Mechanisms involved in down-regulation of intestinal IgA in rats by high cocoa intake

Previous studies have shown that rat intestinal immunoglobulin A (IgA) concentration and lymphocyte composition of the intestinal immune system were influenced by a highly enriched cocoa diet. The aim of this study was to dissect the mechanisms by which a long-term high cocoa intake was capable of modifying gut secretory IgA in Wistar rats. After 7 weeks of nutritional intervention, Peyer's patches, mesenteric lymph nodes and the small intestine were excised for gene expression assessment of IgA, transforming growth factor ß, C-C chemokine receptor-9 (CCR9), interleukin (IL)-6, CD40, retinoic acid receptors (RAR¿ and RARß), C-C chemokine ligand (CCL)-25 and CCL28 chemokines, polymeric immunoglobulin receptor and toll-like receptors (TLR) expression by real-time polymerase chain reaction. As in previous studies, secretory IgA concentration decreased in intestinal wash and fecal samples after cocoa intake. Results from the gene expression showed that cocoa intake reduced IgA and IL¿6 in Peyer's patches and mesenteric lymph nodes, whereas in small intestine, cocoa decreased IgA, CCR9, CCL28, RAR¿ and RARß. Moreover, cocoa-fed animals presented an altered TLR expression pattern in the three compartments studied. In conclusion, a high-cocoa diet down-regulated cytokines such as IL-6, which is required for the activation of B cells to become IgA-secreting cells, chemokines and chemokine receptors, such as CCL28 and CCR9 together with RAR¿ and RARß, which are involved in the gut homing of IgA-secreting cells. Moreover, cocoa modified the cross-talk between microbiota and intestinal cells as was detected by an altered TLR pattern. These overall effects in the intestine may explain the intestinal IgA down-regulatory effect after the consumption of a long-term cocoa-enriched diet.

Chlamydiaceae family, Parachlamydia spp., and Waddlia spp. in porcine abortion.

At present, despite extensive laboratory investigations, most cases of porcine abortion remain without an etiological diagnosis. Due to a lack of recent data on the abortigenic effect of order Chlamydiales, 286 fetuses and their placentae of 113 abortion cases (1-5 fetuses per abortion case) were investigated by polymerase chain reaction (PCR) methods for family Chlamydiaceae and selected Chlamydia-like organisms such as Parachlamydia acanthamoebae and Waddlia chondrophila. In 0.35% of the cases (1/286 fetuses), the Chlamydiaceae real-time PCR was positive. In the Chlamydiaceae-positive fetus, Chlamydia abortus was detected by a commercial microarray and 16S ribosomal RNA PCR followed by sequencing. The positive fetus had a Porcine circovirus-2 coinfection. By the Parachlamydia real-time PCR, 3.5% (10/286 fetuses of 9 abortion cases) were questionable positive (threshold cycle values: 35.0-45.0). In 2 of these 10 cases, a confirmation by Chlamydiales-specific real-time PCR was possible. All samples tested negative by the Waddlia real-time PCR. It seems unlikely that Chlamydiaceae, Parachlamydia, and Waddlia play an important role as abortigenic agents in Swiss sows.

Permissivity of fish cell lines to three Chlamydia-related bacteria: Waddlia chondrophila, Estrella lausannensis and Parachlamydia acanthamoebae.

Epitheliocystis is an infectious disease affecting gills and skin of various freshwater and marine fishes, associated with high mortality and reduced growth of survivors. Candidatus Piscichlamydia salmonis and Clavochlamydia salmonicola have recently been identified as aetiological agents of epitheliocystis in Atlantic Salmon. In addition, several other members of the Chlamydiales order have been identified in other fish species. To clarify the pathogenicity of Chlamydia-like organisms towards fishes, we investigated the permissivity of two fish cell lines, EPC-175 (Fathead Minnow) and RTG-2 (rainbow trout) to three Chlamydia-related bacteria: Waddlia chondrophila, Parachlamydia acanthamoebae and Estrella lausannensis. Quantitative PCR and immunofluorescence demonstrated that W. chondrophila and, to a lesser extent, E. lausannensis were able to replicate in the two cell lines tested. Waddlia chondrophila multiplied rapidly in its host cell and a strong cytopathic effect was observed. During E. lausannensis infection, we observed a limited replication of the bacteria not followed by host cell lysis. Very limited replication of P. acanthamoebae was observed in both cell lines tested. Given its high infectivity and cytopathic effect towards fish cell lines, W. chondrophila represents the most interesting Chlamydia-related bacteria to be used to develop an in vivo model of epitheliocystis disease in fishes.

