Direct and indirect cumulative effects of temperature, nutrients, and light on phytoplankton growth.

Temperature and resource availability are pivotal factors influencing phytoplankton community structures. Numerous prior studies demonstrated their significant influence on phytoplankton stoichiometry, cell size, and growth rates. The growth rate, serving as a reflection of an organism's success within its environment, is linked to stoichiometry and cell size. Consequently, alterations in abiotic conditions affecting cell size or stoichiometry also exert indirect effects on growth. However, such results have their limitations, as most studies used a limited number of factors and factor levels which gives us limited insights into how phytoplankton respond to environmental conditions, directly and indirectly. Here, we tested for the generality of patterns found in other studies, using a combined multiple-factor gradient design and two single species with different size characteristics. We used a structural equation model (SEM) that allowed us to investigate the direct cumulative effects of temperature and resource availability (i.e., light, N and P) on phytoplankton growth, as well as their indirect effects on growth through changes in cell size and cell stoichiometry. Our results mostly support the results reported in previous research thus some effects can be identified as dominant effects. We identified rising temperature as the dominant driver for cell size reduction and increase in growth, and nutrient availability (i.e., N and P) as dominant factor for changes in cellular stoichiometry. However, indirect effects of temperature and resources (i.e., light and nutrients) on species' growth rates through cell size and cell stoichiometry differed across the two species suggesting different strategies to acclimate to its environment.

Dynamic path analysis for exploring treatment effect mediation processes in clinical trials with time-to-event endpoints.

Why does a beneficial treatment effect on a longitudinal biomarker not translate into overall treatment benefit on survival, when the biomarker is in fact a prognostic factor of survival? In a recent exploratory data analysis in oncology, we were faced with this seemingly paradoxical result. To address this problem, we applied a theoretically principled methodology called dynamic path analysis, which allows us to perform mediation analysis with a longitudinal mediator and survival outcome. The aim of the analysis is to decompose the total treatment effect into a direct treatment effect and an indirect treatment effect mediated through a carefully constructed mediation path. The dynamic nature of the underlying methodology enables us to describe how these effects evolve over time, which can add to the mechanistic understanding of the underlying processes. In this paper, we present a detailed description of the dynamic path analysis framework and illustrate its application to survival mediation analysis using simulated and real data. The use case analysis provides clarity on the specific exploratory question of interest while the methodology generalizes to a wide range of applications in drug development where time-to-event is the primary clinical outcome of interest.

Causal mediation analysis with mediator values below an assay limit.

Causal indirect and direct effects provide an interpretable method for decomposing the total effect of an exposure on an outcome into the indirect effect through a mediator and the direct effect through all other pathways. A natural choice for a mediator in a randomized clinical trial is the treatment's targeted biomarker. However, when the mediator is a biomarker, values can be subject to an assay lower limit. The mediator is affected by the treatment and is a putative cause of the outcome, so the assay lower limit presents a compounded problem in mediation analysis. We propose two approaches to estimate indirect and direct effects with a mediator subject to an assay limit: (1) extrapolation and (2) numerical optimization and integration of the observed likelihood. Since these estimation methods solely rely on the so-called Mediation Formula, they apply to most approaches to causal mediation analysis: natural, separable, and organic indirect, and direct effects. A simulation study compares the two estimation approaches to imputing with half the assay limit. Using HIV interruption study data from the AIDS Clinical Trials Group described in Li et al 2016, AIDS; Lok and Bosch 2021, Epidemiology, we illustrate our methods by estimating the organic/pure indirect effect of a hypothetical HIV curative treatment on viral suppression mediated by two HIV persistence measures: cell-associated HIV-RNA and single-copy plasma HIV-RNA.

DP2LM: leveraging deep learning approach for estimation and hypothesis testing on mediation effects with high-dimensional mediators and complex confounders.

