Comprehensive Sensitivity Analysis Research Articles

Transforming abandoned petroleum wells in the Nigerian sector of the Chad basin into triplet borehole heat exchangers for sustainable power generation

This study pioneers a transformative approach to sustainable energy recovery, repurposing abandoned petroleum wells in the Nigerian Chad Basin as a triplet-deep closed-loop heat exchanger. We employed a computational numerical approach to designed two deep closed loop systems, which share a common production well, and to evaluate the thermal performance as well as its sustainability over a period of 25 years. The geometrical configuration consists of two injection wells, which converge to the common production one doubling the flow rate at surface and minimising the heat loss during the ascent of the fluid. The availability of detailed abandoned well specifications enabled us to constrain three-dimensional geological and thermal models of the undisturbed underground and, subsequently, to simulate numerically the production temperature and the thermal disturbance in the surrounding rocks. A comprehensive sensitivity analysis optimized the best operational design for total recovered thermal energy and uninterrupted heat recovery. When the deep close loop heat system operates with a fluid circulation rate of 0.03 m³s⁻1 and an injection temperature of 20 °C, it sustains a production temperature of over 100 °C, which generates total recovered thermal energy of 9730 TWth and 817 TWe over 25 years (9000 days). The average annual thermal and electricity productions of 389 TWth and 33 TWe, respectively, are more than the demand of the entire population of Magumeri district. The study's findings offer practical implications for policymakers, industry stakeholders, and local communities, emphasising the potential for socio-economic development while fostering environmental stewardship in the Nigerian Chad Basin.

A Model of Gastric Mucosal pH Regulation: Extending Sensitivity Analysis Using Sobol' Indices to Understand Higher Moments.

Several recent theoretical studies have indicated that a relatively simple secretion control mechanism in the epithelial cells lining the stomach may be responsible for maintaining a neutral (healthy) pH adjacent to the stomach wall, even in the face of enormous electrodiffusive acid transport from the interior of the stomach. Subsequent work used Sobol' Indices (SIs) to quantify the degree to which this secretion mechanism is "self-regulating" i.e. the degree to which the wall pH is held neutral as mathematical parameters vary. However, questions remain regarding the nature of the control that specific parameters exert over the maintenance of a healthy stomach wall pH. Studying the sensitivity of higher moments of the statistical distribution of a model output can provide useful information, for example, how one parameter may skew the distribution towards or away from a physiologically advantageous regime. In this work, we prove a relationship between SIs and the higher moments and show how it can potentially reduce the cost of computing sensitivity of said moments. We define -indices to quantify sensitivity of variance, skewness, and kurtosis to the choice of value of a parameter, and we propose an efficient strategy that uses both SIs and -indices for a more comprehensive sensitivity analysis. Our analysis uncovers a control parameter which governs the "tightness of control" that the secretion mechanism exerts on wall pH. Finally, we discuss how uncertainty in this parameter can be reduced using expert information about higher moments, and speculate about the physiological advantage conferred by this control mechanism.

Guaranteed minimum withdrawal benefits with high-water mark fee structure.

The Guaranteed Minimum Withdrawal Benefit (GMWB), an adjunct incorporated within variable annuities, commits to reimbursing the entire initial investment regardless of the performance of the underlying funds. While extensive research exists in financial and actuarial literature regarding the modeling and valuation techniques of GMWBs, much of it is founded on a static fee structure. Our study introduces an innovative fee structure based on the high-water mark (HWM) principle and a regime-switch jump-diffusion model for the pricing of GMWBs, employing numerical solutions through the Monte Carlo method for solving the stochastic differential equation (SDE). Furthermore, a companion piece of research addresses the risk management of GMWBs within the same analytical framework as the pricing component, an aspect that has received limited attention in the existing literature. In assessing the necessary capital reserves for unforeseen losses, our methodology involves the computation of two risk metrics associated with the tail distribution of net liability from the insurer's perspective, Value-at-Risk (VaR) and Conditional-Tail-Expectation (CTE). Comprehensive numerical results and sensitivity analyses are also provided.

