This work aimed to study the kinetics of bifenthrin and deltamethrin residues degradation in rice grains under exposure to ozone and evaluate the effect of ozonization on rice grains marketing standards. The grains sprayed with pesticides were exposed to ozone at a concentration of 3 mg L and continuous flow of 1.0 L min for defined periods of up to 10 h. Residues of pesticides were extracted from grains using solid liquid extraction method with low-temperature partition analyzed by gas chromatography with electron capture detection (SLE/LTP-GC/ECD). Data from residual concentrations of bifenthrin and deltamethrin, due to the ozone exposure period, were adjusted to kinetic models of zero order, first order and second order. It was observed that ozone was effective in the degradation bifenthrin and deltamethrin residues, removing 91.9% of bifenthrin and 92.7% of deltamethrin. The kinetic model that best fitted deltamethrin and bifenthrin residues degradation data by ozone, was the one of first order. The marketing standards evaluated were the water content and yield. It was observed the ozone did not alter the rice grains water content and yield.
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http://dx.doi.org/10.1007/s13197-017-2884-1 | DOI Listing |
J Chem Phys
January 2025
Department of Chemistry and Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712, USA.
Inferring underlying microscopic dynamics from low-dimensional experimental signals is a central problem in physics, chemistry, and biology. As a trade-off between molecular complexity and the low-dimensional nature of experimental data, mesoscopic descriptions such as the Markovian master equation are commonly used. The states in such descriptions usually include multiple microscopic states, and the ensuing coarse-grained dynamics are generally non-Markovian.
View Article and Find Full Text PDFNat Genet
January 2025
State Key Laboratory of Plant Environmental Resilience, College of Life Sciences, Zhejiang University, Hangzhou, China.
Rice production is facing substantial threats from global warming associated with extreme temperatures. Here we report that modifying a heat stress-induced negative regulator, a negative regulator of thermotolerance 1 (NAT1), increases wax deposition and enhances thermotolerance in rice. We demonstrated that the C2H2 family transcription factor NAT1 directly inhibits bHLH110 expression, and bHLH110 directly promotes the expression of wax biosynthetic genes CER1/CER1L under heat stress conditions.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai 400085, India; Homi Bhabha National Institute, Mumbai 400094, India. Electronic address:
Arsenic (As) is a potent carcinogen that enters the human food chain mainly through rice, which is one of the staple food crops worldwide. During February 2022, a market survey was conducted and 500 samples of rice grains were collected across 41 different locations in Mumbai/Navi-Mumbai. On the basis of grain As-accumulation, samples were grouped into three categories including low- (0-30 ng g DW), medium- (31-70 ng g DW) or high- (>71 ng g DW).
View Article and Find Full Text PDFEnviron Geochem Health
January 2025
Department of Agricultural Chemistry, National Taiwan University, Taipei, 10617, Taiwan.
Rare earth elements (REEs) are a critical global focus due to their increasing use, raising concerns about their environmental distribution and human exposure, both vital to food safety and human health. Surface soil (0-30 cm) and corresponding rice grain samples (n = 85) were collected from paddy fields in Taiwan. This study investigated the total REE contents in soil through aqua regia digestion, as well as their labile forms extracted using 0.
View Article and Find Full Text PDFJ Genet Genomics
January 2025
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA. Electronic address:
The QTL by environment interaction (Q×E) effect is hard to detect because there are no effective ways to control the genomic background. In this study, we propose a novel linear mixed model that simultaneously analyzes data from multiple environments to detect Q×E interactions. This model incorporates two different kinship matrices derived from the genome-wide markers to control both main and interaction polygenic background effects.
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