With the ever-increasing demand for lithium (Li) for portable energy storage devices, there is a global concern associated with environmental contamination of Li, via the production, use, and ...disposal of Li-containing products, including mobile phones and mood-stabilizing drugs. While geogenic Li is sparingly soluble, Li added to soil is one of the most mobile cations in soil, which can leach to groundwater and reach surface water through runoff. Lithium is readily taken up by plants and has relatively high plant accumulation coefficient, albeit the underlying mechanisms have not been well described. Therefore, soil contamination with Li could reach the food chain due to its mobility in surface- and ground-waters and uptake into plants. High environmental Li levels adversely affect the health of humans, animals, and plants. Lithium toxicity can be considerably managed through various remediation approaches such as immobilization using clay-like amendments and/or chelate-enhanced phytoremediation. This review integrates fundamental aspects of Li distribution and behaviour in terrestrial and aquatic environments in an effort to efficiently remediate Li-contaminated ecosystems. As research to date has not provided a clear picture of how the increased production and disposal of Li-based products adversely impact human and ecosystem health, there is an urgent need for further studies on this field.
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•Biogeochemistry of Li influence bioavailability, toxicity, and remediation.•High Li levels adversely affect human and ecosystem health.•Li is readily taken up by plants and likely transferred into the food chain.•An integrated risk management of Li contaminated environments is needed.•Mechanisms and remediation possibilities of Li warrant further research.
Knowing whether COVID-19 vaccine effectiveness wanes is crucial for informing vaccine policy, such as the need for and timing of booster doses. We aimed to systematically review the evidence for the ...duration of protection of COVID-19 vaccines against various clinical outcomes, and to assess changes in the rates of breakthrough infection caused by the delta variant with increasing time since vaccination.
This study was designed as a systematic review and meta-regression. We did a systematic review of preprint and peer-reviewed published article databases from June 17, 2021, to Dec 2, 2021. Randomised controlled trials of COVID-19 vaccine efficacy and observational studies of COVID-19 vaccine effectiveness were eligible. Studies with vaccine efficacy or effectiveness estimates at discrete time intervals of people who had received full vaccination and that met predefined screening criteria underwent full-text review. We used random-effects meta-regression to estimate the average change in vaccine efficacy or effectiveness 1–6 months after full vaccination.
Of 13 744 studies screened, 310 underwent full-text review, and 18 studies were included (all studies were carried out before the omicron variant began to circulate widely). Risk of bias, established using the risk of bias 2 tool for randomised controlled trials or the risk of bias in non-randomised studies of interventions tool was low for three studies, moderate for eight studies, and serious for seven studies. We included 78 vaccine-specific vaccine efficacy or effectiveness evaluations (Pfizer–BioNTech-Comirnaty, n=38; Moderna-mRNA-1273, n=23; Janssen-Ad26.COV2.S, n=9; and AstraZeneca-Vaxzevria, n=8). On average, vaccine efficacy or effectiveness against SARS-CoV-2 infection decreased from 1 month to 6 months after full vaccination by 21·0 percentage points (95% CI 13·9–29·8) among people of all ages and 20·7 percentage points (10·2–36·6) among older people (as defined by each study, who were at least 50 years old). For symptomatic COVID-19 disease, vaccine efficacy or effectiveness decreased by 24·9 percentage points (95% CI 13·4–41·6) in people of all ages and 32·0 percentage points (11·0–69·0) in older people. For severe COVID-19 disease, vaccine efficacy or effectiveness decreased by 10·0 percentage points (95% CI 6·1–15·4) in people of all ages and 9·5 percentage points (5·7–14·6) in older people. Most (81%) vaccine efficacy or effectiveness estimates against severe disease remained greater than 70% over time.
COVID-19 vaccine efficacy or effectiveness against severe disease remained high, although it did decrease somewhat by 6 months after full vaccination. By contrast, vaccine efficacy or effectiveness against infection and symptomatic disease decreased approximately 20–30 percentage points by 6 months. The decrease in vaccine efficacy or effectiveness is likely caused by, at least in part, waning immunity, although an effect of bias cannot be ruled out. Evaluating vaccine efficacy or effectiveness beyond 6 months will be crucial for updating COVID-19 vaccine policy.
Coalition for Epidemic Preparedness Innovations.
Model uncertainty and VaR aggregation Embrechts, Paul; Puccetti, Giovanni; Rüschendorf, Ludger
Journal of banking & finance,
August 2013, 2013-8-00, 20130801, Volume:
37, Issue:
8
Journal Article
Peer reviewed
•We derive an algorithm for the calculation of bounds on the best and worst VaR.•The numerical results are compared with available analytical bounds.•Worst dependence scenarios are derived using the ...notion of complete mixability.•Examples show one can handle portfolios with several hundreds of risk factors.•The results obtained highlight VaR-based model uncertainty.
