Bayesian Regression Research Articles

Hypothalamus Connectivity in Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling illness of unknown etiology. Increasing evidence suggests hypothalamic involvement in ME/CFS pathophysiology, which has rarely been explored using magnetic resonance imaging (MRI)in the condition. This work aimed to use MRI to examine hypothalamus connectivity in adolescents with ME/CFS and explore how this relates to fatigue severity and illness duration. 25 adolescents with ME/CFS and 23 healthy controls completed a neuroimaging protocol consisting of structural and multishell diffusion-weighted imaging sequences, in addition to the PedsQL Multidimensional Fatigue Scale to assess fatigue severity. Information about illness duration was acquired at diagnosis. Preprocessing and streamlines tractography was performed using QSIPrep combined with a custom parcellation scheme to create structural networks. The number (degree) and weight (strength) of connections between lateralized hypothalamus regions and cortical and subcortical nodes were extracted, and relationships between connectivity measures, fatigue severity, and illness duration were performed using Bayesian regression models. We observed weak-to-moderate evidence of increased degree, but not strength, of connections from the bilateral anterior-inferior (left: pd [%] = 99.18, median [95% CI] = -22.68[-40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = -23.35[-38.47 to 8.20]), left anterior-superior (pd [%] = 99.33, median [95% CI] = -18.83[-33.45 to 4.07]) and total left hypothalamus (pd [%] = 99.44, median [95% CI] = -47.18[-83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: -0.47[-0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:-0.43[-0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group (pd [%] = 99.35, median [95% CI] = -0.28[-0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.

Executive function tests: Normative data for Spanish-speaking adults in the United States.

Normative data for Spanish-speaking populations, particularly Hispanics in the U.S., is notably scarce. This study aims to establish normative data for executive function tests (Modified Wisconsin Card Sorting Test and Stroop Color and Word Test) among Spanish-speaking Hispanics in the U.S. We assessed 245 individuals aged 18-80 from eight U.S. states (California, Connecticut, Florida, Indiana, New Jersey, Oregon, Virginia, and Wisconsin) and employed Bayesian regression to estimate norms, considering various sociodemographic factors influencing performance. The posterior distribution suggests a high probability that age impacts SCWT performance, with older adults likely to show greater declines, particularly among those with high proficiency in Spanish. The posterior distribution suggests a stronger effect of age on M-WCST performance among individuals with longer U.S. residency. Educational attainment demonstrates a robust positive impact on M-WCST outcomes, with lower levels of education associated with a higher probability of increased errors. An interaction between education and Spanish proficiency was observed, influencing SCWT scores differently across proficiency levels. Sex and acculturation levels interact to affect SCWT performance, with distinct patterns observed between men and women. This suggests that the impact of acculturation on cognitive test performance may vary by gender. Establishing culturally sensitive normative data can enhance accurate identification of executive dysfunction and reduce misdiagnosis risks. This study underscores the importance of considering sociocultural factors including acculturation and language proficiency in neuropsychological assessments to better serve diverse populations.

A hierarchical Bayesian regression framework to analyze climate data from Central Asia region

A hierarchical Bayesian regression framework to analyze climate data from Central Asia region

Spatio-temporal Prediction of Air Quality Using Spatio-temporal Clustering and Hierarchical Bayesian Model

