Firm retreats : a step by step guide for CPA firms

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Space Solar Rectifying Antenna On Earth

The realization of solar power from space is becoming increasingly closer as a solution to solving the continued growth in energy demand. Space based solar power is also being perceived as an alternative solution for non-renewable energy resources. Future solar power satellites will be positioned in orbit around the Earth where they will collect solar radiation. That radiation will be transformed into a microwave energy beam that is targeted to a receiving rectifying antenna or “rectenna” located on Earth’s surface. The received microwave energy will be converted into direct current electricity. This presentation focuses on the microwave patch antennas used with integrated rectifiers in ground receivers on Earth. Inset feed and quarter-wave microwave patch antennas and a microwave rectifier were engineered, manufactured, and tested in-house at the University of North Dakota. The results showed a resonant frequency close to the desired 2.45 GHz, but the rectifier demonstrated 21% power conversion efficiency from AC to DC at 15dBm. The antenna and rectifier were combined and analysis was performed for the parameters of distance of the receiving rectenna from the transmitter and power output upon rectification. The innovation of this project is the “Multi-Combinational Renewable Energy Efficient Generator ” that allows such energy attachments as terrestrial solar and wind, geo-thermal facilities, energy storage systems, and the rectenna itself to be integrated into the base structure. The future Global Electrical Grid will use solar power satellites as a space electrical node and, it is hoped, the MCREEG generator will serve as a ground electrical node. Advisors: Dr. Sima Noghanian, Dr. Hossein Salehfar, Dr. Isaac Chang, Dr. James Casler, Dr. Ron Fevi

