Search Results (12)

Background and Objectives: Gestational diabetes mellitus (GDM) is one of the most common medical conditions in pregnancy, with adverse effects on maternal and neonatal outcomes. Evidence suggests a beneficial effect of plant-based dietary patterns, rich in foods derived from plant sources and low in animal foods, on type 2 diabetes; however, their effects on GDM remain unclear. We aimed to investigate the association between pre-pregnancy provegetarian food patterns and the incidence of GDM in a Spanish cohort. Materials and Methods: This subsample of the Seguimiento Universidad de Navarra (SUN) cohort analyzed 3589 Spanish university graduate pregnant women with a mean (standard deviation) age of 28 (±4.3) who were initially free of pre-existing diabetes at baseline. Dietary food consumption was evaluated through a validated, 136-item semi-quantitative food frequency questionnaire. The pre-pregnancy provegetarian food pattern was obtained by assigning positive scores to plant-based food groups and reverse scores to animal food groups. Energy-adjusted quintiles were applied to allocate points to construct the provegetarian food pattern, ranging from 12 to 60 points. Logistic regression models were performed to estimate the odds ratios (OR) of GDM across quintiles of a pre-pregnancy provegetarian food pattern, using the lowest quintile as the reference category. Results: We identified 178 incidence cases of GDM. Women in the highest quintile (Q5) of provegetarian food pattern before pregnancy exhibited a 42% relative reduction in the odds of GDM [adjusted OR (95% CI) Q5 vs. Q1: 0.58 (0.35, 0.97); p-trend = 0.109]. Higher consumption of meat and dairy before pregnancy was associated with a significantly increased risk of GDM [adjusted OR (95% CI) Q5 vs. Q1: 1.94 (1.19, 3.16); p-trend = 0.005] and [adjusted OR (95% CI) Q5 vs. Q1: 1.77 (1.07, 2.94); p-trend = 0.082], respectively. Conclusions: Higher pre-pregnancy consumption of a provegetarian food pattern was associated with a lower risk of developing GDM in Spanish women. Further studies are needed to confirm these findings. Full article

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling. Full article

The idea of experimental design has been widely used in subsampling algorithms to extract a small portion of big data that carries useful information for statistical modeling. Most existing subsampling algorithms of this kind are model-based and designed to achieve the corresponding optimality criteria for the model. However, data generating models are frequently unknown or complicated. Model-free subsampling algorithms are needed for obtaining samples that are robust under model misspecification and complication. This paper introduces two novel algorithms, called the Projection-Uniform Subsampling algorithm and its extension. Both algorithms aim to extract a subset of samples from big data that are space-filling in low-dimensional projections. We show that subdata obtained from our algorithms perform superiorly under the uniform projection criterion and centered $L_2$-discrepancy. Comparisons among our algorithms, model-based and model-free methods are conducted through two simulation studies and two real-world case studies. We demonstrate the robustness of our proposed algorithms in building statistical models in scenarios involving model misspecification and complication. Full article

This paper demonstrates whether the impact of agency costs on firm value depends on the level of managerial power using the fraction of capital held by the manager, as well as their level of voting rights. Focusing on a sample of 120 non-financial French firms incorporated in the CAC All-Tradable Index for the period 2008–2022, the first empirical analysis provides strong evidence that agency costs of equity, as measured in terms of operating expenses, administrative expenses and the agency cost of free cash flow, exert a negative impact on the firm’s market value. In a second empirical analysis, we split our sample into three sub-samples with the aim of capturing the effect of managerial power. The findings lead us to believe that the association between the agency cost measurement and the firm’s market value depend on the level of managerial power. This paper challenges prior studies by strengthening our understanding of managerial behavior (incentive, neutral, and entrenchment) in relation to shareholder wealth. Furthermore, it contributes to the recent literature by enabling a better knowledge of the disparity related to studies conducted in other countries with different governance models. Full article

Poor birth outcomes such as low birth weight, low birth length and short gestational age, are public health concern issues in South Africa (SA). This study utilized structural equation modeling (SEM) to explore how nutritional and social factors contribute to favorable fetal growth conditions (FFGC) in pregnant women living with and without human immunodeficiency virus (HIV), in the Free State Province of SA. Sociodemographic characteristics, stress, health and nutrition-related information, and birth outcomes data were collected and analyzed from a subsample of 305 women enrolled in a cohort study from 2018–2020. Descriptive statistics were analyzed in R version 4.1.2 and SEM was conducted in Lavaan version 0.6–5. Higher gestational body mass index (GBMI) and income levels were associated with higher FFGC (p < 0.05). Household incomes were positively associated with dietary micronutrient quality (p = 0.002), GBMI (p = 0.012) and food security (p = 0.001). Low incomes (p = 0.004) and food insecurity (p < 0.001) were associated with higher stress, while social support was positively associated with food security status (p = 0.008). These findings highlight the complex interconnections between the social and nutritional factors that are associated with fetal growth conditions. Multisectoral community-based programs may be a useful strategy to address these challenges. Full article