Despite well-known shortcomings as a risk measure, Value-at-Risk (VaR) is still the industry and regulatory standard for the calculation of risk capital in banking and insurance. This paper is concerned with the numerical estimation of the VaR for a portfolio position as a function of different dependence scenarios on the factors of the portfolio. Besides summarizing the most relevant analytical bounds, including a discussion of their sharpness, we introduce a numerical algorithm which allows for the computation of reliable (sharp) bounds for the VaR of high-dimensional portfolios with dimensions d possibly in the several hundreds. We show that additional positive dependence information will typically not improve the upper bound substantially. In contrast higher order marginal information on the model, when available, may lead to strongly improved bounds. Several examples of practical relevance show how explicit VaR bounds can be obtained. These bounds can be interpreted as a measure of model uncertainty induced by possible dependence scenarios.
Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological ...processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.
The global sports supplies industry, characterized by its complex and geographically dispersed supply chains, presents companies with a multitude of risk factors. These range from disruptions in raw ...material sourcing to geopolitical instability. This paper explores the challenges and opportunities associated with implementing effective supply chain risk management (SCRM) strategies within this industry. The study delve into the various types of risks faced by companies and propose strategies for mitigating them. One such strategy is Multi-Criteria Decision Making (MCDM). MCDM empowers companies to objectively evaluate and prioritize risks based on a range of factors and facilitates data-driven decision-making regarding supplier selection and risk mitigation approaches, ultimately leading to a more resilient and competitive supply chain. The paper highlights that the sports supplies industry can significantly benefit from the application of Probabilistic Linguistic Term Sets (PLTS) for enhanced risk assessment and management. By integrating PLTS into the decision-making process, companies gain a more nuanced understanding of supplier reliability and can implement proactive mitigation strategies. This translates to an overall increase in supply chain resilience and competitiveness. This paper proposes an advanced approach that leverages PLTS to address uncertainties inherent in decision-making. The study introduces innovative PLTS meticulously designed using the CRADIS method. CRADIS is a robust tool that aids in selecting optimal goals within the decision-making process. It combines various MCDM strategies, offering a powerful approach to navigate complex decision scenarios with multiple criteria. To demonstrate the adaptability and effectiveness of this proposed methodology, a case study is presented, showcasing its practical application and potential impact. To validate the effectiveness and real-world applicability of our improved MCDM CRADIS method, a comparative analysis with existing decision-making methods is conducted. This comparison highlights the strengths and advancements offered by the proposed approach.
Upland river systems in the UK are predicted to be prone to the effects of increased flood magnitudes and frequency, driven by climate change. It is clear from recent events that some headwater ...catchments can be very sensitive to large floods, activating the full sediment system, with implications for flood risk management further down the catchment. We provide a 15‐year record of detailed morphological change on a 500‐m reach of upland gravel‐bed river, focusing upon the geomorphic response to an extreme event in 2007, and the recovery in the decade following. Through novel application of two‐dimensional (2D) hydrodynamic modelling we evaluate the different energy states of pre‐ and post‐flood morphologies of the river reach, exploring how energy state adjusts with recovery following the event. Following the 2007 flood, morphological adjustments resulted in changes to the shear stress population over the reach, resulting in higher shear stresses. Although the proportion of shear stresses in excess of those experienced using the pre‐flood digital elevation model (DEM) varied over the recovery period, they remained substantially in excess of those experienced pre‐2007, suggesting that there is still potential for enhanced bedload transport and morphological adjustment within the reach. Although volumetric change calculated from DEM differencing does indicate a reduction in erosion and deposition volumes in the decade following the flood, we argue that the system still has not fully recovered to the pre‐flood state. We further argue that Thinhope Burn, and other similarly impacted catchments in upland environments, may not recover under the wet climatic phase currently being experienced. Hence systems like Thinhope Burn will continue to deliver large volumes of sediment further down river catchments, providing new challenges for flood risk management into the future.
The current wet phase in UK climate could be pushing some upland catchments closer to tipping points, whereby their sediment systems become activated, increasing sediment delivery downstream and enhancing flood risk. A decade after a large flood in an upland stream in the UK, we demonstrate a shift to a more energetic channel with enhanced sediment transport potential and morphological adjustment.
Count (and count-like) data in finance Cohn, Jonathan B.; Liu, Zack; Wardlaw, Malcolm I.
Journal of financial economics,
November 2022, 2022-11-00, Volume:
146, Issue:
2
Journal Article
Peer reviewed
This paper assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributions, which are increasingly common in corporate finance ...applications. We demonstrate that the common practice of estimating linear regressions of the log of 1 plus the outcome produces estimates with no natural interpretation that can have the wrong sign in expectation. In contrast, a simple fixed-effects Poisson model produces consistent and reasonably efficient estimates under more general conditions than commonly assumed. We also show through replication of existing papers that economic conclusions can be highly sensitive to the regression model employed.
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