Air pollution can cause many negative impacts, so it is meaningful to establish an air quality prediction model with high accuracy for air pollution prevention and control. In previous studies, most models did not consider the spatio-temporal distribution characteristics of air quality. So this study proposes a novel spatio-temporal prediction approach STC-HBM, which combines spatio-temporal clustering (STC) and hierarchical Bayesian model (HBM) to build a spatio-temporal prediction model. In this study, we first perform spatio-temporal clustering on the Beijing-Tianjin-Hebei region in China and then apply hierarchical Bayesian models to different clusters separately. We consider three hierarchical Bayesian models: the separable spatio-temporal (SST) model, the Gaussian process (GP) model, and the autoregressive (AR) model. Since the prior distribution affects the prediction accuracy of the HBM, the resultant output of the Bayesian linear regression (BLR) model is used as the prior input of the HBM, which improves the flexibility of the model. The experimental results show that 13 cities in the BTH region are clustered into two clusters according to their spatio-temporal characteristics. Based on MAE, RMSE, MAPE, and coverage (CVG), cluster 1, and cluster 2 are better in both temporal and spatial prediction compared to the overall prediction model. In addition, for cluster 1, the models with the best prediction in time and space are AR and GP, respectively; for cluster 2, the models with the best prediction in time and space are SST and GP, respectively.

Macro-economics and firm-specific factors impacting the non-performing loans of vietnamese commercial banks in the context of COVID-19

The COVID-19 pandemic has created unprecedented economic disruptions worldwide, with the banking sector being one of the most affected industries. This paper aims to explore the impact of various factors on the nonperforming loans (NPLs) of Vietnamese commercial banks during the COVID-19 pandemic by employing a Bayesian hierarchical regression approach. The analysis highlights that the pandemic has led to a significant and positive increase in the NPLs ratio of Vietnamese banks, indicating that the financial health of these institutions has been adversely affected by the crisis. Moreover, the study uncovers that the impact of COVID-19 on NPLs is not uniform across all banks. Specifically, state-owned banks have experienced a more pronounced increase in NPLs compared to their private counterparts. This disparity suggests that state-owned banks may be more vulnerable to economic shocks due to their broader exposure to sectors and enterprises heavily impacted by the pandemic. The findings of this research offer critical insights into the dynamics of the banking sector in Vietnam during a global health crisis and emphasize the need for targeted policy interventions. The results also underscore the importance of understanding the differential impacts of such crises on various types of financial institutions. By identifying the factors that contribute to the increase in NPLs, this study provides valuable information that can inform future strategies aimed at enhancing the resilience of the banking sector against similar economic disruptions. Furthermore, the findings highlight the effectiveness of the policies implemented during the pandemic to support the banking sector, suggesting areas for improvement and further research.

Sex Differences in Cortical Thickness and Neuropsychiatric Symptom Burden Based on APOE4 Homozygosity in Alzheimer's Disease.

Sex differences in patterns of cortical thickness and neuropsychiatric symptom (NPS) burden were examined among individuals with Alzheimer's disease (AD) and two copies (homozygote carriers) of the e4 allele of the apolipoprotein gene (APOE). A total of 752 participants with a clinical etiologic diagnosis of AD were selected from the National Alzheimer's Coordinating Center (NACC) database. Bayesian multilevel regression was used to examine both the within- and between-sex differences in gray-matter cortical thickness and total NPS burden associated with APOE homozygosity. Female homozygote carriers displayed a high probability of having reduced cortical thickness primarily in medial-lateral temporal regions and a greater burden of NPS, relative to both non-homozygous females and homozygous males. These findings support the notion that APOE4 status affects cortical thickness and symptom burden in men and women with AD differentially, with females showing more pronounced effects in brain areas known to be vulnerable in early AD. Future investigations should attempt to elucidate the proposed pattern of decline longitudinally.

Usage of intellectual analysis of ecological-economic data in the pension insurance system and for forecasting expenditures on social protection and social security

