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

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

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

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

Retrieval of Soil Moisture from FengYun-3D Microwave Radiation Imager Operational and Recalibrated Data Using Random Forest Regression

Three Microwave Radiation Imagers (MWRI) were carried onboard the FengYun-3B/C/D satellites and have collected more than 10 years of data since 2010. To create a robust climate quality of data, MWRI level one data were reprocessed with new calibration. This study evaluates the performance of retrieving global soil moisture from recalibrated MWRI data (RCD) and quantifies the difference of retrieved soil moisture between operational calibration data (OCD) and RCD. Soil Moisture Operational Products System (SMOPS) products from NOAA on four days of different seasons were collocated with MWRI brightness temperatures, and then the collocated data were used for training an algorithm through machine learning. The retrieved soil moisture products using OCD and RCD were evaluated against the independent SMOPS products, in situ networks and SMAP soil moisture product. It is shown that the algorithm from the random forest is suitable for FY-3D recalibrated MWRI data, with a coefficient of determination (R2) of 0.7223, a mean bias of −0.0062 and an unbiased root mean square difference (ubRMSD) of 0.0476 m3 m−3 compared with SMOPS products over the period from 12 July 2018 to 31 December 2019. The difference of retrieved soil moisture using OCD and RCD is spatially heterogeneous. Both temporal and spatial coverage and accuracy of the existing FY-3D operational soil moisture products are significantly improved

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination ( R C V 2 ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models

Correction: Wang, J., et al. Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data. Remote Sens. 2016, 8, 931

The authors wish to make the following corrections to their paper [1].[...