This paper reports an expansion of the English as a second language (L2) component of the Multilingual Eye Movement Corpus (MECO L2), an international database of eye movements during text reading. While the previous Wave 1 of the MECO project (Kuperman et al., 2023) contained English as a L2 reading data from readers with 12 different first language (L1) backgrounds, the newly collected dataset adds eye-tracking data on English text reading from 13 distinct L1 backgrounds (N = 660) as well as participants’ scores on component skills of English proficiency and information about their demographics and language background and use. The paper reports reliability estimates, descriptive statistics, and correlational analyses as means to validate the expansion dataset. Consistent with prior literature and the MECO Wave 1, trends in the MECO Wave 2 data include a weak correlation between reading comprehension and oculomotor measures of reading fluency and a greater L1-L2 contrast in reading fluency than reading comprehension. Jointly with Wave 1, the MECO project includes English reading data from more than 1,200 readers representing a diversity of native writing systems (logographic, abjad, abugida, and alphabetic) and 19 distinct L1 backgrounds. We provide multiple pointers to new venues of how L2 reading researchers can mine this rich publicly available dataset.

Honeybees (Apis mellifera) and native bee species have ecological, economic, social, and cultural importance to smallholder coffee farmers. While the ecological contributions of bees to the sustainability of coffee systems are well documented, particularly in relation to the coffee crop, fewer studies have examined socio-economic dimensions of beekeeping for honey as an agroecological diversification strategy for coffee producers. Yet, understanding the multiple values of different diversification strategies is important as many coffee farmers in different parts of the world are finding it increasingly difficult to make a living on coffee alone and are adopting alternative strategies, such as on-farm diversification. In this Participatory Action Research (PAR) study, we examined the opportunities, limitations, and trade-offs of beekeeping (with A. mellifera) as an agroecological diversification option for smallholder coffee farmers in Chiapas, Mexico. We applied a mixed-methods approach, which consisted of monthly surveys with 25 beekeepers of Campesinos Ecológicos de la Sierra Madre de Chiapas (CESMACH)/Apicultores Miel Real del Triunfo (ART) producer cooperatives for 12 months and five focus groups between 2018 and 2019. We found that beekeeping is less labor-intensive than coffee, and there are opportunities to integrate beekeeping into the annual farming cycle of coffee and maize production without causing competing labor demands or additional time pressures. We also found that beekeeping could generate economic gains for peasant families; however, profitability hinged on various factors, such as the price for honey, yield per hive, and the number of beehives. Our results further show that beekeeping yielded multiple non-monetary benefits by contributing to the nutrition and health of farmer families and their communities, serving as a vehicle for horizontal learning and relationship building, and contributing to the emotional well-being of beekeepers. Finally, producers who hoped to gain economically from beekeeping were generally interested in growing their apiaries but expressed concerns about limited technical knowledge and the impacts of climate change. Given the multiple social, economic, and ecological benefits of beekeeping, it has great promise as a part of agroecological food and farming systems. We argue that efforts to promote beekeeping as a diversification strategy should take a holistic approach, underscoring the potential of apiculture to enhance the well-being and resilience of beekeeping families and strengthen food sovereignty and local economies (including solidarity economies) in peasant communities. These findings can be useful in supporting beekeepers and their organizations in strategic planning for enhancing the long-term sustainability of beekeeping.

Indigenous people worldwide are at increased risk of mental health problems compared with non-Indigenous people. Longstanding impacts of colonisation, systematic exclusion from rights and subsequent discrimination, and lack of access to quality education and healthcare, including mental healthcare, have been identified as contributory factors to these disproportionate mental health problems. With limited access, Indigenous people are less likely to seek healthcare, owing to the insufficient number of healthcare professionals representing Indigenous communities. In the face of growing numbers of mental health problems in Indigenous people in Bangladesh, this paper sheds light on the inadequate number of mental health professionals, particularly from Indigenous communities, and the potential impacts of this on the well-being of Indigenous people, and considers ways to increase representation of Indigenous mental health professionals. The aim is to ensure that the mental health system in Bangladesh is inclusive and embraces the country's diversity.

