Risk Assessment and Prediction of Aflatoxin in Agro-Products

Aflatoxin (AFT), highly toxic and carcinogenic to humans, seriously threatens consumption safety of agro-products. It is necessary to conduct risk assessment of aflatoxin contamination in agro-food products to find out critical control points (CCPs) and develop prediction, prevention and control theories and technologies. In this chapter, risk assessment and prediction of aflatoxin contamination in peanut were taken as an example. The values under the limit of detection (LOD) were replaced by zero, 1/2 LOD or LOD according to their respective proportion, and the distribution of values higher than the LOD was fitted by @RISK software. AFB1 dietary exposure was evaluated based on non-parametric probability risk assessment and margin of exposure (MOE). A risk ranking method was adopted for mycotoxins based on food risk expectation ranking. Spatial analysis of AFB1 contamination was conducted using geographic information system (GIS). Average climatic conditions were calculated by Thiessen polygon method and the relationship between AFB1 concentration and average pre-harvest climatic conditions was obtained through multiple regression. To fulfill the purposes of reducing cost, increasing efficiency, maximizing the role of risk assessment and prediction, and improving the quality and safety of agricultural products, we will continuously focus on developing advanced and integrated technologies and solutions

mTOR Signaling in X/A‐Like Cells Contributes to Lipid Homeostasis in Mice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/1/hep30229-sup-0001-FigS1-S8.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/2/hep30229_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/3/hep30229.pd

Combining Metabolomics and Transcriptomics to Characterize Tanshinone Biosynthesis in Salvia Miltiorrhiza

Plant natural products have been co-opted for millennia by humans for various uses such as flavor, fragrances, and medicines. These compounds often are only produced in relatively low amounts and are difficult to chemically synthesize, limiting access. While elucidation of the underlying biosynthetic processes might help alleviate these issues (e.g., via metabolic engineering), investigation of this is hindered by the low levels of relevant gene expression and expansion of the corresponding enzymatic gene families. However, the often-inducible nature of such metabolic processes enables selection of those genes whose expression pattern indicates a role in production of the targeted natural product. Here, we combine metabolomics and transcriptomics to investigate the inducible biosynthesis of the bioactive diterpenoid tanshinones from the Chinese medicinal herb, Salvia miltiorrhiza(Danshen). Untargeted metabolomics investigation of elicited hairy root cultures indicated that tanshinone production was a dominant component of the metabolic response, increasing at later time points. A transcriptomic approach was applied to not only define a comprehensive transcriptome (comprised of 20,972 non-redundant genes), but also its response to induction, revealing 6,358 genes that exhibited differential expression, with significant enrichment for up-regulation of genes involved in stress, stimulus and immune response processes. Consistent with our metabolomics analysis, there appears to be a slower but more sustained increased in transcript levels of known genes from diterpenoid and, more specifically, tanshinone biosynthesis. Among the co-regulated genes were 70 transcription factors and 8 cytochromes P450, providing targets for future investigation. Our results indicate a biphasic response of Danshen terpenoid metabolism to elicitation, with early induction of sesqui- and tri- terpenoid biosynthesis, followed by later and more sustained production of the diterpenoid tanshinones. Our data provides a firm foundation for further elucidation of tanshinone and other inducible natural product metabolism in Danshen

Learning k-Nearest Neighbor Naive Bayes For Ranking ⋆

Abstract. Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective classification model in decades. However, its performance in ranking is unknown. In this paper, we conduct a systematic study on the ranking performance of KNN. At first, we compare KNN and KNNDW (KNN with distance weighted) to decision trees and naive Bayes in ranking, measured by AUC (the area under the Receiver Operating Characteristics curve). Then, we propose to improve the ranking performance of KNN by combining KNN with naive Bayes (simply NB). The idea is that a naive Bayes is learned using the k nearest neighbors of the test instance as the training data and used to classify the test instance. A critical problem in combining KNN with naive Bayes is the lack of training data when k is small. We propose to deal with it using cloning to expand the training data. That is, each of the k nearest neighbors is “cloned ” and the clones are added to the training data. We call our new model instance cloning local naive Bayes (simply ICLNB). We conduct extensive empirical comparison for the related algorithms in two groups in terms of AUC, using the 36 UCI datasets recommended by Weka[2]. In the first group, we compare ICLNB with KNN, NB, NBTree[3], C4.4[4]. In the second group, we compare ICLNB with KNN, KNNDW and LWNB[5]. Our experimental results show that ICLNB outperforms all those algorithms significantly. From our study, we have two conclusions. First, KNN-relates algorithms performs well in ranking. Second, our new algorithm ICLNB performs best among the algorithms compared in this paper, and could be used in the applications in which an accurate ranking is desired.

