The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform
Takuro Shinano - One of the best experts on this subject based on the ideXlab platform.
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large scale agricultural soil and Food Sampling and radioactivity analysis during nuclear emergencies in japan development of technical and organisational procedures for soil and Food Sampling after the accident
Journal of Environmental Radioactivity, 2020Co-Authors: Mayumi Hachinohe, Takuro ShinanoAbstract:Most available measurement methods and protocols for radioactive materials are focused on the use of high-precision Sampling and analysis and do not consider the practicality of these techniques in the case of large-scale emergencies involving high numbers of samples and measurements. The experience gained after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident has demonstrated a need for optimization of Sampling and measurement programmes in the case of nuclear emergency that affects Food and agriculture. Under these conditions, resources for implementation of monitoring and allocations for Sampling and measurements might be limited, and urgent information is needed for effective emergency response. This paper supplies a historical overview of Sampling and analytical techniques for assessment of radionuclides in the agricultural environments and Foodstuffs and is intended for use in research, policy and decision-making in nuclear emergency preparedness and response, particularly with respect to large scale accidents.
Mayumi Hachinohe - One of the best experts on this subject based on the ideXlab platform.
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large scale agricultural soil and Food Sampling and radioactivity analysis during nuclear emergencies in japan development of technical and organisational procedures for soil and Food Sampling after the accident
Journal of Environmental Radioactivity, 2020Co-Authors: Mayumi Hachinohe, Takuro ShinanoAbstract:Most available measurement methods and protocols for radioactive materials are focused on the use of high-precision Sampling and analysis and do not consider the practicality of these techniques in the case of large-scale emergencies involving high numbers of samples and measurements. The experience gained after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident has demonstrated a need for optimization of Sampling and measurement programmes in the case of nuclear emergency that affects Food and agriculture. Under these conditions, resources for implementation of monitoring and allocations for Sampling and measurements might be limited, and urgent information is needed for effective emergency response. This paper supplies a historical overview of Sampling and analytical techniques for assessment of radionuclides in the agricultural environments and Foodstuffs and is intended for use in research, policy and decision-making in nuclear emergency preparedness and response, particularly with respect to large scale accidents.
Amer M Burhan - One of the best experts on this subject based on the ideXlab platform.
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effects of left dlpfc modulation on social cognitive processes following Food Sampling
Appetite, 2018Co-Authors: Peter A Hall, Cassandra J Lowe, Adrian B Safati, Emilia B Klassen, Amer M BurhanAbstract:Abstract Background The dorsolateral prefrontal cortex (dlPFC) plays a central role in the inhibition of eating, but also the modulation of conscious thought processes that might precede, accompany or follow initial Food tasting. The latter might be particularly important to the extent that post-tasting cognitions may drive prolonged eating beyond the satiety point. However, we know very little about the effect of the dlPFC on conation following initial Food Sampling. This investigation compared the effects of dlPFC attenuation using repetitive transcranial magnetic stimulation (rTMS) on social cognition following (Study 1) and prior to (Study 2) a Food consumption opportunity. Methods In Study 1, participants (N = 21; Mage = 21 years) were randomized to active or sham continuous theta-burst stimulation (cTBS; an inhibitory variant of rTMS) targeting the left dlPFC followed by an interference task. Participants subsequently completed measures of attitudes, norms and perceived control following a mock taste test. In Study 2, a second sample of right handed participants (N = 37; Mage = 21 years) were assigned to active or sham cTBS, followed by an interference task and two measures of attitudes (implicit and explicit), both assessed prior to the same taste test paradigm. Results In Study 1, findings revealed a reliable effect of cTBS on post-tasting attitudes (t(1,19) = 3.055, p = .007; d = 1.34), such that attitudes towards calorie dense snack Foods were significantly more positive following active stimulation than following sham stimulation. Similar effects were found for social norms (t(1,19) = 3.024, p = .007, d = 1.31) and perceived control (t(1,20) = 19.247, p Conclusions The left dlPFC may selectively modulate facilitative social cognition following initial Food Sampling (but not pre-consumption).
Lirong Zheng - One of the best experts on this subject based on the ideXlab platform.
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CONFENIS - Pattern Discovery from Big Data of Food Sampling Inspections Based on Extreme Learning Machine.
Lecture Notes in Business Information Processing, 2018Co-Authors: Xin Li, Junyu Wang, Jianxin Wang, Feng Chen, Lirong ZhengAbstract:Food Sampling programs are implemented from time to time in local areas or throughout the country in order to guarantee Food safety and to improve Food quality. The hidden patterns in the accumulated huge amount of data and their potential values are worthy to research. In this paper, Extreme learning machine (ELM) is employed on real data sets collected from the Food safety inspections of China in recent two years, in order to mine the relationship between Food quality and Food category, manufacturing site and season, inspection site and season, and many other attributes. Experimental results indicate that the ELM approach has better prediction precision and generalization ability than Logistic regression that was adopted in preceding work. The patterns obtained are helpful for making more effective Food Sampling plans and for more targeted Food safety tracing.
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Pattern Discovery from Big Data of Food Sampling Inspections Based on Extreme Learning Machine
2018Co-Authors: Yi Liu, Yiwei Shi, Junyu Wang, Jianxin Wang, Xin Li, Feng Chen, Lirong ZhengAbstract:Food Sampling programs are implemented from time to time in local areas or throughout the country in order to guarantee Food safety and to improve Food quality. The hidden patterns in the accumulated huge amount of data and their potential values are worthy to research. In this paper, Extreme learning machine (ELM) is employed on real data sets collected from the Food safety inspections of China in recent two years, in order to mine the relationship between Food quality and Food category, manufacturing site and season, inspection site and season, and many other attributes. Experimental results indicate that the ELM approach has better prediction precision and generalization ability than Logistic regression that was adopted in preceding work. The patterns obtained are helpful for making more effective Food Sampling plans and for more targeted Food safety tracing.
Kirsty Hope - One of the best experts on this subject based on the ideXlab platform.
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epidemiology and whole genome sequencing of an ongoing point source salmonella agona outbreak associated with sushi consumption in western sydney australia 2015
Epidemiology and Infection, 2017Co-Authors: C. K. Thompson, Qinning Wang, Craig Shadbolt, Emily Fearnley, Vitali Sintchenko, Peter Howard, Neil Franklin, Helen E Quinn, Kirsty HopeAbstract:During May 2015, an increase in Salmonella Agona cases was reported from western Sydney, Australia. We examine the public health actions used to investigate and control this increase. A descriptive case-series investigation was conducted. Six outbreak cases were identified; all had consumed cooked tuna sushi rolls purchased within a western Sydney shopping complex. Onset of illness for outbreak cases occurred between 7 April and 24 May 2015. Salmonella was isolated from Food samples collected from the implicated premise and a prohibition order issued. No further cases were identified following this action. Whole genome sequence (WGS) analysis was performed on isolates recovered during this investigation, with additional S. Agona isolates from sporadic-clinical cases and routine Food Sampling in New South Wales, January to July 2015. Clinical isolates of outbreak cases were indistinguishable from Food isolates collected from the implicated sushi outlet. Five additional clinical isolates not originally considered to be linked to the outbreak were genomically similar to outbreak isolates, indicating the point-source contamination may have started before routine surveillance identified an increase. This investigation demonstrated the value of genomics-guided public health action, where near real-time WGS enhanced the resolution of the epidemiological investigation.