This review aims to assess the prevalence of malaria in pregnancy during antenatal visits and delivery, species-specific burden together with regional variation in the burden of disease. It also aims to estimate the proportions of adverse pregnancy outcomes in malaria-positive women. Based on the PRISMA guidelines, a thorough and systematic search was conducted in July 2023 across two electronic databases (including PubMed and CENTRAL). Forest plots were constructed for each outcome of interest highlighting the effect measure, confidence interval, sample size, and its associated weightage. All the statistical meta-analysis were conducted using R-Studio version 2022.07. Sensitivity analyses, publication bias assessment, and meta-regression analyses were also performed to ensure robustness of the review. According to the pooled estimates of 253 studies, the overall prevalence of malaria was 18.95% (95% CI: 16.95–21.11), during antenatal visits was 20.09% (95% CI: 17.43–23.06), and at delivery was 17.32% (95% CI: 14.47–20.61). The highest proportion of malarial infection was observed in Africa approximating 21.50% (95% CI: 18.52–24.81) during ANC and 20.41% (95% CI: 17.04–24.24) at the time of delivery. Our analysis also revealed that the odds of having anaemia were 2.40 times (95% CI: 1.87–3.06), having low birthweight were 1.99 times (95% CI: 1.60–2.48), having preterm birth were 1.65 times (95% CI: 1.29–2.10), and having stillbirths were 1.40 times (95% CI: 1.15–1.71) in pregnant women with malaria.

OBJECTIVES/GOALS: Scholarly achievement metrics are essential for academic researchers since they are used for promotion and funding opportunities. Our objective was to create awareness among pilot project PIs about how these scholarly activities are evaluated and about the need for continuous auto-evaluation. METHODS/STUDY POPULATION: NIH-sponsored Clinical & Translational Research (CTR) infrastructure grants are a critical mechanism to increase scholarly activity. The Pilot Project Program Core (PPP) of The Alliance IDeA-CTR created a weighted metrics system to evaluate activities including presentations, publications, promotion, honors, & community service. We used the revised evidence-based medicine pyramid to develop the metric instrument. Pilot project PIs received the metric table and met quarterly with a PPP member to discuss progress. The top PIs were acknowledged during the Alliance Research Day with a platform presentation and a monetary award for research expenses or travel to scientific meetings. RESULTS/ANTICIPATED RESULTS: During our first 2 pilot project calls (2020-2022) the PPP funded 7 one-year pilot projects for ($50,000 each). We had a total of 10 PIs, 2 of the projects were MPI. Seven PIs were early or new stage investigators (ESI/NSI). Using the productivity award metric we had a total of 33 presentations, 10 publications, 12 events of community service, and 2 external grant funding. These are significant outcomes considering the pandemics impact on clinical & translational research. A total of 3 awards were given, one award per year for funding and an overall award. The activity was well received by the PIs who actively participated in the tracking of their scholarly activities using the metric. DISCUSSION/SIGNIFICANCE: Productivity metrics are crucial for the career development of ESI and NSI by raising awareness regarding the importance of scholarly activities in their career. This activity will help them track their productivity in an ongoing manner while becoming independent researchers.

In a large healthcare worker cohort, we quantified the association between behaviors and risk of coronavirus disease 2019 (COVID-19) during different pandemic phases, adjusting for prior infection and vaccination. Individual characteristics, including personal concerns, were associated with these behaviors. Public health messaging should target high-risk populations and behaviors as the pandemic evolves.

Stigma against mental disability within the medical field continues to impose significant barriers on physicians and trainees. Here, we examine several implications of this stigma and propose steps toward greater inclusion of persons with mental disabilities in the physician workforce.

Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding routine, such as the ability to interpolate or extrapolate in the initial conditions’ space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using deep Gaussian processes as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses long short-term memory networks, for the interpolation.

Branched-chain amino acids (BCAA) are considered markers of insulin resistance (IR) in subjects with obesity. In this study, we evaluated whether the presence of the SNP of the branched-chain aminotransferase 2 (BCAT2) gene can modify the effect of a dietary intervention (DI) on the plasma concentration of BCAA in subjects with obesity and IR. A prospective cohort study of adult subjects with obesity, BMI ≥ 30 kg/m2, homeostatic model assessment-insulin resistance (HOMA-IR ≥ 2·5) no diagnosed chronic disease, underwent a DI with an energy restriction of 3140 kJ/d and nutritional education for 1 month. Anthropometric measurements, body composition, blood pressure, resting energy expenditure, oral glucose tolerance test results, serum biochemical parameters and the plasma amino acid profile were evaluated before and after the DI. SNP were assessed by the TaqMan SNP genotyping assay. A total of eighty-two subjects were included, and fifteen subjects with a BCAT2 SNP had a greater reduction in leucine, isoleucine, valine and the sum of BCAA. Those subjects also had a greater reduction in skeletal muscle mass, fat-free mass, total body water, blood pressure, muscle strength and biochemical parameters after 1 month of the DI and adjusting for age and sex. This study demonstrated that the presence of the BCAT2 SNP promotes a greater reduction in plasma BCAA concentration after adjusting for age and sex, in subjects with obesity and IR after a 1-month energy-restricted DI.

