The Influence of Fatty Acid and Glycerol on the Kinetics of Fat Hydrolysis by Candida Rugosa Lipase in a Membrane Reactor

The kinetics of lipid-hydrolysis by Candida rugosa lipase was investigated in a membrane reactor and in an emulsion system. Two models were chosen to describe the kinetics of the enzyme:

(1) The hydrolysis of triglycerides to fatty acids was considered to be a chain reaction with the intermediary products di- and mono-glyceride; each step was assumed to be a reversible second-order reaction. The reaction rate constants were determined from batch experiments. The experimental results could be described with this model.

(2) For process optimization and control, a model based on the power law was developed. For this model, the rate of hydrolysis was measured as a function of fatty acid and glycerol concentrations. Relations for the initial rate and equilibrium ester fraction as a function of the glycerol concentration were determined. Further, the reaction rate could be described with the power-law model with a power of 1.75 in the hydrolyzable ester fraction for a wide range of glycerol concentrations. The model with power 1.75 gave much better results when compared to a similar first order model. Although simpler, the first order model can not be used. The power law model was applied in the simulation of a reactor composed of three modules. The fatty acid production rate was calculated for this reactor system as a function of the outgoing glycerol concentration at different conditions.     

Molecular modeling of the core of Abeta amyloid fibrils

Amyloid fibrils, a key pathological feature of Alzheimer's disease (AD) and other amyloidosis implicated in neurodegeneration, have a characteristic cross-beta structure. Here we present a structural model for the core of amyloid fibrils formed by the Abeta peptide using computational approaches and experimental data. Abeta(15-36) was threaded against the parallel beta-helical proteins. Our multi-layer model was constructed using the top scoring template 1lxa, a left-handed parallel beta-helical protein. This six-rung helical model has in-register repeats of the Abeta(15-36) sequence. Each rung has three beta-strands separated by two turns. The model was tested using molecular dynamics simulations in explicit water, and is in good agreement with a number of experimental observations. In addition, a model based on right-handed helical proteins is also described. The core structural model described here might serve as the building block of the Abeta(1-40) amyloid fibril as well as some other amyloid fibrils.     

Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action

Background

Genomic selection is an appealing method to select purebreds for crossbred performance. In the case of crossbred records, single nucleotide polymorphism (SNP) effects can be estimated using an additive model or a breed-specific allele model. In most studies, additive gene action is assumed. However, dominance is the likely genetic basis of heterosis. Advantages of incorporating dominance in genomic selection were investigated in a two-way crossbreeding program for a trait with different magnitudes of dominance. Training was carried out only once in the simulation.

Results

When the dominance variance and heterosis were large and overdominance was present, a dominance model including both additive and dominance SNP effects gave substantially greater cumulative response to selection than the additive model. Extra response was the result of an increase in heterosis but at a cost of reduced purebred performance. When the dominance variance and heterosis were realistic but with overdominance, the advantage of the dominance model decreased but was still significant. When overdominance was absent, the dominance model was slightly favored over the additive model, but the difference in response between the models increased as the number of quantitative trait loci increased. This reveals the importance of exploiting dominance even in the absence of overdominance. When there was no dominance, response to selection for the dominance model was as high as for the additive model, indicating robustness of the dominance model. The breed-specific allele model was inferior to the dominance model in all cases and to the additive model except when the dominance variance and heterosis were large and with overdominance. However, the advantage of the dominance model over the breed-specific allele model may decrease as differences in linkage disequilibrium between the breeds increase. Retraining is expected to reduce the advantage of the dominance model over the alternatives, because in general, the advantage becomes important only after five or six generations post-training.

Conclusion

Under dominance and without retraining, genomic selection based on the dominance model is superior to the additive model and the breed-specific allele model to maximize crossbred performance through purebred selection.     