Robust, extended goal programming with uncertainty sets: an application to a multi-objective portfolio selection problem leveraging DEA

AbstractThis study presents a two-phase approach of Data Envelopment Analysis (DEA) and Goal Programming (GP) for portfolio selection, representing a pioneering attempt at combining these techniques within the context of portfolio selection. The approach expands on the conventional risk and return framework by incorporating additional financial factors and addressing data uncertainty, which allows for a thorough examination of portfolio outcomes while accommodating investor preferences and conservatism levels. The initial phase employs a super-efficiency DEA model to streamline asset selection by identifying suitable investment candidates based on efficiency scores, setting the stage for subsequent portfolio optimization. The second phase leverages the Extended GP (EGP) framework, which facilitates the comprehensive incorporation of investor preferences to determine the optimal weights of the efficient assets previously identified within the portfolio. Each goal is tailored to reflect specific financial factors spanning both technical and fundamental aspects. To tackle data uncertainty, robust optimization is applied. The research contributes to the robust GP (RGP) literature by analyzing new RGP variants, overcoming limitations of traditional and other uncertain GP models by incorporating uncertainty sets. Robust counterparts of the EGP models are accordingly developed using polyhedral and combined interval and polyhedral uncertainty sets, providing a flexible representation of uncertainty in financial markets. Empirical results, based on real data from the Tehran Stock Exchange comprising 779 assets, demonstrate the superiority of the proposed approach over traditional portfolio selection methods across various uncertainty settings. Additionally, a comprehensive sensitivity analysis investigates the impact of uncertainty levels on the robust EGP models. The proposed framework offers guidance to investors and fund managers through a pragmatic approach, enabling informed and robust portfolio decisions by considering efficiency, uncertainty, and extended financial factors.

Policy Assessment for Energy Transition to Zero- and Low-Emission Technologies in Pickup Trucks: Evidence from Mexico

The transport sector is under scrutiny because of its significant greenhouse gas emissions. Essential strategies, particularly the adoption of zero- and low-emission vehicles powered by electricity, are crucial for mitigating emissions in road transport. Pickups, which are integral to Mexico’s fleet, contribute to such emissions. Thus, implementing effective policies targeting pickups is vital for reducing air pollution and aligning with Mexico’s decarbonization objectives. This paper presents a simulation model based on system dynamics to represent the adoption process of zero- and low-emission vehicles, with a focus on pickups and utilizing data from the Mexican case. Three policy evaluation scenarios are proposed based on the simulation model: business as usual; disincentives for zero- and low-emission vehicles; and incentives for unconventional vehicles. One of the most significant findings from this study is that even in a scenario with a greater number of vehicles in circulation, if the technology is fully electric, the environmental impact in terms of emissions is lower. Additionally, a comprehensive sensitivity analysis spanning a wide spectrum is undertaken through an extensive computational process, yielding multiple policy scenarios. The analysis indicates that to achieve a maximal reduction in the country’s emissions, promoting solely hybrid electric vehicles and plug-in hybrid electric vehicles is advisable, whereas internal combustion engines, vehicular natural gas, and battery electric vehicles should be discouraged.

The possible causal relationship between COVID-19 and imaging markers of cerebral small vessel disease: a Mendelian randomization study

ABSTRACT Objectives Observational studies have suggested that SARS-CoV-2 infection may increase the burden of cerebral small vessel disease (CSVD). This study aims to explore the causal correlation between COVID-19 and the imaging markers of CSVD using Mendelian randomization (MR) methods. Methods Summary-level genome-wide association study (GWAS) statistics for COVID-19 susceptibility, hospitalization, and severity were utilized as proxies for exposure. Large-scale meta-analysis GWAS data on three neuroimaging markers of white matter hyperintensity, lacunar stroke, and brain microbleeds, were employed as outcomes. Our primary MR analysis employed the inverse variance weighted (IVW) approach, supplemented by MR-Egger, weighted median, and MR-PRESSO methods. We also conducted multivariable MR analysis to address confounding bias and validate the robustness of the established causal estimates. Comprehensive sensitivity analyses included Cochran’s Q test, Egger-intercept analysis, MR-PRESSO, and leave-one-out analysis. Results The MR analysis revealed a significant causal correlation between the severity of COVID-19 and an increased risk of lacunar stroke, as demonstrated by the IVW method (ORivw = 1.08, 95% CI: 1.03–1.16, pivw = 0.005, FDR = 0.047). Nevertheless, no causal correlations were observed between COVID-19 susceptibility or hospitalization and any CSVD imaging markers. The robustness and stability of these findings were further confirmed by multivariable MR analysis and comprehensive sensitivity analyses. Discussion This study provides compelling evidence of a potential causal effect of severe COVID-19 on the incidence of lacunar stroke, which may bring fresh insights into the understanding of the comorbidity between COVID-19 and CSVD.

