Genome-Wide Association Study Identifies Chromosome 10q24.32 Variants Associated with Arsenic Metabolism and Toxicity Phenotypes in Bangladesh

Arsenic contamination of drinking water is a major public health issue in many countries, increasing risk for a wide array of diseases, including cancer. There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however, the basis of this variation is not well understood. Here, we have performed the first genome-wide association study (GWAS) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health. Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10−8) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations. In a follow-up analysis of 1,085 individuals with arsenic-induced premalignant skin lesions (the classical sign of arsenic toxicity) and 1,794 controls, we show that one of these five variants (rs9527) is also associated with skin lesion risk (P = 0.0005). Using a subset of individuals with prospectively measured arsenic (n = 769), we show that rs9527 interacts with arsenic to influence incident skin lesion risk (P = 0.01). Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10−12) and neighboring gene C10orf32 (P = 10−44), which are involved in C10orf32-AS3MT read-through transcription. This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date, the only GWAS of any arsenic-related trait, and the first study to implicate 10q24.32 variants in both arsenic metabolism and arsenical skin lesion risk. The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species. These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide

Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits

With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer

Krisenmanagement bei Franchisenehmern – eine empirische Untersuchung

Die Bewältigung von Unternehmenskrisen stellt für Franchisesysteme eine zentrale Herausforderung dar. Dies illustrieren prominente Krisenfälle, wie die des Rechtsberatungssystems juraXX oder der Sandwichkette Subway. Zentral gesteuerte Krisenmanagementsysteme können hierbei Abhilfe schaffen. Hinweise dafür liefert ebenso die in diesem Beitrag vorgestellte qualitative empirische Erhebung mit deutschen Franchisenehmern. Sie deckt auf, dass deutsche Franchisegeber und -nehmer derzeit noch keine Vorreiterrolle in der Steuerung von Krisen im Netzwerkkontext einnehmen. Die Ergebnisse zeigen weiterhin, dass eine große Heterogenität unter den vorhandenen Krisenmanagementsystemen vorherrscht. Im Artikel werden erste praktische Implikationen für Franchisegeber abgeleitet und Wege aufgezeigt, wie diese komplexe, noch wenig untersuchte Thematik weiter erforscht werden kann

Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits

With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer

Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles

Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners

Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles

Cloud transients cause rapid fluctuations in the output of photovoltaic (PV) systems, which can significantly affect the voltage levels in a low-voltage (LV) grid with high penetration of PV systems. These voltage fluctuations may lead to violation of the existing power quality standards. This study estimates the impact of rapid PV output fluctuations on the power quality in an existing LV grid by performing load flow analyses for scenarios in the years 2017, 2030 and 2050 using PV data with 20-second resolution. In this study, we propose a system for the mitigation of PV output fluctuations by altering the charging processes of electric vehicles (EVs) and we assess the effectiveness of the proposed system. Results indicate that PV output fluctuations have minor impact on the voltage levels in the year 2030, but PV output fluctuations induce considerable voltage fluctuations in the year 2050. The magnitude of the voltage fluctuations is dependent on the location in the grid, the installed PV capacity and the grid configuration. These voltage fluctuations can induce visible and annoying light flicker for a significant part of the day in the year 2050. Implementing the proposed system shows that EV technology can contribute in reducing the amount of visible and annoying light flicker considerably, however at the expense of increased charging costs for EV owners

Arsenic Exposure, Dietary Patterns, and Skin Lesion Risk in Bangladesh: A Prospective Study

Dietary factors are believed to modulate arsenic toxicity, potentially influencing risk of arsenical skin lesions. The authors evaluated associations among dietary patterns, arsenic exposure, and skin lesion risk using baseline food frequency questionnaire data collected in the Health Effects of Arsenic Longitudinal Study (HEALS) in Araihazar, Bangladesh (2000–2009). They identified dietary patterns and estimated dietary pattern scores using factor analysis. Scores were tested for association with incident skin lesion risk and interaction with water arsenic exposure by using ∼6 years of follow-up data (814 events among 9,677 individuals) and discrete time hazards models (adjusting for key covariates). The authors identified 3 clear dietary patterns: the “gourd and root,” “vegetable,” and “animal protein” patterns. The gourd and root pattern score was inversely associated with skin lesion risk (Ptrend = 0.001), with hazard ratios of 0.86, 0.73, and 0.69 for the second, third, and fourth highest quartiles. Furthermore, the association between water arsenic and skin lesion incidence was stronger among participants with low gourd and root scores (multiplicative Pinteraction < 0.001; additive Pinteraction = 0.05). The vegetable pattern and animal protein pattern showed similar but weaker associations and interactions. Eating a diet rich in gourds and root vegetables and increasing dietary diversity may reduce arsenical skin lesion risk in Bangladesh