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1.
Chemosphere ; 286(Pt 1): 131566, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34293557

RESUMEN

It is well documented that fine particles matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are associated with a range of adverse health outcomes. However, most epidemiologic studies have focused on understanding their additive effects, despite that individuals are exposed to multiple air pollutants simultaneously that are likely correlated with each other. Therefore, we applied a novel method - Bayesian Kernel machine regression (BKMR) and conducted a population-based cohort study to assess the individual and joint effect of air pollutant mixtures (PM2.5, O3, and NO2) on all-cause mortality among the Medicare population in 15 cities with 656 different ZIP codes in the southeastern US. The results suggest a strong association between pollutant mixture and all-cause mortality, mainly driven by PM2.5. The positive association of PM2.5 with mortality appears stronger at lower percentiles of other pollutants. An interquartile range change in PM2.5 concentration was associated with a significant increase in mortality of 1.7 (95% CI: 0.5, 2.9), 1.6 (95% CI: 0.4, 2.7) and 1.4 (95% CI: 0.1, 2.6) standard deviations (SD) when O3 and NO2 were set at the 25th, 50th, and 75th percentiles, respectively. BKMR analysis did not identify statistically significant interactions among PM2.5, O3, and NO2. However, since the small sub-population might weaken the study power, additional studies (in larger sample size and other regions in the US) are in need to reinforce the current finding.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Anciano , Contaminantes Atmosféricos/análisis , Contaminantes Atmosféricos/toxicidad , Contaminación del Aire/análisis , Contaminación del Aire/estadística & datos numéricos , Teorema de Bayes , Estudios de Cohortes , Exposición a Riesgos Ambientales/análisis , Exposición a Riesgos Ambientales/estadística & datos numéricos , Humanos , Dióxido de Nitrógeno/análisis , Dióxido de Nitrógeno/toxicidad , Ozono/análisis , Ozono/toxicidad , Material Particulado/análisis , Material Particulado/toxicidad
2.
Sci Total Environ ; 803: 150139, 2022 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-34525685

RESUMEN

Although significant scientific research strides have been made in mapping the spatial extents and ecohydrological dynamics of wetlands in semi-arid environments, the focus on small wetlands remains a challenge. This is due to the sensing characteristics of remote sensing platforms and lack of robust data processing techniques. Advancements in data analytic tools, such as the introduction of Google Earth Engine (GEE) platform provides unique opportunities for improved assessment of small and scattered wetlands. This study thus assessed the capabilities of GEE cloud-computing platform in characterising small seasonal flooded wetlands, using the new generation Sentinel 2 data from 2016 to 2020. Specifically, the study assessed the spectral separability of different land cover classes for two different wetlands detected, using Sentinel-2 multi-year composite water and vegetation indices and to identify the most suitable GEE machine learning algorithm for accurately detecting and mapping semi-arid seasonal wetlands. This was achieved using the object based Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART) and Naïve Bayes (NB) advanced algorithms in GEE. The results demonstrated the capabilities of using the GEE platform to characterize wetlands with acceptable accuracy. All algorithms showed superiority, in mapping the two wetlands except for the NB method, which had lowest overall classification accuracy. These findings underscore the relevance of the GEE platform, Sentinel-2 data and advanced algorithms in characterizing small and seasonal semi-arid wetlands.


Asunto(s)
Motor de Búsqueda , Humedales , Teorema de Bayes , Monitoreo del Ambiente , Estaciones del Año , Sudáfrica
3.
Sci Total Environ ; 803: 150038, 2022 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-34525726

