Pp2a: At The Crossroads Between Growth and Defence In Plants

Living organisms manage their resources in well evolutionary-preserved manner to grow and reproduce. Plants are no exceptions, beginning from their seed stage they have to perceive environmental conditions to avoid germination at wrong time or rough soil. Under favourable conditions, plants invest photosynthetic end products in cell and organ growth to provide best possible conditions for generation of offspring. Under natural conditions, however, plants are exposed to a multitude of environmental stress factors, including high light and insufficient light, drought and flooding, various bacteria and viruses, herbivores, and other plants that compete for nutrients and light. To survive under environmental challenges, plants have evolved signaling mechanisms that recognise environmental changes and perform fine-tuned actions that maintain cellular homeostasis. Controlled phosphorylation and dephosphorylation of proteins plays an important role in maintaining balanced flow of information within cells. In this study, I examined the role of protein phosphatase 2A (PP2A) on plant growth and acclimation under optimal and stressful conditions. To this aim, I studied gene expression profiles, proteomes and protein interactions, and their impacts on plant health and survival, taking advantage of the model plant Arabidopsis thaliana and the mutant approach. Special emphasis was made on two highly similar PP2A-B regulatory subunits, B’γ and B’ζ. Promoters of B’γ and B’ζ were found to be similarly active in the developing tissues of the plant. In mature leaves, however, the promoter of B’γ was active in patches in leaf periphery, while the activity of B’ζ promoter was evident in leaf edges. The partially overlapping expression patterns, together with computational models of B’γ and B’ζ within trimeric PP2A holoenzymes suggested that B’γ and B’ζ may competitively bind into similar PP2A trimmers and thus influence each other’s actions. Arabidopsis thaliana pp2a-b’γ and pp2a-b’γζ double mutants showed dwarfish phenotypes, indicating that B’γ and B’ζ are needed for appropriate growth regulation under favorable conditions. However, while pp2a-b’γ displayed constitutive immune responses and appearance of premature yellowings on leaves, the pp2a-b’γζ double mutant supressed these yellowings. More detailed analysis of defense responses revealed that B’γ and B’ζ mediate counteracting effects on salicylic acid dependent defense signalling. Associated with this, B’γ and B’ζ were both found to interact in vivo with CALCIUM DEPENDENT PROTEIN KINASE 1 (CPK1), a crucial element of salicylic acid signalling pathway against pathogens in plants. In addition, B’γ was shown to modulate cellular reactive oxygen species (ROS) metabolism by controlling the abundance of ALTERNATIVE OXIDASE 1A and 1D in mitochondria. PP2A B’γ and B’ζ subunits turned out to play crucial roles in the optimization of plant choices during their development. Taken together, PP2A allows fluent responses to environmental changes, maintenance of plant homeostasis, and grant survivability with minimised cost of redirection of resources from growth to defence.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

Thermal reactions of the major hydrocarbon components of biomass gasification gas

Gasification of biomass is an efficient method process to produce liquid fuels, heat and electricity. It is interesting especially for the Nordic countries, where raw material for the processes is readily available. The thermal reactions of light hydrocarbons are a major challenge for industrial applications. At elevated temperatures, light hydrocarbons react spontaneously to form higher molecular weight compounds. In this thesis, this phenomenon was studied by literature survey, experimental work and modeling effort. The literature survey revealed that the change in tar composition is likely caused by the kinetic entropy. The role of the surface material is deemed to be an important factor in the reactivity of the system. The experimental results were in accordance with previous publications on the subject. The novelty of the experimental work lies in the used time interval for measurements combined with an industrially relevant temperature interval. The aspects which are covered in the modeling include screening of possible numerical approaches, testing of optimization methods and kinetic modelling. No significant numerical issues were observed, so the used calculation routines are adequate for the task. Evolutionary algorithms gave a better performance combined with better fit than the conventional iterative methods such as Simplex and Levenberg-Marquardt methods. Three models were fitted on experimental data. The LLNL model was used as a reference model to which two other models were compared. A compact model which included all the observed species was developed. The parameter estimation performed on that model gave slightly impaired fit to experimental data than LLNL model, but the difference was barely significant. The third tested model concentrated on the decomposition of hydrocarbons and included a theoretical description of the formation of carbon layer on the reactor walls. The fit to experimental data was extremely good. Based on the simulation results and literature findings, it is likely that the surface coverage of carbonaceous deposits is a major factor in thermal reactions.

Stability Analysis in Multicriteria Discrete Portfolio Optimization.