A statistical mechanical model of the pre- and subtransitions of lecithin membranes

A statistical mechanical model with experimentally proved facts as starting points is presented. This model explains on molecular level, the pre- and subtransitions appearing in lipid membranes. The model describes the main features of the transitions, the hysteresis of the subtransition and the mobility changes of the heads and chains at these transitions. The model was expanded for phosphatidylcholine homologues with arbitrary chain lengths, and a qualitative agreement in the case of pretransition as far as a quantitative one for the subtransition were found.     

A bioenergetic model of the mitochondrial population undergoing permeability transition

Mitochondrial permeability transition (MPT) is a highly regulated complex phenomenon that is a type of ischemia/reperfusion injury that can lead to cell death and ultimately organ dysfunction. A novel population transition and detailed permeability transition pore regulation model were integrated with an existing bioenergetics model to describe MPT induction under a variety of conditions. The framework of the MPT induction model includes the potential states of the mitochondria (aggregated, orthodox and post-transition), their transitions from one state to another as well as their interaction with the extra-mitochondrial environment. The model encodes the three basic necessary conditions for MPT: a high calcium load, alkaline matrix pH and circumstances which favor de-energization. The MPT induction model was able to reproduce the expected bioenergetic trends observed in a population of mitochondria subjected to conditions that favor MPT. The model was corroborated and used to predict that MPT in an acidic environment is mitigated by an increase in activity of the mitochondrial potassium/hydrogen exchanger. The model was also used to present the beneficial impact of reducing the duration mitochondria spend in the orthodox state on preserving the extra-mitochondrial ATP levels. The model serves as a tool for investigators to use to understand the MPT induction phenomenon, explore alternative hypotheses for PTP regulation, as well as identify endogenous pharmacological targets and evaluate potential therapeutics for MPT mitigation.     

Evaluation of mammalian fed-batch cultivations by two different models

Simulations of fed-batch hybridoma cell cultures were performed with two different models, a new model suggested by Nielsen and an older model suggested by Batt and Kompala. The response of the new Nielsen model was less sensitive to changes in feed concentrations and frequencies than the Batt and Kompala model. The new simulations with the Nielsen model as well as those previously with the Batt and Kompala model indicate, that possible benefits of fed-batch cultivations must be sought in other features than in intrinsic metabolic kinetics.     

NDP52 associates with phosphorylated tau in brains of an Alzheimer disease mouse model

We previously showed that NDP52 (also known as calcoco2) plays a role as an autophagic receptor for phosphorylated tau facilitating its clearance via autophagy. Here, we examined the expression and association of NDP52 with autophagy-regulated gene (ATG) proteins including LC3, as well as phosphorylated tau and amyloid-beta (Aβ) in brains of an AD mouse model. NDP52 was expressed not only in neurons, but also in microglia and astrocytes. NDP52 co-localized with ATGs and phosphorylated tau as expected since it functions as an autophagy receptor for phosphorylated tau in brain. Compared to wild-type mice, the number of autophagic vesicles (AVs) containing NDP52 in both cortex and hippocampal regions was significantly greater in AD model mice. Moreover, the protein levels of NDP52 and phosphorylated tau together with LC3-II were also significantly increased in AD model mice, reflecting autophagy impairment in the AD mouse model. By contrast, a significant change in p62/SQSTM1 level was not observed in this AD mouse model. NDP52 was also associated with intracellular Aβ, but not with the extracellular Aβ of amyloid plaques. We conclude that NDP52 is a key autophagy receptor for phosphorylated tau in brain. Further our data provide clear evidence for autophagy impairment in brains of AD mouse model, and thus strategies that result in enhancement of autophagic flux in AD are likely to be beneficial.     

Development of a compartmental model of human zinc metabolism: identifiability and multiple studies analyses

A compartmental model of zinc metabolism has been developed from stable isotope tracer studies of five healthy adults. Multiple isotope tracers were administered orally and intravenously, and the resulting enrichment was measured in plasma, erythrocytes, urine, and feces for as long as 3 wk. Data from total zinc measurements and model-independent calculations of various steady-state parameters were also modeled with the kinetic data. A structure comprised of 14 compartments and as many as 25 unknown kinetic parameters was developed to adequately model the data from each of the individual studies. The structural identifiability of the model was established using the GLOBI2 identifiability analysis software. Numerical identifiability of parameter estimates was evaluated using statistical data provided by SAAM. A majority of the model parameters was estimated with sufficient statistical certainty to be considered well determined. After the fitting of the model and data from the individual studies using SAAM/CONSAM, results were submitted to SAAM extended multiple studies analysis for aggregation into a single set of population parameters and statistics. The model was judged to be valid based on criteria described elsewhere.     