RESUMEN

Despite several national and local policies towards cleaner air in England, many schools in London breach the WHO-recommended concentrations of air pollutants such as NO2 and PM2.5. This is while, previous studies highlight significant adverse health effects of air pollutants on children's health. In this paper we adopted a Bayesian spatial hierarchical model to investigate factors that affect the odds of schools exceeding the WHO-recommended concentration of NO2 (i.e., 40 µg/m3 annual mean) in Greater London (UK). We considered a host of variables including schools' characteristics as well as their neighbourhoods' attributes from household, socioeconomic, transport-related, land use, built and natural environment characteristics perspectives. The results indicated that transport-related factors including the number of traffic lights and bus stops in the immediate vicinity of schools, and borough-level bus fuel consumption are determinant factors that increase the likelihood of non-compliance with the WHO guideline. In contrast, distance from roads, river transport, and underground stations, vehicle speed (an indicator of traffic congestion), the proportion of borough-level green space, and the area of green space at schools reduce the likelihood of exceeding the WHO recommended concentration of NO2. We repeated our analysis under a hypothetical scenario in which the recommended concentration of NO2 is 35 µg/m3 - instead of 40 µg/m3. Our results underscore the importance of adopting clean fuel technologies on buses, installing green barriers, and reducing motorised traffic around schools in reducing exposure to NO2 concentrations in proximity to schools. Also, our findings highlight the presence of environmental inequalities in the Greater London area. This study would be useful for local authority decision making with the aim of improving air quality for school-aged children in urban settings.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Teorema de Bayes , Niño , Exposición a Riesgos Ambientales/análisis , Monitoreo del Ambiente , Humanos , Londres , Dióxido de Nitrógeno/análisis , Material Particulado/análisis , Instituciones Académicas , Organización Mundial de la Salud
4.
Q J Exp Psychol (Hove) ; 75(1): 1-17, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34414825

RESUMEN

People often learn from experience about the distribution of outcomes of risky options. Typically, people draw small samples, when they can actively sample information from risky gambles to make decisions. We examine how the size of the sample that people experience in decision from experience affects their preferences between risky options. In two studies (N = 40 each), we manipulated the size of samples that people could experience from risky gambles and measured subjective selling prices and the confidence in selling price judgements after sampling. The results show that, on average, sample size influenced neither the selling prices nor confidence. However, cognitive modelling of individual-level learning showed that around half of the participants could be classified as Bayesian learners, whereas the other half adhered to a frequentist learning strategy and that if learning was cognitively simpler more participants adhered to the latter. The observed selling prices of Bayesian learners changed with sample size as predicted by Bayesian principles, whereas sample size affected the judgements of frequentist learners much less. These results illustrate the variability in how people learn from sampled information and provide an explanation for why sample size often does not affect judgements.


Asunto(s)
Conducta de Elección , Juicio , Teorema de Bayes , Toma de Decisiones , Humanos , Tamaño de la Muestra
5.
Sci Total Environ ; 803: 149947, 2022 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-34487905

RESUMEN

The popular concept of wellbeing has added multiple dimensions to the current socio-economic measures of vulnerability from natural hazards. Due to the wellbeing concept's relevance in various policy agendas, there is a need for a stronger integration of what is predominantly a socio-economic concept into the natural hazards space. Graphical methods have been used as transdisciplinary engagement tools to translate verbal descriptions of socio-ecological systems into simulation models able to test hypotheses. The purpose of this article is to identify the graphical methods that have been used in the literature to graphically represent, structure and model different segments of the hazard risk chain. A thorough review of the literature on natural hazards was performed using a set of keywords and filters that resulted in a total of 94 articles, which were then categorised based on the graphical methods used, broad families, properties, hazard types, and segments along the risk chain considered. A case study on volcanic hazards in Mount Taranaki, New Zealand showcased ways forward by conceptually combining methods to link hazards to impacts on wellbeing. Out of the review it was identified that the most widely used methodologies in the natural hazards space are probabilistic graphs (e.g. Bayesian networks) representing the random nature of hazards while mapping methods based on System Dynamic principles (SD) (e.g. causal loop diagrams) are used to characterise the dynamically emergent behaviours of socio-economic agents. While studies linking hazards to wellbeing using graphs are scarce, there is a nascent literature on the characterisation of wellbeing's multi-dimensionality using networks and SD diagrams. Hence, the possibilities to use common methods, or combinations of these, are numerous potentially enabling the creation of graph-based, distilled simulation models that can be used by experts from different backgrounds to quantitatively model the wellbeing impacts exerted by natural hazards.


