Associations between colorectal cancer risk and dietary intake of tomato, tomato products, and lycopene: evidence from a prospective study of 101,680 US adults

BackgroundPrevious epidemiological studies have yielded inconsistent results regarding the effects of dietary tomato, tomato products, and lycopene on the incidence of colorectal cancer (CRC), possibly due to variations in sample sizes and study designs.MethodsThe current study used multivariable Cox regression, subgroup analyses, and restricted cubic spline functions to investigate correlations between CRC incidence and mortality and raw tomato, tomato salsa, tomato juice, tomato catsup, and lycopene intake, as well as effect modifiers and nonlinear dose-response relationships in 101,680 US adults from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.ResultsDuring follow-up 1100 CRC cases and 443 CRC-specific deaths occurred. After adjustment for confounding variables, high consumption of tomato salsa was significantly associated with a reduced risk of CRC incidence (hazard ratio comparing the highest category with the lowest category 0.8, 95% confidence interval 0.65–0.99, p for trend = 0.039), but not with a reduced risk of CRC mortality. Raw tomatoes, tomato juice, tomato catsup, and lycopene consumption were not significantly associated with CRC incidence or CRC mortality. No potential effect modifiers or nonlinear associations were detected, indicating the robustness of the results.ConclusionIn the general US population a higher intake of tomato salsa is associated with a lower CRC incidence, suggesting that tomato salsa consumption has beneficial effects in terms of cancer prevention, but caution is warranted when interpreting these findings. Further prospective studies are needed to evaluate its potential effects in other populations

The impact of immunoglobulin G N-glycosylation level on COVID-19 outcome: evidence from a Mendelian randomization study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID‐19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran’s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92–0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels

Enhancing the resilience of the power system to accommodate the construction of the new power system: key technologies and challenges

The increasingly frequent extreme events pose a serious threat to the resilience of the power system. At the same time, the power grid is transforming into a new type of clean and low-carbon power system due to severe environmental issues. The system shows strong randomness with a high proportion of renewable energy, which has increased the difficulty of maintaining the safe and stable operation of the power system. Therefore, it is urgent to improve the resilience of the new power system. This paper first elaborates on the concept of power system resilience, listing the characteristics of new power systems and their impact on grid resilience. Secondly, the evaluation methods for resilient power grids are classified into two categories, and measures to improve the resilience of the new power system are reviewed from various stages of disasters. Then, the critical technologies for improving the resilience of the new power system are summarized. Finally, the prospective research directions for new power system resilience enhancement are expounded

A review on technologies and methods of mitigating impacts of large-scale intermittent renewable generations on power system

Abstract: With environmental pressure and the development of smart grid, penetration of large-scale renewable energy to power system seems to be imperative. However, it encounters some obstacles because of intermittencies of Renewable Generations (RGs). So mitigating impacts of intermittent RGs is a key issue for development of power system and this study presents a review on recent researches referring the problem from both electrical technologies and human factors. Technologies of storage, advance automation, electric vehicles, demand side management, risk constraint power system management, policies of renewable energies and the future research directions of them were discussed by the bibliographical survey

A review on the economic dispatch and risk management considering wind power in the power market

With the rapid development of world economy, wind power has been given more and more consideration owing to its energy saving and environmental protection. But due to intermittency and unpredictability nature of wind power generation, many new problems come into being when infusing wind power into power network with conventional generators. Aiming at these difficulties, this paper presents a review on the historical research production of this theme. The models of economic dispatch schedule of wind power considering dissimilar actual condition, different optimized algorithms and risk management in the electric market are discussed and the future trend is prospected in this paper.Wind power Economic dispatch model Risk management Optimized algorithm Electric market

Optimal Scheduling of Hydro–PV–Wind Hybrid System Considering CHP and BESS Coordination

Coordination of a hydropower, combined heat and power (CHP), and battery energy storage system (BESS) with multiple renewable energy sources (RES) can effectively reduce the adverse effects of large-scale renewable energy integration in power systems. This paper proposes a concept of a renewable-based hybrid energy system and puts forward an optimal scheduling model of this system, taking into account the cost of operation and risk. An optimization method is proposed based on Latin hypercube sampling, scene reduction, and piecewise linearization. Firstly, a large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with a scene reduction method. Secondly, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene. Finally, the performance of the proposed model and method was evaluated based on case studies with real-life data. Results showed that the renewable-based hybrid system can not only reduce the intermittent and volatility of renewable resources but also ensure the smooth of tie-line power as much as possible. The proposed model and method are universal, feasible, and effective

Privacy-Preserving Energy Scheduling for ESCOs Based on Energy Blockchain Network

Capable of aggregating multiple energy resources, the energy service company (ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitate the consumption of renewable resources in the energy market. However, the issues have become significantly more serious related to the privacy and security of the data in consumption and trading. In this paper, we address the problem by proposing a privacy-preserving energy scheduling (PPES) model based on energy blockchain network. A Lagrangian relaxation method is applied to decompose the model into several individual optimal scheduling problems, and the individual scheduling problems are solved by consensus algorithm and smart contracts in energy blockchain network. The performance of the proposed model and method is evaluated with several case studies based on multiple energy nodes. Simulation results show the rationality and validity of the proposed method, and the model is conducive to the protection of environment and transparent scheduling of energy service companies (ESCOs). In addition, it can reflect the information of energy demand and supply to improve the privacy and security of data

Optimal scheduling of regional integrated energy system considering multiple uncertainties

Integrated energy system (IES) is an effective way to realize the efficient utilization of energy. Under the deregulated electricity market, IES operator gains profits by providing customers with energy service, including electricity, heat or cooling energy. With the deepening of market reform, higher penetration rate of renewable energy, economic risks embed in the IES. Based on this, an optimal scheduling model of regional IES considering uncertainties is proposed, aiming at maximizing the profits. Scenario analysis method has been adopted to model the uncertainties: Markov-Chain-Monte-Carlo (MCMC) sampling method, which has a better performance in fitting the probability distribution, is utilized to generate scenarios; K-means clustering method is applied to narrow down the sampling sets. By replacing the parameters in the deterministic model with the sampling sets, a series of optimal results can be achieved. The case study shows that the cooling storage tank can improve the economic benefits about 4.97% by converting electricity to cooling energy at lower price period and releasing energy at peak hours. Besides, through the proposed optimization model, operators can have a straight understanding of the venture brought by the uncertainties and a more reliable scheduling result is formed for reference

Controllable Load Management Approaches in Smart Grids

With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The “broad controllable loads” management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed