide layers. Based on the proposed mechanism, we have successfully anchored Ir single-atom sites on NiFe oxyhydroxides (Ir0.1/Ni9Fe SAC) via a unique in situ cryogenic-photochemical reduction method that delivers an overpotential of 183 mV at 10 mA ⋅ cm-2 and retains its performance following 100 h of operation in 1 M KOH electrolyte, outperforming the reported catalysts and the commercial IrO2 catalysts. These findings open the avenue toward an atomic-level understanding of the oxygen evolution of catalytic centers under in operando conditions.

Selective anisotropic growth of Bi2S3 nanoparticles with adjustable optical properties.

We report on the controlled synthesis and functionalization in two steps of elongated Bi2S3 nanoparticles within a wide range of sizes. First, we show the effect of the temperature and reaction time on the synthesis of two series of nanoparticles by the reaction of thioacetamide with bismuth(III) neodecanoate in the presence of organic surfactants. At 105 °C and long reaction times, nanoneedles of about 45 nm in length containing larger crystallites are obtained, while highly crystalline nanorods of about 30 nm in length are dominant at 165 °C, regardless of the reaction time. The optical properties of both types of nanoparticles show an enhancement of the band gap compared to bulk Bi2S3. This is likely to arise from quantum confinement effects caused by the small particle dimensions relative to the typical exciton size, together with an increase in near-infrared absorption due to the anisotropic particle shape. Second, a ligand exchange approach has been developed to transfer the Bi2S3 nanoparticles to aqueous solutions by grafting dimercaptosuccinic acid onto the surface of the particles. The as-prepared coated nanoparticles show good stability in water, in a wide biological pH range, and in phosphate-buffered saline solutions. Overall, this work highlights the controlled design at all levels - from the inorganic core to the organic surface coating - of elongated Bi2S3 nanoparticles, leading to a tunable optical response by tuning their morphology and size.

Machine learning with bond information for local structure optimizations in surface science.

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model.

We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. Our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.

Strong Substrate Mediation of Attractive Lateral Interactions of CO on Cu(110).

The mechanism of chemical reactions between adsorbed species is defined by the combined effects of the adsorbate-substrate potential landscape and lateral interactions. Such lateral interactions are therefore integral to catalytic processes, but their study is often complicated by "substrate mediation", the regulation of a two-body potential between adsorbed particles by the surface itself. Substrate mediation can influence the sign and magnitude of lateral interactions. There are notable exceptions of ordered structures forming at low coverage, indicative of short-range attractive forces where repulsive forces are expected to dominate, suggesting a strong substrate-mediated contribution. To explore further the origins of such interactions, we have investigated the adsorption of CO on Cu(110) using a combination of low-temperature microscopy and first-principles calculations. Our studies reveal that lateral adsorbate interactions, which are constrained by the metal surface, regulate the bonding between the adsorbate and substrate. Anisotropic CO-CO coupling is seen to arise from a perfect balance between the intermolecular accumulation of charge that acts as a glue (chemical coupling) at sufficiently large distances to avoid repulsive effects (dipole-dipole coupling and Pauli's repulsion between electron clouds).

Elucidation of temperature-programmed desorption of high-coverage hydrogen on Pt(211), Pt(221), Pt(533) and Pt(553) based on density functional theory calculations.

In this work we compute high-coverage hydrogen adsorption energies and geometries on the stepped platinum surfaces Pt(211) and Pt(533) which contain a (100)-step type and the Pt(221) and Pt(553) surface with a (111) step edge. We discuss these results in relation to ultra-high-vacuum temperature programmed desorption (TPD) data to elucidate the origin of the desorption features. Our results indicated that on surfaces with a (100)-step type, two distinct ranges of adsorption energy for the step and terrace are observed, which mirrors the TPD spectra for which we find a clear separation of the desorption peaks. For the (111) step type, the TPD spectra show much less separation of the step and terrace features, which we assign to the low individual adsorption energies for H atoms on this step edge. From our results we obtain a much clearer understanding of the surface-hydrogen bonding at high coverages and the origin of the different TPD features present for the two step types studied.

On-Demand Final State Control of a Surface-Bound Bistable Single Molecule Switch.

Modern electronic devices perform their defined action because of the complete reliability of their individual active components (transistors, switches, diodes, and so forth). For instance, to encode basic computer units (bits) an electrical switch can be used. The reliability of the switch ensures that the desired outcome (the component's final state, 0 or 1) can be selected with certainty. No practical data storage device would otherwise exist. This reliability criterion will necessarily need to hold true for future molecular electronics to have the opportunity to emerge as a viable miniaturization alternative to our current silicon-based technology. Molecular electronics target the use of single-molecules to perform the actions of individual electronic components. On-demand final state control over a bistable unimolecular component has therefore been one of the main challenges in the past decade (1-5) but has yet to be achieved. In this Letter, we demonstrate how control of the final state of a surface-supported bistable single molecule switch can be realized. On the basis of the observations and deductions presented here, we further suggest an alternative strategy to achieve final state control in unimolecular bistable switches.

Physical Exertion Immediately Prior to Placental Abruption: A Case-Crossover Study.

While there is consistent evidence that episodes of physical exertion are associated with an immediately higher risk of acute ischemic vascular events, the risk of placental abruption immediately following episodes of physical exertion has not been studied. In a multicenter case-crossover study, we interviewed 663 women with placental abruption at 7 Peruvian hospitals between January 2013 and August 2015. We asked women about physical exertion in the hour before symptom onset and compared this with their frequency of physical exertion over the prior week. Compared with times with light or no exertion, the risk of placental abruption was 7.8 (95% confidence interval (CI): 5.5, 11.0) times greater in the hour following moderate or heavy physical exertion. The instantaneous incidence rate ratio of placental abruption within an hour of moderate or heavy physical exertion was lower for women who habitually engaged in moderate or heavy physical activity more than 3 times per week in the year before pregnancy (rate ratio (RR) = 3.0, 95% CI: 1.6, 5.9) compared with more sedentary women (RR = 17.3, 95% CI: 11.3, 26.7; P for homogeneity < 0.001), and the rate ratio was higher among women with preeclampsia/eclampsia (RR = 13.6, 95% CI: 7.0, 26.2) than among women without (RR = 6.7, 95% CI: 4.4, 10.0; P for homogeneity = 0.07).

Serum Estradiol Level at Intensive Care Unit Admission and Mortality in Critically Ill Patients.

CONTEXT: It has been observed that sex hormones may play a role in inflammatory processes and mortality of critically ill patients. AIMS: The aim was evaluated the relationship between serum estradiol level at Intensive Care Unit (ICU) admission and mortality of critically ill patients. SETTINGS AND DESIGN: This study was a prospective cohort conducted in one mixed ICU. SUBJECTS AND METHODS: In heterogeneous group of critically ill patients admitted to the ICU, we measured serum estradiol at admission time. STATISTICAL ANALYSIS USED: The discrimination to predict mortality of serum estradiol level was assessed by the receiver-operating curve (ROC) curve and its association with mortality by logistic regression analysis. RESULTS: We included 131 patients, 57.3% of which were male. The serum estradiol level measured at ICU admission was significantly higher in nonsurvivors than in survivors: 116 versus 67.2 pg/mL, respectively (P < 0.0001). The area under the ROC of serum estradiol level to predict mortality was 0.74 (P < 0.0001). Serum estradiol level ≥97.9 pg/mL had sensitivity of 60%, specificity of 90%, positive predictive value of 64%, negative predictive value of 88%, positive likelihood ratio of 6, and negative likelihood ratio of 0.44, for predicting mortality. In multivariate analysis, it had relative risk of 6.47 (P = 0.002) for ICU mortality. CONCLUSIONS: The serum estradiol level is elevated in critically ill patients, regardless of gender, especially in those who die. It has good discriminative capacity to predict mortality, and it is an independent risk factor for death in this group of patients.

Substance Use, Bullying, and Body Image Disturbances in Adolescents and Young Adults Under the Prism of a 3D Simulation Program: Validation of MySchool4web.

BACKGROUND: The use of virtual reality or three-dimensional (3D) simulation programs has been explored in different mental health problems and contexts. One of the applications that showed evidence of a suitable assessment for behavioral and emotional problems in adolescents is the 3D questionnaire called MySchool4web. The aim of this work was to analyze the psychometric properties of a tailored version of MySchool4web. MATERIALS AND METHODS: A correlational, nonexperimental, cross-sectional study was carried out. The participants were 668 secondary students from schools in Santiago, Chile with a mean age of 15.74 years. Adolescents completed MySchool4web in the informatics classroom of each school. RESULTS: Results indicated that two of the items had to be removed, and a three-factor solution was found representing the dimensions substance use, bullying, and body image. This final model of 10 items showed acceptable internal consistency per factor, and its scores were significantly associated with other related measures in nine items. CONCLUSIONS: These outcomes suggest that this version of MySchool4web is a reliable and valid measure of a 3D instrument for the screening of risky behaviors and emotional problems in adolescents and young adults in a school context.