Arctic mixed-phase clouds simulated by the WRF model: Comparisons with ACLOUD radar and in situ airborne observations and sensitivity of microphysics properties

The microphysical properties of two mixed-phase clouds (MPCs), one over sea ice, and another over open ocean, are investigated using in situ measuring probes and a cloud radar installed on-board two aircraft during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) field campaign, west of Svalbard on 17 June 2017. Comparisons between observations and different configurations of the Weather and Research Forecasting (WRF) model are conducted and reveal discrepancies in the vertical profile of the ice crystal concentrations for both MPCs due to slightly higher temperatures (by approx. 1–2 °C) in WRF than observed, affecting the ice formation. A comprehensive sensitivity analysis is carried out to address the impact of assumptions used for the microphysics (MP) and the atmospheric boundary layer (ABL) schemes on the simulated vertical properties of the MPC systems. The role of the vertical resolution of the large scale data used for model initialisation is also investigated. Results show that the model performance improves for large-scale initialisation with more numerous levels close to the surface. Moreover, modifying the ABL or MP scheme influences the altitudes of the cloud top and base as well as the liquid and ice water amounts. Our study also highlights how an increase in the cloud condensation nuclei and droplet number concentrations can impact the vertical profiles of the liquid and ice water amounts.

Three warehouse inventory model for non-instantaneous deteriorating items with quadratic demand, time-varying holding costs and backlogging over finite time horizon

This paper presents a novel sustainable inventory model tailored for non-instantaneous deteriorating items, incorporating quadratic demand and time-dependent holding costs. Notably, the model accounts for shortages over a finite time horizon, simulating real-world scenarios. The inventory management the strategy involves distributing products across three warehouses: the Own Warehouse (OW) and two Rental Warehouses (RW), with applications spanning supermarkets, bakeries, malls, and floral businesses. The primary objective is minimizing total costs to enhance consumer satisfaction and bolster profits. The theoretical framework is substantiated through numerical examples, focusing on the critical finding regarding optimal total cost. A special methodology is employed to balance inventory dynamics amidst various warehouses, optimizing resource allocation and mitigating shortages. Furthermore, a comprehensive sensitivity analysis using MATLAB Software unveils the repercussions of parameter modifications, providing valuable insights for decision-makers in diverse industrial settings.

Integrating IoT and blockchain for intelligent inventory management in supply chains: A multi-objective optimization approach for the insurance industry

This research aims to develop and optimize an intelligent supply chain framework tailored for the insurance industry by integrating an innovative inventory management policy supported by the Internet of Things (IoT) and blockchain technologies. Through an extensive literature review, key assumptions are established, needs are identified, and objectives are formulated. A multi-objective mathematical planning model is proposed to minimize cost and time within the supply chain, with optimization conducted under deterministic and fuzzy conditions. The model utilizes augmented epsilon constraint and weighted sum methods to address its multi-objectiveness. Noteworthy is the integration of advanced technologies such as Wireless Sensor Networks (WSN), Radio-Frequency Identification (RFID), blockchain, and online sales, distinguishing it from traditional approaches. Initial validation and testing in smaller dimensions demonstrate the model’s adaptability to precise methods, while larger dimensions necessitate the use of meta-heuristic algorithms for efficient problem resolution. Additionally, a comprehensive sensitivity analysis in the insurance industry on objective function coefficients reveals intricate relationships, offering valuable insights into optimization dynamics across diverse conditions.

Optimization of reliability and sensitivity analysis for flange structures

The study delves into the reliability-driven optimization design of the flanges, drawing from reliability design theory, advanced optimization methodologies, and comprehensive sensitivity analysis. Flanges, integral to mechanical systems, demand precise design. Reliability design theory emphasizes consistent operation under diverse conditions. A novel approach to the flange-specific reliability sensitivity analysis method is central to this research. It promises to revolutionize flange design. By understanding the first two moments of core random parameters, we expedite reliability-driven optimization designs for mechanical connections. The programs enhance our approach, streamlining the pursuit of reliability-driven designs and providing rapid, accurate sensitivity analysis data for mechanical connections. This data offers critical insights into parameter influence, enabling targeted design adjustments. In summary, this research represents a significant advancement in mechanical engineering. By integrating reliability design theory with cutting-edge methodologies, it promises to enhance the quality and reliability of mechanical connections, meeting the rigorous demands of contemporary engineering applications. This holistic approach will play a pivotal role in the future of mechanical component design.

