Embryo dry weight (mg) for embryos taken from females captured in Florida springs with varying concentrations of nitrate

<p><b>Copyright information:</b></p><p>Taken from "Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs"</p><p></p><p>Environmental Health Perspectives 2005;114(S-1):69-75.</p><p>Published online 21 Oct 2005</p><p>PMCID:PMC1874177.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Graph shows mean ± 1 SE. = 0.56, = 0.003

Mean hepatic weight, adjusted for body weight, for females captured in Florida springs with varying dissolved oxygen concentrations

<p><b>Copyright information:</b></p><p>Taken from "Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs"</p><p></p><p>Environmental Health Perspectives 2005;114(S-1):69-75.</p><p>Published online 21 Oct 2005</p><p>PMCID:PMC1874177.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Graph shows adjusted mean ± 1 SE. = 0.85, = 0.001

Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs-0

<p><b>Copyright information:</b></p><p>Taken from "Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs"</p><p></p><p>Environmental Health Perspectives 2005;114(S-1):69-75.</p><p>Published online 21 Oct 2005</p><p>PMCID:PMC1874177.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p

Mean embryo number, adjusted for maternal body weight for females captured in Florida springs with varying temperatures

<p><b>Copyright information:</b></p><p>Taken from "Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs"</p><p></p><p>Environmental Health Perspectives 2005;114(S-1):69-75.</p><p>Published online 21 Oct 2005</p><p>PMCID:PMC1874177.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Graph shows mean ± 1 SE. = 0.76, = 0.005

Percentage of nonreproductive, mature females sampled from Florida springs with varying nitrate concentrations

<p><b>Copyright information:</b></p><p>Taken from "Water Quality Influences Reproduction in Female Mosquitofish () from Eight Florida Springs"</p><p></p><p>Environmental Health Perspectives 2005;114(S-1):69-75.</p><p>Published online 21 Oct 2005</p><p>PMCID:PMC1874177.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI</p> Fish were sampled during the reproductive season. Total samplings from each spring consisted of 30 mature females. = 0.57, = 0.03

Using Machine Learning Tools to Model Complex Toxic Interactions with Limited Sampling Regimes

A major impediment to understanding the impact of environmental stress, including toxins and other pollutants, on organisms, is that organisms are rarely challenged by one or a few stressors in natural systems. Thus, linking laboratory experiments that are limited by practical considerations to a few stressors and a few levels of these stressors to real world conditions is constrained. In addition, while the existence of complex interactions among stressors can be identified by current statistical methods, these methods do not provide a means to construct mathematical models of these interactions. In this paper, we offer a two-step process by which complex interactions of stressors on biological systems can be modeled in an experimental design that is within the limits of practicality. We begin with the notion that environment conditions circumscribe an <i>n</i>-dimensional hyperspace within which biological processes or end points are embedded. We then randomly sample this hyperspace to establish experimental conditions that span the range of the relevant parameters and conduct the experiment(s) based upon these selected conditions. Models of the complex interactions of the parameters are then extracted using machine learning tools, specifically artificial neural networks. This approach can rapidly generate highly accurate models of biological responses to complex interactions among environmentally relevant toxins, identify critical subspaces where nonlinear responses exist, and provide an expedient means of designing traditional experiments to test the impact of complex mixtures on biological responses. Further, this can be accomplished with an astonishingly small sample size

Using Machine Learning Tools to Model Complex Toxic Interactions with Limited Sampling Regimes

