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The greater amberjack Seriola dumerili is a pelagic, epibenthic species that is widely distributed in the Atlantic, Pacific, and Indian oceans. Life history samples from a total of 2,729 greater amberjacks were collected between 2000 and 2004 by personnel of the Marine Resource Monitoring Assessment and Prediction program and National Marine Fisheries Service port agents from recreational fisherman and in commercial fish houses from Cape Lookout, North Carolina, to Key West, Florida. Ages were estimated using thin transverse otolith sections from 1,996 specimens; sex and reproductive state were assigned to 2,517 fish based on histological preparations of gonadal tissues. Ages of greater amberjacks sampled ranged from 1 to 13 years; these data were described with a von Bertalanffy growth equation fitted to all aged specimens: L t ¼ 1,241.5 3 [1 À e À0.28(tþ1.56) ]. Sexual dimorphism was evident; females were larger at age than males. Size at 50% maturity was 644 mm fork length (FL) for males and 733 mm FL for females. Age at 50% maturity for females was 1.3 years. Estimates of potential annual fecundity ranged from 18,271,400 to 59,032,800 oocytes for 930-1,296-mm specimens and from 25,472,100 to 47,194,300 oocytes for ages 3-7. Peak spawning occurred primarily off south Florida and the Florida Keys during April and May. Even though the extremely fast growth, early maturation, very high fecundity, and wide distribution of greater amberjacks suggest that the population would be difficult to overexploit, a recent stock assessment of the Gulf of Mexico population shows that the species is vulnerable to overexploitation and should managed more conservatively than the life history characteristics imply.

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Methylmercury (MeHg) is a ubiquitous environmental pollutant and has been shown to affect learning in vertebrates following relatively low exposures. Zebrafish were used to model long-term learning deficits after developmental MeHg exposure. Selenomethionine (SeMet) co-exposure was used to evaluate its role in neuroprotection. Embryos were exposed from 2-24 hours post fertilization to (1) MeHg without SeMet, (2) SeMet without MeHg and (3) in combination of MeHg and SeMet. In case (1), the levels of MeHg were 0.00, 0.01, 0.03, 0.06, 0.10, 0.30 µM. In case (2), the levels of SeMet were 0.00. 0.03, 0.06, 0.10, 0.30 µM. In case (3), co-exposure levels of (MeHg, SeMet) were (0.03, 0.03), (0.03, 0.06), (0.03, 0.10), (0.03, 0.30), (0.10, 0.03), (0.10, 0.06), (0.10, 0.10), (0.10, 0.30) µM. Learning functions were tested in individual adults, four months after developmental exposure using a spatial alternation paradigm with food delivery on alternating sides of the aquarium. Low levels of MeHg (<0.1 µM) exposure delayed learning in treated fish; fish exposed to higher MeHg levels were unable to learn the task; SeMet co-exposure did not prevent this deficit. These data are consistent with findings in laboratory rodents. The dorsal and lateral telencephalon are the primary brain regions in fish involved in spatial learning and memory. Adult telencephalon cell body density decreased significantly at all MeHg exposures >0.01 µM MeHg. SeMet co-exposure ameliorated but did not prevent changes in telencephalon cell body density. In summary, MeHg affected both learning and brain structure, but SeMet only partially reversed the latter.Descriptors developmental exposure; learning; mercury; selenium; spatial alternation; zebrafish

A flexible approach to response surface modeling for the study of the joint action of three active anticancer agents is used to model a complex pattern of synergism, additivity and antagonism in an in vitro cell growth assay. The method for determining a useful nonlinear response surface model depends upon a series of steps using appropriate scaling of drug concentrations and effects, raw data modeling, and hierarchical parameter modeling. The method is applied to a very large in vitro study of the combined effect of Trimetrexate (TMQ), LY309887 (LY), and Tomudex (TDX) on inhibition of cancer cell growth. The base model employed for modeling dose-response effect is the four parameter Hill equation [1]. In the hierarchical aspect of the final model, the base Hill model is treated as a function of the total amount of the three drug mixture and the Hill parameters, background B, dose for 50% effect D50, and slope m, are understood as functions of the three drug fractions. The parameters are modeled using the canonical mixture polynomials from the mixture experiment methodologies introduced by Scheff [2]. We label the model generated a Nonlinear Mixture Amount model with control observations, or zero amounts, an "NLMAZ" model. This modeling paradigm provides for the first time an effective statistical approach to modeling complex patterns of local synergism, additivity, and antagonism in the same data set, the possibility of including additional experimental components beyond those in the mixture, and the capability of modeling three or more drugs.