The paper is devoted to an actual scientific and applied problem – the development of methodology for applying mathematical models and data mining methods for actuarial calculations in mandatory state pension insurance system. The paper describes methodology for modeling changes in the number of pension recipients taking into account the impact of environmental factors, in particular air pollution. The basis of the proposed method is a multi-model approach, characterized by combination of data mining and probabilistic models in the form of Bayesian network, which are appropriate in conditions of statistical, parametric and structural uncertainty.The proposed approach describes the change in number of pension recipients, in particular for disability and breadwinner loss, under influence of air pollution from organic and inorganic compounds. The scientific novelty of the paper is in the use of an ensemble of models including probabilistic and statistical models in the form of Bayesian network and regression models, in the system of actuarial calculations of mandatory state pension insurance.The paper considers several scenarios for the impact of pollutants on the growth of number of pension recipients. The indicator of the share of expenditures on social protection and social security of the population in the gross national product was chosen as the target variable of the process under study. Mathematical models were found to be adequate to the modeling process, and the Bayesian network classification error is about 20%. The model structure is built in Genie 2.0 modeling system. The principal component analysis, is used to reduce the data dimension. The proposed methodology can also be applied to other tasks of forecasting social protection and social security expenditures.

Prenatal and childhood exposure to endocrine-disrupting chemicals and early thelarche in 8-year-old girls: A prospective study using Bayesian kernel regression

BackgroundStudies on the combined effects of persistent and non-persistent endocrine-disrupting chemicals (EDCs) on puberty are insufficient. To date, no studies have analyzed breast development at age 8 years, a key criterion for determining precocious puberty. We investigated the relationship between prenatal or childhood exposure to EDC mixtures and early thelarche, defined as breast development before age 8 years in girls. MethodsThis prospective study included 211 girls with data on prenatal and 8-year-old exposure of cadmium (Cd), lead, mercury, bisphenol-A (BPA), 3-phenoxybenzoic acid, and three phthalate metabolites from the Environment and Development of Children cohort. Prenatal exposure was assessed through samples from pregnant women at 14–27th weeks of gestation. Tanner staging was assessed by a pediatric endocrinologist. The relationship between single and mixed chemical exposures and outcomes was assessed using logistic regression, generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) models. ResultsEarly thelarche was observed in 42 (19.9%) girls at age 8 years. In the logistic regression models, the risk of early thelarche increased with increased exposure to Cd in their mothers (adjusted odds ratio [aOR] per interquartile range [IQR] = 1.80, 95% confidence interval [CI] 1.23–2.65) but decreased with prenatal BPA exposure (aOR per IQR = 0.57, 95% CI 0.35–0.92). None of the 8-year-old chemical exposures was associated with early thelarche. In the GAM, early thelarche was positively correlated with prenatal Cd and inversely associated with prenatal BPA exposure (p = 0.004 for Cd and p = 0.036 for BPA). In the BKMR models, an increase in log-transformed prenatal Cd concentrations from the 25th to 75th percentile was associated with an increase in the estimated probability of early thelarche at age 8 years (risk difference: 0.46 [95% credible interval: 0.04–0.88]) when other chemicals were set at their median values. ConclusionsConsidering the combined effects of persistent and non-persistent chemical mixtures, maternal Cd exposure during the second trimester may be associated with early thelarche in 8-year-old girls.

Natural resource extraction - Sustainable development relationship and energy productivity moderation in resource-rich countries: A panel Bayesian regression analysis

Social development is essential for improving quality of life, fostering stability, driving economic growth, achieving sustainable goals, enhancing human capital, encouraging civic engagement, building resilience, and fostering innovation, thereby ensuring inclusive, equitable, and sustainable progress. The Social Progress Index (SPI), also known as social development, contains all these features. This study aims to examine the impact of natural resource extraction on social development quadratically using data from 2011 to 2022. The focus of the study is on resource-rich countries. In this study, productive capacity in the energy sector is used as a social development determinant and a natural resource consumption moderator, ensuring sustainability in social development. Analyzing the quadratic relationship allows us to model and understand nonlinear relationships between the predictor and the outcome variable, capturing the effects that change direction or strength at different levels of the predictor. In this study, Bayesian regression is incorporated as an econometric approach. It provides more flexible modelling, incorporates prior knowledge, and offers a full distribution of parameter estimates, leading to better uncertainty quantification and more robust predictions. These estimates confirms a U-shaped natural resource extraction and social development relationship. Energy productive capacities in resource-rich countries not only enhance social development but also ensure the sustainable extraction of natural resources leading to sustainable social development. Population density and biodiversity are incorporated as control variables of the model. The impact of both is positive on social development.

