Growth promoters including hormonal substances and antibiotics are used legally and illegally in food producing animals for the growth promotion of livestock animals. Hormonal substances still under debate in terms of their human health impacts are estradiol-17β, progesterone, testosterone, zeranol, trenbolone, and melengestrol acetate (MGA) . Many of the risk assessment results of natural steroid hormones have presented negligible impacts when they are used under good veterinary practices. For synthetic hormonelike substances, ADIs and MRLs have been established for food safety along with the approval of animal treatment. Small amounts of antibiotics added to feedstuff present growth promotion effects via the prevention of infectious diseases at doses lower than therapeutic dose. The induction of antimicrobial resistant bacteria and the disruption of normal human intestinal flora are major concerns in terms of human health impact. Regulatory guidance such as ADIs and MRLs fully reflect the impact on human gastrointestinal microflora. However, before deciding on any risk management options, risk assessments of antimicrobial resistance require large-scale evidence regarding the relationship between antimicrobial use in food-producing animals and the occurrence of antimicrobial resistance in human pathogens. In this article, the risk profiles of hormonal and antibacterial growth promoters are provided based on recent toxicity and human exposure information, and recommendations for risk management to prevent human health impacts by the use of growth promoters are also presented.

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Low-power wireless network for the emerging Internet of Things (IoT) should be reliable enough to satisfy the application requirements, and also energy-efficient for embedded devices to remain battery powered. Synchronized communication methods such as Time Slotted Channel Hopping (TSCH) have shown promising results for these purposes, achieving end-to-end reliability over 99% with low dutycycles. However, they lack one thing: flexibility to support a wide variety of applications and services with unpredictable traffic load and routing topology due to ''fixed'' slotframe sizes. To this end, we propose TESLA, a traffic-aware elastic slotframe adjustment scheme for TSCH networks which enables each node to dynamically self-adjust its slotframe size at run time. TESLA aims to minimize its energy consumption without sacrificing reliable packet delivery by utilizing incoming traffic load to estimate channel contention level experienced by each neighbor. We extensively evaluate the effectiveness of TESLA on large-scale 110-node and 79-node testbeds, demonstrating that it achieves up to 70.2% energy saving compared to Orchestra (the de facto TSCH scheduling mechanism) while maintaining 99% reliability.