Stability assessment of CIPP liner under varied boundary conditions: A theoretical and simulation study

The buckling of liners carries significant implications, including hampered medium transmission, structural damage, and the risk of leaks, adversely affecting production, economy, and the environment. The interface performance between Cured-in-place-pipe (CIPP) liners and host pipes is pivotal for overall structural stability. However, this interface's influence has been underestimated in engineering practices and laboratory research. This study introduces two novel hydrostatic pressure buckling models, enhancing constrained arch and energy-based Glock models, while considering both free and bonded boundary conditions. Subsequently, a validated 3D finite element model of CIPP lined pipes is established from full-scale tests. Through comprehensive sensitivity analysis, the study investigates the influence of diameter-to-wall thickness ratio (DR) and annular gap on liner buckling under free boundary conditions. Additionally, it explores the effects of DR value and ovality ratio on buckling behavior under bonded conditions. Results reveal distinct buckling and post-buckling modes under different boundary conditions. The proposed theoretical analysis method accurately predicts critical buckling pressure within a 10 % error margin, validated through experiments and numerical simulations, offering practical guidance for assessing repaired pipe stability.

Mechanistic modeling of twin screw wet granulation for pharmaceutical formulations: Calibration, sensitivity analysis, and model-driven workflow

Wet granulation, a particle size enlargement process, can significantly enhance the critical quality attributes of powders and improve the ability to form tablets in pharmaceutical manufacturing. In this study, a mechanistic-based population balance model is applied to twin screw wet granulation. This model incorporated a recently developed breakage kernel specifically designed for twin screw granulation, along with nucleation, layering, and consolidation. Calibration and validation were performed on Hydrochlorothiazide and Acetaminophen formulations, which exhibit different particle size and wettability characteristics. Utilizing a compartmental experimental dataset, a comprehensive global sensitivity analysis identified critical inputs impacting quality attributes. The study revealed that the nucleation rate process model, effectively represented particle size distributions for both formulations. Adjustments to nucleation and breakage rate parameters, influenced by material properties and screw configuration, improved the model’s accuracy. A model-driven workflow was proposed, offering step-by-step guidelines and facilitating PBM model usage, providing essential details for future active pharmaceutical ingredient (API) formulations.

Vegetation dynamics regulate baseflow seasonal patterns of the Chaohe watershed in North China

Study regionThe Chaohe watershed is Beijing's primary potable water source. Study focusThe initiation of ecological restoration (ER), combined with the rapid acceleration of climate change (CC), has precipitated severe water shortages in North China. The seasonal responses of baseflow (BF), pivotal for sustaining rivers' fundamental flow and ecological equilibrium, to ER and CC are poorly understood. This study provides a precise depiction of the seasonal variations in BF by leveraging multiple separation methodologies. By applying the BFAST algorithm and a comprehensive sensitivity analysis, we unveil the nuanced seasonal patterns of BF adjustments in reaction to ER and CC. New hydrological insights for the regionBaseflow, primarily influenced by the wet season, constituted 64.21% of the annual aggregate. Considerable decreases in BF during the dry (−32.61%) and wet (−68.21%) seasons pose increasing threats to available water resources. The decrease in sub-surface runoff (−1.91 mm per decade) dominated the reduction of dry season BF. Indeed, vegetation regulated seasonal water distribution, maintaining the essential flow throughout the dry season. In the wet season, the reduction in BF acts as a supplemental water source to fulfill the escalating evapotranspiration needs due to afforestation and a drying climate. This study highlights the persistent hydrological consequences of ER and CC on water resources, emphasizing the crucial function of vegetation in baseflow, a key component for ecological restoration and water resource management in water-limited areas.

Causal effects of immune cells in glioblastoma: a Bayesian Mendelian Randomization study.