Associations of Advanced Glycation End Products with Sleep Disorders in Chinese Adults.

Advanced glycation end products (AGEs), a group of food processing byproducts, have been implicated in the development of various diseases. However, the relationship between circulating AGEs and sleep disorders remains uncertain. This cross-sectional study elucidated the association of plasma AGEs with sleep disorders among 1732 Chinese adults who participated in the initial visit (2019-2020) of the Tongji-Shenzhen Cohort (TJSZC). Sleep behavior was assessed using self-reported questionnaires and precise accelerometers. Plasma levels of AGEs, including Nε-(Carboxymethyl)lysine (CML), Nε-(Carboxyethyl)lysine (CEL), and Nδ-(5-hydro-5-methyl-4-imidazolone-2-yl)-ornithine (MG-H1), were quantified by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). In logistic regression, per IQR increment in individual AGEs was associated with an increased odds ratio of short sleep duration (CML: 1.11 [1.00, 1.23]; CEL: 1.16, [1.04, 1.30]), poor sleep quality (CML: 1.33 [1.10, 1.60]; CEL: 1.53, [1.17, 2.00]; MG-H1: 1.61 [1.25, 2.07]), excessive daytime sleepiness (CML: 1.33 [1.11, 1.60]; MG-H1: 1.39 [1.09, 1.77]), and insomnia (CML: 1.29 [1.05, 1.59]). Furthermore, in weighted quantile sum regression and Bayesian kernel machine regression analyses, elevated overall exposure levels of plasma AGEs were associated with an increased risk of sleep disorders, including short sleep duration, poor sleep quality, excessive daytime sleepiness, and insomnia, with CML being identified as the leading contributor. Insufficient vegetable intake and higher dietary fat intake was associated with an increase in plasma CEL. These findings support a significant association between plasma AGEs and sleep disorders, indicating that AGEs may adversely influence sleep health and reducing the intake of AGEs may facilitate preventing and ameliorating sleep disorders.

Relationship between phthalate exposure and kidney function in Taiwanese adults as determined through covariate-adjusted standardization and cumulative risk assessment

Few studies have investigated the associations between phthalate exposure and kidney function indicators in adults by simultaneously performing covariate-adjusted creatinine standardization, cumulative risk assessment, and mixture analysis. Thus, we applied these methods simultaneously to investigate the aforementioned associations in an adult population. This cross-sectional study analyzed data (N = 839) from a community-based arm of the Taiwan Biobank. The levels of 10 urinary phthalate metabolites were measured and calculated as the sum of the molar concentrations of the dibutyl phthalate metabolite (ΣDBPm) and di(2-ethylhexyl) phthalate (DEHP) metabolite (ΣDEHPm). The hazard index (HI) and daily intake (DI) were estimated by measuring the urinary levels of the phthalate metabolite. Kidney function biomarkers were assessed by measuring the following: blood urea nitrogen (BUN), uric acid, the albumin-to-creatinine ratio (ACR), and the estimated glomerular filtration rate (eGFR). Generalized linear models were implemented to examine the associations between exposure to individual phthalates, HI scores, and kidney function biomarkers. We also employed Bayesian kernel machine regression (BKMR) to analyze the relationships between exposure to various combinations of phthalates and kidney function. ΣDEHPm levels were significantly and positively associated with BUN and ACR levels, and ΣDBPm levels were positively associated with ACR levels. In addition, eGFR was negatively associated with ΣDBPm and ΣDEHPm levels. In the BKMR model, a mixture of 10 phthalate metabolites was significantly associated with BUN, uric acid, ACR, and eGFR results. Higher DIDEHP and higher DIDnBP values were significantly associated with lower eGFRs and higher ACRs, respectively. Higher DIDiBP and DIDEP values were significantly associated with higher uric acid levels. A higher HI was significantly associated with lower eGFRs and higher ACRs. Our results suggest that exposure to environmental phthalates is associated with impaired kidney function in Taiwanese adults.

Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis

Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis

Adoption of Chinese cars and the shift to electric vehicles: Early evidence from Norway

Chinese auto manufacturers have started exporting electric cars to developed markets. I study data from the world’s leading market for electric vehicles, Norway, to provide some early descriptive evidence on the patterns of Chinese-brand new car sales. Using a Bayesian multilevel regression model, I find evidence of local peer effects: A higher proportion of sales of a Chinese branded car in a postal code increases the probability of a new car sale being Chinese. I also hypothesize that municipalities with high penetrations of electric cars will be associated with higher penetrations of Chinese-branded new car sales. I do not find evidence for this.

Urinary metals are associated with obesity in U.S. children and adolescents: A cross-sectional study

Heavy metals are pervasive in the environment, and exposure to these metals may contribute to obesity in children and adolescents. We hypothesized that metal exposures are associated with obesity in children and adolescents. Data were drawn from children and adolescents aged 6 to 19 years from the 2007 to 2018 National Health and Nutrition Examination Survey. We employed weighted multivariate logistic regression and restricted cubic spline to explore the effects of individual metal exposures on obesity, and weighted quantile sum regression, quantile g-computed regression, and Bayesian kernel machine regression to explore the effects of mixed metal exposures on obesity. Subgroup analyses by gender were also performed. All models were adjusted for age, gender, race, poverty to income ratio, and serum cotinine. Among the 3,650 children and adolescents studied, 21.04% had obesity. Logistic regression revealed positive associations between barium (OR = 1.23, 95% CI: 1.07-1.40) and thallium (OR = 1.55, 95% CI: 1.23-2.15) with obesity, while cadmium (OR = 0.74, 95% CI: 0.61-0.89), cobalt (OR = 0.51, 95% CI: 0.41-0.62), and lead (OR = 0.70, 95% CI: 0.57-0.86) were negatively associated with obesity. Restricted cubic spline indicated a nonlinear relationship between lead and thallium and obesity. Quantile g-computed regression demonstrated that mixed metal exposure was negatively associated with obesity (OR = 0.50, 95% CI: 0.42-0.59). Subgroup analyses revealed a gender-specific effect for mercury (P for interaction = 0.03), which was negatively associated with obesity in females (OR = 0.83, 95% CI: 0.69-0.99). In conclusion, metal exposures are associated with obesity in children and adolescents, with gender differences.

Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: a case-control study in Shanxi, China

Maternal exposure to various metallic and non-metallic elements has been linked to the occurrence of orofacial clefts (OFCs), yet there remains a dearth of comprehensive research on the potential ramifications of simultaneous exposure to multiple elements. In this study, we investigated the individual and combined effects of element exposure on OFCs in a cohort of 168 pregnant women (49 cases and 119 controls) in the Shanxi province of northern China from 2010 to 2015. Cord serum samples were obtained from all participants to analyze the levels of 32 elements using inductively coupled plasma-mass spectrometry. The study examined the independent correlation between element concentrations and OFCs using two machine screening models, Boruta and Least Absolute Shrinkage and Selection Operator. Bayesian kernel machine regression (BKMR) was utilized to determine the combined effects of key exposure elements on OFCs and to clarify the interaction between exposed elements through the generalized additive model (GAM). The screening models identified lead (Pb), tin (Sn), iron (Fe), and cesium (Cs) as the most significant risk factors for OFC development in offspring. In the BKMR model, the probability of OFCs increased with higher overall levels of these risk elements, with Pb emerging as the primary contributor to the combined effect of the mixture. The findings of the GAM indicated that the combined exposure to Pb and Sn had a synergistic effect on the risk of developing OFCs. Analysis of elemental exposure in umbilical cord serum suggested that Pb exposure may have detrimental effects on OFC development in offspring, which may be further intensified by a synergistic interaction between Sn and Pb in the occurrence of OFCs.