Asunto(s)
Ecosistema , Políticas , Teorema de Bayes , Humanos , Nueva Zelanda
6.
J Environ Manage ; 301: 113576, 2022 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-34597946

RESUMEN

The approach of applying stressor load limits or thresholds to aid estuarine management is being explored in many global case studies. However, there is growing concern regarding the influence of multiple stressors and their cumulative effects on the functioning of estuarine ecosystems due to the considerable uncertainty around stressor interactions. Recognising that empirical data limitations hinder parameterisation of detailed models of estuarine ecosystem responses to multiple stressors (suspended sediment, sediment mud and metal content, and nitrogen inputs), an expert driven Bayesian network (BN) was developed and validated. Overall, trends in estuarine condition predicted by the BN model were well supported by field observations, including results that were markedly higher than random (71-84% concordance), providing confidence in the overall model dynamics. The general BN framework was then applied to a case study estuary to demonstrate the model's utility for informing management decisions. Results indicated that reductions in suspended sediment loading were likely to result in improvements in estuarine condition, which was further improved by reductions in sediment mud and metal content, with an increased likelihood of high abundance of ecological communities relative to baseline conditions. Notably, reductions in suspended sediment were also associated with an increased probability of high nuisance macroalgae and phytoplankton if nutrient loading was not also reduced (associated with increased water column light penetration). Our results highlight that if stressor limit setting is to be implemented, limits must incorporate ecosystem responses to cumulative stressors, consider the present and desired future condition of the estuary of interest, and account for the likelihood of unexpected ecological outcomes regardless of whether the experts (or empirical data) suggest a threshold has or has not been triggered.


Asunto(s)
Ecosistema , Estuarios , Teorema de Bayes , Nitrógeno , Fitoplancton
7.
J Environ Manage ; 301: 113817, 2022 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-34607136

RESUMEN

Assessing trade-offs among ecosystem services (ESs) that are provided by forests is necessary to support decision-making and to minimize negative effects of timber harvesting. In this study, we examined how spatial data, forest operational rules, ESs, and probabilistic statistics can be combined into a practical tool for trade-off analysis that could guide decision-making towards sustainable forestry. Our main goal was to analyze trade-offs among the wood provisioning ES and other forest ESs at the landscape level using a Bayesian belief network (BBN). We used LiDAR data to derive four ES layers as inputs to a spatial BBN: (i) wood provisioning; (ii) erosion regulating; (iii) climate regulating; and (iv) habitat supporting. We quantified operational constraints with four forest operational rules (FOR) that were defined in terms of: (i) potential harvest block size; (ii) distance between a small potential harvest block and a larger harvest block; (iii) gross merchantable volume (GMV); and (iv) distance to an existing resource road. Maps of the most probable trade-off classes between the wood provisioning ES and other ESs enabled us to identify areas where timber harvesting should be avoided or where timber harvesting should have a very low negative effect on other ESs. Even with our most restrictive management scenario, the total GMV that could be harvested met the annual allowable cut (AAC) volume required to meet sustainable forestry objectives. Through our study, we demonstrated that high-resolution spatial data could be used to quantify trade-offs among wood provisioning ES and other forest-related ESs and to simulate small changes in ES indicators within the BBN. We also demonstrated the potential to evaluate management scenarios to reduce trade-offs by considering FOR as inputs to the BBN. Maps of the most probable trade-off classes among two or three ESs under operational constraints provide key information to guide forest management decision-making towards sustainable forestry.