Traditional linear mediation analysis has inherent limitations when it comes to handling high-dimensional mediators. Particularly, accurately estimating and rigorously inferring mediation effects is challenging, primarily due to the intertwined nature of the mediator selection issue. Despite recent developments, the existing methods are inadequate for addressing the complex relationships introduced by confounders. To tackle these challenges, we propose a novel approach called DP2LM (Deep neural network-based Penalized Partially Linear Mediation). This approach incorporates deep neural network techniques to account for nonlinear effects in confounders and utilizes the penalized partially linear model to accommodate high dimensionality. Unlike most existing works that concentrate on mediator selection, our method prioritizes estimation and inference on mediation effects. Specifically, we develop test procedures for testing the direct and indirect mediation effects. Theoretical analysis shows that the tests maintain the Type-I error rate. In simulation studies, DP2LM demonstrates its superior performance as a modeling tool for complex data, outperforming existing approaches in a wide range of settings and providing reliable estimation and inference in scenarios involving a considerable number of mediators. Further, we apply DP2LM to investigate the mediation effect of DNA methylation on cortisol stress reactivity in individuals who experienced childhood trauma, uncovering new insights through a comprehensive analysis.

A controlled effects approach to assessing immune correlates of protection.

An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.

Contribution of birds to the study of sexual differentiation of brain and behavior.

Behavioral neuroendocrinology has largely relied on mammalian models to understand the relationship between hormones and behavior, even if this discipline has historically used a larger diversity of species than other fields. Recent advances revealed the potential of avian models in elucidating the neuroendocrine bases of behavior. This paper provides a review focused mainly on the contributions of our laboratory to the study of sexual differentiation in Japanese quail and songbirds. Quail studies have firmly established the role of embryonic estrogens in the sexual differentiation of male copulatory behavior. While most sexually differentiated features identified in brain structure and physiology result from the different endocrine milieu of adults, a few characteristics are organized by embryonic estrogens. Among them, a sex difference was identified in the number and morphology of microglia which is not associated with sex differences in the concentration/expression of neuroinflammatory molecules. The behavioral role of microglia and neuroinflammatory processes requires further investigations. Sexual differentiation of singing in zebra finches is not mediated by the same endocrine mechanisms as male copulatory behavior and "direct" genetic effect, i.e., not mediated by gonadal steroids have been identified. Epigenetic contributions have also been considered. Finally sex differences in specific aspects of singing behavior have been identified in canaries after treatment of adults with exogenous testosterone suggesting that these aspects of song are differentiated during ontogeny. Integration of quail and songbirds as alternative models has thus expanded understanding of the interplay between hormones and behavior in the control of sexual differentiation.

Synthesis of lichen response to gaseous nitrogen: ammonia versus nitrogen dioxide.

The dominant chemical form of nitrogen pollution in the atmosphere in the U.S. is shifting from oxidized nitrogen, primarily from combustion of fossil fuels, to reduced nitrogen from agricultural animal waste and fertilizer applications. Does it matter to lichens? In this synthesis, we characterize U.S. air concentrations of the most ubiquitous gaseous forms of reduced and oxidized nitrogen, NO2 and NH3, respectively, and their direct effects on lichens. In the U.S., the 3-year average (2017-2019) of the annual mean for each monitoring site ranges up to 56.4 µg NO2 m-3 (~30 ppb) and 6 µg NH3 m-3 (~9 ppb). The spatial coverage of current routine monitoring of NO2 and NH3 likely does not accurately represent exposures of NO2 to ecosystems in rural areas or capture spikes of NH3 concentrations proximal to intensive agriculture, which are documented to exceed 700 µg NH3 m-3 (~1000 ppb) for short durations. Both NO2 and NH3 can act as nutrients to lichens, but as exposures rise, both can cause physiological stress and mortality that then change community composition and diversity. There is a growing body of evidence that lichen community composition is altered at current levels of exposure in the U.S. with estimated no effect or lowest effect concentrations from <1-3 µg m-3 NO2 and <1 µg m-3 NH3. Better spatial characterization of both NO2 and NH3 concentrations, especially near intensive agriculture, would help to characterize the extent of the impacts across the U.S. These findings are discussed in the context of U.S. air pollution policy.

Increased summer temperature is associated with reduced calf mass of a circumpolar large mammal through direct thermoregulatory and indirect, food quality, pathways.

Climate change represents a growing ecological challenge. The (sub) arctic and boreal regions of the world experience the most rapid warming, presenting an excellent model system for studying how climate change affects mammals. Moose (Alces alces) are a particularly relevant model species with their circumpolar range. Population declines across the southern edge of this range are linked to rising temperatures. Using a long-term dataset (1988-1997, 2017-2019), we examine the relative strength of direct (thermoregulatory costs) and indirect (food quality) pathways linking temperature, precipitation, and the quality of two important food items (birch and fireweed) to variation in moose calf mass in northern Sweden. The direct effects of temperature consistently showed stronger relationships to moose calf mass than did the indirect effects. The proportion of growing season days where the temperature exceeded a 20 °C threshold showed stronger direct negative relationships to moose calf mass than did mean temperature values. Finally, while annual forb (fireweed) quality was more strongly influenced by temperature and precipitation than were perennial (birch) leaves, this did not translate into a stronger relationship to moose calf weight. The only indirect path with supporting evidence suggested that mean growing season temperatures were positively associated with neutral detergent fiber, which was, in turn, negatively associated with calf mass. While indirect impacts of climate change deserve further investigation, it is important to recognize the large direct impacts of temperature on cold-adapted species.