Knowledge gaps and opportunities for conservation with orchid collectors in Vietnam

AbstractUnsustainable harvesting of orchid species is a critical, global threat to orchid diversity and abundance, fueled by domestic and global demand. However, drivers of demand continue to be under‐explored, despite opportunities for proactive engagement and/or behavior change strategies with and/or directed at orchid collectors. In this study, we surveyed self‐identified Vietnamese orchid collectors, some of whom were also known to engage in wild harvesting of endangered species, to understand sociological dimensions including motivations, knowledge, attitudes, behaviors, and conservation ethos. We found that knowledge about domestic and international regulations was low, and that orchid collectors were likely to believe that Vietnam has plenty of orchids left in the wild. Orchid collectors were highly willing to share their expertise and collections with international NGOs, and receive advice on propagation of orchid species. Through Bayesian logistic regression, we found no statistically significant influence of demographic attributes on the behavior of harvesting from the wild; however, having access to propagation equipment and supplies increased the likelihood of wild harvest. Accurate knowledge (awareness) is a key first step in the behavior change process. We suggest the implementation of awareness‐raising campaigns targeted at Vietnamese orchid collectors, which communicate domestic and international restrictions about orchid harvest and trade, as well as the known status of orchid species in the wild. Additionally, we suggest that an opportunity exists to recruit Vietnamese orchid collectors into a formal network for knowledge‐sharing and the co‐creation of sustainable guidelines around keeping and propagating orchids.Abstract in Vietnamese is available with online material.

Exposure to Multiple Metal(loid)s and Hypertension in Chinese Older Adults.

Evidence about effects of metal(loid)s on hypertension among adults is insufficient. The aim of our study was to evaluate the individual and joint associations between seven selected metal(loid)s and hypertension, including lead (Pb), manganese (Mn), nickel (Ni), arsenic (As), cadmium (Cd), chromium (Cr), and vanadium (V)) in Chinese older adults. This study included 1009 older adults, and the blood concentrations of seven metal(loid)s were evaluated by inductively coupled plasma mass spectrometry (ICP-MS). The following conditions were considered as hypertension: (1) either systolic blood pressure ≥ 140mm Hg or diastolic blood pressure ≥ 90mm Hg, (2) a self-reported history of hypertension, or (3) currently taking antihypertensive medications. Logistic regression was utilized to investigate the association between individual metal(loid) and hypertension, while Bayesian kernel machine regression (BKMR) was employed to investigate the association of the metal(loid) mixture with hypertension. Adjusted single-metal(loid) model showed a significant positive association between Pb and hypertension (OR = 1.24, 95%CI = 1.03-1.50). This significant association still existed in multi-metal(loid) model (OR = 1.22, 95%CI = 1.01-1.47). BKMR further indicated a positive linear association of Pb with hypertension. The metal(loid) mixture was positively associated with hypertension in older adults, although not significant. Within the mixture, Pb had the highest posterior inclusion probabilities value (PIP = 0.9192). There were multiplicative interactions of Pb and Mn on hypertension. In addition, Pb and Mn had additive effects on the association of other blood metal(loid)s with hypertension. The associations of multiple metal(loid)s with hypertension are dependent on diabetes, areas, age, and BMI. The metal(loid) mixture exposure may contribute to hypertension in Chinese older adults, mainly driven by Pb and interactions of Pb and Mn. Reducing exposure to these metal(loid)s may prevent hypertension among older adults, which is especially true for those living with diabetes.

Association between urinary mixture metal levels and olfactory function in coal miners.

Exposure to occupational metallic mixtures has a potential impact on olfactory function. However, research evidence is limited on the potential impact of exposure to metallic mixtures and olfactory dysfunction. Furthermore, the coal dust generated contains multiple various metals during coal mining, and no study yet has focus on the olfactory dysfunction of coal miners. In this study, we evaluate the association between urinary metallic mixtures and olfactory function in coal miners, while also exploring the potential applicability of plasma olfactory marker protein (OMP) as a biomarker for assessing olfaction. From July to October 2023, coal workers from seven different coal mining enterprises were recruited for the survey when they come for the employee health checkup. Ultimately, 376 participants were met the inclusion criteria and, respectively, determined with the concentrations of urine (16 metals) and plasma (OMP). Meanwhile, applying UPSIT to access their olfactory function. Binary logistic regression and restricted cubic spline (RCS) model were used to estimate the association of individual metals with olfactory function. Bayesian kernel machine regression (BKMR) and Quantile g-computation (QG-C) regression were employed to assess the overall association between metal mixtures and olfactory function and identify the major contributing elements. In a single-metal model, two metals in urine were found to be significantly associated with olfactory function. RCS analysis further revealed that the association between Iron (Fe) and olfactory function was linear, while Lead (Pb) exhibited a non-linear. The BKMR model demonstrated a significant positive association between metal mixture concentration and olfactory function. Combined QG-C regression analysis suggested that metals Cr, Fe, Se, Sb, and Pb could impact the performance of the olfactory test (UPSIT), with Pb being identified as the most influential contributor. The correlation between plasma OMP protein levels and urinary metal concentrations was weak. Multiple metals are associated with olfactory function in the coal miners. A significant positive association was observed between metal mixture concentrations and olfactory function, with Pb being the most important contributor. In this study, plasma OMP has not been demonstrated to serve as a biomarker for olfactory function.

Handheld ultrasound for portal vein evaluation in pediatric patients.

To assess diagnostic capabilities of a modern handheld ultrasound device (HUD) for portal vein (PV) evaluation in pediatric patients and to verify if age, body mass index (BMI) and object depth correlate to imagequality. 45 patients (3.9 months- 17.9 years; 10.3 years±5.4) were examined using a HUD and cart-based high-end ultrasound system (HEUS). The intra-and extrahepatic PV was scanned using B-Mode and Color-coded Doppler sonography (CCDS). A five-point Likert scale was applied to evaluate image quality. Results were interpreted by two readers in consensus. Scores were compared between the scanners and correlated to age, BMI and skin-to-portal-vein-distance (SPVD). The influence on image quality and the difference between the two devices were analyzed using ordinal and Bayesian logistic regression models. ≥4 points (mild or no limitations) were achieved by the HUD in 60% for B-Mode and 56% for CCDS. There was a statistically significant negative correlation between the image quality achieved by the HUD and the SPVD for B-Mode and CCDS. The effect of BMI and SPVD on having≥4 points differed significantly between the HUD and HEUS for B-Mode (OR = 0.191, p = 0.005, and OR = 0.040, p = 0.008) and for CCDS (OR = 0.209, p = 0.009, and OR = 0.084,p = 0.011). Sufficient to excellent image quality for portal vein assessment in pediatric patients was achieved by the HUD except for the most deep-lying structures. Compared to HEUS, diagnostic performance of the HUD is lower, based on a negative correlation with object depth.

Consonant lengthening marks the beginning of words across a diverse sample of languages.

Speech consists of a continuous stream of acoustic signals, yet humans can segment words and other constituents from each other with astonishing precision. The acoustic properties that support this process are not well understood and remain understudied for the vast majority of the world's languages, in particular regarding their potential variation. Here we report cross-linguistic evidence for the lengthening of word-initial consonants across a typologically diverse sample of 51 languages. Using Bayesian multilevel regression, we find that on average, word-initial consonants are about 13 ms longer than word-medial consonants. The cross-linguistic distribution of the effect indicates that despite individual differences in the phonology of the sampled languages, the lengthening of word-initial consonants is a widespread strategy to mark the onset of words in the continuous acoustic signal of human speech. These findings may be crucial for a better understanding of the incremental processing of speech and speech segmentation.