Asunto(s)
Ecosistema , Agricultura Forestal , Teorema de Bayes , Conservación de los Recursos Naturales , Bosques
8.
Spectrochim Acta A Mol Biomol Spectrosc ; 264: 120251, 2022 Jan 05.
Artículo en Inglés | MEDLINE | ID: mdl-34455387

RESUMEN

Pregnancy diagnosis is essential for rabbit's reproductive management. The early identification of non-pregnant rabbits allows for earlier re-insemination, increases the service rate, and reduces the laboring interval in commercial operations. The objective of this study was to establish the feasibility of using a Vis-NIR spatially resolved spectroscopy for diagnosing pregnancy in female rabbits. A total of 141 female rabbits, including 67 pregnant female rabbits (PRs) and 74 non-pregnant female rabbits (NPRs), were measured spectrally between 350 and 1000 nm with different source-detector distances (SDD). Different preprocessing methods were used to transform and enhance the spectral signal. A partial least squares-discriminant analysis (PLS-DA) classification model of the original and preprocessed spectra was established. The highest accuracy of the calibration set and prediction set was 91.75% and 86.05%, respectively. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were used to select characteristic wavelengths from the variables of VIP > 1 (Variable importance in projection),and four classification models were established based on selected wavelengths, including PLS-DA, support vector machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes. SPA-SVM was the optimal classification model, the sensitivity, specificity, and accuracy of the validation set and prediction set were 93.18%, 94.44%, 93.88%, 86.96%, 90.00%, 90.69% respectively. The results showed that Vis-NIR spatially resolved spectroscopy combined with classification models could discriminate the PRs and NPRs.


Asunto(s)
Espectroscopía Infrarroja Corta , Máquina de Vectores de Soporte , Algoritmos , Animales , Teorema de Bayes , Análisis Discriminante , Femenino , Análisis de los Mínimos Cuadrados , Embarazo , Conejos
9.
J Exp Child Psychol ; 213: 105269, 2022 01.
Artículo en Inglés | MEDLINE | ID: mdl-34416553

RESUMEN

Visualizations are commonly used in educational materials; however, not all visualizations are equally effective at promoting learning. Prior research has supported the idea that both perceptually rich and bland visualizations are beneficial for learning and generalization. We investigated whether the perceptual richness of a life cycle diagram influenced children's learning of metamorphosis, a concept that prior work suggests is difficult for people to generalize. Using identical materials, Study 1 (N = 76) examined learning and generalization of metamorphosis in first- and second-grade students, and Study 2 (N = 53) did so in fourth- and fifth-grade students. Bayesian regression analyses revealed that first and second graders learned more from the lesson with the perceptually rich diagram. In addition, fourth and fifth graders generalized more with the bland diagram, but these generalizations tended to be incorrect (i.e., generalizing metamorphosis to animals that do not undergo this type of change). These findings differ from prior research with adults, in which bland diagrams led to more correct generalizations, suggesting that the effect of perceptual richness on learning and generalization might change over development.


Asunto(s)
Generalización Psicológica , Aprendizaje , Animales , Teorema de Bayes , Humanos , Instituciones Académicas , Estudiantes
10.
Sci Total Environ ; 806(Pt 1): 150281, 2022 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-34562758

