The osmoadaptive response of the wine yeast Saccharomyces cerevisiae K1-V1116 during icewine fermentation

The adapted metabolic response of commercial wine yeast under prolonged exposure to concentrated solutes present in Icewine juice is not fully understood. Presently, there is no information regarding the transcriptomic changes in gene expression associated with the adaptive stress response ofwine yeast during Icewine fermentation compared to table wine fermentation. To understand how and why wine yeast respond differently at the genomic level and ultimately at the metabolic level during Icewine fermentation, the focus ofthis project was to identify and compare these differences in the wine yeast Saccharomyces cerevisiae KI-Vll16 using cDNA microarray technology during the first five days of fermentation. Significant differences in yeast gene expression patterns between fermentation conditions were correlated to differences in nutrient utilization and metabolite production. Sugar consumption, nitrogen usage and metabolite levels were measured using enzyme assays and HPLC. Also, a small subset of differentially expressed genes was verified using Northern analysis. The high osmotic stress experienced by wine yeast throughout Icewine fermentation elicited changes in cell growth and metabolism correlating to several fermentation difficulties, including reduced biomass accumulation and fermentation rate. Genes associated with carbohydrate and nitrogen transport and metabolism were expressed at lower levels in Icewine juice fermenting cells compared to dilute juice fermenting cells. Osmotic stress, not nutrient availability during Icewine fermentation appears to impede sugar and nitrogen utilization. Previous studies have established that glycerol and acetic acid production are increased in yeast during Icewine fermentation. A gene encoding for a glycerollW symporter (STL1) was found to be highly expressed up to 25-fold in the i Icewine juice condition using microarray and Northern analysis. Active glycerol transport by yeast under hyperosmotic conditions to increase cytosolic glycerol concentration may contribute to reduced cell growth observed in the Icewine juice condition. Additionally, genes encoding for two acetyl CoA synthetase isoforms (ACSl and ACS2) were found to be highly expressed, 19- and II-fold respectively, in dilute juice fermenting cells relative to the Icewine juice condition. Therefore, decreased conversion of acetate to acetyl-CoA may contribute to increased acetic acid production during Icewine fermentation. These results further help to explain the response of wine yeast as they adapt to Icewine juice fermentation. ii

Metabolite changes during Riesling icewine fermentation when yeast micronutrients are used /

Icewine is an intensely s\veet dessert \vine fermented from the juice of naturally frozen grapes. Icewine fermentation poses many challenges such as failure to reach desired ethanol levels and production of high levels of volatile acidity in the fonn of acetic acid. This study investigated the impact of micronutrient addition (GO-FERM® and NATSTEP®) during the rehydration stage of the commercial \vine yeast Saccharomyces cerevisiae KI-VIII6 during Ice\vine fermentation. Sterile-filtered and unfiltered Riesling Ice\vine juice was inoculated \vith yeast rehydrated under four different conditions: in water only; with GO-FERM®; with NATSTEP®; or the combination of both micronutrient products in the rehydration water. Using sterile-filtered Icewine juice, yeast rehydration had a positive impact of reducing the rate of acetic acid produced as a function of sugar consumed, reducing the ratio of acetic acid/ethanol and reducing the ratio of acetic acid/glycerol. In the sterile-filtered fermentation, yeast rehydrated with micronutrients generated 9-times less acetic acid per gram of sugar in the first 48 hours compared to yeast rehydrated only \vith water and resulted in a 17% reduction in acetic acid in the final \vine \vhen normalized to sugar consumed. However, the sterile-filtered fermentations likely became stuck due to the overc1arification of the juice as evidenced from the low sugar consumption (117 gIL) that could not be completely overcome by the micronutrient treatments (144 gIL sugar consumed) to reach a target ethanol of IO%v/v. Contrary to \vhat \vas observed in the sterile-filtered treatements, using unfiltered Ice\vine juice, yeast micronutrient addition had no significant impact of reducing the rate of acetic acid produced as a function of sugar consumed, reducing the ratio of acetic acid/ethanol and reducing the ratio of acetic acid/glycerol. However, in the unfiltered fermentation, micronutrient addition during yeast rehydration caused a reduction in the acetic acid produced as a function of sugar consumed up to 150 giL sugar consumed.. In contrast to the sterile-filtered fermentations, the unfiltered fermentations did not become stuck as evidenced from the higher sugar consumption (l47-174g1L). The largest effects of micronutrient addition are evident in the first two days of both sterile and unfiltered fermentations.