Canister core desorption has been successfully applied to coal-bed methane evaluation and exploitation as the technique eliminates the need for time-consuming down-hole fluid retrieval through flow testing. The technique has also been used for the evaluation and exploration of early-stage tight and shale gas reservoirs in recent years, although its success and applicability are poorly understood. In this study, we analyzed a comprehensive canister desorption data set on 230 core samples from nine exploration wells drilled into the Montney Formation in northeastern British Columbia part of the Western Canada Sedimentary Basin (WCSB). The purpose of the study was to illustrate the desorption characteristics of tight rocks and the relationship to reservoir properties and operational parameters. Based on the measured core properties (e.g., porosity, fluid saturation, permeability, total organic carbon (TOC) content, and adsorption isotherms) of canister samples and adjacent core samples, non-isothermal gas transport in cores was modeled to quantify the lost gas during core recovery and lost gas time at the surface. Gas volumes were measured and subsampled by canister desorption tests. The results show that the gas contents measured by on-site canister desorption only accounts for a minor (but significant) portion (about 2 to 25%) of the total gas-in-place in the Montney Formation cores, with the lower percentages being associated with samples of better reservoir qualities (e.g., higher porosity). Over 60–90% (mainly free gas) of the total gas-in-place can be lost during core recovery, and up to 10% can be lost at surface, prior to canister desorption. The measured canister desorption gas is mainly from adsorbed gas, and hence shows strong positive correlation to TOC content. The study shows that the current canister desorption test method severely underestimates in-situ gas content because it fails to correctly estimate the total lost gas content, limiting the successful application of the desorption technique. Nevertheless, the bulk properties and molecular compositions of the desorption gases are strongly correlated to those of the gases produced in the same area, exhibiting distinctive gas composition profiles throughout core desorption for different reservoir types or thermal maturity, and thus can provide invaluable information for the initial evaluation of unconventional plays. A workflow of EOS-based PVT property and compositional modeling is proposed to integrate the core desorption gas test results with core analysis data and mud gas and/or produced gas data for improved characterization of in situ reservoir fluids, and hence, better assessments of hydrocarbons-in-place and evaluations of tight and shale reservoirs. Full article

Children from low-income households and minority families have high cardiometabolic risk. Although breakfast consumption is known to improve cardiometabolic health in children, limited randomized control trials (RCT) have explored this association in low-income and racial/ethnic U.S. minority families. This study conducted secondary analyses from TX Sprouts, a school-based gardening, cooking, and nutrition education RCT, to examine the intervention effect on breakfast consumption and how changes in breakfast consumption impact cardiometabolic risk in predominately low-income, multi-ethnic children. TX Sprouts consisted of 16 schools (8 intervention; 8 control) in greater Austin, TX. A total of 18 lessons were taught, including topics on breakfast consumption benefits and choosing healthy food options at school. Children completed clinical measures (e.g., anthropometrics, body composition via bioelectrical impedance), and the number of breakfast occasions (BO) per week (at home and school) was captured via validated survey at baseline and post-intervention. Post-study—Baseline changes in breakfast consumption were used to categorize students as: maintainers (BO −1 to 1 day/week), decreasers (BO ≤−2 day/week), and increasers (BO ≥2 day/week). Optional fasting blood draws were performed on a subsample. Generalized weighted linear mixed modeling tested differences between intervention and control, with schools as random clusters. Analysis of covariance and linear regression examined changes in breakfast consumption on cardiometabolic outcomes, controlling for age, sex, race/ethnicity, free and reduced-price school meal participation (FRL), school site, breakfast location, physical activity, baseline cardiometabolic measures, and BMI z-score. This study included 1417 children (mean age 9 years; 53% male; 58% Hispanic, 63% FRL; breakfast consumption patterns: 63% maintainers, 16% decreasers, and 21% increasers). There was no intervention effect on changes in breakfast consumption. Compared to decreasers, increasers had an increase in insulin (−0.3 µIU/mL vs. +4.1 µIU/mL; p = 0.01) and a larger increase in HOMA-IR (+0.4 vs. +1.5; p < 0.01). Every one-day increase in breakfast consumption decreased fasting insulin by 0.44 µIU/mL, HOMA-IR by 0.11, and hemoglobin A1c by 0.01% (p ≤ 0.03). Increased breakfast consumption was linked to improved glucose control, suggesting breakfast can mitigate risk in a high-risk population. To better understand underlying mechanisms linking breakfast consumption to improved metabolic health, RCTs focusing on breakfast quality and timing are warranted. Full article

Increased vaccine hesitancy and refusal negatively affects vaccine uptake, leading to the reemergence of vaccine preventable diseases. We aim to quantify the relative importance of factors people consider when making vaccine decisions for themselves, or for their child, with specific attention for underlying motives arising from context, such as required effort (accessibility) and opportunism (free riding on herd immunity). We documented attitudes towards vaccination and performed a discrete choice experiment in 4802 respondents in The United Kingdom, France and Belgium, eliciting preferences for six attributes: (1) vaccine effectiveness, (2) vaccine preventable disease burden, (3) vaccine accessibility in terms of copayment, vaccinator and administrative requirements, (4) frequency of mild vaccine-related side-effects, (5) vaccination coverage in the country’s population and (6) local vaccination coverage in personal networks. We distinguished adults deciding on vaccination for themselves from parents deciding for their youngest child. While all attributes were found to be significant, vaccine effectiveness and accessibility stood out in all (sub)samples, followed by vaccine preventable disease burden. We confirmed that people attach more value to severity of disease compared to its frequency, and discovered that peer influence dominates free-rider motives, especially for the vaccination of children. These behavioral data are insightful for policy and are essential to parameterize dynamic vaccination behavior in simulation models. In contrast to what most game theoretical models assume, social norms dominate free-rider incentives. Policy-makers and healthcare workers should actively communicate on high vaccination coverage, and draw attention to the effectiveness of vaccines while optimizing their practical accessibility. Full article

Perfluoroalkyl acids (PFAS) are known endocrine disrupting chemicals, potentially affecting thyroid function. Smoking has been associated with PFAS levels as well as with thyroid function. The impact of smoking on the association between PFAS and thyroid function remains to be elucidated, so the objective was to assess the effect of PFAS exposure on thyroid function in the general population, stratified by smoking status, using the National Health and Nutrition Examination Survey (NHANES). NHANES adult participants who were part of the 2011–2012 laboratory subsample and had PFAS and thyroid function measured were included (n = 1325). Adjusted linear regression models and stratified analyses were performed. There was a significant positive association between perfluorooctanesulfonic acid (PFOS) (p = 0.003), perfluorononanoic acid (PFNA) (p = 0.014), total PFAS (p = 0.004) concentrations and free T4 (FT4). No significant associations were found between perfluorooctanoic acid (PFOA), PFOS, perfluorohexane sulfonate (PFHxS), PFNA, total PFAS and total T4 (TT4) or thyroid stimulating hormone (TSH). In non-smokers, a significant positive association was found between PFOS (p = 0.003), PFHxS (p = 0.034), PFNA (p = 0.012), total PFAS (p = 0.003) and FT4 while no significant associations were found in smokers. The present study showed that increased PFAS exposure was associated with increased FT4 in non-smokers, while no association was found in smokers. These results confirm that smoking modifies the association between PFAS exposure and thyroid function. Full article

While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site. Full article

The importance of downside risk cannot be denied. In this study, we have replaced beta in the five-factor model of using downside beta and have added a momentum factor to suggest a new six-factor downside beta capital asset pricing model (CAPM). Two models are tested—a beta- and momentum-based six-factor model and a downside-beta- (proxy of downside risk) and momentum-based six-factor model. Beta and downside beta are highly correlated; therefore, portfolios are double-sorted to disentangle the correlation. Factor loadings, i.e., size, value, momentum, profitability, and investment, are constructed. The standard methodologies are applied. Data for sample stocks from different non-financial sectors listed in the Pakistan Stock Exchange (PSX) are taken from January 2000 to December 2018. The PSX-100 index and three-month T-bills are taken as proxies for market and risk-free returns. The study uses three subsamples for robustness—period of very high volatility, period of stability, and period of stability and growth with volatility. The results show that the value factor is redundant in both models. The momentum factor is rejected in the beta-based six-factor model only. The beta-based six-factor model shows very low R² in periods of highly volatility. The R² is high for the other periods. In contrast, the downside beta six-factor model captures the downside trend of the market in an effective manner with a relatively high R². The risk–return relationship is stronger for the downside beta model. These reasons lead us to believe that, overall, the downside beta six-factor model is a better option for investors as compared to the beta-based six-factor model in the area of asset pricing models. Full article

This study aimed to explore the set of variables related to skeletal muscle mass (SMM) in both sexes, and to create age- and sex-related models of changes in SMM, using the most representative indicator of muscular status. Body composition was assessed in 8733 subjects (♀ = 3370 and ♂ = 5363), allocated into subsamples according to age: 18–29.9, 30–39.9, 40–49.9, 50–59.9, 60–69.9, and 70.0–79.9 years. Nine variables were used: protein mass, protein percent, protein mass index, SMM, percent of SMM, SMM index, fat-free mass, fat-free mass index, and protein/fat index. Univariate and multivariate analysis of variance (ANOVA and MANOVA) were used to determine between- and within-sex difference in all variables by age. Correlation analysis established the relationship between age and muscularity variables. Principal Component Analysis extracted the variables that loaded highest in explaining muscularity, while regression analysis determined the linearity of association between the age and indicators of muscular status. Variables SMMI and PSMM were extracted as the most sensitive to age, with SMMI being gender-independent while showing the parabolic and sinusoidal form of change as function of ageing in males and females, respectively; and PSMM being sex-dependent while showing a linear trend of decrease in both sexes. Full article