Augmenting naive bayes for ranking

Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates. Numerous techniques have been proposed to extend naive Bayes for better classification accuracy, of which selective Bayesian classifiers (SBC) (Langley & Sage

Attribute Value Weighted Average of One-Dependence Estimators

Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison

Determination of Aflatoxin B1 and B2 in Vegetable Oils Using Fe3O4/rGO Magnetic Solid Phase Extraction Coupled with High-Performance Liquid Chromatography Fluorescence with Post-Column Photochemical Derivatization

In this study, magnetic graphene nanocomposite Fe3O4/rGO was synthesized by facile one-pot solvothermal method. The nanocomposite was successfully used as magnetic solid phase extraction (MSPE) adsorbents for the determination of aflatoxins in edible vegetable oils through the π–π stacking interactions. MSPE parameters including the amount of adsorbents, extraction and desorption time, washing conditions, and the type and volume of desorption solvent were optimized. Under optimal conditions, good linear relationships were achieved. Limits of detection of this method were as low as 0.02 µg/kg and 0.01 µg/kg for aflatoxin B1 and B2, respectively. Finally, the magnetic graphene nanocomposite was successfully applied to aflatoxin analysis in vegetable oils. The results indicated that the recoveries of the B-group aflatoxins ranged from 80.4% to 106.0%, whereas the relative standard deviations (RSDs) were less than 8.1%. Owing to the simplicity, rapidity and efficiency, Fe3O4/rGO magnetic solid phase extraction coupled with high-performance liquid chromatography fluorescence with post-column photochemical derivatization (Fe3O4/rGO MSPE-HPLC-PCD-FLD) is a promising analytical method for routine and accurate determination of aflatoxins in lipid matrices

Extraction and Quantification of Sulforaphane and Indole-3-Carbinol from Rapeseed Tissues Using QuEChERS Coupled with UHPLC-MS/MS

Rapeseed (Brassica napus L.) is rich in phenols, vitamins, carotenoids, and mineral elements, such as selenium. Additionally, it contains the active ingredients sulforaphane and indole-3-carbinol, which have been demonstrated to have pharmacological effects. In this study, sulforaphane and indole-3-carbinol were extracted and quantified from rapeseeds using quick, easy, cheap, effective, rugged and safe (QuEChERS) method coupled with ultra high performance liquid chromarography tandem mass spectrometry (UHPLC-MS/MS). The major parameters for extraction and purification efficiency were optimized, including the hydrolysis reaction, extraction condition and type and amount of purification adsorbents. The limit of detection (LOD) and the limit of quantification (LOQ) for sulforaphane were 0.05 μg/kg and 0.15 μg/kg, and for indole-3-carbinol were 5 μg/kg and 15 μg/kg, respectively. The developed method was used to successfully analyze fifty rapeseed samples. The QuEChERS coupled with UHPLC-MS/MS simultaneously detect sulforaphane and indole-3-carbinol in vegetable matrix and evaluate the quality and nutrition of rapeseed samples

Simultaneous Determination of Aflatoxins and Benzo(a)pyrene in Vegetable Oils Using Humic Acid-Bonded Silica SPE HPLC–PHRED–FLD

In the present work, a rapid, accurate, and cost-effective method was developed for the simultaneous quantification of aflatoxins and benzo(a)pyrene in lipid matrices, using solid-phase extraction (SPE) via humic acid-bonded silica (HAS) sorbents, followed by high-performance liquid chromatography coupled with photochemical post-column reactor fluorescence spectroscopy (HPLC–PHRED–FLD) analysis. The major parameters of extraction efficiency and HPLC–PHRED–FLD analysis were investigated and this method was fully validated. The limits of quantification and the limits of detection were 0.05–0.30 and 0.01–0.09 µg kg−1, respectively. The recoveries were 66.9%–118.4% with intra-day and inter-day precision less than 7.2%. The results of 80 oil samples from supermarkets indicated a high occurrence of BaP, and most of concentrations were within the requirements of EU and China food safety regulations. This is the first utilization of HAS–SPE HPLC–PHRED–FLD to simultaneously analyze the occurrence of aflatoxins and benzo(a)pyrene in vegetable oils