The effects of yeast inoculation rate and acclimatization to juice on icewine fermentation /

Icewine is an intensely sweet, unique dessert wine fennented from the juice of grapes that have frozen naturally on the vine. The juice pressed from the frozen grapes is highly concentrated, ranging from a minimum of 35° Brix to approximately 42° Brix. Often Icewine fennentations are sluggish, taking months to reach the desired ethanol level, and sometimes become stuck. In 6 addition, Icewines have high levels of volatile acidity. At present, there is no routine method of yeast inoculation for fennenting Icewine. This project investigated two yeast inoculum levels, 0.2 gIL and 0.5 gIL. The fennentation kinetics of inoculating these yeast levels directly into the sterile Icewine juice or conditioning the cells to the high sugar levels using a step wise acclimatization procedure were also compared. The effect of adding GO-FERM, a yeast nutrient, was also assessed. In the sterile fennentations, yeast inoculated at 0.2 gIL stopped fennenting before the required ethanol level was achieved, producing only 7.8% (v/v) and 8.1 % (v/v) ethanol for the direct and conditioned inoculations, respectively. At 0.5 gIL, the stepwise conditioned cells fennented the most sugar, producing 12.2% (v/v) ethanol, whereas the direct inoculum produced 10.5% (v/v) ethanol. The addition of the yeast nutrient GO-FERM increased the rate of biomass accumulation, but reduced the ethanol concentration in wines fennented at 0.5 gIL. There was no significant difference in acetic acid concentration in the final wines across all treatments. Fennentations using unfiltered Icewine juice at the 0.5 gIL inoculum level were also compared to see if the effects of yeast acclimatization and micronutrient addition had the same impact on fennentation kinetics and yeast metabolite production as observed in the sterile-filtered juice fennentations. In addition, a full descriptive analysis of the finished wines was carried out to further assess the impact of yeast inoculation method on Icewine sensory quality. At 0.5 gIL, the stepwise conditioned cells fennented the most sugar, producing 11.5% (v/v) ethanol, whereas the direct inoculum produced 10.0% (v/v) ethanol. The addition of the yeast nutrient GO-FERM increased the peak viable cell numbers, but reduced the ethanol concentration in wines fennented at 0.5 gIL. There was a significant difference 7 in acetic acid concentration in the final wines across all treatments and all treatments affected the sensory profiles of the final wines. Wines produced by direct inoculation were described by grape and raisin aromas and butter flavour. The addition of GO-FERM to the direct inoculation treatment shifted the aroma/flavour profiles to more orange flavour and aroma, and a sweet taste profile. StepWise acclimatizing the cells resulted in wines described more by peach and terpene aroma. The addition of GO-FERM shifted the profile to pineapple and alcohol aromas as well as alcohol flavour. Overall, these results indicate that the addition of GO-FERM and yeast acclimatization shortened the length of fermentation and impacted the sensory profiles of the resultant wines.

A biochemical predictor of performance during mesophilic anaerobic fermentation of starch wastewater

The aim of this study was to determine the potential of biochemical parameters, such as enzyme activity and adenosine triphosphate (ATP) levels, as monitors of process performance in the Upflow Anaerobic Sludge Blanket (UASB) reactor utilizing a starch wastewater. The acid and alkaline phosphatase activity and the ATP content of the UASB sludge were measured in response to changes in flow rate and nutrient loading. Conventional parameters of process performance, such as gas production, acetic acid production, COD, phosphorus, nitrogen and suspended solids loadings and % COD removal were also monitored. The response of both biochemical and conventional parameters to changing process conditions was then compared. Alkaline phosphatase activity exhibited the highest activity over the entire study perioda A high suspended solids loading was observed to upset the system in terms of gas production, acetic acid production and % COD removala The initial rate of increase in alkaline phosphatase activity following an increase in loading was four times as great during process upset than under conditions of good performance. The change in enzyme actiVity was also more sensitive to process upset than changes in acetic acid production. The change in ATP content of the sludge with time suggested that enzyme actiVity was changing independently of the actual viable biomass present. The bacterial composition of the anaerobic sludge granules was similar to that of other sludge bed systems, at the light and scanning electron microscope level. Isolated serum bottle cultures produced several acids involved in anaerobic carbohydrate metabolism. The overall performance of the UASB system indicated that higher loadings of soluble nutrients could have been tolerated by the system.