The importance of being a lumen.

Advances in tissue engineering and microtechnology have enabled researchers to more easily generate in vitro tissue models that mimic the tissue geometry and spatial organization found in vivo (e.g., vessel or mammary duct models with tubular structures). However, the widespread adoption of these models for biological studies has been slow, in part due to the lack of direct comparisons between existing 2-dimensional and 3-dimensional cell culture models and new organotypic models that better replicate tissue structure. Using previously developed vessel and mammary duct models with 3-dimensional lumen structures, we have begun to explore this question. In a direct comparison between these next generation organotypic models and more traditional methods, we observed differences in the levels of several secreted growth factors and cytokines. In addition, endothelial vessel geometry profoundly affects the phenotypic behavior of carcinoma cells, suggesting that more traditional in vitro assays may not capture in vivo events. Here, we seek to review and add to the increasing evidence supporting the hypothesis that using cell culture models with more relevant tissue structure influences cell fate and behavior, potentially increasing the relevance of biological findings.

An Auto-Tuning PI Control System for an Open-Circuit Low-Speed Wind Tunnel Designed for Greenhouse Technology.

Wind tunnels are a key experimental tool for the analysis of airflow parameters in many fields of application. Despite their great potential impact on agricultural research, few contributions have dealt with the development of automatic control systems for wind tunnels in the field of greenhouse technology. The objective of this paper is to present an automatic control system that provides precision and speed of measurement, as well as efficient data processing in low-speed wind tunnel experiments for greenhouse engineering applications. The system is based on an algorithm that identifies the system model and calculates the optimum PI controller. The validation of the system was performed on a cellulose evaporative cooling pad and on insect-proof screens to assess its response to perturbations. The control system provided an accuracy of <0.06 m·s(-1) for airflow speed and <0.50 Pa for pressure drop, thus permitting the reproducibility and standardization of the tests. The proposed control system also incorporates a fully-integrated software unit that manages the tests in terms of airflow speed and pressure drop set points.

ConcreteXAI: A multivariate dataset for concrete strength prediction via deep-learning-based methods.

Concrete is a prominent construction material globally, owing to its reputed attributes such as robustness, endurance, optimal functionality, and adaptability. Formulating concrete mixtures poses a formidable challenge, mainly when introducing novel materials and additives and evaluating diverse design resistances. Recent methodologies for projecting concrete performance in fundamental aspects, including compressive strength, flexural strength, tensile strength, and durability (encompassing homogeneity, porosity, and internal structure), exist. However, actual approaches need more diversity in the materials and properties considered in their analyses. This dataset outlines the outcomes of an extensive 10-year laboratory investigation into concrete materials involving mechanical tests and non-destructive assessments within a comprehensive dataset denoted as ConcreteXAI. This dataset encompasses evaluations of mechanical performances and non-destructive tests. ConcreteXAI integrates a spectrum of analyzed mixtures comprising twelve distinct concrete formulations incorporating diverse additives and aggregate types. The dataset encompasses 18,480 data points, establishing itself as a cutting-edge resource for concrete analysis. ConcreteXAI acknowledges the influence of artificial intelligence techniques in various science fields. Emphatically, deep learning emerges as a precise methodology for analyzing and constructing predictive models. ConcreteXAI is designed to seamlessly integrate with deep learning models, enabling direct application of these models to predict or estimate desired attributes. Consequently, this dataset offers a resourceful avenue for researchers to develop high-quality prediction models for both mechanical and non-destructive tests on concrete elements, employing advanced deep learning techniques.

Spectrum of somatic mutational features of colorectal tumors in ancestrally diverse populations.

Ancestrally diverse and admixed populations, including the Hispanic/Latino/a/x/e community, are underrepresented in cancer genetic and genomic studies. Leveraging the Latino Colorectal Cancer Consortium, we analyzed whole exome sequencing data on tumor/normal pairs from 718 individuals with colorectal cancer (128 Latino, 469 non-Latino) to map somatic mutational features by ethnicity and genetic ancestry. Global proportions of African, East Asian, European, and Native American ancestries were estimated using ADMIXTURE. Associations between global genetic ancestry and somatic mutational features across genes were examined using logistic regression. TP53 , APC , and KRAS were the most recurrently mutated genes. Compared to non-Latino individuals, tumors from Latino individuals had fewer KRAS (OR=0.64, 95%CI=0.41-0.97, p=0.037) and PIK3CA mutations (OR=0.55, 95%CI=0.31-0.98, p=0.043). Genetic ancestry was associated with presence of somatic mutations in 39 genes (FDR-adjusted LRT p<0.05). Among these genes, a 10% increase in African ancestry was associated with significantly higher odds of mutation in KNCN (OR=1.34, 95%CI=1.09-1.66, p=5.74×10 -3 ) and TMEM184B (OR=1.53, 95%CI=1.10-2.12, p=0.011). Among RMGs, we found evidence of association between genetic ancestry and mutation status in CDC27 (LRT p=0.0084) and between SMAD2 mutation status and AFR ancestry (OR=1.14, 95%CI=1.00-1.30, p=0.046). Ancestry was not associated with tumor mutational burden. Individuals with above-average Native American ancestry had a lower frequency of microsatellite instable (MSI-H) vs microsatellite stable tumors (OR=0.45, 95%CI=0.21-0.99, p=0.048). Our findings provide new knowledge about the relationship between ancestral haplotypes and somatic mutational profiles that may be useful in developing precision medicine approaches and provide additional insight into genomic contributions to cancer disparities. Significance: Our data in ancestrally diverse populations adds essential information to characterize mutational features in the colorectal cancer genome. These results will help enhance equity in the development of precision medicine strategies.

Research Infrastructure Core Facilities at Research Centers in Minority Institutions: Part I-Research Resources Management, Operation, and Best Practices.

Funded by the National Institutes of Health (NIH), the Research Centers in Minority Institutions (RCMI) Program fosters the development and implementation of innovative research aimed at improving minority health and reducing or eliminating health disparities. Currently, there are 21 RCMI Specialized (U54) Centers that share the same framework, comprising four required core components, namely the Administrative, Research Infrastructure, Investigator Development, and Community Engagement Cores. The Research Infrastructure Core (RIC) is fundamentally important for biomedical and health disparities research as a critical function domain. This paper aims to assess the research resources and services provided and evaluate the best practices in research resources management and networking across the RCMI Consortium. We conducted a REDCap-based survey and collected responses from 57 RIC Directors and Co-Directors from 98 core leaders. Our findings indicated that the RIC facilities across the 21 RCMI Centers provide access to major research equipment and are managed by experienced faculty and staff who provide expert consultative and technical services. However, several impediments to RIC facilities operation and management have been identified, and these are currently being addressed through implementation of cost-effective strategies and best practices of laboratory management and operation.

Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico.

Nowadays, society faces a catastrophic problem related to respiratory syndrome due to the coronavirus SARS-CoV-2: the Covid-19 disease. This virus has changed our coexistence rules and, in consequence, has reshaped the daily activities in modern societies. Thus, there are many efforts to understand the virus behaviour in order to reduce its negative impact, and these efforts produce an incredible amount of information and data sources every week. Data scientists, which use techniques such as Machine learning, are focusing their abilities to develop mathematical models for analysing this critical situation. This paper uses Machine Learning techniques as tools to help understand some specific new patterns in Covid patients that arise from unknown complex interactions in the transmission-dynamic models of the SARS-CoV-2 virus, and their relation with the corresponding social contact patterns which are often known or can be inferred from populations variables. One of the main motivations of this research is to find the diseases that cause an increase in the risk of death in infected people with the Covid-19 virus. Mexico is the case of study in this research. The general conditions of health that cause death are well known generally in the world. However, these conditions in each country can differ depending on different factors such as the general health status of people. The results show that the principal causes of death in Mexico are related to age, bad eating habits, chronic diseases, and contact with infected people having not proper care. Results from the analysis show a remarkable accuracy of 87%, which is considered satisfactory.

Engineering the Multi-Enzymatic Activity of Cerium Oxide Nanoparticle Coatings for the Antioxidant Protection of Implants.

Imbalance of oxidants is a universal contributor to the failure of implanted devices and tissues. A sustained oxidative environment leads to cytotoxicity, prolonged inflammation, and ultimately host rejection of implanted devices/grafts. The incorporation of antioxidant materials can inhibit this redox/inflammatory cycle and enhance implant efficacy. Cerium oxide nanoparticles (CONP) is a highly promising agent that exhibits potent, ubiquitous, and self-renewable antioxidant properties. Integrating CONP as surface coatings provides ease in translating antioxidant properties to various implants/grafts. Herein, we describe the formation of CONP coatings, generated via the sequential deposition of CONP and alginate, and the impact of coating properties, pH, and polymer molecular weight, on their resulting redox profile. Investigation of CONP deposition, layer formation, and coating uniformity/thickness on their resulting oxidant scavenging activity identified key parameters for customizing global antioxidant properties. Results found lower molecular weight alginates and physiological pH shift CONP activity to a higher H2O2 to O2 --scavenging capability. The antioxidant properties measured for these various coatings translated to distinct antioxidant protection to the underlying encapsulated cells. Information gained from this work can be leveraged to tailor coatings towards specific oxidant-scavenging applications and prolong the function of medical devices and cellular implants.