A favorable impression of vaccination leads to a better vaccination rate for the human papillomavirus vaccine: A Japanese questionnaire survey investigation.

Background: The Japanese vaccination rate for infants and children is seemingly excellent, except for the human papillomavirus (HPV) vaccine. Regardless of its efficacy, the inoculation rate in Japan has dropped to approximately 1 % since 2013 because of negative information about vaccine side effects. We aimed to demonstrate the factors that lead to low vaccine acceptance rates (e.g., caregiver attitudes, popular misconceptions) to inform the relevant target demographic of a stronger intention to immunize and to facilitate recovery of HPV vaccine coverage. Methods: We conducted this study using data from two questionnaire surveys. Statistical analyses of factors affecting vaccine impressions and mediation effects of HPV vaccine impressions were performed. The difference in the knowledge about each vaccine was evaluated. Results: Of the respondents, 95.9 % reported their intent to vaccinate their infants, whereas 58.2-78.3 % felt that they sufficiently understood the aims, efficacy, and risks of vaccination and 66.6 % had a positive impression of vaccines. Overall, 21.3 % of parents responded that they planned to have their child vaccinated against HPV, and 25.8 % had a favorable impression of this vaccine. Among factors affecting vaccine impressions, we found that parents had anxiety about vaccines when they felt that their knowledge of vaccines was insufficient. Additionally, impressions of the HPV vaccine had a mediating effect on the association between the impressions of infant vaccines and parents' intent to provide the vaccine to their children. Conclusion: These findings show that as a society, we need to improve impressions and knowledge regarding vaccines, including but not exclusively the HPV vaccine. Moreover, although the recovery of HPV vaccine coverage is strongly desired for improving public health, simply improving impressions about the HPV vaccine or educating parents with substantive knowledge is insufficient. Instead, improving impressions and understanding of the vaccination itself is necessary.

Moving beyond standard toxicological metrics: The effect of diclofenac on planktonic host-parasite interactions.

Pharmaceuticals are increasingly released into surface waters and therefore ubiquitous in aquatic systems. While pharmaceuticals are known to influence species interactions, their effect on host-parasite interactions is still underexplored despite potential ecosystem-level consequences. Here, we ask whether diclofenac, a widely used non-steroid anti-inflammatory drug, affects the interaction between a phytoplankton host (Staurastrum sp.; green alga) and its obligate fungal parasite (Staurastromyces oculus; chytrid fungus). We hypothesized that the effect of increasing diclofenac concentration on the host-parasite system depends on parasite exposure. We assessed acute and chronic effects of a wide range of diclofenac concentrations (0-150 mg/L) on host and parasite performance using a replicated long gradient design in batch cultures. Overall system response summarizing parameters related to all biotic components in an experimental unit i.e., number of bacteria and phytoplankton host cells along with photosynthetic yield (a measure of algal cell fitness), depended on diclofenac concentration and presence/absence of parasite. While host standing biomass decreased at diclofenac concentrations >10 mg/L in non-parasite-exposed treatments, it increased at ≥10 mg/L in parasite-exposed treatments since losses due to infection declined. During acute phase (0-48 h), diclofenac concentrations <0.1 mg/L had no effect on host net-production neither in parasite-exposed nor non-parasite-exposed treatments, but parasite infection ceased at 10 mg/L. During chronic phase (0-216 h), host net-production declined only at concentrations >10 mg/L in non-parasite-exposed cultures, while it was overall close to zero in parasite-exposed cultures. Our results suggest that chytrid parasites are more sensitive to diclofenac than their host, allowing a window of opportunity for growth of phytoplankton hosts, despite exposure to a parasite. Our work provides a first understanding about effects of a pharmaceutical on a host-parasite interaction beyond those defined by standard toxicological metrics.

Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.