A major impediment to understanding the impact of environmental stress, including toxins and other pollutants, on organisms, is that organisms are rarely challenged by one or a few stressors in natural systems. Thus, linking laboratory experiments that are limited by practical considerations to a few stressors and a few levels of these stressors to real world conditions is constrained. In addition, while the existence of complex interactions among stressors can be identified by current statistical methods, these methods do not provide a means to construct mathematical models of these interactions. In this paper, we offer a two-step process by which complex interactions of stressors on biological systems can be modeled in an experimental design that is within the limits of practicality. We begin with the notion that environment conditions circumscribe an <i>n</i>-dimensional hyperspace within which biological processes or end points are embedded. We then randomly sample this hyperspace to establish experimental conditions that span the range of the relevant parameters and conduct the experiment(s) based upon these selected conditions. Models of the complex interactions of the parameters are then extracted using machine learning tools, specifically artificial neural networks. This approach can rapidly generate highly accurate models of biological responses to complex interactions among environmentally relevant toxins, identify critical subspaces where nonlinear responses exist, and provide an expedient means of designing traditional experiments to test the impact of complex mixtures on biological responses. Further, this can be accomplished with an astonishingly small sample size

Altered Breast Development in Young Girls from an Agricultural Environment-1

<p><b>Copyright information:</b></p><p>Taken from "Altered Breast Development in Young Girls from an Agricultural Environment"</p><p>Environmental Health Perspectives 2006;114(3):471-475.</p><p>Published online Jan 2006</p><p>PMCID:PMC1392245.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p

Examination of Metals from Aerospace-Related Activity in Surface Water Samples from Sites Surrounding the Kennedy Space Center (KSC), Florida

Metal contamination from Space Shuttle launch activity was examined using inductively coupled plasma-atomic emission spectroscopy in a two-tier study sampling surface water collected from several sites at the Kennedy Space Center (KSC) and associated Merritt Island National Wildlife Refuge in east central Florida. The primary study examined both temporal changes in baseline metal concentrations (19 metals) in surface water (1996 to 2009, 11 sites) samples collected at specific long-term monitoring sites and metal deposition directly associated with Space Shuttle launch activity at two Launch Complexes (LC39A and LC39B). A secondary study examined metal concentrations at additional sites and increased the amount of elements measured to 48 elements. Our examination places a heavy focus on those metals commonly associated with launch operations (e.g., Al, Fe, Mn, and Zn), but a brief discussion of other metals (As, Cu, Mo, Ni, and Pb) is also included. While no observable accumulation of metals occurred during the time period of the study, the data obtained postlaunch demonstrated a dramatic increase for Al, Fe, Mn, and Zn. Comparing overall trends between the primary and secondary baseline surface water concentrations, elevated concentrations were generally observed at sampling stations located near the launch complexes and from sites isolated from major water systems. While there could be several natural and anthropogenic sources for metal deposition at KSC, the data in this report indicate that shuttle launch events are a significant source

Health of Common Bottlenose Dolphins (Tursiops truncatus) in Barataria Bay, Louisiana, Following the <i>Deepwater Horizon</i> Oil Spill

The oil spill resulting from the explosion of the <i>Deepwater Horizon</i> drilling platform initiated immediate concern for marine wildlife, including common bottlenose dolphins in sensitive coastal habitats. To evaluate potential sublethal effects on dolphins, health assessments were conducted in Barataria Bay, Louisiana, an area that received heavy and prolonged oiling, and in a reference site, Sarasota Bay, Florida, where oil was not observed. Dolphins were temporarily captured, received a veterinary examination, and were then released. Dolphins sampled in Barataria Bay showed evidence of hypoadrenocorticism, consistent with adrenal toxicity as previously reported for laboratory mammals exposed to oil. Barataria Bay dolphins were 5 times more likely to have moderate–severe lung disease, generally characterized by significant alveolar interstitial syndrome, lung masses, and pulmonary consolidation. Of 29 dolphins evaluated from Barataria Bay, 48% were given a guarded or worse prognosis, and 17% were considered poor or grave, indicating that they were not expected to survive. Disease conditions in Barataria Bay dolphins were significantly greater in prevalence and severity than those in Sarasota Bay dolphins, as well as those previously reported in other wild dolphin populations. Many disease conditions observed in Barataria Bay dolphins are uncommon but consistent with petroleum hydrocarbon exposure and toxicity