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

Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves

Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (> 95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R2cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161 mg/cm2. Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R2CV = 0.70 and RMSECV = 0.0015 mg/cm2). The spectral domain 400–900 nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring

Utilization of a Strongly Inducible DDI2 Promoter to Control Gene Expression in Saccharomyces cerevisiae

Regulating target gene expression is a common method in yeast research. In Saccharomyces cerevisiae, there are several widely used regulated expression systems, such as the GAL and Tet-off systems. However, all current expression systems possess some intrinsic deficiencies. We have previously reported that the DDI2 gene can be induced to very high levels upon cyanamide or methyl methanesulfonate treatment. Here we report the construction of gene expression systems based on the DDI2 promoter in both single- and multi-copy plasmids. Using GFP as a reporter gene, it was demonstrated that the target gene expression could be increased by up to 2,000-fold at the transcriptional level by utilizing the above systems. In addition, a DDI2-based construct was created for promoter shuffling in the budding yeast genome to control endogenous gene expression. Overall, this study offers a set of convenient and highly efficient experimental tools to control target gene expression in budding yeast

Chemotherapeutic Sensitization of Leptomycin B Resistant Lung Cancer Cells by Pretreatment with Doxorubicin

The development of novel targeted therapies has become an important research focus for lung cancer treatment. Our previous study has shown leptomycin B (LMB) significantly inhibited proliferation of lung cancer cells; however, p53 wild type lung cancer cells were resistant to LMB. Therefore, the objective of this study was to develop and evaluate a novel therapeutic strategy to sensitize LMB-resistant lung cancer cells by combining LMB and doxorubicin (DOX). Among the different treatment regimens, pretreatment with DOX (pre-DOX) and subsequent treatment with LMB to A549 cells significantly decreased the 50% inhibitory concentration (IC50) as compared to that of LMB alone (4.4 nM vs. 10.6 nM, P<0.05). Analysis of cell cycle and apoptosis by flow cytometry further confirmed the cytotoxic data. To investigate molecular mechanisms for this drug combination effects, p53 pathways were analyzed by Western blot, and nuclear proteome was evaluated by two dimensional-difference gel electrophoresis (2D-DIGE) and mass spectrometry. In comparison with control groups, the levels of p53, phospho-p53 (ser15), and p21 proteins were significantly increased while phospho-p53 (Thr55) and survivin were significantly decreased after treatments of pre-DOX and LMB (P<0.05). The 2D-DIGE/MS analysis identified that sequestosome 1 (SQSTM1/p62) had a significant increase in pre-DOX and LMB-treated cells (P<0.05). In conclusion, our results suggest that drug-resistant lung cancer cells with p53 wild type could be sensitized to cell death by scheduled combination treatment of DOX and LMB through activating and restoring p53 as well as potentially other signaling pathway(s) involving sequestosome 1

Meta-analysis of the detection of plant pigment concentrations using hyperspectral remotely sensed data

Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a

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

Johnson–Holmquist-II(JH-2) Constitutive Model for Rock Materials: Parameter Determination and Application in Tunnel Smooth Blasting

The Johnson&ndash;Holmquist-II(JH-2) model is introduced as the constitutive model for rock materials in tunnel smooth blasting. However, complicated and/or high-cost experiments need to be carried out to obtain the parameters of the JH-2 constitutive model. This study chooses Barre granite as an example to propose a quick and convenient determination method for the parameters of the JH-2 model using a series of computational and extrapolated methods. The validity of the parameters is verified via comparing the results of 3D numerical simulations with laboratory blast-loading experiments. Subsequently, the verified parameter determination method, together with the JH-2 damage constitutive model, is applied in the numerical simulation of smooth blasting in Zigaojian tunnel, Hangzhou&ndash;Huangshan high-speed railway. The overbreak/underbreak induced by rock blasting and joints/discontinuities is well estimated through comparing the damage contours resulting from the numerical study with the tunnel profiles measured from the tunnel site. The peak particle velocities (PPVs) of the near field are extracted to estimate the damage scope and damage degree for the surrounding rock mass of the tunnel on the basis of PPV damage criteria. This method can be used in the excavation of rock tunnels subjected to large strains, high strain rates, and high pressures, thereby reducing safety risk and economic losses