RESUMEN

Revealing the transport and sources of nitrate in urban stormwater runoff can effectively manage nitrate pollution in urban areas. This study used the chemical properties of stormwater along with δ15N-NO3- and δ18O-NO3- isotopes to identify the transport and sources of nitrate within an urban catchment. The results showed that the NO3-N concentration and total dissolved nitrogen (TDN) composition differed among roof runoff, road runoff, and drainage runoff. The highest NO3-N concentration was found in roof runoff and NH3-N dominated the TDN composition. However, the erosion of pervious surfaces and litter may have led to higher DON/TDN values in road runoff. The TDN composition of drainage runoff was consistent with that of roof runoff. Furthermore, among the various rainfall characteristics, the depth and intensity of rainfall were significantly correlated with the NO3-N concentrations in roof runoff and road runoff, while antecedent dry days had little effect. According to a Bayesian mixing model, the average contributions of the nitrate load in drainage runoff were ranked as road runoff (51.6%) > rainwater (29.2%) > and roof runoff (15%), which is consistent with the results of previous studies. Rainwater nitrate may have ranked second due to the confluence time, pollution level, and other factors that made rainwater reduce the pollution characteristics of roof runoff. The dominant contribution of road runoff to the NO3-N concentration of drainage runoff could be attributed to the large runoff volume. Hence, effective measures should be taken to minimize the NO3-N concentration in roof runoff, while runoff volume reduction should be the primary concern for controlling road runoff pollution. This work is helpful for obtaining a better understanding of the transport and sources of nitrate that vary dynamically within different hydrological flow pathways, and the outcomes are expected to enhance targeted measures to mitigate nitrate pollution in urban water systems.


Asunto(s)
Nitrógeno , Contaminantes Químicos del Agua , Teorema de Bayes , China , Monitoreo del Ambiente , Nitrógeno/análisis , Lluvia , Movimientos del Agua , Contaminantes Químicos del Agua/análisis
11.
Chemosphere ; 287(Pt 4): 132402, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34597642

RESUMEN

Most previous studies have indicated inconsistent relationships between rice cadmium (Cd) and the soil properties of paddy fields at a regional scale under the adverse effects of confounding factors and spatial heterogeneity. In order to reduce these effects, this study integrates Geodetector, a stepwise regression model, and a hierarchical Bayesian method (collectively called GDSH). The GDSH framework is validated in a large typical rice production area in southeastern China. According to the results, significant stratified heterogeneity of the bioaccumulation factor is observed among different subregions and pH strata (q = 0.23, p < 0.01). Additionally, the soil-rice relationships and dominant factors vary by the subregions, and the available soil Cd and pH are found to be the dominant factors in 64% and 50% of subregions, respectively. In the entire region, when the pH < 6, the dominant factors are organic matter and available Cd, and when pH ≥ 6 they are organic matter, pH, and available Cd. Furthermore, these factors presented different sensitivity to the spatial heterogeneity. The results indicate that, at the subregional level, the GDSH framework can reduce the confounding effects and accurately identify the dominant factors of rice Cd. At the regional level, this model can evaluate the sensitivity of the dominant factors to spatial heterogeneity in a large area. This study provides a new scheme for the complete utilization of regional field survey data, which is conducive to formulating precise pollution control strategies.


Asunto(s)
Oryza , Contaminantes del Suelo , Teorema de Bayes , Cadmio/análisis , Suelo , Contaminantes del Suelo/análisis
12.
Acta Trop ; 225: 106181, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34678259

RESUMEN

INTRODUCTION: Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019. METHODS: This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05. RESULTS: The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed. CONCLUSIONS: Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.


Asunto(s)
Leishmaniasis Cutánea , Teorema de Bayes , Análisis por Conglomerados , Humanos , Incidencia , Irán/epidemiología , Leishmaniasis Cutánea/epidemiología , Análisis Espacio-Temporal
13.
Environ Res ; 203: 111810, 2022 01.
Artículo en Inglés | MEDLINE | ID: mdl-34343550