This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted models), and show how a range of estimators can be generated, with different modeling requirements and robustness properties. The primary goal is to help build intuitive appreciation for robust estimation that is conducive to sound practice. We do this by visualizing the target estimand and the estimation strategies. A second goal is to provide a "menu" of estimators that practitioners can choose from for the estimation of marginal natural (in)direct effects. The estimators generated from this exercise include some that coincide or are similar to existing estimators and others that have not previously appeared in the literature. We note several different ways to estimate the weights for cross-world weighting based on three expressions of the weighting function, including one that is novel; and show how to check the resulting covariate and mediator balance. We use a random continuous weights bootstrap to obtain confidence intervals, and also derive general asymptotic variance formulas for the estimators. The estimators are illustrated using data from an adolescent alcohol use prevention study. R-code is provided.

Predicting the long-term impact of rotavirus vaccination in 112 countries from 2006 to 2034: A transmission modeling analysis.

Rotavirus vaccination has been shown to reduce rotavirus burden in many countries, but the long-term magnitude of vaccine impacts is unclear, particularly in low-income countries. We use a transmission model to estimate the long-term impact of rotavirus vaccination on deaths and disability adjusted life years (DALYs) from 2006 to 2034 for 112 low- and middle-income countries. We also explore the predicted effectiveness of a one- vs two- dose series and the relative contribution of direct vs indirect effects to overall impacts. To validate the model, we compare predicted percent reductions in severe rotavirus cases with the percent reduction in rotavirus positivity among gastroenteritis hospital admissions for 10 countries with pre- and post-vaccine introduction data. We estimate that vaccination would reduce deaths from rotavirus by 49.1 % (95 % UI: 46.6-54.3 %) by 2034 under realistic coverage scenarios, compared to a scenario without vaccination. Most of this benefit is due to direct benefit to vaccinated individuals (explaining 69-97 % of the overall impact), but indirect protection also appears to enhance impacts. We find that a one-dose schedule would only be about 57 % as effective as a two-dose schedule 12 years after vaccine introduction. Our model closely reproduced observed reductions in rotavirus positivity in the first few years after vaccine introduction in select countries. Rotavirus vaccination is likely to have a substantial impact on rotavirus gastroenteritis and its mortality burden. To sustain this benefit, the complete series of doses is needed.

Direct and Indirect Effects of Essential Oils for Sustainable Crop Protection.

Plant essential oils (EOs) are gaining interest as biopesticides for crop protection. EOs have been recognized as important ingredients of plant protection products including insecticidal, acaricidal, fungicidal, and nematicidal agents. Considering the growing importance of EOs as active ingredients, the domestication and cultivation of Medicinal and Aromatic Plants (MAPs) to produce chemically stable EOs contributes to species conservation, provides the sustainability of production, and decreases the variations in the active ingredients. In addition to these direct effects on plant pests and diseases, EOs can induce plant defenses (priming effects) resulting in better protection. This aspect is of relevance considering that the EU framework aims to achieve the sustainable use of new plant protection products (PPPs), and since 2020, the use of contaminant PPPs has been prohibited. In this paper, we review the most updated information on the direct plant protection effects of EOs, focusing on their modes of action against insects, fungi, and nematodes, as well as the information available on EOs with plant defense priming effects.

A controlled effects approach to assessing immune correlates of protection.

An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.

Role of black carbon in modulating aerosol direct effects driven by air pollution controls during 2013-2017 in China.