Histogram filtering as a tool in variational Monte Carlo optimization

Optimization of wave functions in quantum Monte Carlo is a difficult task because the statistical uncertainty inherent to the technique makes the absolute determination of the global minimum difficult. To optimize these wave functions we generate a large number of possible minima using many independently generated Monte Carlo ensembles and perform a conjugate gradient optimization. Then we construct histograms of the resulting nominally optimal parameter sets and "filter" them to identify which parameter sets "go together" to generate a local minimum. We follow with correlated-sampling verification runs to find the global minimum. We illustrate this technique for variance and variational energy optimization for a variety of wave functions for small systellls. For such optimized wave functions we calculate the variational energy and variance as well as various non-differential properties. The optimizations are either on par with or superior to determinations in the literature. Furthermore, we show that this technique is sufficiently robust that for molecules one may determine the optimal geometry at tIle same time as one optimizes the variational energy.

Optimization of the valence energy variance of the CuH molecule

We developed the concept of split-'t to deal with the large molecules (in terms of the number of electrons and nuclear charge Z). This naturally leads to partitioning the local energy into components due to each electron shell. The minimization of the variation of the valence shell local energy is used to optimize a simple two parameter CuH wave function. Molecular properties (spectroscopic constants and the dipole moment) are calculated for the optimized and nearly optimized wave functions using the Variational Quantum Monte Carlo method. Our best results are comparable to those from the single and double configuration interaction (SDCI) method.

Optimization of trial wave functions for use in quantum Monte Carlo with application to LiH

Methods for both partial and full optimization of wavefunction parameters are explored, and these are applied to the LiH molecule. A partial optimization can be easily performed with little difficulty. But to perform a full optimization we must avoid a wrong minimum, and deal with linear-dependency, time step-dependency and ensemble-dependency problems. Five basis sets are examined. The optimized wavefunction with a 3-function set gives a variational energy of -7.998 + 0.005 a.u., which is comparable to that (-7.990 + 0.003) 1 of Reynold's unoptimized \fin ( a double-~ set of eight functions). The optimized wavefunction with a double~ plus 3dz2 set gives ari energy of -8.052 + 0.003 a.u., which is comparable with the fixed-node energy (-8.059 + 0.004)1 of the \fin. The optimized double-~ function itself gives an energy of -8.049 + 0.002 a.u. Each number above was obtained on a Bourrghs 7900 mainframe computer with 14 -15 hrs CPU time.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

(A) Optimization of solid body phase synthesis of RNA using the Cpep chemistry. (B) Synthesis of BODIPY labeled oligonucleotides

(A) Solid phase synthesis of oligonucleotides are well documented and are extensively studied as the demands continue to rise with the development of antisense, anti-gene, RNA interference, and aptamers. Although synthesis of RNA sequences faces many challenges, most notably the choice of the 2' -hydroxy protecting group, modified 2' -O-Cpep protected ribonucleotides were synthesized as alternitive building blocks. Altering phosphitylation procedures to incorporate 3' -N,N-diethyl phosphoramidites enhanced the overall reactivity, thus, increased the coupling efficiency without loss of integrety. Furthermore, technical optimizations of solid phase synthesis cycles were carried out to allow for successful synthesis of a homo UIO sequences with a stepwise coupling efficiency reaching 99% and a final yield of 91 %. (B) Over the past few decades, dipyrrometheneboron difluoride (BODIPY) has gained recognition as one of the most versatile fluorophores. Currently, BODIPY labeling of oligonucleotides are carried out post-synthetically and to date, there lacks a method that allows for direct incorporation of BODIPY into oligonucleotides during solid phase synthesis. Therefore, synthesis of BODIPY derived phosphoramidites will provide an alternative method in obtaining fluorescently labelled oligonucleotides. A method for the synthesis and incorporation of the BODIPY analogues into oligonucleotides by phosphoramidite chemistry-based solid phase DNA synthesis is reported here. Using this approach, BODIPY-labeled TlO homopolymer and ISIS 5132 were successfully synthesized.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

Icewine fermentation by Saccharomyces cervisiae : fundamental stress responses and comparative fermentation dynamics

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999.5 B63 P54 2007

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.