Estimation of the Life Span of Red Blood Cells in the Growing Animal in Different Nutritional States

In this study the model of Shemin and Rittenberg for estimating the life span of red blood cells was extended so that non-steady-state conditions, exemplified by growth or changing physiological states, might be considered. The parameters were estimated by use of the modified Gauss-Newton method. The biological data that were used came from growing sheep in different physiological states with regard to copper. The model was extended to include changes in total blood hemin and changes in blood hemin synthesis that may occur with time. In the present study a linear function was taken as a first approximation. The model appeared to be a sufficiently good approximation in the study reported herein. It was found, however, that the parameters associated with changes in hemin should be estimated from ancillary measurements such as blood volume, Hb, body weight, etc., in order to obtain a good fit or definition of the model.     

Modeling of the sensory evaluation of sake by Dempster-Shafer's measure and genetic algorithm

Sensory evaluation data obtained from experts were analyzed by numerical methods. The aim of this study is to identify a model that can objectively estimate the sensory evaluation results based on the concentrations of components in sake. To this aim, a learning model in which Dempster-Shafer's measure was learned by genetic algorithm (GA) was constructed. The learning process was performed by discovery of the assignments of basic probabilities according to the decrease in error between the observed and estimated data. When the model was compared with back propagation and multiple regression analysis by cross validation, the predictive faculty of the present model was as good as that of back propagation. The experiential rule by experts for time series data of sensory evaluation could be more sufficiently explained by the present model than by back propagation. The main advantage of this model was that its predictive faculty was compensated by Bayesian probabilities.     

Evaluation of a bracken (Pteridium aquilinum (L.) Kuhn) growth model in predicting the effects of control strategies across a range of climatic zones in Great Britain

A nationwide bracken control experiment provided data over a three-year period for testing the accuracy of a bracken growth model (BRACON). Objective assessments of model validity identified the model as a reasonably accurate predictor of bracken stand dynamics given the range of environmental conditions currently prevailing in Great Britain, in relation to (1) cutting regimes and (2) spraying asulam. Ranking of treatment efficacy at individual sites was closely reflected in model predictions. Predicted response of the rhizome system to cutting treatments underestimated bracken resilience. Failure to consider the consequences of frond regrowth in the latter portion of the growing season as a means to offsetting energy loss was identified as a potential explanation for this discrepancy. The role of the model as a practical management tool is discussed with particular reference to relating model predictions, based upon generalised environmental data (40 km plusmn; 40 km grid cells), to bracken management at individual sites.     

Kinetics of organic removal in fixed-bed aerobic biological reactor

The process kinetics of a lab-scale upflow aerobic immobilized biomass (UAIB) reactor using simulated sugar-manufacturing wastewater as feed was investigated. The experimental unit consisted of a 22l reactor filled with high porosity pumice stone. The UAIB reactor was tested under different organic loads and different hydraulic retention times (HRT) and the substrate loading removal rate was compared with prediction of Stover-Kincannon model, second-order model and the first order substrate removal model. After obtaining steady-state conditions, organic loading rate was increased from 750 to 4500 g COD/m(3) day to resemble wastewater from sugar production lines, and hydraulic retention time was decreased from 1 to 0.5 days, stepwise. Nine different operational conditions were applied changing these two parameters in a certain program. As a result of the calculations, Stover-Kincannon model and second-order model known as "Grau" model were found to be the most appropriate models for this reactor. Stover-Kincannon model and Grau second-order model gave high correlation coefficients, which were 99.7% and 99.4%, respectively. Therefore, these models could be used in predicting the behavior or design of the UAIB reactors.     