Aerosol direct effects (ADEs) can modulate shortwave radiation as well as atmospheric dynamics and air quality. As the key absorbing component of aerosol, the black carbon (BC) largely determines the aerosol optical properties. Therefore, it is expected that BC emission controls might gain co-benefits from the simultaneous reduction of ADEs. To demonstrate such synergy, here we quantified the ADEs changes and the role of BC controls in China during 2013-2017 using a regional two-way coupled meteorology chemistry transport model. Simulated results suggest that the control action effectively reduced the wintertime PM2.5 concentration (-26.0 µg m-3) and associated ADEs. In January, the influence of ADEs on surface shortwave radiation, 2-meter temperature, and planetary boundary layer height was weakened from -16.7 W m-2, -0.20 °C, and -15.4 m in 2013 to -11.3 W m-2, -0.06 °C, and -10.7 m in 2017, respectively. The enhancement of SO2, NO2, and PM2.5 concentrations due to ADEs was reduced from +3.1%, +5.2%, and +5.4% in 2013 to +2.6%, +4.5%, and +3.3% in 2017, respectively, demonstrating the extra benefit of air pollution controls for improving air quality by reducing ADEs. Meanwhile, the BC emission reduced by 12.5% simultaneously along with the effective controls on SO2 and NO2 emissions during 2013-2017, mainly from domestic combustion (-11.7%), resulting in 30.3% (-0.9 µg m-3) reduction of BC concentration. Such BC controls contributed 15.6-60.2% of such changes in the ADEs influence on meteorological variables, and 32.6-41.1% on air pollutants. More specially, the effectiveness of collaborative reduction of BC further reduced surface shortwave radiation in China by 3.6 W m-2 in January and 1.0 W m-2 in July, leading to a more weakened ADEs that bring extra benefits in reducing PM2.5 concentrations by 1.8 µg m-3 in January and 0.3 µg m-3 in July. Apparently, BC played an important role in modulating the ADEs and associated influences on meteorology and air quality, suggesting a wise control strategy by targeting absorbing component of PM2.5 reduction to address both air pollution and climate change in the future.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations.

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Extracellular Vesicles as Natural Delivery Carriers Regulate Oxidative Stress Under Pathological Conditions.

Extracellular vesicles are cellular secretory particles that can be used as natural drug delivery carriers. They have successfully delivered drugs including chemotherapeutics, proteins, and genes to treat various diseases. Oxidative stress is an abnormal physiological phenomenon, and it is associated with nearly all diseases. In this short review, we summarize the regulation of EVs on oxidative stress. There are direct effects and indirect effects on the regulation of oxidative stress through EVs. On the one hand, they can deliver antioxidant substances or oxides to recipient cells, directly relieving or aggravating oxidative stress. On the other hand, regulate factors of oxidative stress-related signaling pathways can be delivered to recipient cells by the mediation of EVs, realizing the indirect regulation of oxidative stress. To the best of our knowledge, however, only endogenous drugs have been delivered by EVs to regulate oxidative stress till now. And the heterogeneity of EVs may complicate the regulation of oxidative stress. Therefore, this short review aims to draw more attention to the EVs-based regulation of oxidative stress, and we hope excellent EVs-based delivery carriers that can deliver exogenous drugs to regulate oxidative stress can be exploited.

Indirect and direct effects of factors associated with diabetes amongst the rural black population in the Dikgale Health and Demographic Surveillance System, South Africa.

BACKGROUND: Diabetes is an enormous, growing clinical and public health problem, which together with hypertension contributes significantly to the high risk of cardiovascular diseases (CVDs) globally. AIM: To examine the indirect and direct effects of risk factors simultaneously as a network of multiple pathways leading to diabetes in the rurally based adult population (aged 15+) using a household survey. METHODS: This investigation was based on a predictive model using a cross-sectional community-based study to identify the direct and indirect effects of diabetes risk factors in the Dikgale Health and Demographic Surveillance System (HDSS) consisting of 15 villages, with 7200 households and a total population of approximately 36 000. Fasting blood glucose and total cholesterol were measured using ILAB 300 with the following cut-off values: high fasting blood glucose 7 mmol/L and triglycerides 1.70 mmol/L. RESULTS: A total of 1407 individuals were interviewed, of whom 1281 had their blood pressure (BP) measured. The conceptual model was validated by the goodness-of-fit indexes (comparative fit index [CFI] = 1.00, Tucker Lewis index [TLI] = 1.041, root mean square error of approximation [RMSEA] = 0.001). Hypertension had the strongest direct effect of 0.0918 on diabetes, followed by age (0.0039) and high waist circumference (-0.0023). Hypertension also mediates the effects that high waist circumference (0.0005) and triglycerides (0.0060) have on diabetes status. CONCLUSION: The results in this study confirm the conceptual model considered in the risk factors for diabetes and suggest that hypertension, age and high waist circumference are the key variables directly affecting the diabetes status in the South African rural black population. The direct effect of triglycerides on diabetes suggests mediation by some measured factor(s).

Assessment of risks to listed species from the use of atrazine in the USA: a perspective.

