Neural net modeling of estuarine indicators: Hindcasting phytoplankton biomass and net ecosystem production in the Neuse (North Carolina) and Trout (Florida) Rivers, USA

Phytoplankton biomass, as chlorophyll (Chl) a, and net ecosystem production (NEP), were modeled using artificial neural networks (ANNs). Chl a varied seasonally and along a saline gradient throughout the Neuse River (North Carolina). NEP was extremely dynamic in the Trout River (Florida), with phototrophic or heterotrophic conditions occurring over short-term intervals. Physical and chemical variables, arising from meteorological and hydrological conditions, created spatial and/or temporal gradients in both systems and served as interacting predictors for the trends/patterns of Chl a and NEP. ANNs outperformed comparable linear regression models and reliably modeled Chl a concentrations less than 20 μg L-1 and NEP values, denoting the apparent non-linear interactions among abiotic and indicator variables. ANNs underestimated Chl a concentrations greater than 20 μg L-1, likely due to the periodicity of data acquisition not being sufficient to generalize system variability, the designated 'lag' effect for variables not being adequate to portray estuarine flow dynamics, the exclusion of (one or more) variables that would have improved prediction, and/or an unrealistic expectation of network performance. Variables indicative of meteorological and hydrological forcing and/or proxy measurements of phytoplankton had the greatest relative impact on prediction of Chl a and NEP. Except for their predictive capability, ANNs might appear to be of limited value for ecological applications and problem solving; interpreting the absolute impact of and/or interacting relationships among network variables is intrinsically difficult. Statistical methods or 'rule extraction' algorithms that convey comprehensible network interpretation are needed prior to the routine use of ANNs in programs assessing and/or forecasting the response of biotic indicators to perturbation or for a means to discern estuarine function

Genome-Wide Association Study in BRCA1 Mutation Carriers Identifies Novel Loci Associated with Breast and Ovarian Cancer Risk

BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7×10-8, HR = 1.14, 95% CI: 1.09-1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4×10-8, HR = 1.27, 95% CI: 1.17-1.38) and 4q32.3 (rs4691139, P = 3.4×10-8, HR = 1.20, 95% CI: 1.17-1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific associat

Application of linear accelerator technology to the detection of trace amounts of transuranics in waste barrels

Electron linear accelerators (linacs), as sources of photons and neutrons, can produce a significant number of fissions in transuranic isotopes contained in large barrels of waste material. Both photons and thermal neutrons have been used to detect about 1 mg of plutonium in 105-kg matrices. A sequential interrogation with neutrons and photons, easily possible with linacs, can show both fertile and fissile constituents among the heavy-mass isotopes. The advantages of linacs in solving existing assay problems include: (1) high available beam current; (2) variable beam current, beam energy, pulse width, and pulse repetition frequency; and (3) beam-scanning ability. They also are compatible with passive assay instruments. Their versatility makes it likely that they will remain useful as assay technology advances