Establishment of the model of white blood cell membrane chromatography and screening of antagonizing TLR_4 receptor component from Atractylodes macrocephala Koidz

Inflammation is a common and fundamental patho- physiological response. The inflammatory reaction is characterized by activation of white blood cell (WBC) with an accompanying vascular reaction. Recently, it has been found that the inflammatory reaction d…     

Application of deterministic epidemic theory to nasal carriage of staphylococcus aureus

Nasal carriage of antibiotic-resistant Staphylococcus aureus is often used as an index of cross-infection in hospitals. In this paper, a deterministic model of the epidemiology of Staphylococcal nasal carriage was derived employing the concepts of epidemic theory. This theoretical model was tested against experimental data gathered from a large survey. When the association between nasal carriage of tetracycline-resistant Staphylococci and length of stay in hospital derived from the survey was compared with theoretical figures derived from the model, the validity of the model in a real situation was confirmed.     

Dynamic analysis of a microbial process: A systems engineering approach

A general mathematical model of the chemostat system is developed in order to define an experimental program of dynamic testing. A glucose-limited culture ofSaccharomyces cerevisiae was grown in a chemostat using chemically defined medium. The chemostat was perturbed from an initial steady state by changes in input glucose concentration, dilution rate, pH, and temperature. Dynamic responses of cell mass, glucose, cell number, RNA, and protein concentrations were measured. A number of simulation techniques were used in developing a dynamic mathematical model and in comparing the developed model with experimental data as well as the Monod model. The resulting model was found to be quantitatively accurate and superior to the Monod model. The developed model was interpreted in the light of cell physiology. Adjustment of intracellular RNA fraction was found to be rate limiting in acceleration of cell specific growth rate.     

Cross-National Validation of an Eight-Factor Model of Societal Risk Perception

This study cross-nationally tested an eight-factor model of societal risk perception. The factors in the model were: Common individual hazards, Pollutants, Energy production and public transportation, Outdoor activities, Sex, deviance and addictions, Medical care, Weapons, and Psychotropic drugs. Using confirmatory factor analyses, the model was tested on a sample of Greek students and on a sample of French students, and was shown to satisfactorily account for the data in both samples. This model may be considered as a potentially useful tool for studying cross-national as well as individual differences (e.g., age, gender, worldviews or personality) in risk perception. Future studies are needed to determine: (a) whether this model applies to samples composed of persons of different ages or composed of persons from non-Western countries and (b) whether this model could be usefully expanded with one or more factors.     

microRNA‐143‐3p attenuated development of hepatic fibrosis in autoimmune hepatitis through regulation of TAK1 phosphorylation

Autoimmune hepatitis (AIH) is a chronic liver disease due to autoimmune system attacks hepatocytes and causes inflammation and fibrosis. Intracellular signalling and miRNA may play an important role in regulation of liver injury. This study aimed to investigate the potential roles of microRNA 143 in a murine AIH model and a hepatocyte injury model. Murine AIH model was induced by hepatic antigen S100, and hepatocyte injury model was induced by LPS. Mice and AML12 cells were separated into six groups with or without the treatment of miRNA‐143. Inflammation and fibrosis as well as gene expression were examined by different cellular and molecular techniques. The model was successfully established with the elevation of ALT and AST as well as inflammatory and fibrotic markers. Infection or transfection of mir‐143 in mice or hepatocytes significantly attenuated the development of alleviation of hepatocyte injury. Moreover, the study demonstrated phosphorylation of TAK1‐mediated miRNA‐143 regulation of hepatic inflammation and fibrosis as well as hepatocyte injury. Our studies demonstrated a significant role of miRNA‐143 in attenuation of liver injury in AIH mice and hepatocytes. miRNA‐143 regulates inflammation and fibrosis through its regulation of TAK1 phosphorylation, which warrants TAK1 as a target for the development of new therapeutic strategy of autoimmune hepatitis.