Atrazine is a triazine herbicide used predominantly on corn, sorghum, and sugarcane in the US. Its use potentially overlaps with the ranges of listed (threatened and endangered) species. In response to registration review in the context of the Endangered Species Act, we evaluated potential direct and indirect impacts of atrazine on listed species and designated critical habitats. Atrazine has been widely studied, extensive environmental monitoring and toxicity data sets are available, and the spatial and temporal uses on major crops are well characterized. Ranges of listed species are less well-defined, resulting in overly conservative designations of "May Effect". Preferences for habitat and food sources serve to limit exposure among many listed animal species and animals are relatively insensitive. Atrazine does not bioaccumulate, further diminishing exposures among consumers and predators. Because of incomplete exposure pathways, many species can be eliminated from consideration for direct effects. It is toxic to plants, but even sensitive plants tolerate episodic exposures, such as those occurring in flowing waters. Empirical data from long-term monitoring programs and realistic field data on off-target deposition of drift indicate that many other listed species can be removed from consideration because exposures are below conservative toxicity thresholds for direct and indirect effects. Combined with recent mitigation actions by the registrant, this review serves to refine and focus forthcoming listed species assessment efforts for atrazine.Abbreviations: a.i. = Active ingredient (of a pesticide product). AEMP = Atrazine Ecological Monitoring Program. AIMS = Avian Incident Monitoring SystemArach. = Arachnid (spiders and mites). AUC = Area Under the Curve. BE = Biological Evaluation (of potential effects on listed species). BO = Biological Opinion (conclusion of the consultation between USEPA and the Services with respect to potential effects in listed species). CASM = Comprehensive Aquatic System Model. CDL = Crop Data LayerCN = field Curve Number. CRP = Conservation Reserve Program (lands). CTA = Conditioned Taste Avoidance. DAC = Diaminochlorotriazine (a metabolite of atrazine, also known by the acronym DACT). DER = Data Evaluation Record. EC25 = Concentration causing a specified effect in 25% of the tested organisms. EC50 = Concentration causing a specified effect in 50% of the tested organisms. EC50RGR = Concentration causing a 50% reduction in relative growth rate. ECOS = Environmental Conservation Online System. EDD = Estimated Daily Dose. EEC = Expected Environmental Concentration. EFED = Environmental Fate and Effects Division (of the USEPA). EFSA = European Food Safety Agency. EIIS = Ecological Incident Information System. ERA = Environmental Risk Assessment. ESA = Endangered Species Act. ESU = Evolutionarily Significant UnitsFAR = Field Application RateFIFRA = Federal Insecticide, Fungicide, and Rodenticide Act. FOIA = Freedom of Information Act (request). GSD = Genus Sensitivity Distribution. HC5 = Hazardous Concentration for ≤ 5% of species. HUC = Hydrologic Unit Code. IBM = Individual-Based Model. IDS = Incident Data System. KOC = Partition coefficient between water and organic matter in soil or sediment. KOW = Octanol-Water partition coefficient. LC50 = Concentration lethal to 50% of the tested organisms. LC-MS-MS = Liquid Chromatograph with Tandem Mass Spectrometry. LD50 = Dose lethal to 50% of the tested organisms. LAA = Likely to Adversely Affect. LOAEC = Lowest-Observed-Adverse-Effect Concentration. LOC = Level of Concern. MA = May Affect. MATC = Maximum Acceptable Toxicant Concentration. NAS = National Academy of Sciences. NCWQR = National Center of Water Quality Research. NE = No Effect. NLAA = Not Likely to Adversely Affect. NMFS = National Marine Fisheries Service. NOAA = National Oceanic and Atmospheric Administration. NOAEC = No-Observed-Adverse-Effect Concentration. NOAEL = No-Observed-Adverse-Effect Dose-Level. OECD = Organization of Economic Cooperation and Development. PNSP = Pesticide National Synthesis Project. PQ = Plastoquinone. PRZM = Pesticide Root Zone Model. PWC = Pesticide in Water Calculator. QWoE = Quantitative Weight of Evidence. RGR = Relative growth rate (of plants). RQ = Risk Quotient. RUD = Residue Unit Doses. SAP = Science Advisory Panel (of the USEPA). SGR = Specific Growth Rate. SI = Supplemental Information. SSD = Species Sensitivity Distribution. SURLAG = Surface Runoff Lag Coefficient. SWAT = Soil & Water Assessment Tool. SWCC = Surface Water Concentration Calculator. UDL = Use Data Layer (for pesticides). USDA = United States Department of Agriculture. USEPA = United States Environmental Protection Agency. USFWS = United States Fish and Wildlife Service. USGS = United States Geological Survey. WARP = Watershed Regressions for Pesticides.