Chlamydiaceae and Chlamydia-like organisms in the koala (Phascolarctos cinereus)--organ distribution and histopathological findings.

Chlamydial infections in koalas can cause life-threatening diseases leading to blindness and sterility. However, little is known about the systemic spread of chlamydiae in the inner organs of the koala, and data concerning related pathological organ lesions are limited. The aim of this study was to perform a thorough investigation of organs from 23 koalas and to correlate their histopathological lesions to molecular chlamydial detection. To reach this goal, 246 formalin-fixed and paraffin embedded organ samples from 23 koalas were investigated by histopathology, Chlamydiaceae real-time PCR and immunohistochemistry, ArrayTube Microarray for Chlamydiaceae species identification as well as Chlamydiales real-time PCR and sequencing. By PCR, two koalas were positive for Chlamydia pecorum whereas immunohistochemical labelling for Chlamydiaceae was detected in 10 tissues out of nine koalas. The majority of these (n=6) had positive labelling in the urogenital tract related to histopathological lesions such as cystitis, endometritis, pyelonephritis and prostatitis. Somehow unexpected was the positive labelling in the gastrointestinal tract including the cloaca as well as in lung and spleen indicating systemic spread of infection. Uncultured Chlamydiales were detected in several organs of seven koalas by PCR, and four of these suffered from plasmacytic enteritis of unknown aetiology. Whether the finding of Chlamydia-like organisms in the gastrointestinal tract is linked to plasmacytic enteritis is unclear and remains speculative. However, as recently shown in a mouse model, the gastrointestinal tract might play a role being the site for persistent chlamydial infections and being a source for reinfection of the genital tract.

Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

Model for interevent times with long tails and multifractality in human communications: An application to financial trading

Social, technological, and economic time series are divided by events which are usually assumed to be random, albeit with some hierarchical structure. It is well known that the interevent statistics observed in these contexts differs from the Poissonian profile by being long-tailed distributed with resting and active periods interwoven. Understanding mechanisms generating consistent statistics has therefore become a central issue. The approach we present is taken from the continuous-time random-walk formalism and represents an analytical alternative to models of nontrivial priority that have been recently proposed. Our analysis also goes one step further by looking at the multifractal structure of the interevent times of human decisions. We here analyze the intertransaction time intervals of several financial markets. We observe that empirical data describe a subtle multifractal behavior. Our model explains this structure by taking the pausing-time density in the form of a superstatistics where the integral kernel quantifies the heterogeneous nature of the executed tasks. A stretched exponential kernel provides a multifractal profile valid for a certain limited range. A suggested heuristic analytical profile is capable of covering a broader region.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

Kinetic modeling of mechanisms of industrially important organic reactions in gas and liquid phase

This dissertation is based on 5 articles which deal with reaction mechanisms of the following selected industrially important organic reactions: 1. dehydrocyclization of n-butylbenzene to produce naphthalene 2. dehydrocyclization of 1-(p-tolyl)-2-methylbutane (MB) to produce 2,6-dimethylnaphthalene 3. esterification of neopentyl glycol (NPG) with different carboxylic acids to produce monoesters 4. skeletal isomerization of 1-pentene to produce 2-methyl-1-butene and 2-methyl-2-butene The results of initial- and integral-rate experiments of n-butylbenzene dehydrocyclization over selfmade chromia/alumina catalyst were applied when investigating reaction 2. Reaction 2 was performed using commercial chromia/alumina of different acidity, platina on silica and vanadium/calcium/alumina as catalysts. On all catalysts used for the dehydrocyclization, major reactions were fragmentation of MB and 1-(p-tolyl)-2-methylbutenes (MBes), dehydrogenation of MB, double bond transfer, hydrogenation and 1,6-cyclization of MBes. Minor reactions were 1,5-cyclization of MBes and methyl group fragmentation of 1,6- cyclization products. Esterification reactions of NPG were performed using three different carboxylic acids: propionic, isobutyric and 2-ethylhexanoic acid. Commercial heterogeneous gellular (Dowex 50WX2), macroreticular (Amberlyst 15) type resins and homogeneous para-toluene sulfonic acid were used as catalysts. At first NPG reacted with carboxylic acids to form corresponding monoester and water. Then monoester esterified with carboxylic acid to form corresponding diester. In disproportionation reaction two monoester molecules formed NPG and corresponding diester. All these three reactions can attain equilibrium. Concerning esterification, water was removed from the reactor in order to prevent backward reaction. Skeletal isomerization experiments of 1-pentene were performed over HZSM-22 catalyst. Isomerization reactions of three different kind were detected: double bond, cis-trans and skeletal isomerization. Minor side reaction were dimerization and fragmentation. Monomolecular and bimolecular reaction mechanisms for skeletal isomerization explained experimental results almost equally well. Pseudohomogeneous kinetic parameters of reactions 1 and 2 were estimated by usual least squares fitting. Concerning reactions 3 and 4 kinetic parameters were estimated by the leastsquares method, but also the possible cross-correlation and identifiability of parameters were determined using Markov chain Monte Carlo (MCMC) method. Finally using MCMC method, the estimation of model parameters and predictions were performed according to the Bayesian paradigm. According to the fitting results suggested reaction mechanisms explained experimental results rather well. When the possible cross-correlation and identifiability of parameters (Reactions 3 and 4) were determined using MCMC method, the parameters identified well, and no pathological cross-correlation could be seen between any parameter pair.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Canine distemper virus detection in asymptomatic and non vaccinated dogs