Glioblastoma (GBM) is a highly malignant brain tumor, and immune cells play a crucial role in its initiation and progression. The immune system's cellular components, including various types of lymphocytes, macrophages, and dendritic cells, among others, engage in intricate interactions with GBM. However, the precise nature of these interactions remains to be conclusively determined. In this study, a comprehensive two-sample Mendelian Randomization (MR) analysis was conducted to elucidate the causal relationship between immune cell features and the incidence of GBM. Utilizing publicly available genetic data, we investigated the causal associations between 731 immune cell signatures and the risk of GBM. Subsequently, we conducted a reverse Mendelian randomization analysis to rule out reverse causation. Finally, it was concluded that there is a unidirectional causal relationship between three subtypes of immune cells and GBM. Comprehensive sensitivity analyses were employed to validate the results robustness, heterogeneity, and presence of horizontal pleiotropy. To enhance the accuracy of our results, we concurrently subjected them to Bayesian analysis. After conducting MR analyses, we identified 10 immune phenotypes that counteract glioblastoma, with the most protective being FSC-A on Natural Killer T cells (OR = 0.688, CI = 0.515-0.918, P = 0.011). Additionally, we found 11 immune cell subtypes that promote GBM incidence, including CD62L- HLA DR++ monocyte % monocyte (OR = 1.522, CI = 1.004-2.307, P = 0.048), CD4+CD8+ T cell % leukocyte (OR = 1.387, CI = 1.031-1.866, P = 0.031). Following the implementation of reverse MR analysis, where glioblastoma served as the exposure variable and the outcomes included 21 target immune cell subtypes, we discerned that only three cell subtypes (CD45 on CD33+ HLA DR+ CD14dim, CD33+ HLA DR+ Absolute Count, and IgD+ CD24+ B cell Absolute Count) exhibited a unidirectional causal association with glioblastoma. Our study has genetically demonstrated the close relationship between immune cells and GBM, guiding future clinical research.

Exploring causal correlations between inflammatory cytokines and Ménière's disease: a Mendelian randomization.

Previous studies have highlighted associations between certain inflammatory cytokines and Ménière's Disease (MD), such as interleukin (IL) -13 and IL-1β. This Mendelian randomization aims to comprehensively evaluate the causal relationships between 91 inflammatory cytokines and MD. A comprehensive two-sample Mendelian randomization (MR) analysis was conducted to determine the causal association between inflammatory cytokines and MD. Utilizing publicly accessible genetic datasets, we explored causal links between 91 inflammatory cytokines and MD risk. Comprehensive sensitivity analyses were employed to assess the robustness, heterogeneity, and presence of horizontal pleiotropy in our findings. Our findings indicate that MD causally influences the levels of two cytokine types: IL-10 (P=0.048, OR=0.945, 95%CI =0.894~1.000) and Neurotrophin-3 (P=0.045, OR=0954, 95%CI =0.910~0.999). Furthermore, three cytokines exhibited significant causal effects on MD: CD40L receptor (P=0.008, OR=0.865, 95%CI =0.777-0.963), Delta and Notch-like epidermal growth factor-related receptor (DNER) (P=0.010, OR=1.216, 95%CI =1.048-1.412), and STAM binding protein (P=0.044, OR=0.776, 95%CI =0.606-0.993). This study suggests that the CD40L receptor, DNER, and STAM binding protein could potentially serve as upstream determinants of MD. Furthermore, our results imply that when MD is regarded as the exposure variable in MR analysis, it may causally correlate with elevated levels of IL-10 and Neurotrophin-3. Using these cytokines for MD diagnosis or as potential therapeutic targets holds great clinical significance.

Investigating the Effects of Ageing on Transmission System Dependability Through the Use of an Artificial Neural Network

As power transmission systems age, their dependability becomes increasingly critical for maintaining reliable electricity supply. However, accurately predicting the impact of ageing on system dependability remains a challenging task due to the complex interactions among various components and environmental factors. In this study, we propose the utilization of an artificial neural network (ANN) approach to investigate the effects of ageing on transmission system dependability. The ANN model is trained on historical data encompassing a wide range of parameters including equipment age, maintenance records, environmental conditions, and system performance metrics. By capturing the nonlinear relationships within the data, the ANN can effectively model the intricate dependencies between ageing and system dependability. Through comprehensive simulations and sensitivity analyses, we demonstrate the capability of the ANN to forecast the degradation of transmission system dependability over time. Moreover, the model enables the identification of key factors driving system reliability decline, thereby facilitating proactive maintenance strategies and resource allocation.

Dynamics of an [formula omitted] epidemic model with stochastic optimal control and awareness programs