RESUMEN

With a recent surge of the new severe acute respiratory syndrome-coronavirus 2 (SARS-Cov-2, COVID-19) in South Korea, this study attempts to investigate the effects of environmental conditions such as air pollutants (PM2.5) and meteorological covariate (Temperature) on COVID-19 transmission in Seoul. To account for unobserved heterogeneity in the daily confirmed cases of COVID-19 across 25 contiguous districts within Seoul, we adopt a full Bayesian hierarchical approach for the generalized linear mixed models. A formal statistical analysis suggests that there exists a positive correlation between a 7-day lagged effect of PM2.5 concentration and the number of confirmed COVID-19 cases, which implies an elevated risk of the infectious disease. Conversely, temperature has shown a negative correlation with the number of COVID-19 cases, leading to reduction in relative risks. In addition, we clarify that the random fluctuation in the relative risks of COVID-19 mainly originates from temporal aspects, whereas no significant evidence of variability in relative risks is observed in terms of spatial alignment of the 25 districts. Nevertheless, this study provides empirical evidence using model-based formal assessments regarding COVID-19 infection risks in 25 districts of Seoul from a different perspective.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , COVID-19 , Contaminantes Atmosféricos/efectos adversos , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Teorema de Bayes , Humanos , Material Particulado/análisis , República de Corea/epidemiología , SARS-CoV-2 , Seúl/epidemiología , Temperatura
14.
Crit Care Clin ; 38(1): 51-67, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34794631

RESUMEN

Clinical reasoning is prone to errors in judgment. Error is comprised of 2 components-bias and noise; each has an equally important role in the promulgation of error. Biases or systematic errors in reasoning are the product of misconceptions of probability and statistics. Biases arise because clinicians frequently rely on mental shortcuts or heuristics to make judgments. The most frequently used heuristics are representativeness, availability, and anchoring/adjustment which lead to the common biases of base rate neglect, misconceptions of regression, insensitivities to sample size, and fallacies of conjunctive, and disjunctive events. Bayesian reasoning is the framework within which posterior probabilities of events is identified. Familiarity with these mathematical concepts will likely enhance clinical reasoning. Noise is defined as inter or intraobserver variability in judgment that should be identical. Guidelines in medicine are a technique to reduce noise.


Asunto(s)
Heurística , Juicio , Teorema de Bayes , Humanos , Unidades de Cuidados Intensivos
15.
Chemosphere ; 287(Pt 1): 132052, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34478965

RESUMEN

The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.


Asunto(s)
Carbón Mineral , Hidrógeno , Teorema de Bayes , Biomasa , Temperatura
16.
Chemosphere ; 287(Pt 2): 132159, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34509013

RESUMEN

BACKGROUND: Heavy metals may play an important role as environmental risk factors in diabetes mellitus. This study aimed to explore the association of HbA1c with As, Cd, Cu, Ni, Pb, and Zn in single-metal exposure and multi-metal co-exposure models. METHODS: A cross-sectional study involving 3472 participants was conducted. Plasma concentrations of heavy metals were determined by inductively coupled plasma mass spectrometry. We estimated the association of each metal with HbA1c by linear regression. Potential heterogeneities by sex, age, and smoking were investigated, and metal mixtures and interactions were assessed by the Bayesian kernel machine regression (BKMR). RESULTS: In linear regression, Cu (ß = 0.324, p < 0.05) and Ni (ß = -0.19, p < 0.05) showed significant association with HbA1c in all participants. In BKMR analyses, all exposure-response relationships were approximately linear. Cu was significantly and positively associated with HbA1c levels in overall participants, women, participants aged 60 years old and above, and nonsmokers. Ni was significantly and negatively associated with HbA1c levels in overall participants. We did not observe the overall effect of plasma metal mixtures on HbA1c or the interaction effect of the metals on HbA1c. CONCLUSION: Cu was positively correlated with HbA1c, whereas Ni was negatively correlated with HbA1c, when evaluated individually or in a metal mixture. Additional studies are necessary to confirm these correlations and to control for exposure to different metals in the general population.