Introduction. Smoking cessation after a cancer diagnosis can significantly improve treatment outcomes and reduce the risk of cancer recurrence and all-cause mortality. Aim. We sought to measure the association between cancer diagnosis and subsequent smoking cessation. Methods. Data was sourced from the Population Assessment of Health and Tobacco (PATH) study, a representative population-based sample of United States adults. Our analytic sample included all adult smokers at Wave I, our baseline. The exposure of interest was either a tobacco-related cancer diagnosis, nontobacco-related cancer diagnosis, or no cancer diagnosis (the referent) reported at Wave II or III. The primary outcome was smoking cessation after diagnosis, at Wave IV. Results/Findings. Our sample was composed of 7,286 adult smokers at the baseline representing an estimated 40.9 million persons. Smoking cessation rates after a diagnosis differed after a tobacco-related cancer (25.9%), a nontobacco-related cancer (8.9%), and no cancer diagnosis (17.9%). After adjustment, diagnosis with a tobacco-related cancer was associated with a higher odds of smoking cessation (OR 1.83, 95% CI 1.00-3.33) compared to no cancer diagnosis. Diagnosis with a nontobacco-related cancer was not significantly linked to smoking cessation (OR 0.52, 95% CI 0.48-1.45). Conclusion. Diagnosis with a tobacco-related cancer is associated with greater odds of subsequent smoking cessation compared to no cancer diagnosis, suggesting that significant behavioral change may occur in this setting.

Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

Coronavirus disease 2019 (COVID-19) has rapidly spread globally, forcing countries to apply lockdowns and strict social distancing measures. The aim of this study was to assess eating habits and lifestyle behaviours among residents of the Middle East and North Africa (MENA) region during the lockdown. A cross-sectional study among adult residents of the MENA region was conducted using an online questionnaire designed on Google Forms during April 2020. A total of 2970 participants from eighteen countries participated in the present study. During the pandemic, over 30 % reported weight gain, 6·2 % consumed five or more meals per d compared with 2·2 % before the pandemic (P < 0·001) and 48·8 % did not consume fruits on a daily basis. Moreover, 39·1 % did not engage in physical activity, and over 35 % spent more than 5 h/d on screens. A significant association between the frequency of training during the pandemic and the reported change in weight was found (P < 0·001). A significantly higher percentage of participants reported physical and emotional exhaustion, irritability and tension either all the time or a large part of the time during the pandemic (P < 0·001). Although a high percentage of participants reported sleeping more hours per night during the pandemic, 63 % had sleep disturbances. The study highlights that the lockdown due to the COVID-19 pandemic caused a variety of lifestyle changes, physical inactivity and psychological problems among adults in the MENA region.

We study the effect of insoluble surfactants on the wave dynamics of vertically falling liquid films. We use three-dimensional numerical simulations and employ a hybrid interface-tracking/level-set method, taking into account Marangoni stresses induced by gradients of interfacial surfactant concentration. Our numerical predictions for the evolution of the surfactant-free, three-dimensional wave topology are validated against the experimental work of Park & Nosoko (AIChE J., vol. 49, 2003, pp. 2715–2727). The addition of surfactants is found to influence significantly the development of horseshoe-shaped waves. At low Marangoni numbers, we show that the wave fronts exhibit spanwise oscillations before eventually acquiring a quasi-two-dimensional shape. In addition, the presence of Marangoni stresses is found to suppress the peaks of the travelling waves and preceding capillary wave structures. At high Marangoni numbers, a near-complete rigidification of the interface is observed.

In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.

Lymphopenia is common in adults who have had a Fontan operation although its aetiology and clinical implications remain unknown. Previous work suggests an association between lymphopenia and both liver disease and splenomegaly. The objective of this study was to assess the prevalence of lymphopenia in adults with a Fontan circulation and evaluate its associations with risk factors and clinical outcomes. Using a retrospective cohort study design, we studied 73 adult Fontan patients (age 25.0 ± 8.4 years) who had a complete blood count and abdominal imaging performed. Patients with protein-losing enteropathy were excluded. Clinical data were extracted from hospital records. The mean white blood cell count was 6580 ± 220/ml with a mean lymphocyte count of 1223 ± 508/ml. Lymphopenia, defined as lymphocyte count <1000/ml, was present in 23 (32%) patients. Patients with lymphopenia had a lower total white blood cell count (5556 ± 2517 versus 7136 ± 1924/ml, p = 0.009) and a lower platelet count (162 ± 69 versus 208 ± 69 k/ml, p = 0.008). Lymphopenia was also associated with findings of portal hypertension, including splenomegaly (36 versus 14%, p = 0.04), varices (22 versus 6%, p = 0.04), and ascites (39 versus 14%, p = 0.02). Lymphopenia did not correlate with any cardiac imaging, haemodynamic or exercise testing variables. In conclusion, lymphopenia is common in adult Fontan patients and is associated with markers of portal hypertension. Larger studies are needed to better define the relationship between lymphopenia and clinical outcomes.