Methylation of CYP1A1 and VKORC1 promoter associated with stable dosage of warfarin in Chinese patients

Objective To investigate the association between DNA methylation and the stable warfarin dose through genome-wide DNA methylation analysis and pyrosequencing assay. Method This study included 161 patients and genome-wide DNA methylation analysis was used to screen potential warfarin dose-associated CpGs through Illumina Infinium HumanMethylation 450 K BeadChip; then, the pyrosequencing assay was used to further validate the association between the stable warfarin dose and alterations in the methylation of the screened CpGs. GenomeStudio Software and R were used to analyze the differentially methylated CpGs. Results The methylation levels of CpGs surrounding the xenobiotic response element (XRE) within the CYP1A1 promoter, differed significantly between the different dose groups (P  0, P < 0.05) with an increase in the stable dose of warfarin. At the VKORC1 promoter, two CpGs methylation levels were significantly different between the differential dose groups (P < 0.05), and one CpG (Chr16: 31106793) presented a significant negative correlation (r <  0, P <  0.05) among different dose (low, medium, and high) groups. Conclusion This is a novel report of the methylation levels of six CpGs surrounding the XRE within the CYP1A1 promoter and one differential CpG at the VKORC1 promoter associated with stable warfarin dosage; these methylation levels might be applied as molecular signatures for warfarin

Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data

Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% &lt; SL &le; 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL &gt; 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% canopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress

Listeria monocytogenes Prevalence and Characteristics in Retail Raw Foods in China.

The prevalence and levels of Listeria monocytogenes in retail raw foods covering most provincial capitals in China were studied with testing of 1036 samples of vegetables, edible mushrooms, raw meat, aquatic products and quick-frozen products from September 2012 to January 2014. The total prevalence of Listeria monocytogenes was 20.0% (207/1036), and the most probable number (MPN) values of 65.7% of the positive samples ranged from 0.3 to 110 MPN/g. Geographical differences were observed in this survey, and the results of both qualitative and quantitative methods indicated that the levels in the samples from North China were higher than those in the samples from South China. A total of 248 isolates were analyzed, of which approximately half belonged to molecular serogroup 1/2a-3a (45.2%), followed by 1/2b-3b-7 (30.6%), 1/2c-3c (16.1%), 4b-4d-4e (5.2%) and 4a-4c (2.8%). Most of the isolates carried hly (100%), inlB (98.8%), inlA (99.6%), inlC (98.0%) and inlJ (99.2%), and 44.8% of the isolates were llsX-positive. Seventeen epidemic clones (ECs) were detected, with 7 strains belonging to ECI (2.8%) and 10 belonging to ECIII (4.03%). Resistance to clindamycin (46.8%) was commonly observed, and 59 strains (23.8%) were susceptible to all 14 tested antibiotics, whereas 84 (33.9%) showed an intermediate level of resistance or were resistant to two or more antibiotics, including 7 multi-resistant strains that exhibited resistance to more than 10 antibiotics. The data obtained in the present study provides useful information for assessment of the possible risk posed to Chinese consumers, and this information will have a significant public health impact in China. Furthermore, the presence of virulence markers, epidemic clones, as well as the antibiotic resistance amongst the isolates strongly implies that many of these strains might be capable of causing listeriosis, and more accurate treatment of human listeriosis with effective antibiotics should be considered. This research represents a more full-scale and systematical investigation of the prevalence of L. monocytogenes in retail raw foods in China, and it provides baseline information for Chinese regulatory authorities that will aid in the formulation of a regulatory framework for controlling L. monocytogenes with the aim of improving the microbiological safety of raw foods

Results of antimicrobial susceptibility tests of <i>Listeria monocytogenes</i> isolates obtained from retail raw food in China.

<p>^a CI, critically important; HI, highly important; I, important</p><p>Results of antimicrobial susceptibility tests of <i>Listeria monocytogenes</i> isolates obtained from retail raw food in China.</p

Prevalence and level of <i>Listeria monocytogenes</i> at different sampling sites.

<p>^a These two cities are direct-controlled municipalities.</p><p>^b The values are weighted averages shown as the geometric means of positive samples.</p><p>Prevalence and level of <i>Listeria monocytogenes</i> at different sampling sites.</p