Almost every problem of design, planning and management in the technical and organizational systems has several conflicting goals or interests. Nowadays, multicriteria decision models represent a rapidly developing area of operation research. While solving practical optimization problems, it is necessary to take into account various kinds of uncertainty due to lack of data, inadequacy of mathematical models to real-time processes, calculation errors, etc. In practice, this uncertainty usually leads to undesirable outcomes where the solutions are very sensitive to any changes in the input parameters. An example is the investment managing. Stability analysis of multicriteria discrete optimization problems investigates how the found solutions behave in response to changes in the initial data (input parameters). This thesis is devoted to the stability analysis in the problem of selecting investment project portfolios, which are optimized by considering different types of risk and efficiency of the investment projects. The stability analysis is carried out in two approaches: qualitative and quantitative. The qualitative approach describes the behavior of solutions in conditions with small perturbations in the initial data. The stability of solutions is defined in terms of existence a neighborhood in the initial data space. Any perturbed problem from this neighborhood has stability with respect to the set of efficient solutions of the initial problem. The other approach in the stability analysis studies quantitative measures such as stability radius. This approach gives information about the limits of perturbations in the input parameters, which do not lead to changes in the set of efficient solutions. In present thesis several results were obtained including attainable bounds for the stability radii of Pareto optimal and lexicographically optimal portfolios of the investment problem with Savage's, Wald's criteria and criteria of extreme optimism. In addition, special classes of the problem when the stability radii are expressed by the formulae were indicated. Investigations were completed using different combinations of Chebyshev's, Manhattan and Hölder's metrics, which allowed monitoring input parameters perturbations differently.

Optimization and Measuring Techniques for Collect-and-Place Machines in Printed Circuit Board Industry

This thesis considers optimization problems arising in printed circuit board assembly. Especially, the case in which the electronic components of a single circuit board are placed using a single placement machine is studied. Although there is a large number of different placement machines, the use of collect-and-place -type gantry machines is discussed because of their flexibility and increasing popularity in the industry. Instead of solving the entire control optimization problem of a collect-andplace machine with a single application, the problem is divided into multiple subproblems because of its hard combinatorial nature. This dividing technique is called hierarchical decomposition. All the subproblems of the one PCB - one machine -context are described, classified and reviewed. The derived subproblems are then either solved with exact methods or new heuristic algorithms are developed and applied. The exact methods include, for example, a greedy algorithm and a solution based on dynamic programming. Some of the proposed heuristics contain constructive parts while others utilize local search or are based on frequency calculations. For the heuristics, it is made sure with comprehensive experimental tests that they are applicable and feasible. A number of quality functions will be proposed for evaluation and applied to the subproblems. In the experimental tests, artificially generated data from Markov-models and data from real-world PCB production are used. The thesis consists of an introduction and of five publications where the developed and used solution methods are described in their full detail. For all the problems stated in this thesis, the methods proposed are efficient enough to be used in the PCB assembly production in practice and are readily applicable in the PCB manufacturing industry.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Optimization of a dynamic district heating power plant

The purpose of this Thesis is to find the most optimal heat recovery solution for Wärtsilä’s dynamic district heating power plant considering Germany energy markets as in Germany government pays subsidies for CHP plants in order to increase its share of domestic power production to 25 % by 2020. Different heat recovery connections have been simulated dozens to be able to determine the most efficient heat recovery connections. The purpose is also to study feasibility of different heat recovery connections in the dynamic district heating power plant in the Germany markets thus taking into consideration the day ahead electricity prices, district heating network temperatures and CHP subsidies accordingly. The auxiliary cooling, dynamical operation and cost efficiency of the power plant is also investigated.

Optimization of the RNeasy Mini Kit to obtain high-quality total RNA from sessile cells of Staphylococcus aureus

Biofilm formed by Staphylococcus aureus is considered an important virulence trait in the pathogenesis of infections associated with implantable medical devices. Gene expression analyses are important strategies for determining the mechanisms involved in production and regulation of biofilm. Obtaining intact RNA preparations is the first and most critical step for these studies. In this article, we describe an optimized protocol for obtaining total RNA from sessile cells of S. aureus using the RNeasy Mini Kit. This method essentially consists of a few steps, as follows: 1) addition of acetone-ethanol to sessile cells, 2) lysis with lysostaphin at 37°C/10 min, 3) vigorous mixing, 4) three cycles of freezing and thawing, and 5) purification of the lysate in the RNeasy column. This simple pre-kit procedure yields high-quality total RNA from planktonic and sessile cells of S. aureus.

