A hydrochemical and multi-isotopic study of groundwater sulfate origin and contribution in the coal mining area

Coal mining cities are universally confronted with the degradation of groundwater quality, and the sulfate pollution of groundwater has become a widely studied environmental problem. In this study, we combined multi-isotope (δ³⁴S, δ¹⁸O-SO₄^2- and ⁸⁷Sr/⁸⁶Sr) approach with hydrochemical technique and a Bayesian mixed model to clarify sources and transformations and to quantitatively assess the contribution of sulfate from potential sources. The concentrations of SO₄^2- in groundwater ranged from 7.7 mg/L to 172.9 mg/L, and the high-value areas were located in coal mining area and residential area. The total values of δ³⁴S and δ¹⁸O-SO₄^2- varied from 10.6‰ to 26.9‰ and 6.9‰ to 14.1‰, respectively, in the groundwater. Analyses of SO₄^2- and Sr isotopes and water chemistry indicated that SO₄^2- in groundwater originated from various sources, such as atmospheric precipitation, sulfide mineral oxidation, evaporite dissolution, sewage and mine drainage. The oxidation of pyrite and bacterial sulfate reduction (BSR) had no significant impact on the stable isotopes of groundwater. At the same time, the calculation results of the Bayesian mixed model showed that the sources of SO₄^2- in groundwater mainly include evaporite dissolution in aquifer and mine drainage in the mixture of shallow and deep groundwater, with high contribution proportions of 39.8 ± 10.9% and 31.9 ± 5.7%, respectively, while the contributions of sewage (13.9 ± 8.5%), atmospheric precipitation (9.6 ± 8.6%) and the oxidation of sulfide (4.7 ± 3.3%) to SO₄^2- were lower. The research results revealed the source of SO₄^2- pollution in shallow groundwater in the coal mine area and provided an important scientific basis for the effective management and protection of groundwater resources.

Analysis of the Interaction between Lake and Groundwater Based on Water-Salt Balance Method and Stable Isotopic Characteristics

To better protect lacustrine ecologies and understand the evolutionary process of lake environments, it is critical to study the interacting mechanisms between lakes and the surrounding groundwater. The Wuliangsu Lake watershed is the largest wetland in the Yellow River basin and is the discharge area of the Hetao Irrigation District (HID), which is one of the three largest agricultural production areas in China. Due to the influence of human activities, the discharge water from the HID has led to the deterioration of the Wuliangsu Lake ecology and the degradation of the lake environment. Based on long-term observation data and water sampling data collected in 2021, a water-salt equilibrium model was used to analyze the recharge rate of groundwater to the lake. The contribution rate of groundwater to lake recharge in the study area was calculated with a Bayesian mixing model by combining D and ¹⁸O stable isotope data. Furthermore, the environmental evolutionary process of the lake was also analyzed using the collected water quality data. The results show that channel drainage was the main source of recharge to Wuliangsu Lake, accounting for more than 75%, while groundwater contributed less than 5% of lake recharge. After implementing the ecological water supplement plan, the concentration of various ions in the lake decreased, the concentration of the total dissolved solids (TDS) in the lake decreased from 1.7 g/L in 2016 to 1.28 g/L in 2021, and the ecological environment was improved. The contribution of groundwater to lake recharge was quantitatively analyzed. The results of this study can facilitate the development of vital strategies for preventing the further deterioration of lake water quality and for protecting wetland ecologies.

Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

Factors associated with the availability of iodized salt at household level: a case study in Bangladesh

OBJECTIVE

The availability of iodized salt in households remains low in Bangladesh, which calls for improving the salt iodization quality and its coverage. The present study assessed the socio-economic disparity in Bangladesh to characterize the availability of iodized salt at household level.

DESIGN

Associations between different socio-economic factors and availability of iodized salt at household level were explored using Bayesian mixed-effects logistic models after adjusting the district- and cluster-level random effects.

SETTING

Bangladesh Multiple Indicator Cluster Survey (MICS), 2012-13.ParticipantsHouseholds (sample size, n 50981).

RESULTS

Results showed that 73·15 % of household salt samples were iodized to some extent although iodization level varied. According to the regression model, houses with young (adjusted odds ratio of posterior mean (OR) = 1·31; 95 % credible interval (CI) 1·09, 1·64) and educated (OR = 3·66; 95 % CI 3·25, 4·23) household heads had significantly higher likelihood of availability of iodized salt. In addition, iodized salt was less likely be found in poor and rural households, as urban households were 2·88 times (95 % CI 2·41, 3·34) more likely have iodized salt. Moreover, the regional locations of the households were an important component that contributed to the local iodized salt coverage. As per the district-wise distribution, the north-west part of Bangladesh and Cox's Bazar in the far south seemed to lack household-level iodized salt.

CONCLUSIONS

Our findings suggest that iodized salt intervention should be promoted considering the area variations, which could potentially help policy makers to design interventions in the context of Bangladesh.

Dissecting the Genetic Architecture of Shoot Growth in Carrot (Daucus carota L.) Using a Diallel Mating Design

Crop establishment in carrot (Daucus carota L.) is limited by slow seedling growth and delayed canopy closure, resulting in high management costs for weed control. Varieties with improved growth habit (i.e., larger canopy and increased shoot biomass) may help mitigate weed control, but the underlying genetics of these traits in carrot is unknown. This project used a diallel mating design coupled with recent Bayesian analytical methods to determine the genetic basis of carrot shoot growth. Six diverse carrot inbred lines with variable shoot size were crossed in WI in 2014. F1 hybrids, reciprocal crosses, and parental selfs were grown in a randomized complete block design with two blocks in WI (2015) and CA (2015, 2016). Measurements included canopy height, canopy width, shoot biomass, and root biomass. General and specific combining abilities were estimated using Griffing’s Model I, which is a common analysis for plant breeding experiments. In parallel, additive, inbred, cross-specific, and maternal effects were estimated from a Bayesian mixed model, which is robust to dealing with data imbalance and outliers. Both additive and nonadditive effects significantly influenced shoot traits, with nonadditive effects playing a larger role early in the growing season, when weed control is most critical. Results suggest the presence of heritable variation and thus potential for improvement of these phenotypes in carrot. In addition, results present evidence of heterosis for root biomass, which is a major component of carrot yield.

Bayesian Diallel Analysis Reveals Mx1-Dependent and Mx1-Independent Effects on Response to Influenza A Virus in Mice

Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but the host genetic architecture is not well understood. Here, we obtain a broad view of how heritable factors affect a mouse model of response to IAV infection using an 8 × 8 diallel of the eight inbred founder strains of the Collaborative Cross (CC). Expanding on a prior statistical framework for modeling treatment response in diallels, we explore how a range of heritable effects modify acute host response to IAV through 4 d postinfection. Heritable effects in aggregate explained ∼57% of the variance in IAV-induced weight loss. Much of this was attributable to a pattern of additive effects that became more prominent through day 4 postinfection and was consistent with previous reports of antiinfluenza myxovirus resistance 1 (Mx1) polymorphisms segregating between these strains; these additive effects largely recapitulated haplotype effects observed at the Mx1 locus in a previous study of the incipient CC, and are also replicated here in a CC recombinant intercross population. Genetic dominance of protective Mx1 haplotypes was observed to differ by subspecies of origin: relative to the domesticus null Mx1 allele, musculus acts dominantly whereas castaneus acts additively. After controlling for Mx1, heritable effects, though less distinct, accounted for ∼34% of the phenotypic variance. Implications for future mapping studies are discussed.