Effect of the Addition of Agribusiness and Industrial Wastes as a Partial Substitution of Portland Cement for the Carbonation of Mortars.

The present research work shows the effect on the carbonation of Portland cement-based mortars (PC) with the addition of green materials, specifically residues from two groups: agricultural and industrial wastes, and minerals and fibres. These materials have the purpose of helping with the waste disposal, recycling, and improving the durability of concrete structures. The specimens used for the research were elaborated with CPC 30R RS, according to the Mexican standard NMX-C-414, which is equivalent to the international ASTM C150. The aggregates were taken from the rivers Lerma and Huajumbaro, in the State of Michoacan, Mexico, and the water/cement relation was 1:1 in weight. The carbonation analyses were performed with cylinder specimens in an accelerated carbonation test chamber with conditions of 65 +/- 5% of humidity and 25 +/- 2 °C temperature. The results showed that depending on the PC substitutions, the carbonation front advance of the specimens can increase or decrease. It is highlighted that the charcoal ashes, blast-furnace slags, and natural perlite helped to reduce the carbonation advance compared to the control samples, consequently, they contributed to the durability of concrete structures. Conversely, the sugarcane bagasse ash, brick manufacturing ash, bottom ash, coal, expanded perlite, metakaolin, and opuntia ficus-indica dehydrated fibres additions increased the velocity of carbonation front, helping with the sequestration of greenhouse gases, such as CO2, and reducing environmental pollution.

Ibero-American Endometriosis Patient Phenome: Demographics, Obstetric-Gynecologic Traits, and Symptomatology.

Background: An international collaborative study was conducted to determine the demographic and clinical profiles of Hispanic/Latinx endometriosis patients from Latin America and Spain using the Minimal Clinical Questionnaire developed by the World Endometriosis Research Foundation (WERF) Endometriosis Phenome and Biobanking Harmonization Project (EPHect). Methods: This is a cross-sectional study to collect self-reported data on demographics, lifestyle, and endometriosis symptoms of Hispanic/Latinx endometriosis patients from April 2019 to February 2020. The EPHect Minimal Clinical Questionnaire (EPQ-M) was translated into Spanish. Comprehension and length of the translated survey were assessed by Spanish-speaking women. An electronic link was distributed via social media of endometriosis patient associations from 11 Latin American countries and Spain. Descriptive statistics (frequency, means and SD, percentages, and proportions) and correlations were conducted using SPSSv26. Results: The questionnaire was completed by 1,378 participants from 23 countries; 94.6% had self-reported diagnosis of endometriosis. Diagnostic delay was 6.6 years. Most participants had higher education, private health insurance, and were employed. The most common symptoms were back/leg pain (85.4%) and fatigue (80.7%). The mean number of children was 1.5; 34.4% had miscarriages; the mean length of infertility was 3.7 years; 47.2% reported pregnancy complications. The most common hormone treatment was oral contraceptives (47.0%). The most common comorbidities were migraines (24.1%), polycystic ovary syndrome (PCOS) (22.2%), and irritable bowel syndrome (21.1%). Most participants (97.0%) experienced pelvic pain during menses; for 78.7%, pain was severe; 86.4% reported dyspareunia. The mean age of dysmenorrhea onset was 16.2 years (SD ± 6.1). Hormone treatments were underutilized, while impact was substantial. Pain catastrophizing scores were significantly correlated with pain intensity (p < 0.001). Conclusion: This is the first comprehensive effort to generate a clinical-demographic profile of Hispanic/Latinx endometriosis patients. Differences in clinical presentation compared to other cohorts included higher prevalence and severity of dysmenorrhea and dyspareunia and high levels of pain catastrophizing. Though future studies are needed to dissect the impact of race and ethnicity on pain and impact, this profile is the first step to facilitate the recognition of risk factors and diagnostic features and promote improved clinical management of this patient population. The EPHect questionnaire is an efficient tool to capture data to allow comparisons across ethnicities and geographic regions and tackle disparities in endometriosis research.