Use of the Wasserman equation in optimization of the duration of the power ramp in a cardiopulmonary exercise test: a study of Brazilian men

This study aimed to analyze the agreement between measurements of unloaded oxygen uptake and peak oxygen uptake based on equations proposed by Wasserman and on real measurements directly obtained with the ergospirometry system. We performed an incremental cardiopulmonary exercise test (CPET), which was applied to two groups of sedentary male subjects: one apparently healthy group (HG, n=12) and the other had stable coronary artery disease (n=16). The mean age in the HG was 47±4 years and that in the coronary artery disease group (CG) was 57±8 years. Both groups performed CPET on a cycle ergometer with a ramp-type protocol at an intensity that was calculated according to the Wasserman equation. In the HG, there was no significant difference between measurements predicted by the formula and real measurements obtained in CPET in the unloaded condition. However, at peak effort, a significant difference was observed between oxygen uptake (V˙O2)peak(predicted)and V˙O2peak(real)(nonparametric Wilcoxon test). In the CG, there was a significant difference of 116.26 mL/min between the predicted values by the formula and the real values obtained in the unloaded condition. A significant difference in peak effort was found, where V˙O2peak(real)was 40% lower than V˙O2peak(predicted)(nonparametric Wilcoxon test). There was no agreement between the real and predicted measurements as analyzed by Lin’s coefficient or the Bland and Altman model. The Wasserman formula does not appear to be appropriate for prediction of functional capacity of volunteers. Therefore, this formula cannot precisely predict the increase in power in incremental CPET on a cycle ergometer.

Optimization of random amplified polymorphic DNA protocol for molecular identification of Lophius gastrophysus

Lophius gastrophysus has important commercial value in Brazil particularly for foreign trade. In this study, we described the optimization of Random Amplified Polymorphic DNA (RAPD) protocol for identification of L. gastrophysus. Different conditions (annealing temperatures, MgCl concentrations, DNA quantity) were tested to find reproducible and adequate profiles. Amplifications performed with primers A01, ² A02 and A03 generate the best RAPD profiles when the conditions were annealing temperature of 36ºC, 25 ng of DNA quantity and 2.5 mM MgCl2. Exact identification of the species and origin of marine products is necessary and RAPD could be used as an accurate, rapid tool to expose commercial fraud.

Optimization and validation of a methodology to determine total arsenic, As(III) and As(V), in water samples, through graphite furnace atomic absorption spectrometry

The Graphite furnace atomic absorption spectrometry (GF AAS) was the technique chosen by the inorganic contamination laboratory (INCQ/ FIOCRUZ) to be validated and applied in routine analysis for arsenic detection and quantification. The selectivity, linearity, sensibility, detection, and quantification limits besides accuracy and precision parameters were studied and optimized under Stabilized Temperature Platform Furnace (STPF) conditions. The limit of detection obtained was 0.13 µg.L-1 and the limit of quantification was 1.04 µg.L-1, with an average precision, for total arsenic, less than 15% and an accuracy of 96%. To quantify the chemical species As(III) and As(V), an ion-exchange resin (Dowex 1X8, Cl- form) was used and the physical-chemical parameters were optimized resulting in a recuperation of 98% of As(III) and of 90% of As(V). The method was applied to groundwater, mineral water, and hemodialysis purified water samples. All results obtained were lower than the maximum limit values established by the legal Brazilian regulations, in effect, 50, 10, and 5 µg.L-1 para As total, As(III) e As(V), respectively. All results were statistically evaluated.

Docosahexaenoic acid ethyl esther (DHAEE) microcapsule production by spray-drying: optimization by experimental design

Docosahexaenoic acid is an essential polyunsaturated fatty acid with important metabolic activities. Its conjugated double bonds make it susceptible to decomposition. Its stability may be improved through fatty acid entrapment with a spray-drying technique; however, the many parameters involved in this technique must be considered to avoid affecting the final product quality. Therefore, this study aimed to evaluate the entrapment conditions and yields of fish oil enriched with docosahexaenoic acid ethyl ester. Microcapsules were obtained from Acacia gum using a spray-drying technique. The experimental samples were analyzed by chromatography and delineated by Statistica software, which found the following optimum entrapment conditions: an inlet temperature of 188 °C; 30% core material; an N2 flow rate of 55 mm; and a pump flow rate of 12.5 mL/minute. These conditions provided a 66% yield of docosahexaenoic acid ethyl ester in the oil, corresponding to 19.8% of entrapped docosahexaenoic acid ethyl ester (w/w). This result was considered significant since 30% corresponded to wall material.