Improved Working Memory but No Effect on Striatal Vesicular Monoamine Transporter Type 2 after Omega-3 Polyunsaturated Fatty Acid Supplementation

Studies in rodents indicate that diets deficient in omega-3 polyunsaturated fatty acids (n-3 PUFA) lower dopamine neurotransmission as measured by striatal vesicular monoamine transporter type 2 (VMAT2) density and amphetamine-induced dopamine release. This suggests that dietary supplementation with fish oil might increase VMAT2 availability, enhance dopamine storage and release, and improve dopamine-dependent cognitive functions such as working memory. To investigate this mechanism in humans, positron emission tomography (PET) was used to measure VMAT2 availability pre- and post-supplementation of n-3 PUFA in healthy individuals. Healthy young adult subjects were scanned with PET using [11C]-(+)-α-dihydrotetrabenzine (DTBZ) before and after six months of n-3 PUFA supplementation (Lovaza, 2 g/day containing docosahexaenonic acid, DHA 750 mg/d and eicosapentaenoic acid, EPA 930 mg/d). In addition, subjects underwent a working memory task (n-back) and red blood cell membrane (RBC) fatty acid composition analysis pre- and post-supplementation. RBC analysis showed a significant increase in both DHA and EPA post-supplementation. In contrast, no significant change in [11C]DTBZ binding potential (BPND) in striatum and its subdivisions were observed after supplementation with n-3 PUFA. No correlation was evident between n-3 PUFA induced change in RBC DHA or EPA levels and change in [11C]DTBZ BPND in striatal subdivisions. However, pre-supplementation RBC DHA levels was predictive of baseline performance (i.e., adjusted hit rate, AHR on 3-back) on the n-back task (y = 0.19+0.07, r2 = 0.55, p = 0.009). In addition, subjects AHR performance improved on 3-back post-supplementation (pre 0.65±0.27, post 0.80±0.15, p = 0.04). The correlation between n-back performance, and DHA levels are consistent with reports in which higher DHA levels is related to improved cognitive performance. However, the lack of change in [11C]DBTZ BPND indicates that striatal VMAT2 regulation is not the mechanism of action by which n-3 PUFA improves cognitive performance. © 2012 Narendran et al

Biotechnology and the Environment: A Regulatory Proposal

The human race now holds the ability to alter the hereditary characteristics of all life forms through the use of biotechnology. Although the benefits seem limitless, there is a great deal of uncertainty about the risks this technology poses to human health and the environment. In Canada, the biotechnology industry is largely unregulated. The authors explore the potential and associated risks, and propose some suggestions for its regulation

Fibonacci numbers and resolutions of domino ideals

This paper considers a class of monomial ideals, called domino ideals, whose generating sets correspond to the sets of domino tilings of a $2\times n$ tableau. The multi-graded Betti numbers are shown to be in one-to-one correspondence with equivalence classes of sets of tilings. It is well-known that the number of domino tilings of a $2\times n$ tableau is given by a Fibonacci number. Using the bijection, this relationship is further expanded to show the relationship between the Fibonacci numbers and the graded Betti numbers of the corresponding domino ideal

Assessment for Learning: An Outcomes-Based Approach to Enhance Learning

This paper draws on two case studies from UK universities to advance debate regarding assessment strategies and methods in the ERAU Worldwide. It focuses on the use of summative and formative assessment, the role of feedback, and the importance of learning outcomes for continuous academic improvement. Findings from the first case study, with three cohorts of graduate students, show that, where students are encouraged to learn from their mistakes via formative feedback, improvement is more likely than when standard approaches to assessment are employed. The second case study identifies one university\u27s approach to changing the design, delivery and assessment of its courses. Findings reveal the need to match assessment and learning outcomes in order to enhance students\u27 learning experiences

Participant‐Reported Health Status Predicts Cardiovascular and All‐Cause Mortality Independent of Established and Nontraditional Biomarkers: Evidence From a Representative US Sample

Background: Participant‐reported health status is a key indicator of cardiovascular health, but its predictive value relative to traditional and nontraditional risk factors is unknown. We evaluated whether participant‐reported health status, as indexed by self‐rated health, predicted cardiovascular disease, and all‐cause mortality risk excess of 10‐year atherosclerotic cardiovascular disease (ASCVD) risk scores and 5 nontraditional risk biomarkers. Methods and Results: Analyses used prospective observational data from the 1999–2002 National Health and Nutrition Examination Surveys among those aged 40 to 79 years (N=4677). Vital status was ascertained through 2011, during which there were 850 deaths, 206 from cardiovascular disease (CVD). We regressed CVD and all‐cause mortality on standardized values of self‐rated health in survival models, adjusting for age, sex, education, existing chronic disease, race/ethnicity, ASCVD risk, and standardized biomarkers (fibrinogen, C‐reactive protein [CRP], triglycerides, albumin, and uric acid). In sociodemographically adjusted models, a 1‐SD decrease in self‐rated health was associated with increased risk of CVD mortality (hazard ratio [HR], 1.92; 95% CI, 1.51–2.45; P<0.001), and this hazard remained strong after adjusting for ASCVD risk and nontraditional biomarkers (HR, 1.79; 95% CI, 1.42–2.26; P<0.001). Self‐rated health also predicted all‐cause mortality even after adjustment for ASCVD risk and nontraditional biomarkers (HR, 1.50; 95% CI, 1.35–1.66; P<0.001). Conclusions: Self‐rated health provides prognostic information beyond that captured by traditional ASCVD risk assessments and by nontraditional CVD biomarkers. Consideration of self‐rated health in combination with traditional risk factors may facilitate risk assessment and clinical care

Monotonic properties of the shift and penetration factors

We study derivatives of the shift and penetration factors of collision theory with respect to energy, angular momentum, and charge. Definitive results for the signs of these derivatives are found for the repulsive Coulomb case. In particular, we find that the derivative of the shift factor with respect to energy is positive for the repulsive Coulomb case, a long anticipated but heretofore unproven result. These results are closely connected to the properties of the sum of squares of the regular and irregular Coulomb functions; we also present investigations of this quantity.Comment: 13 pages, 1 figur

Rocket Testing and Integrated System Health Management

Integrated System Health Management (ISHM) describes a set of system capabilities that in aggregate perform: determination of condition for each system element, detection of anomalies, diagnosis of causes for anomalies, and prognostics for future anomalies and system behavior. The ISHM should also provide operators with situational awareness of the system by integrating contextual and timely data, information, and knowledge (DIaK) as needed. ISHM capabilities can be implemented using a variety of technologies and tools. This chapter provides an overview of ISHM contributing technologies and describes in further detail a novel implementation architecture along with associated taxonomy, ontology, and standards. The operational ISHM testbed is based on a subsystem of a rocket engine test stand. Such test stands contain many elements that are common to manufacturing systems, and thereby serve to illustrate the potential benefits and methodologies of the ISHM approach for intelligent manufacturing

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data