Asunto(s)
Metales Pesados , Teorema de Bayes , China , Estudios Transversales , Femenino , Hemoglobina A Glucada , Humanos , Persona de Mediana Edad
17.
Chemosphere ; 287(Pt 3): 132358, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34583294

RESUMEN

Previous studies suggested immunotoxicity of perfluoroalkyl substances (PFASs), but contradictory findings were reported for the associations of PFASs with allergies. The current study aimed to investigate the association of serum PFASs with incident chronic spontaneous urticaria (CSU) in adults. A nested case-control study within a longitudinal cohort of 7051 government employees in China was conducted. Participants with urticaria at the baseline were excluded. During the first follow-up, 70 incident CSU cases were included, and 70 matched healthy controls were randomly selected. In serum samples collected at the baseline, eight PFASs were determined using the UHPLC-MS/MS approach. The median serum concentrations of perfluorobutanoic acid (PFBA) and perfluoroheptanoic acid (PFHpA) were significantly higher in participants with incident CSU. The area under the receiver operating characteristic curve was 0.714 (95% CI: 0.60-0.83) based on the joint prediction by PFBA and PFHpA. The Bayesian kernel machine regression showed a nonlinear positive overall effect of the mixture of PFASs, and identified significant single effects of PFBA and PFHpA. Serum interleukin-4 was significantly higher in the case group at baseline, and was positively associated with PFHpA (r = 0.24). Causal mediation analysis indicated interleukin-4 as a partial mediator (14.8%) in the association of PFHpA with CSU. In conclusion, serum PFASs are associated with an increased risk of incident CSU, and PFBA and PFHpA might be the effective compounds.


Asunto(s)
Ácidos Alcanesulfónicos , Urticaria Crónica , Fluorocarburos , Adulto , Teorema de Bayes , Estudios de Casos y Controles , Fluorocarburos/toxicidad , Humanos , Espectrometría de Masas en Tándem
18.
Environ Pollut ; 292(Pt A): 118247, 2022 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-34624398

RESUMEN

Dyslipidemia may be a potential mechanism linking air pollution to adverse cardiovascular outcomes and this may differ among obese and normal-weight populations. However, the joint effect of multiple air pollutants on lipid profiles and the role of each pollutant are still unclear. This panel study aims to investigate and compare the overall associations of major air pollutants with lipid parameters in obese and normal-weight adults, and assess the relative importance of each pollutant for lipid parameters. Forty-four obese and 53 normal-weight young adults were recruited from December 2017 to June 2018 in Beijing, China. Their fasting blood was collected and serum lipid levels were measured in three visits. Six major air pollutants were included in this study, which were PM2.5, PM10, NO2, SO2, O3 and CO. Bayesian kernel machine regression (BKMR) was implemented to estimate the joint effect of the six air pollutants on various lipid parameters. We found that decreased high-density lipoprotein cholesterol (HDL-C) in the obese group and increased low-density lipoprotein cholesterol (LDL-C) and non-HDL-C in the normal-weight group were associated with the exposure to the mixture of six air pollutants above. Significant increases in total cholesterol (TC)/HDL-C and non-HDL-C/HDL-C were observed in both groups, and the effect was stronger in obese group. Of the six air pollutants above, O3 had the largest posterior inclusion probability in above lipid indices, ranging from 0.75 to 1.00. In the obese group, approximately linear exposure-response relationships were observed over the whole range of logarithmic O3-8 h max concentration, while in the normal-weight group, these relationships existed when the logarithmic concentration exceeded about 2.8. Therefore, lipid profiles of obese adults may be more sensitive to air pollution and this study highlights the importance of strengthening emissions control efforts for O3 in the future.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Teorema de Bayes , China , Humanos , Lípidos , Obesidad , Ozono/análisis , Material Particulado/análisis , Adulto Joven
19.
Environ Pollut ; 292(Pt A): 118362, 2022 Jan 01.
Artículo en Inglés | MEDLINE | ID: mdl-34648836