A quantitative real time polymerase chain reaction (PCR) revealed canine distemper virus presence in peripheral blood samples from asymptomatic and non vaccinated dogs. Samples from eleven domestic dogs with no signs of canine distemper and not vaccinated at the month of collection were used. Canine distemper virus vaccine samples in VERO cells were used as positive controls. RNA was isolated with Trizol®, and treated with a TURBO DNA-free kit. Primers were designed for canine distemper virus nucleocapsid protein coding region fragment amplification (84 bp). Canine b-actin (93 bp) was utilized as the endogenous control for normalization. Quantitative results of real time PCR generated by ABI Prism 7000 SDS Software showed that 54.5% of dogs with asymptomatic canine distemper were positive for canine distemper virus. Dissociation curves confirmed the specificity of the real time PCR fragments. This technique could detect even a few copies of viral RNA and identificate subclinically infected dogs providing accurate diagnosis of this disease at an early stage.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Inverting ionospheric D-region electron density pro-file from riometer measurement

The aim of this work is to invert the ionospheric electron density profile from Riometer (Relative Ionospheric opacity meter) measurement. The newly Riometer instrument KAIRA (Kilpisjärvi Atmospheric Imaging Receiver Array) is used to measure the cosmic HF radio noise absorption that taking place in the D-region ionosphere between 50 to 90 km. In order to invert the electron density profile synthetic data is used to feed the unknown parameter Neq using spline height method, which works by taking electron density profile at different altitude. Moreover, smoothing prior method also used to sample from the posterior distribution by truncating the prior covariance matrix. The smoothing profile approach makes the problem easier to find the posterior using MCMC (Markov Chain Monte Carlo) method.

Propofol exerts anti-inflammatory effects in rats with lipopolysaccharide-induced acute lung injury by inhibition of CD14 and TLR4 expression

We investigated the effect of propofol (Prop) administration (10 mg kg-1 h-1, intravenously) on lipopolysaccharide (LPS)-induced acute lung injury and its effect on cluster of differentiation (CD) 14 and Toll-like receptor (TLR) 4 expression in lung tissue of anesthetized, ventilated rats. Twenty-four male Wistar rats were randomly divided into three groups of 8 rats each: control, LPS, and LPS+Prop. Lung injury was assayed via blood gas analysis and lung histology, and tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) levels were determined in bronchoalveolar lavage fluid using ELISA. Real-time polymerase chain reaction was used to detect CD14 and TLR4 mRNA levels, and CD14 and TLR4 protein expression was determined by Western blot. The pathological scores were 1.2 ± 0.9, 3.3 ± 1.1, and 1.9 ± 1.0 for the control, LPS, and LPS+Prop groups, respectively, with statistically significant differences between control and LPS groups (P < 0.05) and between LPS and LPS+Prop groups (P < 0.05). The administration of LPS resulted in a significant increase in TNF-α and IL-1β levels, 7- and 3.5-fold, respectively (P < 0.05), while treatment with propofol partially blunted the secretion of both cytokines (P < 0.05). CD14 and TLR4 mRNA levels were increased in the LPS group (1.48 ± 0.05 and 1.26 ± 0.03, respectively) compared to the control group (1.00 ± 0.20 and 1.00 ± 0.02, respectively; P < 0.05), while propofol treatment blunted this effect (1.16 ± 0.05 and 1.12 ± 0.05, respectively; P < 0.05). Both CD14 and TLR4 protein levels were elevated in the LPS group compared to the control group (P < 0.05), while propofol treatment partially decreased the expression of CD14 and TLR4 protein versus LPS alone (P < 0.05). Our study indicates that propofol prevents lung injury, most likely by inhibition of CD14 and TLR4 expression.