Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

Multi-user resource-sharing problem for the Internet

In this thesis we study a series of multi-user resource-sharing problems for the Internet, which involve distribution of a common resource among participants of multi-user systems (servers or networks). We study concurrently accessible resources, which for end-users may be exclusively accessible or non-exclusively. For all kinds we suggest a separate algorithm or a modification of common reputation scheme. Every algorithm or method is studied from different perspectives: optimality of protocols, selfishness of end users, fairness of the protocol for end users. On the one hand the multifaceted analysis allows us to select the most suited protocols among a set of various available ones based on trade-offs of optima criteria. On the other hand, the future Internet predictions dictate new rules for the optimality we should take into account and new properties of the networks that cannot be neglected anymore. In this thesis we have studied new protocols for such resource-sharing problems as the backoff protocol, defense mechanisms against Denial-of-Service, fairness and confidentiality for users in overlay networks. For backoff protocol we present analysis of a general backoff scheme, where an optimization is applied to a general-view backoff function. It leads to an optimality condition for backoff protocols in both slot times and continuous time models. Additionally we present an extension for the backoff scheme in order to achieve fairness for the participants in an unfair environment, such as wireless signal strengths. Finally, for the backoff algorithm we suggest a reputation scheme that deals with misbehaving nodes. For the next problem -- denial-of-service attacks, we suggest two schemes that deal with the malicious behavior for two conditions: forged identities and unspoofed identities. For the first one we suggest a novel most-knocked-first-served algorithm, while for the latter we apply a reputation mechanism in order to restrict resource access for misbehaving nodes. Finally, we study the reputation scheme for the overlays and peer-to-peer networks, where resource is not placed on a common station, but spread across the network. The theoretical analysis suggests what behavior will be selected by the end station under such a reputation mechanism.

Optimaalinen rangaistus talousrikoksesta : Tutkimus velallisen rikoksista

Optimal Punishment of Economic Crime: A Study on Bankruptcy Crime This thesis researches whether the punishment practise of bankruptcy crimes is optimal in light of Gary S. Becker’s theory of optimal punishment. According to Becker, a punishment is optimal if it eliminates the expected utility of the crime for the offender and - on the other hand - minimizes the cost of the crime to society. The decision process of the offender is observed through their expected utility of the crime. The expected utility is calculated based on the offender's probability of getting caught, the cost of getting caught and the profit from the crime. All objects including the punishment are measured in cash. The cost of crimes to the society is observed defining the disutility caused by the crime to the society. The disutility is calculated based on the cost of crime prevention, crime damages, punishment execution and the probability of getting caught. If the goal is to minimize the crime profits, the punishments of bankruptcy crimes are not optimal. If the debtors would decide whether or not to commit the crime solely based on economical consideration, the crime rate would be multiple times higher than the current rate is. The prospective offender relies heavily on non-economic aspects in their decision. Most probably social pressure and personal commitment to oblige the laws are major factors in the prospective criminal’s decision-making. The function developed by Becker measuring the cost to society was not useful in the measurement of the optimality of a punishment. The premise of the function that the costs of the society correlate to the costs for the offender from the punishment proves to be unrealistic in observation of the bankruptcy crimes. However, it was observed that majority of the cost of crime for the society are caused by the crime damages. This finding supports the preventive criminal politics.