RESUMEN

The fetus is prenatally exposed to a mixture of organochlorine pesticides (OCPs), mercury (Hg), docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and selenium (Se) through maternal seafood consumption in real-life scenario. Prenatal exposure to these contaminants and nutrients has been suggested to affect thyroid hormone (TH) status in newborns, but the potential relationships between them are unclear and the joint effects of the mixture are seldom analyzed. The aim of the study is to investigate the associations of prenatal exposure to a mixture of OCPs, Hg, DHA, EPA and Se with TH parameters in newborns. 228 mother-infant pairs in Shanghai, China were included. We measured 20 OCPs, total Hg, DHA, EPA and Se in cord blood samples as exposure variables. The total thyroxine (TT4), free thyroxine (FT4), total triiodothyronine (TT3), free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH) levels and the FT3/FT4 ratio in cord serum were determined as outcomes. Using linear regression models, generalized additive models and Bayesian kernel machine regression, we found dose-response relationships of the mixture component with outcomes: among the contaminants, p,p'-DDE was the most important positive predictor of TT3, while HCB was predominantly positively associated with FT3 and the FT3/FT4 ratio, indicating different mechanisms underlying these relationships; among the nutrients, EPA was first found to be positively related to the FT3/FT4 ratio. Additionally, we found suggestive evidence of interactions between p,p'-DDE and HCB on both TT3 and FT3, and EPA by HCB interactions for TT3, FT3 and FT3/FT4 ratio. However, the overall effects of the mixture on thyroid hormone parameters were not significant. Our result suggests that prenatal exposure to p,p'-DDE, HCB and EPA as part of a mixture might affect thyroid function of newborns in independent and interactive ways. The potential biological mechanisms merit further investigation.


Asunto(s)
Mercurio , Plaguicidas , Teorema de Bayes , China , Femenino , Sangre Fetal , Humanos , Recién Nacido , Nutrientes , Embarazo , Hormonas Tiroideas , Tirotropina , Tiroxina
20.
Anal Chim Acta ; 1189: 339223, 2022 Jan 02.
Artículo en Inglés | MEDLINE | ID: mdl-34815054

RESUMEN

The rapid detection of the pathogenic bacteria in patient samples is crucial to expedient patient care. The proposed approach reports the development of a novel lab-on-a-chip device for the rapid detection of P. aeruginosa based on immunomagnetic separation, optical scattering, and machine learning. The immunomagnetic particles with a diameter of 5 µm were synthesized for isolating P. aeruginosa from the test sample. A microfluidic chip was fabricated, and three optical fibers were embedded for connecting a laser light and two photodetectors. The laser light was pointed towards the channel to pass light through the sample. A pair of photodetectors via optical fibers were arranged symmetrically at 45° to the channel. The photodetectors acquired scattered light from the flowing sample and converted the light to an electrical signal. The sample containing immunomagnetic beads linked with bacteria was injected into the microfluidic chip. The optimized conditions for performing the experiments were characterized for real-time detection of P. aeruginosa. The data acquisition system recorded the real-time light scattering from the test sample. After removing noise from the output waveform, five different time-domain statistical features were extracted from each waveform: standard mean, standard variance, skewness, kurtosis, and coefficient of variation. The pathogens classification was performed by training the discrimination model using extracted features based on machine learning algorithms. The support vector machines (SVM) with a sigmoid function kernel showed superior classification performance with 97.9% accuracy among other classifiers, including k-nearest neighbors (KNN), logistic regression (LR), and naïve Bayes (NB). The method can detect P. aeruginosa specifically and quantitatively with a limit of detection of 102 CFU/mL. The device can classify P. aeruginosa within 10 min with a total assay time of 25 min. The device was used to test its ability to detect pathogen from the serum and sputum specimens spiked with 105 CFU/mL concentration of P. aeruginosa. The results indicate that light scattering combined with machine learning can be used to detect P. aeruginosa. The proposed technique is anticipated to be helpful as a rapid device for diagnosing P. aeruginosa related infections.


Asunto(s)
Dispositivos Laboratorio en un Chip , Pseudomonas aeruginosa , Teorema de Bayes , Humanos , Separación Inmunomagnética , Aprendizaje Automático , Máquina de Vectores de Soporte
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