US EPA: Is there room to open a new window for evaluating potential sub-threshold effects and ecological risks?

Agathokleous, Evgenios; Barceló, Damià; Calabrese, Edward J.

doi:10.1016/j.envpol.2021.117372

Cited by 14 publications

(10 citation statements)

References 37 publications

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“…Consequently, any given dose of a specific stressor will fall within a different dose-response range according to the responding organism and the observed output variable [ 4 ]. Therefore, eustress doses must be experimentally determined before proposing their implementation [ 12 , 13 ]. Moreover, until very recently, studies on plant stress responses usually focused on determining a damaging-dose threshold rather than depicting the whole dose-response curve [ 6 , 7 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, eustress doses must be experimentally determined before proposing their implementation [ 12 , 13 ]. Moreover, until very recently, studies on plant stress responses usually focused on determining a damaging-dose threshold rather than depicting the whole dose-response curve [ 6 , 7 , 13 ]. For these reasons, applying novel technologies for enhancing the scopes of hormesis management in agriculture is crucial [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Rico-Chávez

Franco²,

Fernandez‐Jaramillo³

et al. 2022

Plants

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Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Rico-Chávez

Franco²,

Fernandez‐Jaramillo³

et al. 2022

Plants

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“…These authors discussed in some detail that chemical speciation is an important governing factor in toxic response. They did not, however, state how an audience would link speciation to “potentially toxic element”, which is likewise inadequate to convey meaning regarding emerging and more sophisticated models of toxicity (e.g., hormesis) …”

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confidence: 99%

“…They did not, however, state how an audience would link speciation to "potentially toxic element", which is likewise inadequate to convey meaning regarding emerging and more sophisticated models of toxicity (e.g., hormesis). 8 Unquestionably, toxicity is a function of exposure and bioavailability, and bioavailability in turn depends on concentration and speciation. In the last 20 years, some of the authors of this Viewpoint have published numerous papers confirming this and attesting to the value of taking bioavailability into account in risk assessment.…”

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confidence: 99%

“Potentially Toxic Element”─Something that Means Everything Means Nothing

Zhang

Barceló

Clougherty³

et al. 2022

Environ. Sci. Technol.

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“…Responses suggestive of hormesis were found for molecular (molecules involved in oxidative stress), cellular (e.g., growth and density), and whole-organism (e.g., growth and body mass) endpoints, as well as endpoints suggesting potential sub-NOAEL effects on organismic interactions, such as via altered feeding activity [83] . Such responses were induced by chemicals such as antibiotics/antifungals [38,39,42,45,50,51,67,70,71,75] , steroid hormones [41,83] , and other human drugs [63,69] , bisphenol A and its substitutes [66] , chemical leached from disposed light sticks [55] , electroplating processemitted particulate matter [40] , effluents from textile-dyeing wastewater treatment plants [46] , fullerene crystals (nC 60 ) [57] , metals and ionic liquids [62,77,81,82] , micro/nanoplastics and their leachates [43,44,47,53,60,64,80,84] , engineered nanomaterials [58,59,61,65] , pesticides [48,49,52,56,[72][73][74]79,84] , phthalic acid esters ...…”

mentioning

confidence: 99%

Contaminants of emerging concern and aquatic organisms: the need to consider hormetic responses in effect evaluations

Agathokleous¹,

Barceló²,

Fatta‐Kassinos³

et al. 2021

WECN

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Contaminants of emerging concern are widespread in the world’s waters, raising concerns regarding their effects on living organisms. To evaluate the effects of and predict risks associated with such chemicals, dose-response studies are needed, while the nature of the dose-response relationship is critical for the outcomes of such evaluations. Here, we summarize the literature reporting hormetic responses of aquatic organisms to contaminants of emerging concern. Hormesis is a biphasic dose response encompassing stimulatory responses to low doses and inhibitory responses to high doses. We demonstrate that it occurs widely in numerous aquatic organisms exposed to a wide array of contaminants, including nano/microplastics, suggesting potential effects at doses/concentrations that are considerably lower than the traditional toxicological threshold, which cannot be identified or predicted unless hormesis is considered in the study design. To tackle the effects and associated risks of nano/microplastics and other contaminants on aquatic organisms, hormesis should therefore be taken into account early in the design of studies as well as in relevant risk assessments.

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US EPA: Is there room to open a new window for evaluating potential sub-threshold effects and ecological risks?

Cited by 14 publications

References 37 publications

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

“Potentially Toxic Element”─Something that Means Everything Means Nothing

Contaminants of emerging concern and aquatic organisms: the need to consider hormetic responses in effect evaluations

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