An unaware susceptible-aware susceptible-vaccinated-infected-recovered (SuSaV IR) epidemic disease model has been developed and extensively examined to better understand the intricate dynamics of the disease transmission with saturated recovery function. A non-linear, monotonically increasing awareness function has been incorporated and its application is contingent upon the number of infected individuals. The spreading threshold R0 and its sensitivity indices are computed to determine both the prevalence and the potential decline of the spread of disease. Contour plots have been generated to explain how alterations in key parameters affect the evolution of infected individuals. In deterministic model, the condition R0<1 ensures the global stability of a disease-free state while R0>1 indicates the presence of a globally stable prevailing state. A numerical approach has been employed to pinpoint the stability switches for R0 which indicates transcritical bifurcation. Subsequently, a corresponding stochastic model is developed to investigate the impact of random external factors represented as white noise. In addition, the global existence and uniqueness of solutions, and the asymptotic behavior of solutions in the vicinity of steady-states are exhibited in the stochastic model. In the stochastic model, the extinction threshold Res is consistently below R0 due to noise intensity, leading to quicker disease extinction. In order to elucidate the influence of parameters and noise intensities on persistence threshold Rps and extinction threshold Res, a comprehensive sensitivity analysis and the variation of each noise intensity have been performed. This study shows that treatment can be used as an effective tool for controlling the spread of disease by lowering R0 and Rps. It has been empirically observed that high environmental fluctuations can serve as a suppressive factor for disease spread in stochastic model with R0,Rps>1 and the higher noise intensities lead to a faster disappearance of the disease. It is noticed that randomness in susceptible and infected individuals can exert a more significant influence on disease mitigation. The spread of infection exhibits a noteworthy dependence on environmental fluctuations. Additionally, to combat and mitigate the spread of disease, we have executed stochastic optimal control measures. Consequently, the control strategy has emerged as a potent tools for effectively managing the propagation of disease.

Serum ferritin and the risk of myocardial infarction: A Mendelian randomization study.

The potential role of serum ferritin as a risk factor for myocardial infarction (MI) is controversial, necessitating a systematic exploration of the causal relationship between ferritin and MI through Mendelian randomization (MR) methods. Genetic data were derived from a genome-wide association study (GWAS), employing the inverse variance-weighted (IVW) method as the primary approach. Comprehensive sensitivity analyses were conducted to validate the robustness of the results. Evaluation of instrumental variables was performed using the F-statistic, and a meta-analysis was employed to assess the average gene-predicted effect between ferritin and MI. The MR study revealed a negative correlation between ferritin and MI. The odds ratios (ORs) in the IVW method were 0.83 [95% confidence interval (CI) = 0.72-0.97; P = .017] and 0.86 (95% CI = 0.72-1.02; P = .080). Additionally, meta-analysis consistently indicated a negative causal relationship between ferritin and MI, with no heterogeneity or horizontal pleiotropy, thereby indicating a negative correlation between ferritin levels and the risk of MI. The genetic evidence sheds light on the causal relationship between ferritin levels and MI risk, providing new perspectives for future interventions in acute myocardial infarction (AMI).

Comprehensive analysis of long-term trends, meteorological influences, and ozone formation sensitivity in the Jakarta Greater Area

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010–2019) and rural Bogor sites (2017–2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia’ s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration.

Mendelian randomization and colocalization analysis reveal novel drug targets for myasthenia gravis

ObjectiveMyasthenia gravis (MG) is a complex autoimmune disease affecting the neuromuscular junction with limited drug options, but the field of MG treatment recently benefits from novel biological agents. We performed a drug-targeted Mendelian randomization (MR) study to identify novel therapeutic targets of MG.MethodsCis-expression quantitative loci (cis-eQTL), which proxy expression levels for 2176 druggable genes, were used for MR analysis. Causal relationships between genes and disease, identified by eQTL MR analysis, were verified by comprehensive sensitivity, colocalization, and protein quantitative loci (pQTL) MR analyses. The protein-protein interaction (PPI) analysis was also performed to extend targets, followed by enzyme-linked immunosorbent assay (ELISA) to explore the serum level of drug targets in MG patients. A phenome-wide MR analysis was then performed to assess side effects with a clinical trial review assessing druggability.ResultsThe eQTL MR analysis has identified eight potential targets for MG, one for early-onset MG and seven for late-onset MG. Further colocalization analyses indicated that CD226, CDC42BPB, PRSS36, and TNFSF12 possess evidence for colocalization with MG or late-onset MG. pQTL MR analyses identified the causal relations of TNFSF12 and CD226 with MG and late-onset MG. Furthermore, PPI analysis has revealed the protein interaction between TNFSF12-TNFSF13(APRIL) and TNFSF12-TNFSF13B(BLyS). Elevated TNFSF13 serum level of MG patients was also identified by ELISA experiments. This study has ultimately proposed three promising therapeutic targets (TNFSF12, TNFSF13, TNFSF13B) of MG.ConclusionsThree drug targets associated with the BLyS/APRIL pathway have been identified. Multiple biological agents, including telitacicept and belimumab, are promising for MG therapy.