Models for risk assessment of reactive chemicals in aquatic toxicology

A quantitative structure property relationship (QSPR) for a,b-unsaturated carboxylates (mainly acrylates and methacrylates) was established in chapter 2. Chemical reaction rate constants were measured for 12 different chemicals with three different nucleophiles, namely H 2 O, OH - and glutathione (GSH). Relatively small differences were found in hydrolysis rates (reaction with H 2 O and OH - ). At an elevated pH (8.8) the hydrolysis half-life of the compound ranged between 7 and 40 days, with exception of diethyl fumarate (0.4 day). A separation in two groups was observed for the reaction with GSH (Michael addition), where acrylates reacted approximately 100 times as fast as methacrylates. This difference was con- sistent with differences found in electronic structure, which was determined by quantum- chemical calculations. Because no single parameter could describe the electrophilic charac- ter of the unsaturated carboxylates satisfactory, four descriptors were pooled, using a multivariate correlation (partial least squares regression, PLS). The resulting QSPR for Michael addition was able to predict the reactivity of structurally related, unsaturated carboxylates. Acute fish toxicity of a set of acrylates and methacrylates was evaluated in chapter 3. Published four-day LC 50 data for fathead minnow were compared to the chemical reactivity of the compounds towards GSH, because Michael addition was expected to be the mecha- nism that causes harmful binding to essential biological thiol-sites in the fish (e. g. proteins and enzymes). A simple equation was used to model the interaction of electrophilic chemi- cals with GSH. The degree of GSH depletion, which was used to estimate the toxic effect, was found to be related to the product of aqueous exposure concentration and chemical reaction rate of the reactive compound. Although, all acrylates and methacrylates poten- tially could react with GSH, narcosis was judged to be an alternative mode of toxic action responsible for the observed acute toxicity. Potencies for GSH depletion and narcosis were compared on the bases of critical body residues and critical depletion rates. Five out of 12 compounds were thereby identified as narcosis chemicals on the bases of their high calcu- lated lethal body burden. It was concluded that, although the tested chemicals all contained a similar functional group, their mode of action regarding acute fish toxicity was not the same. Therefore, a correlation between chemical reaction rate and LC 50 for the whole test set of chemicals would not be meaningful. The results from chapter 3 indicated, that narcosis was an interfering mode of action in QSARÕs for fish toxicity of reactive chemicals. To evaluate this hypothesis, data of reactive chemicals from three different classes (unsaturated carboxylates, organophosphorus esters?126 Chapter 8 Summary and General Discussion and nitrobenzenes) were taken from the literature and subjected to an analysis for multiple modes of action (chapter 4). The Toxic ratio, being the ratio between the observed LC 50 and the LC 50 , predicted for the same compound by a narcosis QSAR was used to estimate the probability of a compound to act by narcosis. In total, 40 % of the 61 compounds tested were identified as Òprobably acting by narcosisÓ. For these compounds, a narcosis QSAR using the octanol/water partitioning coefficient (K OW ) as sole descriptor was found to describe the toxicity. QSARÕs using reactivity descriptors, which in earlier work had been found insufficient to describe the toxicity of these classes of compounds improved considerably, if the Ònarcotic chemicalsÓ were excluded from the data sets. It was concluded, that narcosis should always be considered as a possible alternative cause of death in acute fish toxicity test, even if the chemicals seem to have a very specific mode of action. Additionally, it was shown, that QSARÕs should only be established for sets of chemicals with an identical mode of action. Modes of action clearly should not be confused with functional groups. The toxic effect of acrylates and methacrylates on a cellular level were investigated in chapter 5. Cellular glutathione (GSH) concentrations were recorded in isolated cells of rat livers. These cells have a continuous high expression of GSH and a broad range of metabo- lism. Potentially toxic metabolites of the acrylates and methacrylates were therefore likely to be produced in these cells. Furthermore, the additivity of the toxic effect of these chemi- cals was investigated in this in-vitro test. For each chemical, an EC 50 for GSH depletion was determined and used as an effect equivalent to compare their potencies. By testing two mixtures, each containing six individual chemicals, it could be shown that the depletion of GSH was dose-additive. This means that in a mixture of acrylates and methacrylates each individual chemical will contribute to the total toxic effect of the mixture. As expected, the compounds were metabolized by the hepatocytes. For one of them, allyl methacrylate, the very toxic metabolic product acrolein could be identified in the cell-culture medium. The production of this metabolite is most probably responsible for the high toxicity of this spe- cific compound towards the liver cells as well as towards fish (chapter 3). A preliminary physiologically based pharmacokinetic and -dynamic model (PBPK-PD) for ethyl acrylate (EA) was presented in chapter 6. It was based on an existing PBPK model for inert compounds in fish, which had been established by the US-EPA in Duluth, MN ( 1, 2). The model was adapted to be used with EA by adding elimination processes in several tissue compartments. Elimination rates of EA, which had been measured in-vitro, were extrapolated to whole organs. The turnover of GSH in the gills was modeled separately and was used to describe the toxic effect of EA on biological targets. Once the model was estab-?lished, several aspects of an aqueous exposure scenario were investigated. The uptake of EA in different organs of the fish was predicted to occur very rapidly (steady state concen- trations reached in minutes to a few hours) with exception of the fat tissue. The metabolic elimination of EA in the gills was not sufficient to cause a notable first pass effect. Conse- quently, the EA concentration in the gill tissue was predicted to be almost instantaneously at equilibrium with the aqueous exposure concentration. The EA concentration in the gills was subsequently used in the biological effect sub-model to describe the depletion of GSH. For a simulated exposure scenario close to a lethal aqueous concentration, the GSH concen- tration in the gills decreased by 60 % during the first 6 hours. This forecast was in agree- ment with experimental observations. In contrast to an existing rat model for EA, the trout model did not predict a first pass elimination of EA and therefore a systemic distribution can be expected in the fish. In both models, however, a local depletion of the GSH level at the site of adsorption was evident. In chapter 7, several findings from the previous chapters were combined to postulate an elementary approach to model toxic effects of reactive chemicals in aquatic organisms. The most important simplification of this approach was, to disregard the pharmacokinetics of moderately hydrophobic reactive chemical in aquatic organisms. This resulted in a elemen- tary pharmacodynamic model (EPD), which describes a target and the interaction of a reac- tive chemical with this target. This approach can be used to describe time and concentration dependent toxicological effects. Models, based on this approach were found to give excel- lent description of experimental data on acetylcholine-esterase inhibition due to OP-esters in several aquatic animals. The approach was also able to predict time dependent effect concentrations (e. g. LC 50 ). Under certain conditions, the EPD model can be reduced to an equivalent of HaberÕs Law, which states that the product of concentration and exposure time will be constant. In addition to this, the EPD model can give a rational interpretation of threshold concentration, which are often observed in toxicity experiments

Increase your personal marketing power : relationship skills for CPAs

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Diagnostic markers based on a computational model of lipoprotein metabolism

Abstract Background: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. Results: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. Conclusions: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible

Use of Physiologically Based Biokinetic (PBBK) Modeling to Study Estragole Bioactivation and Detoxification in Humans as Compared with Male Rats

The extent of bioactivation of the herbal constituent estragole to its ultimate carcinogenic metabolite 1′-sulfooxyestragole depends on the relative levels of bioactivation and detoxification pathways. The present study investigated the kinetics of the metabolic reactions of both estragole and its proximate carcinogenic metabolite 1′-hydroxyestragole in humans in incubations with relevant tissue fractions. Based on the kinetic data obtained a physiologically based biokinetic (PBBK) model for estragole in human was defined to predict the relative extent of bioactivation and detoxification at different dose levels of estragole. The outcomes of the model were subsequently compared with those previously predicted by a PBBK model for estragole in male rat to evaluate the occurrence of species differences in metabolic activation. The results obtained reveal that formation of 1′-oxoestragole, which represents a minor metabolic route for 1′-hydroxyestragole in rat, is the main detoxification pathway of 1′-hydroxyestragole in humans. Due to a high level of this 1′-hydroxyestragole oxidation pathway in human liver, the predicted species differences in formation of 1′-sulfooxyestragole remain relatively low, with the predicted formation of 1′-sulfooxyestragole being twofold higher in human compared with male rat, even though the formation of its precursor 1′-hydroxyestragole was predicted to be fourfold higher in human. Overall, it is concluded that in spite of significant differences in the relative extent of different metabolic pathways between human and male rat there is a minor influence of species differences on the ultimate overall bioactivation of estragole to 1′-sulfooxyestragol

Relationships Between Base-Catalyzed Hydrolysis Rates or Glutathione Reactivity for Acrylates and Methacrylates and Their NMR Spectra or Heat of Formation

The NMR chemical shift, i.e., the π-electron density of the double bond, of acrylates and methacrylates is related to the reactivity of their monomers. We investigated quantitative structure-property relationships (QSPRs) between the base-catalyzed hydrolysis rate constants (k1) or the rate constant with glutathione (GSH) (log kGSH) for acrylates and methacrylates and the 13C NMR chemical shifts of their α,β-unsaturated carbonyl groups (δCα and δCβ) or heat of formation (Hf) calculated by the semi-empirical MO method. Reported data for the independent variables were employed. A significant linear relationship between k1 and δCβ, but not δCα, was obtained for methacrylates (r2 = 0.93), but not for acrylates. Also, a significant relationship between k1 and Hf was obtained for both acrylates and methacrylates (r2 = 0.89). By contrast, log kGSH for acrylates and methacrylates was linearly related to their δCβ (r2 = 0.99), but not to Hf. These findings indicate that the 13C NMR chemical shifts and calculated Hf values for acrylates and methacrylates could be valuable for estimating the hydrolysis rate constants and GSH reactivity of these compounds. Also, these data for monomers may be an important tool for examining mechanisms of reactivity

Mechanisms of Action of (Meth)acrylates in Hemolytic Activity, in Vivo Toxicity and Dipalmitoylphosphatidylcholine (DPPC) Liposomes Determined Using NMR Spectroscopy

We investigated the quantitative structure-activity relationships between hemolytic activity (log 1/H50) or in vivo mouse intraperitoneal (ip) LD50 using reported data for α,β-unsaturated carbonyl compounds such as (meth)acrylate monomers and their 13C-NMR β-carbon chemical shift (δ). The log 1/H50 value for methacrylates was linearly correlated with the δCβ value. That for (meth)acrylates was linearly correlated with log P, an index of lipophilicity. The ipLD50 for (meth)acrylates was linearly correlated with δCβ but not with log P. For (meth)acrylates, the δCβ value, which is dependent on the π-electron density on the β-carbon, was linearly correlated with PM3-based theoretical parameters (chemical hardness, η; electronegativity, χ; electrophilicity, ω), whereas log P was linearly correlated with heat of formation (HF). Also, the interaction between (meth)acrylates and DPPC liposomes in cell membrane molecular models was investigated using 1H-NMR spectroscopy and differential scanning calorimetry (DSC). The log 1/H50 value was related to the difference in chemical shift (ΔδHa) (Ha: H (trans) attached to the β-carbon) between the free monomer and the DPPC liposome-bound monomer. Monomer-induced DSC phase transition properties were related to HF for monomers. NMR chemical shifts may represent a valuable parameter for investigating the biological mechanisms of action of (meth)acrylates

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales